Podcasts about Markdown

At the bottom of the LessWrong post editor, if you have at least 100 global karma, you may have noticed this button.The button Many people click the button, and are jumpscared when it starts an Intercom chat with a professional editor (me), asking what sort of feedback they'd like. So, that's what it does. It's a summon Justis button. Why summon Justis? To get feedback on your post, of just about any sort. Typo fixes, grammar checks, sanity checks, clarity checks, fit for LessWrong, the works. If you use the LessWrong editor (as opposed to the Markdown editor) I can leave comments and suggestions directly inline. I also provide detailed narrative feedback (unless you explicitly don't want this) in the Intercom chat itself. The feedback is totally without pressure. You can throw it all away, or just keep the bits you like. Or use it all! In any case [...] ---Outline:(00:35) Why summon Justis?(01:19) Why Justis in particular?(01:48) Am I doing it right?(01:59) How often can I request feedback?(02:22) Can I use the feature for linkposts/crossposts?(02:49) What if I click the button by mistake?(02:59) Should I credit you?(03:16) Couldnt I just use an LLM?(03:48) Why does Justis do this?--- First published: May 12th, 2025 Source: https://www.lesswrong.com/posts/bkDrfofLMKFoMGZkE/psa-the-lesswrong-feedback-service --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Gros épisode qui couvre un large spectre de sujets : Java, Scala, Micronaut, NodeJS, l'IA et la compétence des développeurs, le sampling dans les LLMs, les DTO, le vibe coding, les changements chez Broadcom et Red Hat ainsi que plusieurs nouvelles sur les licences open source. Enregistré le 7 mai 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-325.mp3 ou en vidéo sur YouTube. News Langages A l'occasion de JavaOne et du lancement de Java 24, Oracle lance un nouveau site avec des ressources vidéo pour apprendre le langage https://learn.java/ site plutôt à destination des débutants et des enseignants couvre la syntaxe aussi, y compris les ajouts plus récents comme les records ou le pattern matching c'est pas le site le plus trendy du monde. Martin Odersky partage un long article sur l'état de l'écosystème Scala et les évolutions du language https://www.scala-lang.org/blog/2025/03/24/evolving-scala.html Stabilité et besoin d'évolution : Scala maintient sa position (~14ème mondial) avec des bases techniques solides, mais doit évoluer face à la concurrence pour rester pertinent. Axes prioritaires : L'évolution se concentre sur l'amélioration du duo sécurité/convivialité, le polissage du langage (suppression des “rugosités”) et la simplification pour les débutants. Innovation continue : Geler les fonctionnalités est exclu ; l'innovation est clé pour la valeur de Scala. Le langage doit rester généraliste et ne pas se lier à un framework spécifique. Défis et progrès : L'outillage (IDE, outils de build comme sbt, scala-cli, Mill) et la facilité d'apprentissage de l'écosystème sont des points d'attention, avec des améliorations en cours (partenariat pédagogique, plateformes simples). Des strings encore plus rapides ! https://inside.java/2025/05/01/strings-just-got-faster/ Dans JDK 25, la performance de la fonction String::hashCode a été améliorée pour être principalement constant foldable. Cela signifie que si les chaînes de caractères sont utilisées comme clés dans une Map statique et immuable, des gains de performance significatifs sont probables. L'amélioration repose sur l'annotation interne @Stable appliquée au champ privé String.hash. Cette annotation permet à la machine virtuelle de lire la valeur du hash une seule fois et de la considérer comme constante si elle n'est pas la valeur par défaut (zéro). Par conséquent, l'opération String::hashCode peut être remplacée par la valeur de hash connue, optimisant ainsi les lookups dans les Map immuables. Un cas limite est celui où le code de hachage de la chaîne est zéro, auquel cas l'optimisation ne fonctionne pas (par exemple, pour la chaîne vide “”). Bien que l'annotation @Stable soit interne au JDK, un nouveau JEP (JEP 502: Stable Values (Preview)) est en cours de développement pour permettre aux utilisateurs de bénéficier indirectement de fonctionnalités similaires. AtomicHash, une implémentation Java d'une HashMap qui est thread-safe, atomique et non-bloquante https://github.com/arxila/atomichash implémenté sous forme de version immutable de Concurrent Hash Trie Librairies Sortie de Micronaut 4.8.0 https://micronaut.io/2025/04/01/micronaut-framework-4-8-0-released/ Mise à jour de la BOM (Bill of Materials) : La version 4.8.0 met à jour la BOM de la plateforme Micronaut. Améliorations de Micronaut Core : Intégration de Micronaut SourceGen pour la génération interne de métadonnées et d'expressions bytecode. Nombreuses améliorations dans Micronaut SourceGen. Ajout du traçage de l'injection de dépendances pour faciliter le débogage au démarrage et à la création des beans. Nouveau membre definitionType dans l'annotation @Client pour faciliter le partage d'interfaces entre client et serveur. Support de la fusion dans les Bean Mappers via l'annotation @Mapping. Nouvelle liveness probe détectant les threads bloqués (deadlocked) via ThreadMXBean. Intégration Kubernetes améliorée : Mise à jour du client Java Kubernetes vers la version 22.0.1. Ajout du module Micronaut Kubernetes Client OpenAPI, offrant une alternative au client officiel avec moins de dépendances, une configuration unifiée, le support des filtres et la compatibilité Native Image. Introduction d'un nouveau runtime serveur basé sur le serveur HTTP intégré de Java, permettant de créer des applications sans dépendances serveur externes. Ajout dans Micronaut Micrometer d'un module pour instrumenter les sources de données (traces et métriques). Ajout de la condition condition dans l'annotation @MetricOptions pour contrôler l'activation des métriques via une expression. Support des Consul watches dans Micronaut Discovery Client pour détecter les changements de configuration distribuée. Possibilité de générer du code source à partir d'un schéma JSON via les plugins de build (Gradle et Maven). Web Node v24.0.0 passe en version Current: https://nodejs.org/en/blog/release/v24.0.0 Mise à jour du moteur V8 vers la version 13.6 : intégration de nouvelles fonctionnalités JavaScript telles que Float16Array, la gestion explicite des ressources (using), RegExp.escape, WebAssembly Memory64 et Error.isError. npm 11 inclus : améliorations en termes de performance, de sécurité et de compatibilité avec les packages JavaScript modernes. Changement de compilateur pour Windows : abandon de MSVC au profit de ClangCL pour la compilation de Node.js sur Windows. AsyncLocalStorage utilise désormais AsyncContextFrame par défaut : offrant une gestion plus efficace du contexte asynchrone. URLPattern disponible globalement : plus besoin d'importer explicitement cette API pour effectuer des correspondances d'URL. Améliorations du modèle de permissions : le flag expérimental --experimental-permission devient --permission, signalant une stabilité accrue de cette fonctionnalité. Améliorations du test runner : les sous-tests sont désormais attendus automatiquement, simplifiant l'écriture des tests et réduisant les erreurs liées aux promesses non gérées. Intégration d'Undici 7 : amélioration des capacités du client HTTP avec de meilleures performances et un support étendu des fonctionnalités HTTP modernes. Dépréciations et suppressions : Dépréciation de url.parse() au profit de l'API WHATWG URL. Suppression de tls.createSecurePair. Dépréciation de SlowBuffer. Dépréciation de l'instanciation de REPL sans new. Dépréciation de l'utilisation des classes Zlib sans new. Dépréciation du passage de args à spawn et execFile dans child_process. Node.js 24 est actuellement la version “Current” et deviendra une version LTS en octobre 2025. Il est recommandé de tester cette version pour évaluer son impact sur vos applications. Data et Intelligence Artificielle Apprendre à coder reste crucial et l'IA est là pour venir en aide : https://kyrylo.org/software/2025/03/27/learn-to-code-ignore-ai-then-use-ai-to-code-even-better.html Apprendre à coder reste essentiel malgré l'IA. L'IA peut assister la programmation. Une solide base est cruciale pour comprendre et contrôler le code. Cela permet d'éviter la dépendance à l'IA. Cela réduit le risque de remplacement par des outils d'IA accessibles à tous. L'IA est un outil, pas un substitut à la maîtrise des fondamentaux. Super article de Anthropic qui essaie de comprendre comment fonctionne la “pensée” des LLMs https://www.anthropic.com/research/tracing-thoughts-language-model Effet boîte noire : Stratégies internes des IA (Claude) opaques aux développeurs et utilisateurs. Objectif : Comprendre le “raisonnement” interne pour vérifier capacités et intentions. Méthode : Inspiration neurosciences, développement d'un “microscope IA” (regarder quels circuits neuronaux s'activent). Technique : Identification de concepts (“features”) et de “circuits” internes. Multilinguisme : Indice d'un “langage de pensée” conceptuel commun à toutes les langues avant de traduire dans une langue particulière. Planification : Capacité à anticiper (ex: rimes en poésie), pas seulement de la génération mot par mot (token par token). Raisonnement non fidèle : Peut fabriquer des arguments plausibles (“bullshitting”) pour une conclusion donnée. Logique multi-étapes : Combine des faits distincts, ne se contente pas de mémoriser. Hallucinations : Refus par défaut ; réponse si “connaissance” active, sinon risque d'hallucination si erreur. “Jailbreaks” : Tension entre cohérence grammaticale (pousse à continuer) et sécurité (devrait refuser). Bilan : Méthodes limitées mais prometteuses pour la transparence et la fiabilité de l'IA. Le “S” dans MCP veut dire Securité (ou pas !) https://elenacross7.medium.com/%EF%B8%8F-the-s-in-mcp-stands-for-security-91407b33ed6b La spécification MCP pour permettre aux LLMs d'avoir accès à divers outils et fonctions a peut-être été adoptée un peu rapidement, alors qu'elle n'était pas encore prête niveau sécurité L'article liste 4 types d'attaques possibles : vulnérabilité d'injection de commandes attaque d'empoisonnement d'outils redéfinition silencieuse de l'outil le shadowing d'outils inter-serveurs Pour l'instant, MCP n'est pas sécurisé : Pas de standard d'authentification Pas de chiffrement de contexte Pas de vérification d'intégrité des outils Basé sur l'article de InvariantLabs https://invariantlabs.ai/blog/mcp-security-notification-tool-poisoning-attacks Sortie Infinispan 15.2 - pre rolling upgrades 16.0 https://infinispan.org/blog/2025/03/27/infinispan-15-2 Support de Redis JSON + scripts Lua Métriques JVM désactivables Nouvelle console (PatternFly 6) Docs améliorées (métriques + logs) JDK 17 min, support JDK 24 Fin du serveur natif (performances) Guillaume montre comment développer un serveur MCP HTTP Server Sent Events avec l'implémentation de référence Java et LangChain4j https://glaforge.dev/posts/2025/04/04/mcp-client-and-server-with-java-mcp-sdk-and-langchain4j/ Développé en Java, avec l'implémentation de référence qui est aussi à la base de l'implémentation dans Spring Boot (mais indépendant de Spring) Le serveur MCP est exposé sous forme de servlet dans Jetty Le client MCP lui, est développé avec le module MCP de LangChain4j c'est semi independant de Spring dans le sens où c'est dépendant de Reactor et de ses interface. il y a une conversation sur le github d'anthropic pour trouver une solution, mais cela ne parait pas simple. Les fallacies derrière la citation “AI won't replace you, but humans using AI will” https://platforms.substack.com/cp/161356485 La fallacie de l'automatisation vs. l'augmentation : Elle se concentre sur l'amélioration des tâches existantes avec l'IA au lieu de considérer le changement de la valeur de ces tâches dans un nouveau système. La fallacie des gains de productivité : L'augmentation de la productivité ne se traduit pas toujours par plus de valeur pour les travailleurs, car la valeur créée peut être capturée ailleurs dans le système. La fallacie des emplois statiques : Les emplois sont des constructions organisationnelles qui peuvent être redéfinies par l'IA, rendant les rôles traditionnels obsolètes. La fallacie de la compétition “moi vs. quelqu'un utilisant l'IA” : La concurrence évolue lorsque l'IA modifie les contraintes fondamentales d'un secteur, rendant les compétences existantes moins pertinentes. La fallacie de la continuité du flux de travail : L'IA peut entraîner une réimagination complète des flux de travail, éliminant le besoin de certaines compétences. La fallacie des outils neutres : Les outils d'IA ne sont pas neutres et peuvent redistribuer le pouvoir organisationnel en changeant la façon dont les décisions sont prises et exécutées. La fallacie du salaire stable : Le maintien d'un emploi ne garantit pas un salaire stable, car la valeur du travail peut diminuer avec l'augmentation des capacités de l'IA. La fallacie de l'entreprise stable : L'intégration de l'IA nécessite une restructuration de l'entreprise et ne se fait pas dans un vide organisationnel. Comprendre le “sampling” dans les LLMs https://rentry.co/samplers Explique pourquoi les LLMs utilisent des tokens Les différentes méthodes de “sampling” : càd de choix de tokens Les hyperparamètres comme la température, top-p, et leur influence réciproque Les algorithmes de tokenisation comme Byte Pair Encoding et SentencePiece. Un de moins … OpenAI va racheter Windsurf pour 3 milliards de dollars. https://www.bloomberg.com/news/articles/2025-05-06/openai-reaches-agreement-to-buy-startup-windsurf-for-3-billion l'accord n'est pas encore finalisé Windsurf était valorisé à 1,25 milliards l'an dernier et OpenAI a levé 40 milliards dernièrement portant sa valeur à 300 milliards Le but pour OpenAI est de rentrer dans le monde des assistants de code pour lesquels ils sont aujourd'hui absent Docker desktop se met à l'IA… ? Une nouvelle fonctionnalité dans docker desktop 4.4 sur macos: Docker Model Runner https://dev.to/docker/run-genai-models-locally-with-docker-model-runner-5elb Permet de faire tourner des modèles nativement en local ( https://docs.docker.com/model-runner/ ) mais aussi des serveurs MCP ( https://docs.docker.com/ai/mcp-catalog-and-toolkit/ ) Outillage Jetbrains défend la suppression des commentaires négatifs sur son assistant IA https://devclass.com/2025/04/30/jetbrains-defends-removal-of-negative-reviews-for-unpopular-ai-assistant/?td=rt-3a L'IA Assistant de JetBrains, lancée en juillet 2023, a été téléchargée plus de 22 millions de fois mais n'est notée que 2,3 sur 5. Des utilisateurs ont remarqué que certaines critiques négatives étaient supprimées, ce qui a provoqué une réaction négative sur les réseaux sociaux. Un employé de JetBrains a expliqué que les critiques ont été supprimées soit parce qu'elles mentionnaient des problèmes déjà résolus, soit parce qu'elles violaient leur politique concernant les “grossièretés, etc.” L'entreprise a reconnu qu'elle aurait pu mieux gérer la situation, un représentant déclarant : “Supprimer plusieurs critiques d'un coup sans préavis semblait suspect. Nous aurions dû au moins publier un avis et fournir plus de détails aux auteurs.” Parmi les problèmes de l'IA Assistant signalés par les utilisateurs figurent : un support limité pour les fournisseurs de modèles tiers, une latence notable, des ralentissements fréquents, des fonctionnalités principales verrouillées aux services cloud de JetBrains, une expérience utilisateur incohérente et une documentation insuffisante. Une plainte courante est que l'IA Assistant s'installe sans permission. Un utilisateur sur Reddit l'a qualifié de “plugin agaçant qui s'auto-répare/se réinstalle comme un phénix”. JetBrains a récemment introduit un niveau gratuit et un nouvel agent IA appelé Junie, destiné à fonctionner parallèlement à l'IA Assistant, probablement en réponse à la concurrence entre fournisseurs. Mais il est plus char a faire tourner. La société s'est engagée à explorer de nouvelles approches pour traiter les mises à jour majeures différemment et envisage d'implémenter des critiques par version ou de marquer les critiques comme “Résolues” avec des liens vers les problèmes correspondants au lieu de les supprimer. Contrairement à des concurrents comme Microsoft, AWS ou Google, JetBrains commercialise uniquement des outils et services de développement et ne dispose pas d'une activité cloud distincte sur laquelle s'appuyer. Vos images de README et fichiers Markdown compatibles pour le dark mode de GitHub: https://github.blog/developer-skills/github/how-to-make-your-images-in-markdown-on-github-adjust-for-dark-mode-and-light-mode/ Seulement quelques lignes de pure HTML pour le faire Architecture Alors, les DTOs, c'est bien ou c'est pas bien ? https://codeopinion.com/dtos-mapping-the-good-the-bad-and-the-excessive/ Utilité des DTOs : Les DTOs servent à transférer des données entre les différentes couches d'une application, en mappant souvent les données entre différentes représentations (par exemple, entre la base de données et l'interface utilisateur). Surutilisation fréquente : L'article souligne que les DTOs sont souvent utilisés de manière excessive, notamment pour créer des API HTTP qui ne font que refléter les entités de la base de données, manquant ainsi l'opportunité de composer des données plus riches. Vraie valeur : La valeur réelle des DTOs réside dans la gestion du couplage entre les couches et la composition de données provenant de sources multiples en formes optimisées pour des cas d'utilisation spécifiques. Découplage : Il est suggéré d'utiliser les DTOs pour découpler les modèles de données internes des contrats externes (comme les API), ce qui permet une évolution et une gestion des versions indépendantes. Exemple avec CQRS : Dans le cadre de CQRS (Command Query Responsibility Segregation), les réponses aux requêtes (queries) agissent comme des DTOs spécifiquement adaptés aux besoins de l'interface utilisateur, pouvant inclure des données de diverses sources. Protection des données internes : Les DTOs aident à distinguer et protéger les modèles de données internes (privés) des changements externes (publics). Éviter l'excès : L'auteur met en garde contre les couches de mapping excessives (mapper un DTO vers un autre DTO) qui n'apportent pas de valeur ajoutée. Création ciblée : Il est conseillé de ne créer des DTOs que lorsqu'ils résolvent des problèmes concrets, tels que la gestion du couplage ou la facilitation de la composition de données. Méthodologies Même Guillaume se met au “vibe coding” https://glaforge.dev/posts/2025/05/02/vibe-coding-an-mcp-server-with-micronaut-and-gemini/ Selon Andrey Karpathy, c'est le fait de POC-er un proto, une appli jetable du weekend https://x.com/karpathy/status/1886192184808149383 Mais Simon Willison s'insurge que certains confondent coder avec l'assistance de l'IA avec le vibe coding https://simonwillison.net/2025/May/1/not-vibe-coding/ Guillaume c'est ici amusé à développer un serveur MCP avec Micronaut, en utilisant Gemini, l'IA de Google. Contrairement à Quarkus ou Spring Boot, Micronaut n'a pas encore de module ou de support spécifique pour faciliter la création de serveur MCP Sécurité Une faille de sécurité 10/10 sur Tomcat https://www.it-connect.fr/apache-tomcat-cette-faille-activement-exploitee-seulement-30-heures-apres-sa-divulgation-patchez/ Une faille de sécurité critique (CVE-2025-24813) affecte Apache Tomcat, permettant l'exécution de code à distance Cette vulnérabilité est activement exploitée seulement 30 heures après sa divulgation du 10 mars 2025 L'attaque ne nécessite aucune authentification et est particulièrement simple à exécuter Elle utilise une requête PUT avec une charge utile Java sérialisée encodée en base64, suivie d'une requête GET L'encodage en base64 permet de contourner la plupart des filtres de sécurité Les serveurs vulnérables utilisent un stockage de session basé sur des fichiers (configuration répandue) Les versions affectées sont : 11.0.0-M1 à 11.0.2, 10.1.0-M1 à 10.1.34, et 9.0.0.M1 à 9.0.98 Les mises à jour recommandées sont : 11.0.3+, 10.1.35+ et 9.0.99+ Les experts prévoient des attaques plus sophistiquées dans les prochaines phases d'exploitation (upload de config ou jsp) Sécurisation d'un serveur ssh https://ittavern.com/ssh-server-hardening/ un article qui liste les configurations clés pour sécuriser un serveur SSH par exemple, enlever password authentigfication, changer de port, desactiver le login root, forcer le protocol ssh 2, certains que je ne connaissais pas comme MaxStartups qui limite le nombre de connections non authentifiées concurrentes Port knocking est une technique utile mais demande une approche cliente consciente du protocol Oracle admet que les identités IAM de ses clients ont leaké https://www.theregister.com/2025/04/08/oracle_cloud_compromised/ Oracle a confirmé à certains clients que son cloud public a été compromis, alors que l'entreprise avait précédemment nié toute intrusion. Un pirate informatique a revendiqué avoir piraté deux serveurs d'authentification d'Oracle et volé environ six millions d'enregistrements, incluant des clés de sécurité privées, des identifiants chiffrés et des entrées LDAP. La faille exploitée serait la vulnérabilité CVE-2021-35587 dans Oracle Access Manager, qu'Oracle n'avait pas corrigée sur ses propres systèmes. Le pirate a créé un fichier texte début mars sur login.us2.oraclecloud.com contenant son adresse email pour prouver son accès. Selon Oracle, un ancien serveur contenant des données vieilles de huit ans aurait été compromis, mais un client affirme que des données de connexion aussi récentes que 2024 ont été dérobées. Oracle fait face à un procès au Texas concernant cette violation de données. Cette intrusion est distincte d'une autre attaque contre Oracle Health, sur laquelle l'entreprise refuse de commenter. Oracle pourrait faire face à des sanctions sous le RGPD européen qui exige la notification des parties affectées dans les 72 heures suivant la découverte d'une fuite de données. Le comportement d'Oracle consistant à nier puis à admettre discrètement l'intrusion est inhabituel en 2025 et pourrait mener à d'autres actions en justice collectives. Une GitHub action très populaire compromise https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised Compromission de l'action tj-actions/changed-files : En mars 2025, une action GitHub très utilisée (tj-actions/changed-files) a été compromise. Des versions modifiées de l'action ont exposé des secrets CI/CD dans les logs de build. Méthode d'attaque : Un PAT compromis a permis de rediriger plusieurs tags de version vers un commit contenant du code malveillant. Détails du code malveillant : Le code injecté exécutait une fonction Node.js encodée en base64, qui téléchargeait un script Python. Ce script parcourait la mémoire du runner GitHub à la recherche de secrets (tokens, clés…) et les exposait dans les logs. Dans certains cas, les données étaient aussi envoyées via une requête réseau. Période d'exposition : Les versions compromises étaient actives entre le 12 et le 15 mars 2025. Tout dépôt, particulièrement ceux publiques, ayant utilisé l'action pendant cette période doit être considéré comme potentiellement exposé. Détection : L'activité malveillante a été repérée par l'analyse des comportements inhabituels pendant l'exécution des workflows, comme des connexions réseau inattendues. Réaction : GitHub a supprimé l'action compromise, qui a ensuite été nettoyée. Impact potentiel : Tous les secrets apparaissant dans les logs doivent être considérés comme compromis, même dans les dépôts privés, et régénérés sans délai. Loi, société et organisation Les startup the YCombinateur ont les plus fortes croissances de leur histoire https://www.cnbc.com/2025/03/15/y-combinator-startups-are-fastest-growing-in-fund-history-because-of-ai.html Les entreprises en phase de démarrage à Silicon Valley connaissent une croissance significative grâce à l'intelligence artificielle. Le PDG de Y Combinator, Garry Tan, affirme que l'ensemble des startups de la dernière cohorte a connu une croissance hebdomadaire de 10% pendant neuf mois. L'IA permet aux développeurs d'automatiser des tâches répétitives et de générer du code grâce aux grands modèles de langage. Pour environ 25% des startups actuelles de YC, 95% de leur code a été écrit par l'IA. Cette révolution permet aux entreprises de se développer avec moins de personnel - certaines atteignant 10 millions de dollars de revenus avec moins de 10 employés. La mentalité de “croissance à tout prix” a été remplacée par un renouveau d'intérêt pour la rentabilité. Environ 80% des entreprises présentées lors du “demo day” étaient centrées sur l'IA, avec quelques startups en robotique et semi-conducteurs. Y Combinator investit 500 000 dollars dans les startups en échange d'une participation au capital, suivi d'un programme de trois mois. Red Hat middleware (ex-jboss) rejoint IBM https://markclittle.blogspot.com/2025/03/red-hat-middleware-moving-to-ibm.html Les activités Middleware de Red Hat (incluant JBoss, Quarkus, etc.) vont être transférées vers IBM, dans l'unité dédiée à la sécurité des données, à l'IAM et aux runtimes. Ce changement découle d'une décision stratégique de Red Hat de se concentrer davantage sur le cloud hybride et l'intelligence artificielle. Mark Little explique que ce transfert était devenu inévitable, Red Hat ayant réduit ses investissements dans le Middleware ces dernières années. L'intégration vise à renforcer l'innovation autour de Java en réunissant les efforts de Red Hat et IBM sur ce sujet. Les produits Middleware resteront open source et les clients continueront à bénéficier du support habituel sans changement. Mark Little affirme que des projets comme Quarkus continueront à être soutenus et que cette évolution est bénéfique pour la communauté Java. Un an de commonhaus https://www.commonhaus.org/activity/253.html un an, démarré sur les communautés qu'ils connaissaient bien maintenant 14 projets et put en accepter plus confiance, gouvernance legère et proteger le futur des projets automatisation de l'administratif, stabiilité sans complexité, les developpeurs au centre du processus de décision ils ont besoins de members et supporters (financiers) ils veulent accueillir des projets au delà de ceux du cercles des Java Champions Spring Cloud Data Flow devient un produit commercial et ne sera plus maintenu en open source https://spring.io/blog/2025/04/21/spring-cloud-data-flow-commercial Peut-être sous l'influence de Broadcom, Spring se met à mettre en mode propriétaire des composants du portefeuille Spring ils disent que peu de gens l'utilisaent en mode OSS et la majorité venait d'un usage dans la plateforme Tanzu Maintenir en open source le coutent du temps qu'ils son't pas sur ces projets. La CNCF protège le projet NATS, dans la fondation depuis 2018, vu que la société Synadia qui y contribue souhaitait reprendre le contrôle du projet https://www.cncf.io/blog/2025/04/24/protecting-nats-and-the-integrity-of-open-source-cncfs-commitment-to-the-community/ CNCF : Protège projets OS, gouvernance neutre. Synadia vs CNCF : Veut retirer NATS, licence non-OS (BUSL). CNCF : Accuse Synadia de “claw back” (reprise illégitime). Revendications Synadia : Domaine nats.io, orga GitHub. Marque NATS : Synadia n'a pas transféré (promesse rompue malgré aide CNCF). Contestation Synadia : Juge règles CNCF “trop vagues”. Vote interne : Mainteneurs Synadia votent sortie CNCF (sans communauté). Support CNCF : Investissement majeur ($ audits, légal), succès communautaire (>700 orgs). Avenir NATS (CNCF) : Maintien sous Apache 2.0, gouvernance ouverte. Actions CNCF : Health check, appel mainteneurs, annulation marque Synadia, rejet demandes. Mais finalement il semble y avoir un bon dénouement : https://www.cncf.io/announcements/2025/05/01/cncf-and-synadia-align-on-securing-the-future-of-the-nats-io-project/ Accord pour l'avenir de NATS.io : La Cloud Native Computing Foundation (CNCF) et Synadia ont conclu un accord pour sécuriser le futur du projet NATS.io. Transfert des marques NATS : Synadia va céder ses deux enregistrements de marque NATS à la Linux Foundation afin de renforcer la gouvernance ouverte du projet. Maintien au sein de la CNCF : L'infrastructure et les actifs du projet NATS resteront sous l'égide de la CNCF, garantissant ainsi sa stabilité à long terme et son développement en open source sous licence Apache-2.0. Reconnaissance et engagement : La Linux Foundation, par la voix de Todd Moore, reconnaît les contributions de Synadia et son soutien continu. Derek Collison, PDG de Synadia, réaffirme l'engagement de son entreprise envers NATS et la collaboration avec la Linux Foundation et la CNCF. Adoption et soutien communautaire : NATS est largement adopté et considéré comme une infrastructure critique. Il bénéficie d'un fort soutien de la communauté pour sa nature open source et l'implication continue de Synadia. Finalement, Redis revient vers une licence open source OSI, avec la AGPL https://foojay.io/today/redis-is-now-available-under-the-agplv3-open-source-license/ Redis passe à la licence open source AGPLv3 pour contrer l'exploitation par les fournisseurs cloud sans contribution. Le passage précédent à la licence SSPL avait nui à la relation avec la communauté open source. Salvatore Sanfilippo (antirez) est revenu chez Redis. Redis 8 adopte la licence AGPL, intègre les fonctionnalités de Redis Stack (JSON, Time Series, etc.) et introduit les “vector sets” (le support de calcul vectoriel développé par Salvatore). Ces changements visent à renforcer Redis en tant que plateforme appréciée des développeurs, conformément à la vision initiale de Salvatore. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 mai 2025 : GOSIM AI Paris - Paris (France) 7-9 mai 2025 : Devoxx UK - London (UK) 15 mai 2025 : Cloud Toulouse - Toulouse (France) 16 mai 2025 : AFUP Day 2025 Lille - Lille (France) 16 mai 2025 : AFUP Day 2025 Lyon - Lyon (France) 16 mai 2025 : AFUP Day 2025 Poitiers - Poitiers (France) 22-23 mai 2025 : Flupa UX Days 2025 - Paris (France) 24 mai 2025 : Polycloud - Montpellier (France) 24 mai 2025 : NG Baguette Conf 2025 - Nantes (France) 3 juin 2025 : TechReady - Nantes (France) 5-6 juin 2025 : AlpesCraft - Grenoble (France) 5-6 juin 2025 : Devquest 2025 - Niort (France) 10-11 juin 2025 : Modern Workplace Conference Paris 2025 - Paris (France) 11-13 juin 2025 : Devoxx Poland - Krakow (Poland) 12 juin 2025 : Positive Design Days - Strasbourg (France) 12-13 juin 2025 : Agile Tour Toulouse - Toulouse (France) 12-13 juin 2025 : DevLille - Lille (France) 13 juin 2025 : Tech F'Est 2025 - Nancy (France) 17 juin 2025 : Mobilis In Mobile - Nantes (France) 19-21 juin 2025 : Drupal Barcamp Perpignan 2025 - Perpignan (France) 24 juin 2025 : WAX 2025 - Aix-en-Provence (France) 25-26 juin 2025 : Agi'Lille 2025 - Lille (France) 25-27 juin 2025 : BreizhCamp 2025 - Rennes (France) 26-27 juin 2025 : Sunny Tech - Montpellier (France) 1-4 juillet 2025 : Open edX Conference - 2025 - Palaiseau (France) 7-9 juillet 2025 : Riviera DEV 2025 - Sophia Antipolis (France) 5 septembre 2025 : JUG Summer Camp 2025 - La Rochelle (France) 12 septembre 2025 : Agile Pays Basque 2025 - Bidart (France) 18-19 septembre 2025 : API Platform Conference - Lille (France) & Online 23 septembre 2025 : OWASP AppSec France 2025 - Paris (France) 25-26 septembre 2025 : Paris Web 2025 - Paris (France) 2-3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 3 octobre 2025 : DevFest Perros-Guirec 2025 - Perros-Guirec (France) 6-10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 7 octobre 2025 : BSides Mulhouse - Mulhouse (France) 9-10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 9-10 octobre 2025 : EuroRust 2025 - Paris (France) 16 octobre 2025 : PlatformCon25 Live Day Paris - Paris (France) 16-17 octobre 2025 : DevFest Nantes - Nantes (France) 30-31 octobre 2025 : Agile Tour Bordeaux 2025 - Bordeaux (France) 30-31 octobre 2025 : Agile Tour Nantais 2025 - Nantes (France) 30 octobre 2025-2 novembre 2025 : PyConFR 2025 - Lyon (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 13 novembre 2025 : DevFest Toulouse - Toulouse (France) 15-16 novembre 2025 : Capitole du Libre - Toulouse (France) 20 novembre 2025 : OVHcloud Summit - Paris (France) 21 novembre 2025 : DevFest Paris 2025 - Paris (France) 27 novembre 2025 : Devfest Strasbourg 2025 - Strasbourg (France) 28 novembre 2025 : DevFest Lyon - Lyon (France) 5 décembre 2025 : DevFest Dijon 2025 - Dijon (France) 10-11 décembre 2025 : Devops REX - Paris (France) 10-11 décembre 2025 : Open Source Experience - Paris (France) 28-31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) 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/

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 19/04 a 25/04.

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 19/04 a 25/04.

Fredrik snackar med Daniel Raniz Raneland om att skapa och hålla presentationer. Ämnen finns överallt bara man börjar se dem, och man ska inte göra det svårt för sig. Att berätta hur man själv lärt sig något blir en alldeles utmärkt presentation. Skulle du kunna skriva en bloggpost om något? Då kan du också göra en presentation av det, du behöver bara anpassa formen lite. En presentation behöver vara lite mer av en resa och ge lite mer av en kontext. Du vet inte vilken kunskap du sitter på som är vardagsmat för dig men ett guldkorn för någon annan! Våga hålla en presentation! Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Raniz Factor10 Besöket i Varberg - då avsnitt 609 spelades in Softhouse Raniz blogg Jfokus TDD Kodkata Parprogrammering Sessionize Seecfp - mejllista Devoxx We are developers Berlin Raniz Øredevpresentation 2024 - Pipeline patterns and antipatterns Swetugg Slidev - koda dina presentationer i Markdown, HTML, och CSS Mermaid Magic code Stöd oss på Ko-fi! En version av Raniz Java på AWS lambda-presentation Papercall Martin Fowler Myconf - konferens i Varberg i maj Devopsdays i Zürich NDC Titlar Myndig på mjukvaruutveckling Vad gör ni här? Jag skriver abstrakt Alldeles för höga förväntningar En väldigt bra struktur En chans att dra sig ur Slutsatsen i början Prata väldigt fort istället 22 minuter inspelat material Ingen presentation är den andra lik En konferens i födelsedagspresent Broar som leder vidare

Topics covered in this episode: Git Town solves the problem that using the Git CLI correctly PEP 751 – A file format to record Python dependencies for installation reproducibility git-who and watchgha Share Python Scripts Like a Pro: uv and PEP 723 for Easy Deployment Extras Joke Watch on YouTube About the show Sponsored by Posit Package Manager: pythonbytes.fm/ppm 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: Git Town solves the problem that using the Git CLI correctly Git Town is a reusable implementation of Git workflows for common usage scenarios like contributing to a centralized code repository on platforms like GitHub, GitLab, or Gitea. Think of Git Town as your Bash scripts for Git, but fully engineered with rock-solid support for many use cases, edge cases, and error conditions. Keep using Git the way you do now, but with extra commands to create various branch types, keep them in sync, compress, review, and ship them efficiently. Basic workflow Commands to create, work on, and ship features. git town hack - create a new feature branch git town sync - update the current branch with all ongoing changes git town switch - switch between branches visually git town propose - propose to ship a branch git town ship - deliver a completed feature branch Additional workflow commands Commands to deal with edge cases. git town delete - delete a feature branch git town rename - rename a branch git town repo - view the Git repository in the browser Brian #2: PEP 751 – A file format to record Python dependencies for installation reproducibility Accepted From Brett Cannon “PEP 751 has been accepted! This means Python now has a lock file standard that can act as an export target for tools that can create some sort of lock file. And for some tools the format can act as their primary lock file format as well instead of some proprietary format.” File name: pylock.toml or at least something that starts with pylock and ends with .toml It's exciting to see the start of a standardized lock file Michael #3: git-who and watchgha git-who is a command-line tool for answering that eternal question: Who wrote this code?! Unlike git blame, which can tell you who wrote a line of code, git-who tells you the people responsible for entire components or subsystems in a codebase. You can think of git-who sort of like git blame but for file trees rather than individual files. And watchgha - Live display of current GitHub action runs by Ned Batchelder Brian #4: Share Python Scripts Like a Pro: uv and PEP 723 for Easy Deployment Dave Johnson Nice full tutorial discussing single file Python scripts using uv with external dependencies Starting with a script with dependencies. Using uv add --script [HTML_REMOVED] [HTML_REMOVED] to add a /// script block to the top Using uv run Adding #!/usr/bin/env -S uv run --script shebang Even some Windows advice Extras Brian: April 1 pranks done well BREAKING: Guido van Rossum Returns as Python's BDFL including Brett Cannon noted as “Famous Python Quotationist” Guido taking credit for “I came for the language but I stayed for the community” which was from Brett then Brett's title of “Famous Python Quotationist” is crossed out. Barry Warsaw asking Guido about releasing Python 2.8 Barry is the FLUFL, “Friendly Language Uncle For Life “ Mariatta can't get Guido to respond in chat until she addresses him as “my lord”. “… becoming one with whitespace.” “Indentation is Enlightenment” Upcoming new keyword: maybe Like “if” but more Pythonic as in Maybe: print("Python The Documentary - Coming This Summer!") I'm really hoping there is a documentary April 1 pranks done poorly Note: pytest-repeat works fine with Python 3.14, and never had any problems If you have to explain the joke, maybe it's not funny. The explanation pi, an irrational number, as in it cannot be expressed by a ratio of two integers, starts with 3.14159 and then keeps going, and never repeats. Python 3.14 is in alpha and people could be testing with it for packages Test & Code is doing a series on pytest plugins pytest-repeat is a pytest plugin, and it happened to not have any tests for 3.14 yet. Now the “joke”. I pretended that I had tried pytest-repeat with Python 3.14 and it didn't work. Test & Code: Python 3.14 won't repeat with pytest-repeat Thus, Python 3.14 won't repeat. Also I mentioned that there was no “rational” explanation. And pi is an irrational number. Michael: pysqlscribe v0.5.0 has the “parse create scripts” feature I suggested! Markdown follow up Prettier to format Markdown via Hugo Been using mdformat on some upcoming projects including the almost done Talk Python in Production book. Command I like is mdformat --number --wrap no ./ uv tool install --with is indeed the pipx inject equivalent, but requires multiple --with's: pipx inject mdformat mdformat-gfm mdformat-frontmatter mdformat-footnote mdformat-gfm-alerts uv tool install mdformat --with mdformat-gfm --with mdformat-frontmatter --with mdformat-footnote --with mdformat-gfm-alerts uv follow up From James Falcon As a fellow uv enthusiast, I was still holding out for a use case that uv hasn't solved. However, after last week's episode, you guys finally convinced me to switch over fully, so I figured I'd explain the use case and how I'm working around uv's limitations. I maintain a python library supported across multiple python versions and occasionally need to deal with bugs specific to a python version. Because of that, I have multiple virtualenvs for one project. E.g., mylib38 (for python 3.8), mylib313 (for python 3.13), etc. I don't want a bunch of .venv directories littering my project dir. For this, pyenv was fantastic. You could create the venv with pyenv virtualenv 3.13.2 mylib313, then either activate the venv with pyenv activate mylib313 and create a .python-version file containing mylib313 so I never had to manually activate the env I want to use by default on that project. uv doesn't have a great solution for this use case, but I switched to a workflow that works well enough for me: Define my own central location for venvs. For me that's ~/v Create venvs with something like uv venv --python 3.13 ~/v/mylib313 Add a simple function to my bashrc: `workon() { source ~/v/$1/bin/activate } so now I can run workon mylib313orworkon mylib38when I need to work in a specific environment. uv's.python-version` support works much differently than pyenv's, and that lack of support is my biggest frustration with this approach, but I am willing to live without it. Do you Firefox but not Zen? You can now make pure Firefox more like Zen's / Arc's layout. Joke: So here it will stay See the follow up thread too! Also: Guido as Lord Python via Nick Muoh

Show notes here in Markdown, No HTML. No relative links. In this episode: Mark has started developing a self-hosted replacement for the Yoto or Tonie audiobook players. Alan has taken a look at Docs, but didn’t use it. Martin has upgraded his home networking with Deco and YuanLey devices. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community, you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

Show notes here in Markdown, No HTML. No relative links. In this episode: Mark has started developing a self-hosted replacement for the Yoto or Tonie audiobook players. Alan has taken a look at Docs, but didn’t use it. Martin has upgraded his home networking with Deco and YuanLey devices. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community, you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

Topics covered in this episode: mdformat pre-commit-uv PEP 758 and 781 Serie: rich git commit graph in your terminal, like magic Extras Joke Watch on YouTube About the show Sponsored by Posit Connect Cloud: pythonbytes.fm/connect-cloud 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. Brian #1: mdformat Suggested by Matthias Schöttle Last episode Michael covered blacken-docs, and I mentioned it'd be nice to have an autoformatter for text markdown. Matthias delivered with suggesting mdformat “Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.” A python project that can be run on the command line. Uses a style guide I mostly agree with. I'm not a huge fan of numbered list items all being “1.”, but that can be turned off with --number, so I'm happy. Converts underlined headings to #, ##, etc. headings. Lots of other sane conventions. The numbering thing is also sane, I just think it also makes the raw markdown hard to read. Has a plugin system to format code blocks Michael #2: pre-commit-uv via Ben Falk Use uv to create virtual environments and install packages for pre-commit. Brian #3: PEP 758 and 781 PEP 758 – Allow except and except* expressions without parentheses accepted PEP 781 – Make TYPE_CHECKING a built-in constant draft status Also, PEP Index by Category kinda rocks Michael #4: Serie: rich git commit graph in your terminal, like magic While some users prefer to use Git via CLI, they often rely on a GUI or feature-rich TUI to view commit logs. Others may find git log --graph sufficient. Goals Provide a rich git log --graph experience in the terminal. Offer commit graph-centric browsing of Git repositories. Extras Michael: Sunsetting Search? (Startpage) Ruff in or out? Joke: Wishing for wishes

Topics covered in this episode: Why aren't you using uv? Python Developer Tooling Handbook Calling all doc writers: blacken-docs Reinventing notebooks as reusable Python programs Extras Joke Watch on YouTube About the show Brought to you by Posit Connect: pythonbytes.fm/connect. 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: Why aren't you using uv? Fun conversation on X by Armin Ronacher. Interesting quotes from the thread I get it replaces pip/pyenv, but should I also use it instead of the built in 'python -m venv .venv'? But I need python installed to make python programs? Because it places the venv in the project folder and I can't run executables from there due to corporate policy. Many such cases. No idea why astral doesn't address this with more urgency. Sounds like a bad corporate policy :) i'm too lazy to switch from pyenv and pip trust issues, what if they do a bait and switch … Because everyone said that about poetry and I'm not sure I'm really ready to get hurt again. Masochism Many times I tried a lot of similar tools and always come back to pip and pip-tools. Them are just work, why should I spend my time for something "cool" that will bring more problems? I tried this week but I was expecting a "uv install requests" instead of "uv add". Switched back to pipenv. we partially use it. will transition when Dependabot support is available. I'll leave it with → Jared Scheel: Seeing a whole lotta Stockholm Syndrome in the replies to this question. Brian #2: Python Developer Tooling Handbook Tim Hopper “This is not a book about programming Python. Instead, the goal of this book is to help you understand the ecosystem of tools used to make Python development easier and more productive” Covers tools related to packaging, linting, formatting, and managing dependencies. Michael #3: Calling all doc writers: blacken-docs Run black on python code blocks in documentation files You can also install blacken-docs as a pre-commit hook. It supports Markdown, reStructuredText, and LaTex files. Additionally, you can run it on Python files to reformat Markdown and reStructuredText within docstrings. Brian #4: Reinventing notebooks as reusable Python programs marimo allows you to store notebooks as plaintext Python files properties Git-friendly: small code change => small diff easy for both humans and computers to read importable as a Python module, without executing notebook cells executable as a Python script editable with a text editor Also, … testing with pytest “Because marimo notebooks are just Python files, they are interoperable with other tools for Python — including pytest. “ “Testing cells. Any cell named as test_* is automatically discoverable and testable by pytest. The same goes for any cell that contains only test_ functions and Test classes.” “Importantly, because cells are wrapped in functions, running pytest test_notebook.py doesn't execute the entire notebook — just its tests.” Extras Brian: PyConUS announces Refund Policy for International Attendees New format now live for The Complete pytest Course Bundle and component courses Each course now available separately also pytest Primary Power is 13 lessons, 3.9 hours Using pytest with Projects, 10 lessons, 3.4 hours pytest Booster Rockets, 6 lessons, 1.3 hours of content New format is easier to navigate Better for people who like different speeds. I'm usually a 1.25x-1.5x speed person. Now also with Congratulations! lessons (with fireworks) and printable certificates. Michael: PyCon Taiwan is currently calling for proposals HN trends follow up via Shinjitsu I'm sure some other Hacker News reader has already given you the feedback, but in the unlikely case that they haven't, You read those headlines in this segment exactly wrong. “Ask HN: Who is hiring?" is a monthly post that asks employers to post about jobs they have available “Ask HN: Who wants to be hired?” is a monthly topic where they ask people who are looking for jobs to post about themselves in the hope that their skillset it is a good match (and not an LLM generated resume) So unfortunately your rosy analysis might need a less rosy interpretation. Joke: Top 12 things likely to be overheard if you had a Klingon Programmer From Holgi on Mastodon

Descubre An Otter Wiki, una wiki minimalista con Git y Markdown para organizar tus notas eficientemente. Ideal para documentación personal y colaborativa.De nuevo vuelvo al ataque con los servicios de notas, y se que no podrás resistirte a probarlo, porque es realmente sencillo, pero es justo lo que necesitas para organizar todo tu conocimiento y sin perder el tiempo. Y es que hace poco en el episodio 673 titulado Ocho imprescindibles para desarrolladores, te hablé de Docmost, como la herramienta para organizar todo tu conocimiento, un sitio donde guardar todos esos KB. Recientemente, me tropecé con An Otter Wiki, y no he tardado ni dos días en reemplazar al primero. Y la razón de reemplazarlo no ha sido ni mas ni menos que la simplicidad. Uno de los grandes obstáculos para documentar es utilizar una herramienta que te complica la vida. La herramienta que utilicemos para gestionar nuestro conocimiento tiene que ser sencilla, muy sencilla. Tiene que ser una herramienta que tengamos siempre a mano, y que podamos utilizar en cualquier momento. Y rápida, muy rápida, y estas son las características precisamente que tiene este servicio del que te voy a hablar en este episodio.Más información y enlaces en las notas del episodio

Join Matt and Simon as they discuss the state of the crypto market, highlighting a quiet week on the news front. They analyse the implications of the upcoming Federal Reserve announcement, the potential for the U.S. government to accumulate more Bitcoin, Strategy's new plan to add to its Bitcoin holdings, and more.Key Takeaways While prices rebounded last week, the crypto market is essentially in a holding pattern until further updates about tariffs, especially on April 2. To a lesser extent, the market is also awaiting the Fed's next policy update on March 19.U.S. Senator Cynthia Lummis reintroduced The Bitcoin Act last week. The bill would direct the government to purchase 1 million BTC over five years.Strategy, formerly MicroStrategy, announced a $21B stock offering to expand its BTC holdings. This is in addition to the company's previously announced ‘21/21 plan', which targets a total capital raise of $42B specifically for buying more BTC.Timestamps:00:00 Market Overview & Current Sentiment03:14 Federal Reserve & Economic Indicators05:45 Bitcoin Act & Government Involvement08:56 Strategy's Plan For Buying More Bitcoin12:07 Altcoin Market Trends14:47 Upcoming Events & Market Predictions

Coming up in this episode * Syncing the Notes * The History of Snaps * And How Much We Absolutely Adore Them 0:00 Cold Open 1:34 Seeking Syncthing 16:42 The History of Snaps 33:52 How'd 9 Years of Snaps Go? 1:01:54 Next Time 1:04:49 Stinger The Video Version https://youtu.be/izDzKkuEyRw It is all about the notes Leo goes back to basics and uses SyncThing (https://syncthing.net/) to move his markdown files around that he edits using a standard text editor (https://code.visualstudio.com/).

Our 202nd episode with a summary and discussion of last week's big AI news! Recorded on 03/07/2025 Hosted by Andrey Kurenkov and Jeremie Harris. Feel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai Read out our text newsletter and comment on the podcast at https://lastweekin.ai/. Join our Discord here! https://discord.gg/nTyezGSKwP In this episode: Alibaba released Qwen-32B, their latest reasoning model, on par with leading models like DeepMind's R1. Anthropic raised $3.5 billion in a funding round, valuing the company at $61.5 billion, solidifying its position as a key competitor to OpenAI. DeepMind introduced BigBench Extra Hard, a more challenging benchmark to evaluate the reasoning capabilities of large language models. Reinforcement Learning pioneers Andrew Bartow and Rich Sutton were awarded the prestigious Turing Award for their contributions to the field. Timestamps + Links: cle picks: (00:00:00) Intro / Banter (00:01:41) Episode Preview (00:02:50) GPT-4.5 Discussion (00:14:13) Alibaba's New QwQ 32B Model is as Good as DeepSeek-R1 ; Outperforms OpenAI's o1-mini (00:21:29) With Alexa Plus, Amazon finally reinvents its best product (00:26:08) Another DeepSeek moment? General AI agent Manus shows ability to handle complex tasks (00:29:14) Microsoft's new Dragon Copilot is an AI assistant for healthcare (00:32:24) Mistral's new OCR API turns any PDF document into an AI-ready Markdown file (00:33:19) A.I. Start-Up Anthropic Closes Deal That Values It at $61.5 Billion (00:35:49) Nvidia-Backed CoreWeave Files for IPO, Shows Growing Revenue (00:38:05) Waymo and Uber's Austin robotaxi expansion begins today (00:38:54) UK competition watchdog drops Microsoft-OpenAI probe (00:41:17) Scale AI announces multimillion-dollar defense deal, a major step in U.S. military automation (00:44:43) DeepSeek Open Source Week: A Complete Summary (00:45:25) DeepSeek AI Releases DualPipe: A Bidirectional Pipeline Parallelism Algorithm for Computation-Communication Overlap in V3/R1 Training (00:53:00) Physical Intelligence open-sources Pi0 robotics foundation model (00:54:23) BIG-Bench Extra Hard (00:56:10) Cognitive Behaviors that Enable Self-Improving Reasoners (01:01:49) The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems (01:05:32) Pioneers of Reinforcement Learning Win the Turing Award (01:06:56) OpenAI launches $50M grant program to help fund academic research (01:07:25) The Nuclear-Level Risk of Superintelligent AI (01:13:34) METR's GPT-4.5 pre-deployment evaluations (01:17:16) Chinese buyers are getting Nvidia Blackwell chips despite US export controls

Allen Pike on the JavaScript ecosystem after a decade away, Lars Wirzenius was there at the birth of Linux, Piotr Migdał archives things in Markdown, Jacob Stopak is gamifying Git with Devlands & Juan Diego Rodríguez runs down how CSS functions (will) work.

D&D and RPG news and commentary by Mike Shea of https://slyflourish.com Contents 00:00 Show Start 01:11 Sly Flourish News: City of Arches in Markdown and EPUB 17:55 D&D & RPG News: Blog of Holding Monster Manual 2024 Stats in the Creative Commons 22:34 DM Tip: Challenge Rating Deep Dive 54:19 Patreon Question: Fantastic Locations in Contemporary Worlds Links Subscribe to the Sly Flourish Newsletter Support Sly Flourish on Patreon Buy Sly Flourish Books: City of Arches Blog of Holding 2025 Monster Manual on a Business Card What Does Challenge Rating Mean in D&D 5e? The Lazy Encounter Benchmark

Allen Pike on the JavaScript ecosystem after a decade away, Lars Wirzenius was there at the birth of Linux, Piotr Migdał archives things in Markdown, Jacob Stopak is gamifying Git with Devlands & Juan Diego Rodríguez runs down how CSS functions (will) work.

Allen Pike on the JavaScript ecosystem after a decade away, Lars Wirzenius was there at the birth of Linux, Piotr Migdał archives things in Markdown, Jacob Stopak is gamifying Git with Devlands & Juan Diego Rodríguez runs down how CSS functions (will) work.

Sun, 02 Mar 2025 22:00:00 GMT http://relay.fm/mpu/786 http://relay.fm/mpu/786 Catching up with John Soliman 786 David Sparks and Stephen Hackett John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. clean 5917 John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. This episode of Mac Power Users is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code MPU. Google Gemini: Supercharge your creativity and productivity. Indeed: Join more than 3.5 million businesses worldwide using Indeed to hire great talent fast. Incogni: Take your personal data back with Incogni! Use code MACPOWERUSERS with this link and get 60% off an annual plan. Guest Starring: John Soliman Links and Show Notes: Sign up for the MPU email newsletter and join the MPU forums. More Power Users: Ad-free episodes with regular bonus segments Submit Feedback Mac Power Users #785: First of All, I'm David Sparks - Relay If you can't update or restore your iPad - Apple Support Mac Power Users #618: Making Movies at Pixar, with John Soliman - Relay John (@solimander.bsky.social) - Bluesky John (@Solimander@mstdn.social) - Mastodon Mac mini - Apple Goodbye, Old Mac Pro (2013) - MacSparky Hello, New Mac Pro (2019) - MacSparky Disney+ on Apple Vision Pro Ushers in a New Era of Storytelling Innovation and Immersive Entertainment | Disney Plus Press Schoolhouse Rock #1 Three is a Magic Number - YouTube Watch Win or Lose | Full Episodes | Disney+ Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration by Ed Catmull Keyboard Maestro HandBrake Lisa Melton's Video Transcoding Scripts Shutter Encoder MakeMKV Video-compare: Split screen video comparison tool Typora — simple yet powerful Markdown reader. Openvibe — Town Square for Open Social Media macOS Icons CandyBar - Wikipedia Accidental Tech Podcast Plex Product List | Synology Inc. Rsync Project dupeGuru | finds duplicate files Bare Bones Software | BBEdit Pattern Playgrounds

Sun, 02 Mar 2025 22:00:00 GMT http://relay.fm/mpu/786 http://relay.fm/mpu/786 David Sparks and Stephen Hackett John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. clean 5917 John Soliman returns to the show to detail his journey with Apple silicon, share his work on Pixar's "Win or Lose," and discuss video transcoding. This episode of Mac Power Users is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code MPU. Google Gemini: Supercharge your creativity and productivity. Indeed: Join more than 3.5 million businesses worldwide using Indeed to hire great talent fast. Incogni: Take your personal data back with Incogni! Use code MACPOWERUSERS with this link and get 60% off an annual plan. Guest Starring: John Soliman Links and Show Notes: Sign up for the MPU email newsletter and join the MPU forums. More Power Users: Ad-free episodes with regular bonus segments Submit Feedback Mac Power Users #785: First of All, I'm David Sparks - Relay If you can't update or restore your iPad - Apple Support Mac Power Users #618: Making Movies at Pixar, with John Soliman - Relay John (@solimander.bsky.social) - Bluesky John (@Solimander@mstdn.social) - Mastodon Mac mini - Apple Goodbye, Old Mac Pro (2013) - MacSparky Hello, New Mac Pro (2019) - MacSparky Disney+ on Apple Vision Pro Ushers in a New Era of Storytelling Innovation and Immersive Entertainment | Disney Plus Press Schoolhouse Rock #1 Three is a Magic Number - YouTube Watch Win or Lose | Full Episodes | Disney+ Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration by Ed Catmull Keyboard Maestro HandBrake Lisa Melton's Video Transcoding Scripts Shutter Encoder MakeMKV Video-compare: Split screen video comparison tool Typora — simple yet powerful Markdown reader. Openvibe — Town Square for Open Social Media macOS Icons CandyBar - Wikipedia Accidental Tech Podcast Plex Product List | Synology Inc. Rsync Project dupeGuru | finds duplicate files Bare Bones Software | BBEdit Pattern Playgrounds

Markdown reports as either text or markdown tables.Two fun plugins discussed.Links:pytest-md-reportpytest-mdTop pytest Plugins  Learn pytestpytest is the number one test framework for Python.Learn the basics super fast with Hello, pytest!Then later you can become a pytest expert with The Complete pytest CourseBoth courses are at courses.pythontest.com ★ Support this podcast on Patreon ★

Today's episode is with Paul Klein, founder of Browserbase. We talked about building browser infrastructure for AI agents, the future of agent authentication, and their open source framework Stagehand.* [00:00:00] Introductions* [00:04:46] AI-specific challenges in browser infrastructure* [00:07:05] Multimodality in AI-Powered Browsing* [00:12:26] Running headless browsers at scale* [00:18:46] Geolocation when proxying* [00:21:25] CAPTCHAs and Agent Auth* [00:28:21] Building “User take over” functionality* [00:33:43] Stagehand: AI web browsing framework* [00:38:58] OpenAI's Operator and computer use agents* [00:44:44] Surprising use cases of Browserbase* [00:47:18] Future of browser automation and market competition* [00:53:11] Being a solo founderTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.swyx [00:00:12]: Hey, and today we are very blessed to have our friends, Paul Klein, for the fourth, the fourth, CEO of Browserbase. Welcome.Paul [00:00:21]: Thanks guys. Yeah, I'm happy to be here. I've been lucky to know both of you for like a couple of years now, I think. So it's just like we're hanging out, you know, with three ginormous microphones in front of our face. It's totally normal hangout.swyx [00:00:34]: Yeah. We've actually mentioned you on the podcast, I think, more often than any other Solaris tenant. Just because like you're one of the, you know, best performing, I think, LLM tool companies that have started up in the last couple of years.Paul [00:00:50]: Yeah, I mean, it's been a whirlwind of a year, like Browserbase is actually pretty close to our first birthday. So we are one years old. And going from, you know, starting a company as a solo founder to... To, you know, having a team of 20 people, you know, a series A, but also being able to support hundreds of AI companies that are building AI applications that go out and automate the web. It's just been like, really cool. It's been happening a little too fast. I think like collectively as an AI industry, let's just take a week off together. I took my first vacation actually two weeks ago, and Operator came out on the first day, and then a week later, DeepSeat came out. And I'm like on vacation trying to chill. I'm like, we got to build with this stuff, right? So it's been a breakneck year. But I'm super happy to be here and like talk more about all the stuff we're seeing. And I'd love to hear kind of what you guys are excited about too, and share with it, you know?swyx [00:01:39]: Where to start? So people, you've done a bunch of podcasts. I think I strongly recommend Jack Bridger's Scaling DevTools, as well as Turner Novak's The Peel. And, you know, I'm sure there's others. So you covered your Twilio story in the past, talked about StreamClub, you got acquired to Mux, and then you left to start Browserbase. So maybe we just start with what is Browserbase? Yeah.Paul [00:02:02]: Browserbase is the web browser for your AI. We're building headless browser infrastructure, which are browsers that run in a server environment that's accessible to developers via APIs and SDKs. It's really hard to run a web browser in the cloud. You guys are probably running Chrome on your computers, and that's using a lot of resources, right? So if you want to run a web browser or thousands of web browsers, you can't just spin up a bunch of lambdas. You actually need to use a secure containerized environment. You have to scale it up and down. It's a stateful system. And that infrastructure is, like, super painful. And I know that firsthand, because at my last company, StreamClub, I was CTO, and I was building our own internal headless browser infrastructure. That's actually why we sold the company, is because Mux really wanted to buy our headless browser infrastructure that we'd built. And it's just a super hard problem. And I actually told my co-founders, I would never start another company unless it was a browser infrastructure company. And it turns out that's really necessary in the age of AI, when AI can actually go out and interact with websites, click on buttons, fill in forms. You need AI to do all of that work in an actual browser running somewhere on a server. And BrowserBase powers that.swyx [00:03:08]: While you're talking about it, it occurred to me, not that you're going to be acquired or anything, but it occurred to me that it would be really funny if you became the Nikita Beer of headless browser companies. You just have one trick, and you make browser companies that get acquired.Paul [00:03:23]: I truly do only have one trick. I'm screwed if it's not for headless browsers. I'm not a Go programmer. You know, I'm in AI grant. You know, browsers is an AI grant. But we were the only company in that AI grant batch that used zero dollars on AI spend. You know, we're purely an infrastructure company. So as much as people want to ask me about reinforcement learning, I might not be the best guy to talk about that. But if you want to ask about headless browser infrastructure at scale, I can talk your ear off. So that's really my area of expertise. And it's a pretty niche thing. Like, nobody has done what we're doing at scale before. So we're happy to be the experts.swyx [00:03:59]: You do have an AI thing, stagehand. We can talk about the sort of core of browser-based first, and then maybe stagehand. Yeah, stagehand is kind of the web browsing framework. Yeah.What is Browserbase? Headless Browser Infrastructure ExplainedAlessio [00:04:10]: Yeah. Yeah. And maybe how you got to browser-based and what problems you saw. So one of the first things I worked on as a software engineer was integration testing. Sauce Labs was kind of like the main thing at the time. And then we had Selenium, we had Playbrite, we had all these different browser things. But it's always been super hard to do. So obviously you've worked on this before. When you started browser-based, what were the challenges? What were the AI-specific challenges that you saw versus, there's kind of like all the usual running browser at scale in the cloud, which has been a problem for years. What are like the AI unique things that you saw that like traditional purchase just didn't cover? Yeah.AI-specific challenges in browser infrastructurePaul [00:04:46]: First and foremost, I think back to like the first thing I did as a developer, like as a kid when I was writing code, I wanted to write code that did stuff for me. You know, I wanted to write code to automate my life. And I do that probably by using curl or beautiful soup to fetch data from a web browser. And I think I still do that now that I'm in the cloud. And the other thing that I think is a huge challenge for me is that you can't just create a web site and parse that data. And we all know that now like, you know, taking HTML and plugging that into an LLM, you can extract insights, you can summarize. So it was very clear that now like dynamic web scraping became very possible with the rise of large language models or a lot easier. And that was like a clear reason why there's been more usage of headless browsers, which are necessary because a lot of modern websites don't expose all of their page content via a simple HTTP request. You know, they actually do require you to run this type of code for a specific time. JavaScript on the page to hydrate this. Airbnb is a great example. You go to airbnb.com. A lot of that content on the page isn't there until after they run the initial hydration. So you can't just scrape it with a curl. You need to have some JavaScript run. And a browser is that JavaScript engine that's going to actually run all those requests on the page. So web data retrieval was definitely one driver of starting BrowserBase and the rise of being able to summarize that within LLM. Also, I was familiar with if I wanted to automate a website, I could write one script and that would work for one website. It was very static and deterministic. But the web is non-deterministic. The web is always changing. And until we had LLMs, there was no way to write scripts that you could write once that would run on any website. That would change with the structure of the website. Click the login button. It could mean something different on many different websites. And LLMs allow us to generate code on the fly to actually control that. So I think that rise of writing the generic automation scripts that can work on many different websites, to me, made it clear that browsers are going to be a lot more useful because now you can automate a lot more things without writing. If you wanted to write a script to book a demo call on 100 websites, previously, you had to write 100 scripts. Now you write one script that uses LLMs to generate that script. That's why we built our web browsing framework, StageHand, which does a lot of that work for you. But those two things, web data collection and then enhanced automation of many different websites, it just felt like big drivers for more browser infrastructure that would be required to power these kinds of features.Alessio [00:07:05]: And was multimodality also a big thing?Paul [00:07:08]: Now you can use the LLMs to look, even though the text in the dome might not be as friendly. Maybe my hot take is I was always kind of like, I didn't think vision would be as big of a driver. For UI automation, I felt like, you know, HTML is structured text and large language models are good with structured text. But it's clear that these computer use models are often vision driven, and they've been really pushing things forward. So definitely being multimodal, like rendering the page is required to take a screenshot to give that to a computer use model to take actions on a website. And it's just another win for browser. But I'll be honest, that wasn't what I was thinking early on. I didn't even think that we'd get here so fast with multimodality. I think we're going to have to get back to multimodal and vision models.swyx [00:07:50]: This is one of those things where I forgot to mention in my intro that I'm an investor in Browserbase. And I remember that when you pitched to me, like a lot of the stuff that we have today, we like wasn't on the original conversation. But I did have my original thesis was something that we've talked about on the podcast before, which is take the GPT store, the custom GPT store, all the every single checkbox and plugin is effectively a startup. And this was the browser one. I think the main hesitation, I think I actually took a while to get back to you. The main hesitation was that there were others. Like you're not the first hit list browser startup. It's not even your first hit list browser startup. There's always a question of like, will you be the category winner in a place where there's a bunch of incumbents, to be honest, that are bigger than you? They're just not targeted at the AI space. They don't have the backing of Nat Friedman. And there's a bunch of like, you're here in Silicon Valley. They're not. I don't know.Paul [00:08:47]: I don't know if that's, that was it, but like, there was a, yeah, I mean, like, I think I tried all the other ones and I was like, really disappointed. Like my background is from working at great developer tools, companies, and nothing had like the Vercel like experience. Um, like our biggest competitor actually is partly owned by private equity and they just jacked up their prices quite a bit. And the dashboard hasn't changed in five years. And I actually used them at my last company and tried them and I was like, oh man, like there really just needs to be something that's like the experience of these great infrastructure companies, like Stripe, like clerk, like Vercel that I use in love, but oriented towards this kind of like more specific category, which is browser infrastructure, which is really technically complex. Like a lot of stuff can go wrong on the internet when you're running a browser. The internet is very vast. There's a lot of different configurations. Like there's still websites that only work with internet explorer out there. How do you handle that when you're running your own browser infrastructure? These are the problems that we have to think about and solve at BrowserBase. And it's, it's certainly a labor of love, but I built this for me, first and foremost, I know it's super cheesy and everyone says that for like their startups, but it really, truly was for me. If you look at like the talks I've done even before BrowserBase, and I'm just like really excited to try and build a category defining infrastructure company. And it's, it's rare to have a new category of infrastructure exists. We're here in the Chroma offices and like, you know, vector databases is a new category of infrastructure. Is it, is it, I mean, we can, we're in their office, so, you know, we can, we can debate that one later. That is one.Multimodality in AI-Powered Browsingswyx [00:10:16]: That's one of the industry debates.Paul [00:10:17]: I guess we go back to the LLMOS talk that Karpathy gave way long ago. And like the browser box was very clearly there and it seemed like the people who were building in this space also agreed that browsers are a core primitive of infrastructure for the LLMOS that's going to exist in the future. And nobody was building something there that I wanted to use. So I had to go build it myself.swyx [00:10:38]: Yeah. I mean, exactly that talk that, that honestly, that diagram, every box is a startup and there's the code box and then there's the. The browser box. I think at some point they will start clashing there. There's always the question of the, are you a point solution or are you the sort of all in one? And I think the point solutions tend to win quickly, but then the only ones have a very tight cohesive experience. Yeah. Let's talk about just the hard problems of browser base you have on your website, which is beautiful. Thank you. Was there an agency that you used for that? Yeah. Herb.paris.Paul [00:11:11]: They're amazing. Herb.paris. Yeah. It's H-E-R-V-E. I highly recommend for developers. Developer tools, founders to work with consumer agencies because they end up building beautiful things and the Parisians know how to build beautiful interfaces. So I got to give prep.swyx [00:11:24]: And chat apps, apparently are, they are very fast. Oh yeah. The Mistral chat. Yeah. Mistral. Yeah.Paul [00:11:31]: Late chat.swyx [00:11:31]: Late chat. And then your videos as well, it was professionally shot, right? The series A video. Yeah.Alessio [00:11:36]: Nico did the videos. He's amazing. Not the initial video that you shot at the new one. First one was Austin.Paul [00:11:41]: Another, another video pretty surprised. But yeah, I mean, like, I think when you think about how you talk about your company. You have to think about the way you present yourself. It's, you know, as a developer, you think you evaluate a company based on like the API reliability and the P 95, but a lot of developers say, is the website good? Is the message clear? Do I like trust this founder? I'm building my whole feature on. So I've tried to nail that as well as like the reliability of the infrastructure. You're right. It's very hard. And there's a lot of kind of foot guns that you run into when running headless browsers at scale. Right.Competing with Existing Headless Browser Solutionsswyx [00:12:10]: So let's pick one. You have eight features here. Seamless integration. Scalability. Fast or speed. Secure. Observable. Stealth. That's interesting. Extensible and developer first. What comes to your mind as like the top two, three hardest ones? Yeah.Running headless browsers at scalePaul [00:12:26]: I think just running headless browsers at scale is like the hardest one. And maybe can I nerd out for a second? Is that okay? I heard this is a technical audience, so I'll talk to the other nerds. Whoa. They were listening. Yeah. They're upset. They're ready. The AGI is angry. Okay. So. So how do you run a browser in the cloud? Let's start with that, right? So let's say you're using a popular browser automation framework like Puppeteer, Playwright, and Selenium. Maybe you've written a code, some code locally on your computer that opens up Google. It finds the search bar and then types in, you know, search for Latent Space and hits the search button. That script works great locally. You can see the little browser open up. You want to take that to production. You want to run the script in a cloud environment. So when your laptop is closed, your browser is doing something. The browser is doing something. Well, I, we use Amazon. You can see the little browser open up. You know, the first thing I'd reach for is probably like some sort of serverless infrastructure. I would probably try and deploy on a Lambda. But Chrome itself is too big to run on a Lambda. It's over 250 megabytes. So you can't easily start it on a Lambda. So you maybe have to use something like Lambda layers to squeeze it in there. Maybe use a different Chromium build that's lighter. And you get it on the Lambda. Great. It works. But it runs super slowly. It's because Lambdas are very like resource limited. They only run like with one vCPU. You can run one process at a time. Remember, Chromium is super beefy. It's barely running on my MacBook Air. I'm still downloading it from a pre-run. Yeah, from the test earlier, right? I'm joking. But it's big, you know? So like Lambda, it just won't work really well. Maybe it'll work, but you need something faster. Your users want something faster. Okay. Well, let's put it on a beefier instance. Let's get an EC2 server running. Let's throw Chromium on there. Great. Okay. I can, that works well with one user. But what if I want to run like 10 Chromium instances, one for each of my users? Okay. Well, I might need two EC2 instances. Maybe 10. All of a sudden, you have multiple EC2 instances. This sounds like a problem for Kubernetes and Docker, right? Now, all of a sudden, you're using ECS or EKS, the Kubernetes or container solutions by Amazon. You're spending up and down containers, and you're spending a whole engineer's time on kind of maintaining this stateful distributed system. Those are some of the worst systems to run because when it's a stateful distributed system, it means that you are bound by the connections to that thing. You have to keep the browser open while someone is working with it, right? That's just a painful architecture to run. And there's all this other little gotchas with Chromium, like Chromium, which is the open source version of Chrome, by the way. You have to install all these fonts. You want emojis working in your browsers because your vision model is looking for the emoji. You need to make sure you have the emoji fonts. You need to make sure you have all the right extensions configured, like, oh, do you want ad blocking? How do you configure that? How do you actually record all these browser sessions? Like it's a headless browser. You can't look at it. So you need to have some sort of observability. Maybe you're recording videos and storing those somewhere. It all kind of adds up to be this just giant monster piece of your project when all you wanted to do was run a lot of browsers in production for this little script to go to google.com and search. And when I see a complex distributed system, I see an opportunity to build a great infrastructure company. And we really abstract that away with Browserbase where our customers can use these existing frameworks, Playwright, Publisher, Selenium, or our own stagehand and connect to our browsers in a serverless-like way. And control them, and then just disconnect when they're done. And they don't have to think about the complex distributed system behind all of that. They just get a browser running anywhere, anytime. Really easy to connect to.swyx [00:15:55]: I'm sure you have questions. My standard question with anything, so essentially you're a serverless browser company, and there's been other serverless things that I'm familiar with in the past, serverless GPUs, serverless website hosting. That's where I come from with Netlify. One question is just like, you promised to spin up thousands of servers. You promised to spin up thousands of browsers in milliseconds. I feel like there's no real solution that does that yet. And I'm just kind of curious how. The only solution I know, which is to kind of keep a kind of warm pool of servers around, which is expensive, but maybe not so expensive because it's just CPUs. So I'm just like, you know. Yeah.Browsers as a Core Primitive in AI InfrastructurePaul [00:16:36]: You nailed it, right? I mean, how do you offer a serverless-like experience with something that is clearly not serverless, right? And the answer is, you need to be able to run... We run many browsers on single nodes. We use Kubernetes at browser base. So we have many pods that are being scheduled. We have to predictably schedule them up or down. Yes, thousands of browsers in milliseconds is the best case scenario. If you hit us with 10,000 requests, you may hit a slower cold start, right? So we've done a lot of work on predictive scaling and being able to kind of route stuff to different regions where we have multiple regions of browser base where we have different pools available. You can also pick the region you want to go to based on like lower latency, round trip, time latency. It's very important with these types of things. There's a lot of requests going over the wire. So for us, like having a VM like Firecracker powering everything under the hood allows us to be super nimble and spin things up or down really quickly with strong multi-tenancy. But in the end, this is like the complex infrastructural challenges that we have to kind of deal with at browser base. And we have a lot more stuff on our roadmap to allow customers to have more levers to pull to exchange, do you want really fast browser startup times or do you want really low costs? And if you're willing to be more flexible on that, we may be able to kind of like work better for your use cases.swyx [00:17:44]: Since you used Firecracker, shouldn't Fargate do that for you or did you have to go lower level than that? We had to go lower level than that.Paul [00:17:51]: I find this a lot with Fargate customers, which is alarming for Fargate. We used to be a giant Fargate customer. Actually, the first version of browser base was ECS and Fargate. And unfortunately, it's a great product. I think we were actually the largest Fargate customer in our region for a little while. No, what? Yeah, seriously. And unfortunately, it's a great product, but I think if you're an infrastructure company, you actually have to have a deeper level of control over these primitives. I think it's the same thing is true with databases. We've used other database providers and I think-swyx [00:18:21]: Yeah, serverless Postgres.Paul [00:18:23]: Shocker. When you're an infrastructure company, you're on the hook if any provider has an outage. And I can't tell my customers like, hey, we went down because so-and-so went down. That's not acceptable. So for us, we've really moved to bringing things internally. It's kind of opposite of what we preach. We tell our customers, don't build this in-house, but then we're like, we build a lot of stuff in-house. But I think it just really depends on what is in the critical path. We try and have deep ownership of that.Alessio [00:18:46]: On the distributed location side, how does that work for the web where you might get sort of different content in different locations, but the customer is expecting, you know, if you're in the US, I'm expecting the US version. But if you're spinning up my browser in France, I might get the French version. Yeah.Paul [00:19:02]: Yeah. That's a good question. Well, generally, like on the localization, there is a thing called locale in the browser. You can set like what your locale is. If you're like in the ENUS browser or not, but some things do IP, IP based routing. And in that case, you may want to have a proxy. Like let's say you're running something in the, in Europe, but you want to make sure you're showing up from the US. You may want to use one of our proxy features so you can turn on proxies to say like, make sure these connections always come from the United States, which is necessary too, because when you're browsing the web, you're coming from like a, you know, data center IP, and that can make things a lot harder to browse web. So we do have kind of like this proxy super network. Yeah. We have a proxy for you based on where you're going, so you can reliably automate the web. But if you get scheduled in Europe, that doesn't happen as much. We try and schedule you as close to, you know, your origin that you're trying to go to. But generally you have control over the regions you can put your browsers in. So you can specify West one or East one or Europe. We only have one region of Europe right now, actually. Yeah.Alessio [00:19:55]: What's harder, the browser or the proxy? I feel like to me, it feels like actually proxying reliably at scale. It's much harder than spending up browsers at scale. I'm curious. It's all hard.Paul [00:20:06]: It's layers of hard, right? Yeah. I think it's different levels of hard. I think the thing with the proxy infrastructure is that we work with many different web proxy providers and some are better than others. Some have good days, some have bad days. And our customers who've built browser infrastructure on their own, they have to go and deal with sketchy actors. Like first they figure out their own browser infrastructure and then they got to go buy a proxy. And it's like you can pay in Bitcoin and it just kind of feels a little sus, right? It's like you're buying drugs when you're trying to get a proxy online. We have like deep relationships with these counterparties. We're able to audit them and say, is this proxy being sourced ethically? Like it's not running on someone's TV somewhere. Is it free range? Yeah. Free range organic proxies, right? Right. We do a level of diligence. We're SOC 2. So we have to understand what is going on here. But then we're able to make sure that like we route around proxy providers not working. There's proxy providers who will just, the proxy will stop working all of a sudden. And then if you don't have redundant proxying on your own browsers, that's hard down for you or you may get some serious impacts there. With us, like we intelligently know, hey, this proxy is not working. Let's go to this one. And you can kind of build a network of multiple providers to really guarantee the best uptime for our customers. Yeah. So you don't own any proxies? We don't own any proxies. You're right. The team has been saying who wants to like take home a little proxy server, but not yet. We're not there yet. You know?swyx [00:21:25]: It's a very mature market. I don't think you should build that yourself. Like you should just be a super customer of them. Yeah. Scraping, I think, is the main use case for that. I guess. Well, that leads us into CAPTCHAs and also off, but let's talk about CAPTCHAs. You had a little spiel that you wanted to talk about CAPTCHA stuff.Challenges of Scaling Browser InfrastructurePaul [00:21:43]: Oh, yeah. I was just, I think a lot of people ask, if you're thinking about proxies, you're thinking about CAPTCHAs too. I think it's the same thing. You can go buy CAPTCHA solvers online, but it's the same buying experience. It's some sketchy website, you have to integrate it. It's not fun to buy these things and you can't really trust that the docs are bad. What Browserbase does is we integrate a bunch of different CAPTCHAs. We do some stuff in-house, but generally we just integrate with a bunch of known vendors and continually monitor and maintain these things and say, is this working or not? Can we route around it or not? These are CAPTCHA solvers. CAPTCHA solvers, yeah. Not CAPTCHA providers, CAPTCHA solvers. Yeah, sorry. CAPTCHA solvers. We really try and make sure all of that works for you. I think as a dev, if I'm buying infrastructure, I want it all to work all the time and it's important for us to provide that experience by making sure everything does work and monitoring it on our own. Yeah. Right now, the world of CAPTCHAs is tricky. I think AI agents in particular are very much ahead of the internet infrastructure. CAPTCHAs are designed to block all types of bots, but there are now good bots and bad bots. I think in the future, CAPTCHAs will be able to identify who a good bot is, hopefully via some sort of KYC. For us, we've been very lucky. We have very little to no known abuse of Browserbase because we really look into who we work with. And for certain types of CAPTCHA solving, we only allow them on certain types of plans because we want to make sure that we can know what people are doing, what their use cases are. And that's really allowed us to try and be an arbiter of good bots, which is our long term goal. I want to build great relationships with people like Cloudflare so we can agree, hey, here are these acceptable bots. We'll identify them for you and make sure we flag when they come to your website. This is a good bot, you know?Alessio [00:23:23]: I see. And Cloudflare said they want to do more of this. So they're going to set by default, if they think you're an AI bot, they're going to reject. I'm curious if you think this is something that is going to be at the browser level or I mean, the DNS level with Cloudflare seems more where it should belong. But I'm curious how you think about it.Paul [00:23:40]: I think the web's going to change. You know, I think that the Internet as we have it right now is going to change. And we all need to just accept that the cat is out of the bag. And instead of kind of like wishing the Internet was like it was in the 2000s, we can have free content line that wouldn't be scraped. It's just it's not going to happen. And instead, we should think about like, one, how can we change? How can we change the models of, you know, information being published online so people can adequately commercialize it? But two, how do we rebuild applications that expect that AI agents are going to log in on their behalf? Those are the things that are going to allow us to kind of like identify good and bad bots. And I think the team at Clerk has been doing a really good job with this on the authentication side. I actually think that auth is the biggest thing that will prevent agents from accessing stuff, not captchas. And I think there will be agent auth in the future. I don't know if it's going to happen from an individual company, but actually authentication providers that have a, you know, hidden login as agent feature, which will then you put in your email, you'll get a push notification, say like, hey, your browser-based agent wants to log into your Airbnb. You can approve that and then the agent can proceed. That really circumvents the need for captchas or logging in as you and sharing your password. I think agent auth is going to be one way we identify good bots going forward. And I think a lot of this captcha solving stuff is really short-term problems as the internet kind of reorients itself around how it's going to work with agents browsing the web, just like people do. Yeah.Managing Distributed Browser Locations and Proxiesswyx [00:24:59]: Stitch recently was on Hacker News for talking about agent experience, AX, which is a thing that Netlify is also trying to clone and coin and talk about. And we've talked about this on our previous episodes before in a sense that I actually think that's like maybe the only part of the tech stack that needs to be kind of reinvented for agents. Everything else can stay the same, CLIs, APIs, whatever. But auth, yeah, we need agent auth. And it's mostly like short-lived, like it should not, it should be a distinct, identity from the human, but paired. I almost think like in the same way that every social network should have your main profile and then your alt accounts or your Finsta, it's almost like, you know, every, every human token should be paired with the agent token and the agent token can go and do stuff on behalf of the human token, but not be presumed to be the human. Yeah.Paul [00:25:48]: It's like, it's, it's actually very similar to OAuth is what I'm thinking. And, you know, Thread from Stitch is an investor, Colin from Clerk, Octaventures, all investors in browser-based because like, I hope they solve this because they'll make browser-based submission more possible. So we don't have to overcome all these hurdles, but I think it will be an OAuth-like flow where an agent will ask to log in as you, you'll approve the scopes. Like it can book an apartment on Airbnb, but it can't like message anybody. And then, you know, the agent will have some sort of like role-based access control within an application. Yeah. I'm excited for that.swyx [00:26:16]: The tricky part is just, there's one, one layer of delegation here, which is like, you're authoring my user's user or something like that. I don't know if that's tricky or not. Does that make sense? Yeah.Paul [00:26:25]: You know, actually at Twilio, I worked on the login identity and access. Management teams, right? So like I built Twilio's login page.swyx [00:26:31]: You were an intern on that team and then you became the lead in two years? Yeah.Paul [00:26:34]: Yeah. I started as an intern in 2016 and then I was the tech lead of that team. How? That's not normal. I didn't have a life. He's not normal. Look at this guy. I didn't have a girlfriend. I just loved my job. I don't know. I applied to 500 internships for my first job and I got rejected from every single one of them except for Twilio and then eventually Amazon. And they took a shot on me and like, I was getting paid money to write code, which was my dream. Yeah. Yeah. I'm very lucky that like this coding thing worked out because I was going to be doing it regardless. And yeah, I was able to kind of spend a lot of time on a team that was growing at a company that was growing. So it informed a lot of this stuff here. I think these are problems that have been solved with like the SAML protocol with SSO. I think it's a really interesting stuff with like WebAuthn, like these different types of authentication, like schemes that you can use to authenticate people. The tooling is all there. It just needs to be tweaked a little bit to work for agents. And I think the fact that there are companies that are already. Providing authentication as a service really sets it up. Well, the thing that's hard is like reinventing the internet for agents. We don't want to rebuild the internet. That's an impossible task. And I think people often say like, well, we'll have this second layer of APIs built for agents. I'm like, we will for the top use cases, but instead of we can just tweak the internet as is, which is on the authentication side, I think we're going to be the dumb ones going forward. Unfortunately, I think AI is going to be able to do a lot of the tasks that we do online, which means that it will be able to go to websites, click buttons on our behalf and log in on our behalf too. So with this kind of like web agent future happening, I think with some small structural changes, like you said, it feels like it could all slot in really nicely with the existing internet.Handling CAPTCHAs and Agent Authenticationswyx [00:28:08]: There's one more thing, which is the, your live view iframe, which lets you take, take control. Yeah. Obviously very key for operator now, but like, was, is there anything interesting technically there or that the people like, well, people always want this.Paul [00:28:21]: It was really hard to build, you know, like, so, okay. Headless browsers, you don't see them, right. They're running. They're running in a cloud somewhere. You can't like look at them. And I just want to really make, it's a weird name. I wish we came up with a better name for this thing, but you can't see them. Right. But customers don't trust AI agents, right. At least the first pass. So what we do with our live view is that, you know, when you use browser base, you can actually embed a live view of the browser running in the cloud for your customer to see it working. And that's what the first reason is the build trust, like, okay, so I have this script. That's going to go automate a website. I can embed it into my web application via an iframe and my customer can watch. I think. And then we added two way communication. So now not only can you watch the browser kind of being operated by AI, if you want to pause and actually click around type within this iframe that's controlling a browser, that's also possible. And this is all thanks to some of the lower level protocol, which is called the Chrome DevTools protocol. It has a API called start screencast, and you can also send mouse clicks and button clicks to a remote browser. And this is all embeddable within iframes. You have a browser within a browser, yo. And then you simulate the screen, the click on the other side. Exactly. And this is really nice often for, like, let's say, a capture that can't be solved. You saw this with Operator, you know, Operator actually uses a different approach. They use VNC. So, you know, you're able to see, like, you're seeing the whole window here. What we're doing is something a little lower level with the Chrome DevTools protocol. It's just PNGs being streamed over the wire. But the same thing is true, right? Like, hey, I'm running a window. Pause. Can you do something in this window? Human. Okay, great. Resume. Like sometimes 2FA tokens. Like if you get that text message, you might need a person to type that in. Web agents need human-in-the-loop type workflows still. You still need a person to interact with the browser. And building a UI to proxy that is kind of hard. You may as well just show them the whole browser and say, hey, can you finish this up for me? And then let the AI proceed on afterwards. Is there a future where I stream my current desktop to browser base? I don't think so. I think we're very much cloud infrastructure. Yeah. You know, but I think a lot of the stuff we're doing, we do want to, like, build tools. Like, you know, we'll talk about the stage and, you know, web agent framework in a second. But, like, there's a case where a lot of people are going desktop first for, you know, consumer use. And I think cloud is doing a lot of this, where I expect to see, you know, MCPs really oriented around the cloud desktop app for a reason, right? Like, I think a lot of these tools are going to run on your computer because it makes... I think it's breaking out. People are putting it on a server. Oh, really? Okay. Well, sweet. We'll see. We'll see that. I was surprised, though, wasn't I? I think that the browser company, too, with Dia Browser, it runs on your machine. You know, it's going to be...swyx [00:30:50]: What is it?Paul [00:30:51]: So, Dia Browser, as far as I understand... I used to use Arc. Yeah. I haven't used Arc. But I'm a big fan of the browser company. I think they're doing a lot of cool stuff in consumer. As far as I understand, it's a browser where you have a sidebar where you can, like, chat with it and it can control the local browser on your machine. So, if you imagine, like, what a consumer web agent is, which it lives alongside your browser, I think Google Chrome has Project Marina, I think. I almost call it Project Marinara for some reason. I don't know why. It's...swyx [00:31:17]: No, I think it's someone really likes the Waterworld. Oh, I see. The classic Kevin Costner. Yeah.Paul [00:31:22]: Okay. Project Marinara is a similar thing to the Dia Browser, in my mind, as far as I understand it. You have a browser that has an AI interface that will take over your mouse and keyboard and control the browser for you. Great for consumer use cases. But if you're building applications that rely on a browser and it's more part of a greater, like, AI app experience, you probably need something that's more like infrastructure, not a consumer app.swyx [00:31:44]: Just because I have explored a little bit in this area, do people want branching? So, I have the state. Of whatever my browser's in. And then I want, like, 100 clones of this state. Do people do that? Or...Paul [00:31:56]: People don't do it currently. Yeah. But it's definitely something we're thinking about. I think the idea of forking a browser is really cool. Technically, kind of hard. We're starting to see this in code execution, where people are, like, forking some, like, code execution, like, processes or forking some tool calls or branching tool calls. Haven't seen it at the browser level yet. But it makes sense. Like, if an AI agent is, like, using a website and it's not sure what path it wants to take to crawl this website. To find the information it's looking for. It would make sense for it to explore both paths in parallel. And that'd be a very, like... A road not taken. Yeah. And hopefully find the right answer. And then say, okay, this was actually the right one. And memorize that. And go there in the future. On the roadmap. For sure. Don't make my roadmap, please. You know?Alessio [00:32:37]: How do you actually do that? Yeah. How do you fork? I feel like the browser is so stateful for so many things.swyx [00:32:42]: Serialize the state. Restore the state. I don't know.Paul [00:32:44]: So, it's one of the reasons why we haven't done it yet. It's hard. You know? Like, to truly fork, it's actually quite difficult. The naive way is to open the same page in a new tab and then, like, hope that it's at the same thing. But if you have a form halfway filled, you may have to, like, take the whole, you know, container. Pause it. All the memory. Duplicate it. Restart it from there. It could be very slow. So, we haven't found a thing. Like, the easy thing to fork is just, like, copy the page object. You know? But I think there needs to be something a little bit more robust there. Yeah.swyx [00:33:12]: So, MorphLabs has this infinite branch thing. Like, wrote a custom fork of Linux or something that let them save the system state and clone it. MorphLabs, hit me up. I'll be a customer. Yeah. That's the only. I think that's the only way to do it. Yeah. Like, unless Chrome has some special API for you. Yeah.Paul [00:33:29]: There's probably something we'll reverse engineer one day. I don't know. Yeah.Alessio [00:33:32]: Let's talk about StageHand, the AI web browsing framework. You have three core components, Observe, Extract, and Act. Pretty clean landing page. What was the idea behind making a framework? Yeah.Stagehand: AI web browsing frameworkPaul [00:33:43]: So, there's three frameworks that are very popular or already exist, right? Puppeteer, Playwright, Selenium. Those are for building hard-coded scripts to control websites. And as soon as I started to play with LLMs plus browsing, I caught myself, you know, code-genning Playwright code to control a website. I would, like, take the DOM. I'd pass it to an LLM. I'd say, can you generate the Playwright code to click the appropriate button here? And it would do that. And I was like, this really should be part of the frameworks themselves. And I became really obsessed with SDKs that take natural language as part of, like, the API input. And that's what StageHand is. StageHand exposes three APIs, and it's a super set of Playwright. So, if you go to a page, you may want to take an action, click on the button, fill in the form, etc. That's what the act command is for. You may want to extract some data. This one takes a natural language, like, extract the winner of the Super Bowl from this page. You can give it a Zod schema, so it returns a structured output. And then maybe you're building an API. You can do an agent loop, and you want to kind of see what actions are possible on this page before taking one. You can do observe. So, you can observe the actions on the page, and it will generate a list of actions. You can guide it, like, give me actions on this page related to buying an item. And you can, like, buy it now, add to cart, view shipping options, and pass that to an LLM, an agent loop, to say, what's the appropriate action given this high-level goal? So, StageHand isn't a web agent. It's a framework for building web agents. And we think that agent loops are actually pretty close to the application layer because every application probably has different goals or different ways it wants to take steps. I don't think I've seen a generic. Maybe you guys are the experts here. I haven't seen, like, a really good AI agent framework here. Everyone kind of has their own special sauce, right? I see a lot of developers building their own agent loops, and they're using tools. And I view StageHand as the browser tool. So, we expose act, extract, observe. Your agent can call these tools. And from that, you don't have to worry about it. You don't have to worry about generating playwright code performantly. You don't have to worry about running it. You can kind of just integrate these three tool calls into your agent loop and reliably automate the web.swyx [00:35:48]: A special shout-out to Anirudh, who I met at your dinner, who I think listens to the pod. Yeah. Hey, Anirudh.Paul [00:35:54]: Anirudh's a man. He's a StageHand guy.swyx [00:35:56]: I mean, the interesting thing about each of these APIs is they're kind of each startup. Like, specifically extract, you know, Firecrawler is extract. There's, like, Expand AI. There's a whole bunch of, like, extract companies. They just focus on extract. I'm curious. Like, I feel like you guys are going to collide at some point. Like, right now, it's friendly. Everyone's in a blue ocean. At some point, it's going to be valuable enough that there's some turf battle here. I don't think you have a dog in a fight. I think you can mock extract to use an external service if they're better at it than you. But it's just an observation that, like, in the same way that I see each option, each checkbox in the side of custom GBTs becoming a startup or each box in the Karpathy chart being a startup. Like, this is also becoming a thing. Yeah.Paul [00:36:41]: I mean, like, so the way StageHand works is that it's MIT-licensed, completely open source. You bring your own API key to your LLM of choice. You could choose your LLM. We don't make any money off of the extract or really. We only really make money if you choose to run it with our browser. You don't have to. You can actually use your own browser, a local browser. You know, StageHand is completely open source for that reason. And, yeah, like, I think if you're building really complex web scraping workflows, I don't know if StageHand is the tool for you. I think it's really more if you're building an AI agent that needs a few general tools or if it's doing a lot of, like, web automation-intensive work. But if you're building a scraping company, StageHand is not your thing. You probably want something that's going to, like, get HTML content, you know, convert that to Markdown, query it. That's not what StageHand does. StageHand is more about reliability. I think we focus a lot on reliability and less so on cost optimization and speed at this point.swyx [00:37:33]: I actually feel like StageHand, so the way that StageHand works, it's like, you know, page.act, click on the quick start. Yeah. It's kind of the integration test for the code that you would have to write anyway, like the Puppeteer code that you have to write anyway. And when the page structure changes, because it always does, then this is still the test. This is still the test that I would have to write. Yeah. So it's kind of like a testing framework that doesn't need implementation detail.Paul [00:37:56]: Well, yeah. I mean, Puppeteer, Playwright, and Slenderman were all designed as testing frameworks, right? Yeah. And now people are, like, hacking them together to automate the web. I would say, and, like, maybe this is, like, me being too specific. But, like, when I write tests, if the page structure changes. Without me knowing, I want that test to fail. So I don't know if, like, AI, like, regenerating that. Like, people are using StageHand for testing. But it's more for, like, usability testing, not, like, testing of, like, does the front end, like, has it changed or not. Okay. But generally where we've seen people, like, really, like, take off is, like, if they're using, you know, something. If they want to build a feature in their application that's kind of like Operator or Deep Research, they're using StageHand to kind of power that tool calling in their own agent loop. Okay. Cool.swyx [00:38:37]: So let's go into Operator, the first big agent launch of the year from OpenAI. Seems like they have a whole bunch scheduled. You were on break and your phone blew up. What's your just general view of computer use agents is what they're calling it. The overall category before we go into Open Operator, just the overall promise of Operator. I will observe that I tried it once. It was okay. And I never tried it again.OpenAI's Operator and computer use agentsPaul [00:38:58]: That tracks with my experience, too. Like, I'm a huge fan of the OpenAI team. Like, I think that I do not view Operator as the company. I'm not a company killer for browser base at all. I think it actually shows people what's possible. I think, like, computer use models make a lot of sense. And I'm actually most excited about computer use models is, like, their ability to, like, really take screenshots and reasoning and output steps. I think that using mouse click or mouse coordinates, I've seen that proved to be less reliable than I would like. And I just wonder if that's the right form factor. What we've done with our framework is anchor it to the DOM itself, anchor it to the actual item. So, like, if it's clicking on something, it's clicking on that thing, you know? Like, it's more accurate. No matter where it is. Yeah, exactly. Because it really ties in nicely. And it can handle, like, the whole viewport in one go, whereas, like, Operator can only handle what it sees. Can you hover? Is hovering a thing that you can do? I don't know if we expose it as a tool directly, but I'm sure there's, like, an API for hovering. Like, move mouse to this position. Yeah, yeah, yeah. I think you can trigger hover, like, via, like, the JavaScript on the DOM itself. But, no, I think, like, when we saw computer use, everyone's eyes lit up because they realized, like, wow, like, AI is going to actually automate work for people. And I think seeing that kind of happen from both of the labs, and I'm sure we're going to see more labs launch computer use models, I'm excited to see all the stuff that people build with it. I think that I'd love to see computer use power, like, controlling a browser on browser base. And I think, like, Open Operator, which was, like, our open source version of OpenAI's Operator, was our first take on, like, how can we integrate these models into browser base? And we handle the infrastructure and let the labs do the models. I don't have a sense that Operator will be released as an API. I don't know. Maybe it will. I'm curious to see how well that works because I think it's going to be really hard for a company like OpenAI to do things like support CAPTCHA solving or, like, have proxies. Like, I think it's hard for them structurally. Imagine this New York Times headline, OpenAI CAPTCHA solving. Like, that would be a pretty bad headline, this New York Times headline. Browser base solves CAPTCHAs. No one cares. No one cares. And, like, our investors are bored. Like, we're all okay with this, you know? We're building this company knowing that the CAPTCHA solving is short-lived until we figure out how to authenticate good bots. I think it's really hard for a company like OpenAI, who has this brand that's so, so good, to balance with, like, the icky parts of web automation, which it can be kind of complex to solve. I'm sure OpenAI knows who to call whenever they need you. Yeah, right. I'm sure they'll have a great partnership.Alessio [00:41:23]: And is Open Operator just, like, a marketing thing for you? Like, how do you think about resource allocation? So, you can spin this up very quickly. And now there's all this, like, open deep research, just open all these things that people are building. We started it, you know. You're the original Open. We're the original Open operator, you know? Is it just, hey, look, this is a demo, but, like, we'll help you build out an actual product for yourself? Like, are you interested in going more of a product route? That's kind of the OpenAI way, right? They started as a model provider and then…Paul [00:41:53]: Yeah, we're not interested in going the product route yet. I view Open Operator as a model provider. It's a reference project, you know? Let's show people how to build these things using the infrastructure and models that are out there. And that's what it is. It's, like, Open Operator is very simple. It's an agent loop. It says, like, take a high-level goal, break it down into steps, use tool calling to accomplish those steps. It takes screenshots and feeds those screenshots into an LLM with the step to generate the right action. It uses stagehand under the hood to actually execute this action. It doesn't use a computer use model. And it, like, has a nice interface using the live view that we talked about, the iframe, to embed that into an application. So I felt like people on launch day wanted to figure out how to build their own version of this. And we turned that around really quickly to show them. And I hope we do that with other things like deep research. We don't have a deep research launch yet. I think David from AOMNI actually has an amazing open deep research that he launched. It has, like, 10K GitHub stars now. So he's crushing that. But I think if people want to build these features natively into their application, they need good reference projects. And I think Open Operator is a good example of that.swyx [00:42:52]: I don't know. Actually, I'm actually pretty bullish on API-driven operator. Because that's the only way that you can sort of, like, once it's reliable enough, obviously. And now we're nowhere near. But, like, give it five years. It'll happen, you know. And then you can sort of spin this up and browsers are working in the background and you don't necessarily have to know. And it just is booking restaurants for you, whatever. I can definitely see that future happening. I had this on the landing page here. This might be a slightly out of order. But, you know, you have, like, sort of three use cases for browser base. Open Operator. Or this is the operator sort of use case. It's kind of like the workflow automation use case. And it completes with UiPath in the sort of RPA category. Would you agree with that? Yeah, I would agree with that. And then there's Agents we talked about already. And web scraping, which I imagine would be the bulk of your workload right now, right?Paul [00:43:40]: No, not at all. I'd say actually, like, the majority is browser automation. We're kind of expensive for web scraping. Like, I think that if you're building a web scraping product, if you need to do occasional web scraping or you have to do web scraping that works every single time, you want to use browser automation. Yeah. You want to use browser-based. But if you're building web scraping workflows, what you should do is have a waterfall. You should have the first request is a curl to the website. See if you can get it without even using a browser. And then the second request may be, like, a scraping-specific API. There's, like, a thousand scraping APIs out there that you can use to try and get data. Scraping B. Scraping B is a great example, right? Yeah. And then, like, if those two don't work, bring out the heavy hitter. Like, browser-based will 100% work, right? It will load the page in a real browser, hydrate it. I see.swyx [00:44:21]: Because a lot of people don't render to JS.swyx [00:44:25]: Yeah, exactly.Paul [00:44:26]: So, I mean, the three big use cases, right? Like, you know, automation, web data collection, and then, you know, if you're building anything agentic that needs, like, a browser tool, you want to use browser-based.Alessio [00:44:35]: Is there any use case that, like, you were super surprised by that people might not even think about? Oh, yeah. Or is it, yeah, anything that you can share? The long tail is crazy. Yeah.Surprising use cases of BrowserbasePaul [00:44:44]: One of the case studies on our website that I think is the most interesting is this company called Benny. So, the way that it works is if you're on food stamps in the United States, you can actually get rebates if you buy certain things. Yeah. You buy some vegetables. You submit your receipt to the government. They'll give you a little rebate back. Say, hey, thanks for buying vegetables. It's good for you. That process of submitting that receipt is very painful. And the way Benny works is you use their app to take a photo of your receipt, and then Benny will go submit that receipt for you and then deposit the money into your account. That's actually using no AI at all. It's all, like, hard-coded scripts. They maintain the scripts. They've been doing a great job. And they build this amazing consumer app. But it's an example of, like, all these, like, tedious workflows that people have to do to kind of go about their business. And they're doing it for the sake of their day-to-day lives. And I had never known about, like, food stamp rebates or the complex forms you have to do to fill them. But the world is powered by millions and millions of tedious forms, visas. You know, Emirate Lighthouse is a customer, right? You know, they do the O1 visa. Millions and millions of forms are taking away humans' time. And I hope that Browserbase can help power software that automates away the web forms that we don't need anymore. Yeah.swyx [00:45:49]: I mean, I'm very supportive of that. I mean, forms. I do think, like, government itself is a big part of it. I think the government itself should embrace AI more to do more sort of human-friendly form filling. Mm-hmm. But I'm not optimistic. I'm not holding my breath. Yeah. We'll see. Okay. I think I'm about to zoom out. I have a little brief thing on computer use, and then we can talk about founder stuff, which is, I tend to think of developer tooling markets in impossible triangles, where everyone starts in a niche, and then they start to branch out. So I already hinted at a little bit of this, right? We mentioned more. We mentioned E2B. We mentioned Firecrawl. And then there's Browserbase. So there's, like, all this stuff of, like, have serverless virtual computer that you give to an agent and let them do stuff with it. And there's various ways of connecting it to the internet. You can just connect to a search API, like SERP API, whatever other, like, EXA is another one. That's what you're searching. You can also have a JSON markdown extractor, which is Firecrawl. Or you can have a virtual browser like Browserbase, or you can have a virtual machine like Morph. And then there's also maybe, like, a virtual sort of code environment, like Code Interpreter. So, like, there's just, like, a bunch of different ways to tackle the problem of give a computer to an agent. And I'm just kind of wondering if you see, like, everyone's just, like, happily coexisting in their respective niches. And as a developer, I just go and pick, like, a shopping basket of one of each. Or do you think that you eventually, people will collide?Future of browser automation and market competitionPaul [00:47:18]: I think that currently it's not a zero-sum market. Like, I think we're talking about... I think we're talking about all of knowledge work that people do that can be automated online. All of these, like, trillions of hours that happen online where people are working. And I think that there's so much software to be built that, like, I tend not to think about how these companies will collide. I just try to solve the problem as best as I can and make this specific piece of infrastructure, which I think is an important primitive, the best I possibly can. And yeah. I think there's players that are actually going to like it. I think there's players that are going to launch, like, over-the-top, you know, platforms, like agent platforms that have all these tools built in, right? Like, who's building the rippling for agent tools that has the search tool, the browser tool, the operating system tool, right? There are some. There are some. There are some, right? And I think in the end, what I have seen as my time as a developer, and I look at all the favorite tools that I have, is that, like, for tools and primitives with sufficient levels of complexity, you need to have a solution that's really bespoke to that primitive, you know? And I am sufficiently convinced that the browser is complex enough to deserve a primitive. Obviously, I have to. I'm the founder of BrowserBase, right? I'm talking my book. But, like, I think maybe I can give you one spicy take against, like, maybe just whole OS running. I think that when I look at computer use when it first came out, I saw that the majority of use cases for computer use were controlling a browser. And do we really need to run an entire operating system just to control a browser? I don't think so. I don't think that's necessary. You know, BrowserBase can run browsers for way cheaper than you can if you're running a full-fledged OS with a GUI, you know, operating system. And I think that's just an advantage of the browser. It is, like, browsers are little OSs, and you can run them very efficiently if you orchestrate it well. And I think that allows us to offer 90% of the, you know, functionality in the platform needed at 10% of the cost of running a full OS. Yeah.Open Operator: Browserbase's Open-Source Alternativeswyx [00:49:16]: I definitely see the logic in that. There's a Mark Andreessen quote. I don't know if you know this one. Where he basically observed that the browser is turning the operating system into a poorly debugged set of device drivers, because most of the apps are moved from the OS to the browser. So you can just run browsers.Paul [00:49:31]: There's a place for OSs, too. Like, I think that there are some applications that only run on Windows operating systems. And Eric from pig.dev in this upcoming YC batch, or last YC batch, like, he's building all run tons of Windows operating systems for you to control with your agent. And like, there's some legacy EHR systems that only run on Internet-controlled systems. Yeah.Paul [00:49:54]: I think that's it. I think, like, there are use cases for specific operating systems for specific legacy software. And like, I'm excited to see what he does with that. I just wanted to give a shout out to the pig.dev website.swyx [00:50:06]: The pigs jump when you click on them. Yeah. That's great.Paul [00:50:08]: Eric, he's the former co-founder of banana.dev, too.swyx [00:50:11]: Oh, that Eric. Yeah. That Eric. Okay. Well, he abandoned bananas for pigs. I hope he doesn't start going around with pigs now.Alessio [00:50:18]: Like he was going around with bananas. A little toy pig. Yeah. Yeah. I love that. What else are we missing? I think we covered a lot of, like, the browser-based product history, but. What do you wish people asked you? Yeah.Paul [00:50:29]: I wish people asked me more about, like, what will the future of software look like? Because I think that's really where I've spent a lot of time about why do browser-based. Like, for me, starting a company is like a means of last resort. Like, you shouldn't start a company unless you absolutely have to. And I remain convinced that the future of software is software that you're going to click a button and it's going to do stuff on your behalf. Right now, software. You click a button and it maybe, like, calls it back an API and, like, computes some numbers. It, like, modifies some text, whatever. But the future of software is software using software. So, I may log into my accounting website for my business, click a button, and it's going to go load up my Gmail, search my emails, find the thing, upload the receipt, and then comment it for me. Right? And it may use it using APIs, maybe a browser. I don't know. I think it's a little bit of both. But that's completely different from how we've built software so far. And that's. I think that future of software has different infrastructure requirements. It's going to require different UIs. It's going to require different pieces of infrastructure. I think the browser infrastructure is one piece that fits into that, along with all the other categories you mentioned. So, I think that it's going to require developers to think differently about how they've built software for, you know

In this episode: Alan switches from self hosted markdown to self hosted mark down served from a docker container running CodiMD. Mark puts on his robe and wizard’s hat, and ventures into the Caves of Qud. Martin switches his console to KMSCON. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

In this episode: Alan switches from self hosted markdown to self hosted mark down served from a docker container running CodiMD. Mark puts on his robe and wizard’s hat, and ventures into the Caves of Qud. Martin switches his console to KMSCON. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

In this episode: Alan switches from self hosted markdown to self hosted mark down served from a docker container running CodiMD. Mark puts on his robe and wizard's hat, and ventures into the Caves of Qud. Martin switches his console to KMSCON.   You can send your feedback via show@linuxmatters.sh or the Contact Form. If... Read More

In this episode of iOS Today, hosts Mikah Sargent and Rosemary Orchard dive into a comprehensive comparison of popular note-taking apps for iOS, examining their unique features and use cases. They also discuss Apple's surprise launch of a new event planning app called Apple Invites. Apple Notes: New iOS 18 features including Math Notes, collaborative note-taking capabilities, and family wish list sharing functionality Freeform: Apple's infinite canvas app that enables collaborative brainstorming, room planning, and freeform content organization with drawing support Bear: A beautiful Markdown-based notes app with custom keyboards and nested tags, available with a free tier and subscription options Obsidian: A powerful, customizable note-taking app with community plugins and the ability to create personal wikis Drafts: An advanced text editor that creates new drafts automatically, featuring powerful actions, custom themes, and extensive widget support Notion: A versatile workspace tool with templates for various use cases, featuring databases, calendars, and collaborative features News Apple launches new "Invites" app requiring iCloud+ subscription to create invitations. It features shared photo albums, weather information, Apple Music playlist integration, and Apple Intelligence for custom backgrounds Feedback Question about limiting group chat notifications in driving focus mode Request for help with continuous reading of non-Kindle ebooks using iOS accessibility features Discussion of potential workarounds including sending content to Kindle and exploring alternative solutions Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

In this episode of iOS Today, hosts Mikah Sargent and Rosemary Orchard dive into a comprehensive comparison of popular note-taking apps for iOS, examining their unique features and use cases. They also discuss Apple's surprise launch of a new event planning app called Apple Invites. Apple Notes: New iOS 18 features including Math Notes, collaborative note-taking capabilities, and family wish list sharing functionality Freeform: Apple's infinite canvas app that enables collaborative brainstorming, room planning, and freeform content organization with drawing support Bear: A beautiful Markdown-based notes app with custom keyboards and nested tags, available with a free tier and subscription options Obsidian: A powerful, customizable note-taking app with community plugins and the ability to create personal wikis Drafts: An advanced text editor that creates new drafts automatically, featuring powerful actions, custom themes, and extensive widget support Notion: A versatile workspace tool with templates for various use cases, featuring databases, calendars, and collaborative features News Apple launches new "Invites" app requiring iCloud+ subscription to create invitations. It features shared photo albums, weather information, Apple Music playlist integration, and Apple Intelligence for custom backgrounds Feedback Question about limiting group chat notifications in driving focus mode Request for help with continuous reading of non-Kindle ebooks using iOS accessibility features Discussion of potential workarounds including sending content to Kindle and exploring alternative solutions Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

In this episode of iOS Today, hosts Mikah Sargent and Rosemary Orchard dive into a comprehensive comparison of popular note-taking apps for iOS, examining their unique features and use cases. They also discuss Apple's surprise launch of a new event planning app called Apple Invites. Apple Notes: New iOS 18 features including Math Notes, collaborative note-taking capabilities, and family wish list sharing functionality Freeform: Apple's infinite canvas app that enables collaborative brainstorming, room planning, and freeform content organization with drawing support Bear: A beautiful Markdown-based notes app with custom keyboards and nested tags, available with a free tier and subscription options Obsidian: A powerful, customizable note-taking app with community plugins and the ability to create personal wikis Drafts: An advanced text editor that creates new drafts automatically, featuring powerful actions, custom themes, and extensive widget support Notion: A versatile workspace tool with templates for various use cases, featuring databases, calendars, and collaborative features News Apple launches new "Invites" app requiring iCloud+ subscription to create invitations. It features shared photo albums, weather information, Apple Music playlist integration, and Apple Intelligence for custom backgrounds Feedback Question about limiting group chat notifications in driving focus mode Request for help with continuous reading of non-Kindle ebooks using iOS accessibility features Discussion of potential workarounds including sending content to Kindle and exploring alternative solutions Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

In this episode of iOS Today, hosts Mikah Sargent and Rosemary Orchard dive into a comprehensive comparison of popular note-taking apps for iOS, examining their unique features and use cases. They also discuss Apple's surprise launch of a new event planning app called Apple Invites. Apple Notes: New iOS 18 features including Math Notes, collaborative note-taking capabilities, and family wish list sharing functionality Freeform: Apple's infinite canvas app that enables collaborative brainstorming, room planning, and freeform content organization with drawing support Bear: A beautiful Markdown-based notes app with custom keyboards and nested tags, available with a free tier and subscription options Obsidian: A powerful, customizable note-taking app with community plugins and the ability to create personal wikis Drafts: An advanced text editor that creates new drafts automatically, featuring powerful actions, custom themes, and extensive widget support Notion: A versatile workspace tool with templates for various use cases, featuring databases, calendars, and collaborative features News Apple launches new "Invites" app requiring iCloud+ subscription to create invitations. It features shared photo albums, weather information, Apple Music playlist integration, and Apple Intelligence for custom backgrounds Feedback Question about limiting group chat notifications in driving focus mode Request for help with continuous reading of non-Kindle ebooks using iOS accessibility features Discussion of potential workarounds including sending content to Kindle and exploring alternative solutions Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

In this episode of iOS Today, hosts Mikah Sargent and Rosemary Orchard dive into a comprehensive comparison of popular note-taking apps for iOS, examining their unique features and use cases. They also discuss Apple's surprise launch of a new event planning app called Apple Invites. Apple Notes: New iOS 18 features including Math Notes, collaborative note-taking capabilities, and family wish list sharing functionality Freeform: Apple's infinite canvas app that enables collaborative brainstorming, room planning, and freeform content organization with drawing support Bear: A beautiful Markdown-based notes app with custom keyboards and nested tags, available with a free tier and subscription options Obsidian: A powerful, customizable note-taking app with community plugins and the ability to create personal wikis Drafts: An advanced text editor that creates new drafts automatically, featuring powerful actions, custom themes, and extensive widget support Notion: A versatile workspace tool with templates for various use cases, featuring databases, calendars, and collaborative features News Apple launches new "Invites" app requiring iCloud+ subscription to create invitations. It features shared photo albums, weather information, Apple Music playlist integration, and Apple Intelligence for custom backgrounds Feedback Question about limiting group chat notifications in driving focus mode Request for help with continuous reading of non-Kindle ebooks using iOS accessibility features Discussion of potential workarounds including sending content to Kindle and exploring alternative solutions Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Join us for two hours of tech as we first welcome David Goldfield to give us updates all about the revisions to the BT Speak. In the second hour we talk about the upcoming BITS web accessibility course, the importance of Mark Down and much more on current technology trends. We also welcome your questions and comments.

Java leads by example regarding documentation: JavaDoc inspires trust in developers through its transparency on each Java API functionality, and the javadoc tool helps developers generate equally great documentation for their APIs and libraries. In this episode, Ana hosts Jonathan Gibbons, core contributor and maintainer of JDK tools, to discuss JavaDoc/javadoc developments, focusing on markdown in JavaDoc documentation comments. Given the importance of having code that is as easy to understand as it is functional, Jonathan dives into significant changes in Java's documentation component and associated tools, how JavaDoc is maintained, code documentation practices, and more.

Allen Wyma talks with Matthias Endler, the creator of lychee, a stream-based link checker written in Rust that finds broken hyperlinks and mail addresses inside of HTML and Markdown documents as well as websites. Contributing to Rustacean Station Rustacean Station is a community project; get in touch with us if you'd like to suggest an idea for an episode or offer your services as a host or audio editor! Twitter: @rustaceanfm Discord: Rustacean Station Github: @rustacean-station Email: hello@rustacean-station.org Timestamps [@00:00] - Meet Matthias: Rust consultant and creator of lychee [@01:55] - Protocol support, valid links, and lychee features [@14:51] - What inspired the creation of lychee [@19:25] - Supporting open-source projects and advice for creators starting their own [@32:17] - Staying on top of dependencies: why upgrading matters [@47:45] - New features being added to lychee Other links RUSTAsia Conf 2025 Credits Intro Theme: Aerocity Audio Editing: Plangora Hosting Infrastructure: Jon Gjengset Show Notes: Plangora Hosts: Allen Wyma

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

This show has been flagged as Explicit by the host. Greetings and welcome to Hacker Public Radio. My name is Peter Paterson, also known as SolusSpider, a Scotsman living in Kentucky, USA. This is my second HPR recording. The first was episode 4258 where I gave my introduction and computer history. Once again I am recording the audio on my Samsung Galaxy S21 Ultra phone, running Android 14, with Audio Recorder by Axet. The app was installed from F-Droid. Markdown For my Shownotes I learned to use Markdown by using the ReText app, which allows me to write in one window and preview the result in another. What is this show about? When I visited Archer72, AKA Mark Rice, in November 2024 in his University of Kentucky trauma room I reminded him that I work for God's Pantry Food Bank. He said he wanted to hear more, and highly suggested that I record the story as an HPR show, so here we are. I plan to ask the questions I hear from so many, and attempt to answer them as best I am able. What is the History of God's Pantry Food Bank? Reading directly from the About-Us page of Godspantry.org Mim Hunt, the founder of God's Pantry Food Bank, vowed to leave "the heartbreaking profession of social work" behind when she returned to her hometown of Lexington after serving as a child welfare worker in 1940's New York City. She and her husband, Robert, opened "Mim's," a combination gift shop, antique gallery, and health food store, but after seeing poverty in Lexington that rivaled what she'd fought against in New York, she found herself unable to remain silent. Mim began her work in Lexington by filling her station wagon with food, clothing, and bedding, and distributing it directly to individuals in need. Soon, neighbors were bringing food donations to what became known as "Mim's Pantry" located at her home on Lexington's Parkers Mill Road. But Mim quickly corrected them. "I don't fill these shelves," she said. "God does. This is God's Pantry." God's Pantry Food Bank was born out of this work in 1955 and remained mobile until the first pantry was opened in 1959. Since its founding, the food bank has grown in many ways. What started with one woman attempting to do what she could to address a need is now an organization serving 50 counties in Central and Eastern Kentucky through a number of programs with a dedicated staff committed to the mission of solving hunger. Mim Hunt devoted her life to helping others, and we continue to honor her legacy at God's Pantry Food Bank. Her work is proof that one person, with every small action, can make a large impact. We invite you to join us in continuing Mim's work. Where have been the locations of the main Food Bank facility? My ex-workmate Robert Srodulski recently wrote a reply in Facebook when our newest building was announced. He stated: "If I count right, this is the 6th main warehouse location in Lexington. Congratulations! > Mim's house and car Oldham Avenue garage A building next to Rupp Arena (which is now gone) Forbes Road Jaggie Fox Way, Innovation Drive." My friend Robert was employed by the Food Bank for 26 years. I am chasing his time as the longest lasting male employee. Two ladies have longer service times: Debbie Amburgey with 36.5 years in our Prestonsburg facility. She started on 19th October 1987. Sadly my good friend Debbie passed earlier this year, and I miss her greatly. She never retired. Danielle Bozarth with currently just under 30 years. She started on 30th May 1995. It would take me just over 11 years to catch up with Debbie's service record, which would take me to the age of 68. Unsure if I shall still be employed by then! What exactly do I mean by Food Bank? In February 2023 I wrote a blog post with my explanation of Food Bank. My website is LinuxSpider.net, and you will find the direct link in the shownotes. The blog was written as a response to friends, mostly from the United Kingdom, asking me very this question. To many there, and indeed here in USA also, what is called a Food Bank is what I call a local Food Pantry. Nobody is wrong here at all. We all gather food from various sources and distribute it to our neighbours who are in food insecure need. Most Pantries are totally staffed by volunteers and often open limited hours. The Food Bank has a larger scope in where we source food from, the amount sourced, does have paid staff but still dependent on volunteers, and we are open at least 40 hours a week. More if you include projects that involve evenings and Saturdays. God's Pantry Food Bank has a service area which includes 50 of the 120 Counties of Kentucky, covering central, southern, and eastern, including part of Appalachia. When I started in 1999 we were distributing 6 million pounds weight of food per year. This is about 150 semi-truckloads. Over 25 years later we are looking at distributing about 50 million pounds this year, about 1,250 truckloads. Over 40% of our distribution is fresh produce. We are an hunger relief organisation, so this amount of food is assisting our neighbours in need. In those 50 Counties we have about 400 partner agencies. Many of these agencies are Soup Kitchens, Children's Programs, Senior Programs, as well as Food Pantries. God's Pantry Food Bank is partnered with the Feeding America network of 198 Food Banks. In my early years I knew them as America's Second Harvest. In 2008 they changed name to Feeding America. Their website is FeedingAmerica.org What they do is outlined in their our-work page, including: Ensuring everyone can get the food they need with respect and dignity. Advocating for policies that improve food security for everyone. Partnering to address the root causes of food insecurity, like the high cost of living and lack of access to affordable housing. Working with local food banks and meal programs. Ending hunger through Food Access, Food Rescue, Disaster Response, and Hunger Research. I have visited a few other Food Banks, but not as many as I would have liked. We all have our own areas of service, but do often interact as the needs arise, especially in times of disaster. The Feeding America network came to Kentucky's aid in the past few years with the flooding in the East and tornadoes in the West. Feeding America aided the Food Banks affected by the devastation from Hurricanes Helene and Milton. How did I get started at the Food Bank? As mentioned in my introduction show I moved from Scotland to Kentucky in May 1999 and married Arianna in June 1999. Before our wedding I had received my green card. My future Mother-in-Law Eva recommended I check with God's Pantry Food Bank to see if they were hiring. She was working for Big Lots and had applied for a warehouse job at the Food Bank. Unfortunately for her she never got the job, but she was quite impressed by the organisation. She knew that I had warehouse and driving experience. So, one day after dropping Arianna at her University of Kentucky Medical Staff Office I stopped by the Food Bank on South Forbes Road to ask. The answer was that they were indeed hiring for the warehouse, and to come back that afternoon to meet with CW Drury, the Warehouse Manager. I drove home, put on smarter clothes, and drove back. It was a pleasure meeting CW and hearing about the job. Although most of the explanation of what they did in their mission went over my head at the time, I knew needed a job, and wanted to join this company. A few days before our wedding I received a phone call from CW offering me the position. I accepted and went for my medical the next day. My first day with God's Pantry Food Bank was on Tuesday 6th July 1999, the day after our honeymoon. I will admit that although my previous job in Scotland was a physical one, quite a few months had passed, and the heat was hot that Summer in Kentucky! I went home exhausted everyday, but totally enjoying the work I was doing. I started off mostly picking orders, assisting Agencies that came in, going to the local Kroger supermarkets to pick up bread, deliver and pick up food barrels of donations, and all the other duties CW assigned me to. I particularly enjoyed the software part of the job. I forget the name of the software back then, but do remember learning the 10 digit Item Codes. 1st is the source 2nd and 3rd are the category. There are 31 officially with Feeding America. next 6 is the unique UPC - usually from the item bar code 10th is the storage code of dry, cooler, or freezer The first code I memorised was Bread Products: 1040010731 This broke down to Donated, Bread Category, UPC number, and Dry Storage. I must admit we did not create a new code when we started storing Bread Product in the Cooler. That is probably the only exception It has been my responsibility all these years to maintain the Item Category Code sheet with different codings we have used and had to invent. An example is that when the source digit had already used 1 to 9, we had to start using letters. Although there were concerns at the time, everything worked out well. When I started at South Forbes Road there were 11 employees there and Debbie in Prestonsburg. 12 in total, in 2 locations. These days we have over 80 employees in 5 locations: Lexington, Prestonsburg, London, Morehead, and a Volunteer Center on Winchester Road, Lexington, near the Smuckers JIF Peanut Butter plant. My time at 104 South Forbes Road was for a full 4 weeks! In August 1999 we moved to 1685 Jaggie Fox Way, into a customised warehouse with 3 pallet tall racking, and lots of office space. It felt so large back then! On my first couple of days of unloading trucks there I totally wore out a pair of trainers!! Jaggie Fox does sound like a strange name for a street, but I later learned it came from 2 ladies, Mrs Jaggie and Mrs Fox who owned the land before the business park purchase. Anyway, that's what I have been told by mulitple people. Technology was fun in 1999, as we had a 56K phone modem, about 10 computers, and 1 printer. You can imagine the shared internet speed. I forget how long, but we eventually got DSL, then Cable. What have been my duties at the Food Bank? For my first decade of employment I worked the warehouse and as a driver. This included delivering food to the 4 to 5 local pantries that we ran ourselves in local church buildings in Fayette County. Funny story is that a couple of years into the job, I was approached by the Development Manager and asked if I knew websites and HTML. I informed her that I was familiar, and she made me responsible for the maintenance of the website that University of Kentucky students had created. It indeed was quite basic with only HTML and images. I had this duty for a few years before a professional company was hired. I mentioned Inventory software. In early 2000 we moved to an ERP, that is an Enterprise Resource Planning suite named Navision written by a Danish company. That company was then taken over by Microsoft. For as while it was called Microsoft NAV, and these days it is part of Dynamics 365. Feeding America commissioned a module named CERES which assisted us non-profits to use profit orientated software. Inhouse, we just call the software CERES. Even though I was no longer maintaining the website, I was still involved in IT to a degree. I became the inhouse guy who would set up new employees with their own computer. Ah, the days of Active Directory. I never did like it! I was also the guy the staff came to first with their computer problems. Funny how a lot of these issues were fixed when I walked in their office. If I could not fix an issue there and then, we did have a contract company on-call. They maintained our server and other high level software. This was still when I was in the warehouse role. After that first decade I was allocated to be our Welcome Center person, which I did for 3 years. This involved welcoming agencies, guests, salespersons, volunteers, and assisting other staff members in many ways. I also went from being a driver to the person who handed out delivery and pick-up routes to the drivers. During these years I became a heavy user of CERES working with the agencies and printing out pick-sheets to our warehouse picking staff. Although I really enjoyed the work, I will openly admit that I am not always the best in heavily social situations. I did have some difficulty when the Welcome Center was full of people needing my attention and I was trying to get software and paperwork duties done. Somehow I survived! My next stage of employment was moving into the offices and becoming the assistant to the Operations Director. This is when I really took on the role of food purchaser, ordering fresh produce and food from vendors as part of our budget. I also took over the responsibility of bidding for food donations from the Feeding America portal named Choice. National Donors offer truckloads of food and other items to the network, and we Food Banks bid on them in an allocated share system. The donations are free, but we pay for the truck freight from the shipping locations. A full time IT person was hired. We are now on our 4th IT Manager. The last 2 each had assistants. Although I am grandfathered in as an admin, my duties in this regard are very low, but still have the abity to install software as needed. Quite handy on my own laptop. As well as being the Food Procurement Officer I also became the Reporting Officer. This has been greatly aided by our team receiving the ability to write our own reports from the Navision SQL database using Jet Reporting. This is an Excel extension that allows us to access field data not directly obtainable in the CERES program. The fore-mentioned Robert Srodulski used to spend a day creating a monthly report that included all of our 50 counties across multiple categories of data. He would step by step complete an Excel worksheet with all this information. I took his spreadsheet, converted it into a Jet Report, and it now runs in about 5 minutes! It is my responsibility to supply reports on a regular monthly, quarterly, and yearly basis to my Directors, fellow staff, and to Feeding America. Yes, I do have an orange mug on my desk that says "I submitted my MPR". That is the Monthly Pulse Report. It sits next to my red swingline stapler! What are God's Pantry Food Bank's sources of food? This is probably the question I get asked the most when friends and online contacts find out what I do for a career. We receive and obtain food from various sources, including: Local donations from people like you. Thank you! Local farmers. Local retail companies and other businesses giving food directly to us and to our Partner Agencies. We are the official food charity of many retailers, including Walmart and Kroger. National Companies, mostly through the Feeding America Choice Program. The USDA, U.S. Department of Agriculture, supplies us with multiple programs of food: TEFAP (the Emergency Food Assistance Program), CCC (Commodity Credit Corporation), and CSFP (Commodity Supplemental Food Program). Purchased food, including Fresh Produce, via donations and grants. Without all this food coming in, we would not be able to distribute to our internal programs or to our partner agencies, allowing them to run Backpacks for Kids, Food Boxes for Seniors, Food Pantries, Mobile Distributions, Sharing Thanksgiving, and a multitude of other services we offer our neighbours. We have a team of Food Sourcers that work directly with the retail companies, so I am not fully involved there, but I am the main Food Purchaser for the majority of the food we buy. Specialised internal programs like Backpack and local Pantries do order specific foods that they need on a regular basis. I try to supply for the long term. With the USDA CSFP program I am responsible for the ordering of that food through a Government website. Often 6 to 12 months ahead of time. Here's a truth that staggers many people when I inform them: If you are spending cash on food donations to God's Pantry Food Bank, the most efficient use of those funds is to donate it to us. I truly can obtain about $10 worth of food for every $1 given. An example is that I recently obtained a full truckload donation of 40,000lb of Canned Sliced Beets (yum!) that we are paying only freight on. Do the maths. #Where is God's Pantry Food Bank located? As mentioned we have 5 locations, not including our own local pantries, but our main head office is at 2201 Innovation Drive Please check out our webpage at GodsPantry.org/2201innovationdrive as it includes an excellent animated walk-through tour of the offices and warehouse, including the Produce Cooler, Deli Cooler, and Freezer. They are massive! I personally waited until the very last day, Friday 13th of December, to move out of my Jaggie Fox office and into my new one at Innovation. Our official first day was on Monday 16th December 2024. What I tooted and posted on that Friday caught the eye of my CEO, Michael Halligan, and he asked me if he could share it with others. Of course he should! In the Shownotes I have included a link to my Mastodon toot. It's too long a number to read out. I am absolutely loving our new location. It's my challenge to fill the cooler, freezer, and dry warehouse with donated food! My new office is 97% set up to my workflow, including my infamous hanging report boards, and spiders everywhere. The last line of my blog says: All that said, it truly is the only job I have ever had which I absolutely enjoy, but totally wish did not exist!! This remains true. Our mission is: Reducing hunger by working together to feed Kentucky communities. Our vision is: A nourished life for every Kentuckian. #How may HPR listeners support God's Pantry Food Bank The quick answer is to go to our website of GodsPantry.org and click on Take Action. From there you will be given a list to choose from: Donate Food Volunteer Host a Food Drive or Fundraiser Become a Partner Attend an Event Advocate Other Ways to Help Thank you so much for listening to my HPR show on God's Pantry Food Bank. Apart from leaving a comment on the HPR show page, the easiest ways for people to contact me are via Telegram: at t.me/solusspider or Mastodon at @SolusSpider@linuxrocks.online I look forward to hearing from you. Now go forth, be there for your fellow neighbours, and record your own HPR show! … Adding this comment to the Shownotes, that I shall not be speaking aloud. Although I consider this show topic to be Clean, as it is basically about my life and work, not my beliefs, there may be some worldwide who hear the name God's Pantry and consider it to be religious. Therefore I am flagging the show as Explicit. just in case. It is merely the name of our non-profit Food Bank, as called by our founder Mim Hunt. Although the majority of our Partner Agencies are faith based non-profit organisations, the Food Bank itself is not faith based. … Provide feedback on this episode.

Mike looks back over a year in the TTRPG hobby! 00:00 Show Start 02:19 100 Games in 2024 03:16 D&D 2024 PHB and DMG Released 05:00 Tales of the Valiant 07:03 Black Flag System Reference Document 10:06 2024 D&D on Foundry, Fantasy Grounds, and Roll20 13:35 Shadow of the Weird Wizard 14:37 Third Party Publishers on D&D Beyond 22:41 Shadowdark 23:25 City of Arches 25:24 RPGs and Generative AI 27:11 TTRPG People Moving to Bluesky 31:24 Other Non-Shittified TTRPG Platforms 33:59 Other WOTC Corporate Stuff and Bob World Builder's Survey 40:10 Looking Forward to 2025 45:42 Top Ten Sly Flourish Articles of 2024 Links Subscribe to the Sly Flourish Newsletter Support Sly Flourish on Patreon Buy Sly Flourish Books: Lazy GM Tools in Markdown on Github Obsidian Web Clipper

The guys are back, this time with Intel news, Microsoft's new open source tool, and the dust-up between bottles and OpenSuse. We finally cover the Pi 500 and Pi monitor, review the latest Framework laptops, and take a look at Microsoft's new Open Source MarkItDown tool. For tips we have comm for comparing files, a tip on bash expansion, fbi for displaying images right on the frame buffer, and abcde for super simple audio CD extraction. The show notes are at https://bit.ly/4gtMyQ0 Merry Christmas, and we'll see you next year! Host: Jonathan Bennett Co-Hosts: Rob Campbell, Jeff Massie, and David Ruggles Want access to the video version and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

D&D and RPG news and commentary by Mike Shea of https://slyflourish.com Contents 00:00 Show Start 02:09 D&D & RPG News: Cities Without Number Bundle of Holding 07:51 D&D & RPG News: Shadowrun Bundle of Holding 08:58 D&D & RPG News: Join the Arcane Library Newsletter for Two Free Adventures 11:02 DM Tip: GM Resources in Markdown for Obsidian 25:27 DM Tip: Use the Obsidian Web Clipper to Save Web Pages 29:31 Sly Flourish News: Free Dice Roller Web App 35:23 Commentary: Notable Sections of the 2024 D&D Dungeon Master's Guide 59:44 Patreon Question: Pondering Conclusions for Sandbox Games 01:02:55 Patreon Question: Using a Big Monitor on a Gaming Table 01:04:37 Patreon Question: Advice for Bridge and Travel Sessions Links Subscribe to the Sly Flourish Newsletter Support Sly Flourish on Patreon Buy Sly Flourish Books: Cities Without Number Bundle of Holding Shadowrun 4e Bundle of Holding Arcane Library Lazy GM Tools in Markdown on Github Obsidian Web Clipper Free Dice Roller Lazy Encounter Benchmark Running Hoards

This week, we discuss Jeff Barr's departure from AWS, OpenAI's latest announcements, and Broadcom's AI ambitions. Plus, Matt debates the finer points of Australian vs. American Apple Intelligence. Watch the YouTube Live Recording of Episode (https://www.youtube.com/live/PY1z81cRZiU?si=w1F7i-d7frDG27DN) 498 (https://www.youtube.com/live/PY1z81cRZiU?si=w1F7i-d7frDG27DN) Runner-up Titles That's a streak That's not a thing I miss the cold-calling lifestyle I miss being a JSON engineer I have trust issues with AI The metaphor was good Welcome to the treadmill Rundown Jeff Barr leaves AWS: And that's a wrap! (https://aws.amazon.com/blogs/aws/and-thats-a-wrap/) 12 Days of OpenAI (https://openai.com/12-days/) (6-9) Day 6: Advanced voice with video & Santa mode (https://youtu.be/NIQDnWlwYyQ) Day 7: Projects in ChatGPT (https://youtu.be/FcB97h3vrzk) Day 8: Search (https://youtu.be/OzgNJJ2ErEE) Day 9: OpenAI o1 and new tools for developers (https://openai.com/index/o1-and-new-tools-for-developers/) API, ChatGPT & Sora Facing Issues (https://status.openai.com/incidents/ctrsv3lwd797) Broadcom Broadcom shares rise 13% on profit beat, 'massive' opportunity in AI (https://www.cnbc.com/2024/12/12/broadcom-avgo-earnings-report-q4-2024-.html) Nvidia falls into correction territory, down more than 10% from its record close (https://www.cnbc.com/2024/12/16/nvidia-falls-into-correction-territory-down-more-than-10percent-from-its-record-close.html) VMware And Custom AI Chips: Broadcom's Recipe For Explosive Growth (https://seekingalpha.com/article/4744807-vmware-and-custom-ai-chips-broadcoms-recipe-for-explosive-growth) Relevant to your Interests Republican lawmakers ask Trump to kill IRS Direct File (https://www.nextgov.com/digital-government/2024/12/republican-lawmakers-ask-trump-kill-irs-direct-file/401595/) Adobe delivers strong Q4, record Firefly generations, but light outlook (https://www.constellationr.com/blog-news/insights/adobe-delivers-strong-q4-record-firefly-generations-light-outlook) Data Exports for FOCUS 1.0 is now in general availability (https://aws.amazon.com/blogs/aws-cloud-financial-management/data-exports-for-focus-1-0-is-now-generally-available/) Duolingo has bucked the post-pandemic blues in edtech (https://www.threads.net/@techmeme/post/DDj5oW5q8-N?xmt=AQGzIRoyTuZ2pO3q5kMBDSUXzruFwt7tqsJmvg732iQ_KQ) Satya Nadella | BG2 w/ Bill Gurley & Brad Gerstner (https://podcasts.apple.com/us/podcast/bg2pod-with-brad-gerstner-and-bill-gurley/id1727278168?i=1000680168104) API, ChatGPT & Sora Facing Issues Incident Report for OpenAI (https://status.openai.com/incidents/ctrsv3lwd797) AWS re:Invent 2024 - Best practices and new tools for cost reporting and estimation (https://www.youtube.com/watch?v=L6di_mQ2sKE) BlackBerry sells Cylance for $160M, a fraction of the $1.4B it paid in 2018 (https://techcrunch.com/2024/12/16/blackberry-sells-cylance-for-160m-a-fraction-of-the-1-4b-it-paid-in-2018/) EU signs $11B deal for sovereign satellite constellation to rival Musk's Starlink (https://techcrunch.com/2024/12/16/eu-signs-11b-deal-for-sovereign-satellite-constellation-to-rival-musks-starlink/) Nuon Seed + Series-A Funding (https://nuon.co/blog/byoc-for-everyone/) Databricks to Hit $62 Billion Valuation in Massive Funding Round (https://www.bloomberg.com/news/articles/2024-12-17/databricks-to-hit-62-billion-valuation-in-massive-funding-round) Android XR: The Gemini era comes to headsets and glasses (https://blog.google/products/android/android-xr/) A vision for Android XR (https://www.youtube.com/watch?v=Pn5uG1ys-pE) Gemini 2.0: Our latest, most capable AI model yet (https://blog.google/products/gemini/google-gemini-ai-collection-2024/) China orbits first Guowang Internet satellites, with thousands more to come (https://arstechnica.com/space/2024/12/china-orbits-first-guowang-internet-satellites-with-thousands-more-to-come/) Microsoft just released a tool that lets you convert Office files to Markdown (https://github.com/microsoft/markitdown) Nonsense Trump says GOP will push to eliminate daylight saving time (https://thehill.com/homenews/campaign/5039673-trump-gop-daylight-saving-time/) Gen Z says no to slim fit pants (https://bsky.app/profile/dieworkwear.bsky.social/post/3ldakaoeuhs24) The 1000-Foot High Rollercoaster Dream (https://interthemepark.com/1000rollercoaster.html) Timey Wimey (https://timeywimey.co/?ref=labnotes.org) (https://bsky.app/profile/dieworkwear.bsky.social/post/3ldakaoeuhs24)## Listener Feedback Great site collating AWS reInvent sessions along with their slides (https://reinvent-planner.cloud/sessions?catalog.view=cards&catalog.cardSize=large) Conferences CfgMgmtCamp (https://cfgmgmtcamp.org/ghent2025/), February 2-5, 2025. Civo Navigate North America (https://www.civo.com/navigate/north-america), San Francisco, Feb 10-11, 2025 DevOpsDayLA (https://www.socallinuxexpo.org/scale/22x/events/devopsday-la) at SCALE22x (https://www.socallinuxexpo.org/scale/22x), March 6-9, 2025, discount code DEVOP SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: ChatGPT Mac App (https://openai.com/chatgpt/desktop/) Photo Credits Header (https://unsplash.com/photos/sydney-opera-house-australia-jK9dT34TfuI) Artwork (https://unsplash.com/photos/a-black-background-with-a-red-and-purple-light-5-lnaaMenBI)

In this episode, Benedict shows some of the tools he loves to use including Markdown (producing PDFs and other docs using Pandoc), AWK, and Graphviz. A lot of tutorials and getting-started links in this practical-oriented episode for you. NOTES This episode of BSDNow is brought to you by Tarsnap (https://www.tarsnap.com/bsdnow) and the BSDNow Patreon (https://www.patreon.com/bsdnow) Headlines The Markdown Guide (https://www.markdownguide.org/basic-syntax/) The Pandoc Website (https://pandoc.org) Using Pandoc and Typst to Produce PDFs (https://imaginarytext.ca/posts/2024/pandoc-typst-tutorial) Eisvogel LaTeX Pandoc template (https://github.com/enhuiz/eisvogel) News Roundup Awk in 20 Minutes (https://ferd.ca/awk-in-20-minutes.html) Awk by Example (https://developer.ibm.com/tutorials/l-awk1/) W3 Schools Tutorials (https://www.w3schools.com) The dot Guide (https://graphviz.org/pdf/dotguide.pdf) Introduction to Graphviz (https://ncona.com/2020/06/create-diagrams-with-code-using-graphviz/) Browser-based Graphviz Editor SketchViz (https://sketchviz.com/) Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Producer Note Once we reach Episode 600, I will be backfilling out fireside website with the older episodes (before 283), depending on how your podcast feed service works, you may get a bunch of new notifications of episodes. Sadly there's nothing I can do about that, but I wanted everyone to be aware that. Also once we hit 600, we will be announcing some new Patreon Perks and new ways you can engage and get involved with the show. More to come in the upcoming weeks as we finalize those plans amongst the team. Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv (mailto:feedback@bsdnow.tv) Join us and other BSD Fans in our BSD Now Telegram channel (https://t.me/bsdnow)

Alex Russell answers the question, "If not React, then what?" Csaba Okrona identifies four core problems that create and reinforce knowledge silos, Rob Koch's Markwhen is like Markdown for timelines, Jeff Geerling is quite impressed by Apple's latest iteration on the Mac mini & Sylvain Kerkour took the time to draw a comparison of Amazon's O.G. S3 service with Cloudflare's R2 competitor.

Alex Russell answers the question, "If not React, then what?" Csaba Okrona identifies four core problems that create and reinforce knowledge silos, Rob Koch's Markwhen is like Markdown for timelines, Jeff Geerling is quite impressed by Apple's latest iteration on the Mac mini & Sylvain Kerkour took the time to draw a comparison of Amazon's O.G. S3 service with Cloudflare's R2 competitor.

In this episode: Alan uses vhs to make a short video Mark uses MARP to build a presentation in MarkDown for a lightning talk at OggCamp Martin uses pueue to manage all his mainframe jobs You can send your feedback via show@linuxmatters.sh. If you’d like to hang out with other listeners and share your feedback with the community you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

In this episode: Alan uses vhs to make a short video Mark uses MARP to build a presentation in MarkDown for a lightning talk at OggCamp Martin uses pueue to manage all his mainframe jobs You can send your feedback via show@linuxmatters.sh. If you’d like to hang out with other listeners and share your feedback with the community you can join: The Linux Matters Chatters on Telegram. The #linux-matters channel on the Late Night Linux Discord server. If you enjoy the show, please consider supporting us using Patreon or PayPal. For $5 a month on Patreon, you can enjoy an ad-free feed of Linux Matters, or for $10, get access to all the Late Night Linux family of podcasts ad-free.

OggCampIn this episode: Alan uses vhs to make a short video Mark uses MARP to build a presentation in MarkDown for a lightning talk at OggCamp Martin uses pueue to manage all his mainframe jobs     You can send your feedback via show@linuxmatters.sh or the Contact Form. If you'd like to hang out with... Read More

Changelog Merch is now on sale, IronCalc sets out to democratize spreadsheets, Grant Slatton writes about algorithms we develop software by, Mark Rainey gives respect to the ultimate in debugging, Gitpod is leaving Kubernetes & Johannes Kaufmann's html-to-markdown converts entire websites into Markdown.

Topics covered in this episode: Briefer: Dashboards and notebooks in a single place Introduction to programming with Python setup-uv HTML for people Extras Joke Watch on YouTube About the show Sponsored by ScoutAPM: pythonbytes.fm/scout Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org 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: Briefer: Dashboards and notebooks in a single place Notebooks and dashboards with Python, SQL, scheduling, native visualizations, code generation, and more. In Briefer, you can: Create notebooks and dashboards using Markdown, Python, SQL, and native visualizations. Build interactive data apps using inputs, dropdowns, and date pickers. Generate code and queries using an AI that understands your database schema and your notebook's context. Schedule notebooks and dashboards to run and update periodically. Create and test ad-hoc pipelines using writebacks. Briefer vs. Traditional BI Tools: Briefer is better than traditional BI tools because it's faster and more flexible, thanks to Python. Briefer vs. Traditional Notebooks: In Briefer, you can run SQL queries against connected data sources directly in your notebook. Then, Briefer will automatically turn your query into a data frame and store it in a variable that you can use in your Python blocks. Brian #2: Introduction to programming with Python Jose Blanca “Python intro aimed at students with no prior programming experience.” “Relies mainly on examples and exercises.” “Does not try to cover every detail of the Python language, but just what a beginner might need to start the journey.” Tech: “… built with the quarto publishing system complemented by the quarto live extension that allows Python to run in the web browser by using pyodide.” Runs on anything, since it doesn't require a local install of Python Running 3.12.1, looks like. Although that's a bit hidden. Seems like it should be more visible. Michael #3: setup-uv Set up your GitHub Actions workflow with a specific version of uv Install a version of uv and add it to PATH Cache the installed version of uv to speed up consecutive runs on self-hosted runners Register problem matchers for error output (Optional) Persist the uv's cache in the GitHub Actions Cache (Optional) Verify the checksum of the downloaded uv executable Brian #4: HTML for people Teaching HTML in a rather fun way. Includes basic CSS Extras Michael: A new article: We Must Replace uWSGI With Something Else Django unique email login Joke: So much O'Really

What did investors make of changes to Southwest's famous seating strategy? And why did two big AI tech-stock names go in opposite directions? Plus, why did Costco shares get a markdown? Host Francesca Fontana discusses the biggest stock moves of the week and the news that drove them. Sign up for the WSJ's free Markets A.M. newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

What did investors make of changes to Southwest's famous seating strategy? And why did two big AI tech-stock names go in opposite directions? Plus, why did Costco shares get a markdown? Host Francesca Fontana discusses the biggest stock moves of the week and the news that drove them. Sign up for the WSJ's free Markets A.M. newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

Scott walks Wes through the new Syntax Production Assistant Desktop App, designed to streamline and automate their complex publishing process. From tech stack choices like Svelte5 and Rust to AI-driven features, they dive into how this tool keeps everything consistent. Show Notes 00:00 Welcome to Syntax! 00:44 Brought to you by Sentry.io. 01:37 What was the idea? 05:42 The tech. Svelte5, Tauri, Rust, FFMPEG. 08:32 Markdown editor. ink-mde, Dillinger. 09:32 Epoch timestamps. Epoch.vercel. 10:01 Updating front-matter. 10:10 Dexie.js function. 11:25 Backing up data. 11:58 Rust functions. 12:58 Why a desktop app and not a website? 14:38 Some small AI features. 16:26 Challenges with OAuth. 20:03 Publishing challenges. 23:29 Could this work on Windows? 23:54 Debugging. 26:23 Deciphering Apple logs. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads