Podcasts about dynamodb

Neste episódio do podcast OsProgramadores, Marcelo conversa com Antonio Pinheiro sobre desenvolvimento de software, modernização de sistemas legados, arquitetura de APIs e os desafios de manter aplicações críticas evoluindo ao longo do tempo.Antonio compartilha sua experiência em projetos envolvendo C#, Delphi, Oracle, SQL Server, integração de sistemas e gestão de dívida técnica, mostrando como equilibrar inovação e manutenção em ambientes corporativos complexos.Antonio Pinheiro é um profissional de desenvolvimento de software com ampla experiência na construção, integração e modernização de sistemas corporativos.Ao longo da carreira, atuou em projetos utilizando tecnologias como:

In this episode, we discuss the October 2025 Amazon outage. The conversation took place during the outage, and though it's been a few months now, the insights and discussions are still very interesting. We trace how a DynamoDB and DNS-related failure cascaded through core AWS services and had a larger blast radius than expected. We also look at whether the outage was accidental or malicious and compare it to previous large cloud outages caused by internal errors or cascading failures. Some really interesting ideas come up around redundancy, failover, local infrastructure, and how data-centered business models change priorities around accountability, compliance, and valuation. Transcript: https://otter.ai/u/0SHTGqt3cmSEDX5v8YLsK7eDyIE?utm_source=copy_url

AWS Morning Brief for the week of May 25th, with Corey Quinn. Links:Amazon Bedrock expands support for request-level usage attributionAmazon ECS introduces pause and continue controls for service deploymentsAWS announces AWS Interconnect - multicloud connectivity with Oracle Cloud Infrastructure in previewAWS Organizations now supports higher quotas for service control policies (SCPs)Amazon Aurora MySQL 8.4 is now generally availableIntroducing ExtendDB: An open source DynamoDB-compatible adapter with pluggable storage backendsNine Entertainment's journey: Achieving 98% cost savings with Amazon ElastiCache Serverless for ValkeyAnnouncing updated retry behavior for AWS SDKs and ToolsAnnouncing AWS CDK Mixins: Composable Abstractions for AWS ResourcesCVE-2026-8838 - Remote Code Execution in amazon-redshift-python-driverCVE-2026-9133 - Arbitrary file read in rabbitmq-aws plugin

Hosted by Dave Farley - co-author of Continuous Delivery (Jolt Award winner), author of Modern Software Engineering, and inventor of the Deployment Pipeline.David Yanacek is a Senior Principal Engineer at AWS and a lead advisor on the Agentic AI team, having played a foundational role in the development of DynamoDB and CloudWatch. He is currently a primary driver behind the Kiro IDE and Amazon's operational agents, specializing in building and operating resilient, high-scale distributed systems.David and Dave talk about the transition from simple AI code completion to autonomous agentic development and the fundamental engineering principles are more critical than ever in an era of AI-generated code.------------------------Only Patreon supporters get to see the full length video episodes of "The Engineering Room” Sign up here: https://www.patreon.com/c/continuousdeliveryLinkedIn David Yanacek - https://www.linkedin.com/in/david-yanacek/?isSelfProfile=falseEqual Experts is a product software development consultancy with a network of over 1,000 experienced technology consultants globally. They increase the pace of innovation by using modern software engineering practices that embrace Continuous Delivery, Security, and Operability from the outset ➡️ https://bit.ly/3ASy8n0

Vercel's VP of Engineering Lindsey Simon unpacks how AI is transforming web deployment — from self-driving infrastructure and automatic rollbacks to prompt-to-app magic and what coding will look like by 2027.Topics Include:Vercel turns a simple git push into a live URL, fastCustomers range from solo indie devs to major enterprises globallyUnder Armour, Reuters, and Polymarket all run on Vercel's platformAI agents can detect failed deployments and automatically suggest fixesVercel calls this approach "self-driving infrastructure" — a post-AI evolutionThe AI SDK lets developers write model-agnostic code across any providerV0 lets anyone generate a full app just by typing a promptThe AI Gateway provides intelligent model fallbacks and cost efficiencyVitamins vs. painkillers: Vercel only builds what customers genuinely needAI works best when code has structure, types, and tests already in placeAWS Bedrock helped Vercel scale V0 when it went unexpectedly viralVercel's AI philosophy: "let it rip" — with smart guardrails around dataWeekly "Demo Days" put every engineer in front of 200 colleagues to presentDesign is a company-wide discipline, from product UI to office signageA new AWS integration makes provisioning Aurora and DynamoDB just a few clicksParticipants:Lindsey Simon – Vice President of Engineering, VercelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Quali sono le principali novità annunciate al re:Invent 2025 in ambito database? Cosa sono i Database Savings Plans e come possono far risparmiare fino al 35% sui costi? Quali miglioramenti sono stati introdotti per RDS Oracle e SQL Server? Cosa significa Optimize CPU e come può ridurre i costi di licenza? Quali sono le novità per Aurora DSQL, ElastiCache, DynamoDB e DocumentDB? Oggi ne parliamo con Domenico di Salvia, Senior Solutions Architect e database specialist di AWS.Link utili:- Amazon Aurora- Oracle Database@AWS

An airhacks.fm conversation with Thorsten Hoeger (@hoegertn) about: first computer experience with an IBM 8086 and learning programming by modifying the QBasic Gorilla game, early programming journey from QBasic to Visual Basic and the discovery of event-driven programming, building a password security script for autoexec.bat as a childhood project, transition from Visual Basic to Java around 2005 starting with Java 1.4.2, working at a small bank in Stuttgart building a core banking system, experience with Eclipse RCP rich client platform and the overhead of plugin architecture in business software, migration from Swing to Eclipse RCP frontend with JBoss application server backend, building a custom Spring-based microservice framework called Dwallin (Icelandic for dwarf) before Spring Boot existed, using Apache CXF for REST and RPC over messaging with ActiveMQ, comparison of Java development trajectories between annotation-based and XML-heavy approaches, discussion of the infamous Java and XML O'Reilly book that popularized XML configuration, xdoclet as a precursor to Java annotations, contrasting approaches of JBoss-based thin WAR deployments versus Spring-based embedded server microservices, university experience learning Ada programming language and its strict compiler as excellent for learning programming, PL/SQL's Ada-based origins, brief experience with OSGi and strong criticism of its complexity and poor developer experience, comparison of OSGi with Java Platform Module System (JPMS), founding Taimos consulting company 10 years ago originally building BlackBerry enterprise software, pivoting to AWS migration consulting for regulated industries including banks and insurance companies, strong preference for serverless architecture with lambda Step Functions API Gateway and DynamoDB, criticism of running kubernetes on AWS versus using native services like ECS Fargate, the distinction between running "in the cloud" versus "on the cloud", detailed discussion of why GraalVM native images are unnecessary on AWS Lambda due to compliance overhead and memory allocation model, quarkus and SnapStart as solutions for Lambda cold start problems, Java's cost efficiency on Lambda due to fast execution times, involvement with AWS CDK since 2018-2019 including building L2 constructs for EC2 and AppSync, shift from code contributions to community organizing and prioritization work with the CDK team, launching CDK Terrain as successor to CDK for Terraform, nuanced discussion of open source economics when the project primarily benefits a paid cloud provider, using GitHub as a personal index and dashboard for reusable project templates, consulting perspective on contributing to open source for code reuse across multiple clients, teaser for a future deep-dive episode on CDK internals and promoting Java usage with CDK Thorsten Hoeger on twitter: @hoegertn

Le D.E.V. de la semaine est Simon Parisot, CEO et cofondateur de Blank. Simon a fait un pari, un peu fou, au début de l’aventure Blank : avoir un environnement 100% serverless ! Lambda, DynamoDB, S3, … il connait tous les services AWS, mais n’utilise pas une seule EC2 !! Il vient nous raconter comment il a construit cette plateforme, et surtout pourquoi ! Il nous explique aussi les changements que cela a sur le travail des dev (le dev en local est compllqué), les impératifs de qualité du code que cela implique et aussi comment le recrutement doit s’adapter à ce choix technique.Liens évoqués pendant l’émissionIFTTD avec Olivier Dupuis - Faites entrer le hackeurFramework serverless🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

At AWS re:Invent I spoke to Woon Ho Jung, CTO for Cloud Native at Commvault, to talk about how they are helping AWS customers protect more than just one type of workloadWe spoke about how they started with BackTrack for S3 and now support DynamoDB and Apache Iceberg, and what real problem that solves when your data is spread across so many services!For teams who are new to Apache Iceberg on AWS, I asked Woon to break down the basics. What do you need in place so that recovery is not a theory, but something you can rely on when a table, job, or pipeline goes wrong!If you care about resilience across modern AWS workloads, this one will be worth watching.#data #ai #awsreinvent #aws #agents Amazon Web Services (AWS) AWS Partners AWS Events #awspartners #awscompetencypartners #agenticai #theravitshow

AI workloads do not fail where people expect. They fail where resilience was never designed in. At AWS re:Invent, I spoke with my friend Michael Fasulo, Senior Director for Portfolio, AI Resilience and ResOps, Commvault to understand where AI systems on AWS are actually breaking and what teams should do differently.This conversation was practical and grounded in what customers are seeing today. We covered- Where resilience breaks first in real world AI workloads running on AWS- What ResOps means in simple terms and why it is becoming essential for AWS customers running AI at scale- How architects should think about recovery when AI spreads across S3, DynamoDB, and Iceberg based data lakes- The design rules that make recovery easier instead of more complex- The most important AI driven resilience capability Commvault is building into its AWS portfolio over the next yearIf you are building AI on AWS and assuming resilience will take care of itself, this conversation is a good wake up call.#data #ai #awsreinvent #aws #agents #awspartners #awscompetencypartners #agenticai #theravitshow

Mojtaba Sarooghi, a Distinguished Product Architect at Queue-it, speaks with host Jeremy Jung about virtual waiting rooms for high-traffic events such as concerts and limited-quantity product releases. They explore using a virtual queue to prevent overloading systems, how most traffic is from bots, using edge workers to reduce requests to the customer's origin servers, and strategies for detecting bots in cooperation with vendors. Mojtaba discusses using AWS services like Elastic Load Balancing, DynamoDB, and Simple Notification Service, and explains why DynamoDB's eventual consistency is a good fit for their domain. To explain the approach, he walks us through how his team resolved an incident in which a traffic spike overloaded their services. Brought to you by IEEE Computer Society and IEEE Software magazine.

De afgelopen maanden hebben grote storingen bij Cloudflare, AWS en andere cloudproviders laten zien hoe kwetsbaar onze internetinfrastructuur is. Cloudflare verwerkt 20% van het wereldwijde internetverkeer, maar één verkeerd configuratiebestand kan het halve internet platleggen. In deze aflevering van Techzine Talks duiken we in de oorzaken van deze grootschalige uitval.Opmerkelijk is dat bijna alle grote storingen worden veroorzaakt door menselijke fouten, niet door cyberaanvallen. Van een configuratiebestand met te veel regels bij Cloudflare tot DNS-problemen bij AWS.  Automatisering en configuratiefouten of een combinatie daarvan blijken de grootste kwetsbaarheden. We bespreken waarom deze systemen zo moeilijk te testen zijn en wat de uitdagingen zijn van centralisatie in de cloudinfrastructuur.De impact van deze storingen reikt verder dan alleen de directe klanten. Door de verwevenheid van moderne microservices-architecturen kunnen problemen bij één provider een olievlekwerking hebben op het hele internet. We onderzoeken of meer redundantie en decentralisatie de oplossing zijn, en waarom zelfs AI vooralsnog geen antwoord biedt op deze complexe uitdagingen.Keywords: Cloudflare storing, AWS uitval, internet infrastructuur, DNS problemen, cloud redundantie, configuratiefouten, botmanagement, cybersecurity, automatisering, cloudprovidersChapters:0:10 - Storingen in de infrastructuur0:34 - Cloudflare en internetverkeer2:54 - Menselijke fouten versus cyberaanvallen3:00 - Het configuratieprobleem bij Cloudflare7:16 - AWS DNS-storing8:17 - DNS als telefoonboek van het internet9:49 - Centralisatie en afhankelijkheid15:38 - Testen en preventie uitdagingen

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

When a pair of high-profile internet outages took down large chunks of the internet last month, the events briefly brought hundreds of organizations to a near-halt and prevented millions of users from accessing core services for everyday business needs. From Starbucks to crypto exchanges to the messaging app Signal, the outages rippled across nearly every sector, shining a spotlight onto the country's — and even the government's — reliance on a mere handful of cloud service providers. In the wake of those incidents, watchdog groups are calling on federal regulators to scrutinize the role that massive cloud companies like Amazon and Microsoft play in owning and maintaining much of our collective backend IT infrastructure. Meanwhile, technology and cybersecurity experts point out that, because of financial and business realities, there are very few alternatives to the large companies that now dominate the market. The Amazon Web Services outage began Oct. 19 and lasted into Oct. 20. According to Amazon's post-mortem, a single software bug in DynamoDB — the system that manages website addresses, along with efforts to repair it — caused all services in the Northern Virginia region that relied on the tool to go down for 15 hours. Just over a week later, Microsoft's Azure cloud platform experienced an outage impacting several of its services. According to Microsoft, an “inadvertent tenant configuration change” occurred in Azure Front Door, the company's content delivery network. The outages exposed just how fragile the country's digital infrastructure is and showed the risks of letting a few companies hold so much power. As a result, some groups are urging federal regulators to address the issue. Federal agencies would be required to report artificial intelligence-related layoffs to the Department of Labor under a new bill from a bipartisan pair of senators. The AI-Related Job Impacts Clarity Act from Sens. Mark Warner, D-Va., and Josh Hawley, R-Mo., calls on agencies and major companies to deliver quarterly reports to DOL on the impact AI has on their workforces, detailing job cuts and displacements. Hawley said in a press release“Artificial intelligence is already replacing American workers, and experts project AI could drive unemployment up to 10-20% in the next five years. The American people need to have an accurate understanding of how AI is affecting our workforce, so we can ensure that AI works for the people, not the other way around.” The bill would also require agencies and companies to report hirings that can be “substantially” credited to AI, as well as the number of individuals they are retraining because of AI. There's also a callout to keep track of open positions an agency or company decided not to fill because of automation. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

Dans cet épisode, Arnaud et Guillaume discutent des dernières évolutions dans le monde de la programmation, notamment les nouveautés de Java 25, JUnit 6, et Jackson 3. Ils abordent également les récents développements en IA, les problèmes rencontrés dans le cloud, et l'état actuel de React et du web. Dans cette conversation, les intervenants abordent divers sujets liés à la technologie, notamment les spécifications de Wasteme, l'utilisation des UUID dans les bases de données, l'approche RAG en intelligence artificielle, les outils MCP, et la création d'images avec Nano Banana. Ils discutent également des complexités du format YAML, des récents dramas dans la communauté Ruby, de l'importance d'une bonne documentation, des politiques de retour au bureau, et des avancées de Cloud Code. Enfin, ils évoquent l'initiative de cafés IA pour démystifier l'intelligence artificielle. Enregistré le 24 octobre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-331.mp3 ou en vidéo sur YouTube. News Langages GraalVM se détache du release train de Java https://blogs.oracle.com/java/post/detaching-graalvm-from-the-java-ecosystem-train Un article de Loic Mathieu sur Java 25 et ses nouvelles fonctionalités https://www.loicmathieu.fr/wordpress/informatique/java-25-whats-new/ Sortie de Groovy 5.0 ! https://groovy-lang.org/releasenotes/groovy-5.0.html Groovy 5: Évolution des versions précédentes, nouvelles fonctionnalités et simplification du code. Compatibilité JDK étendue: Full support JDK 11-25, fonctionnalités JDK 17-25 disponibles sur les JDK plus anciens. Extension majeure des méthodes: Plus de 350 méthodes améliorées, opérations sur tableaux jusqu'à 10x plus rapides, itérateurs paresseux. Améliorations des transformations AST: Nouveau @OperatorRename, génération automatique de @NamedParam pour @MapConstructor et copyWith. REPL (groovysh) modernisé: Basé sur JLine 3, support multi-plateforme, coloration syntaxique, historique et complétion. Meilleure interopérabilité Java: Pattern Matching pour instanceof, support JEP-512 (fichiers source compacts et méthodes main d'instance). Standards web modernes: Support Jakarta EE (par défaut) et Javax EE (héritage) pour la création de contenu web. Vérification de type améliorée: Contrôle des chaînes de format plus robuste que Java. Additions au langage: Génération d'itérateurs infinis, variables d'index dans les boucles, opérateur d'implication logique ==>. Améliorations diverses: Import automatique de java.time.**, var avec multi-assignation, groupes de capture nommés pour regex (=~), méthodes utilitaires de graphiques à barres ASCII. Changements impactants: Plusieurs modifications peuvent nécessiter une adaptation du code existant (visibilité, gestion des imports, comportement de certaines méthodes). **Exigences JDK*: Construction avec JDK17+, exécution avec JDK11+. Librairies Intégration de LangChain4j dans ADK pour Java, permettant aux développeurs d'utiliser n'importe quel LLM avec leurs agents ADK https://developers.googleblog.com/en/adk-for-java-opening-up-to-third-party-language-models-via-langchain4j-integration/ ADK pour Java 0.2.0 : Nouvelle version du kit de développement d'agents de Google. Intégration LangChain4j : Ouvre ADK à des modèles de langage tiers. Plus de choix de LLM : En plus de Gemini et Claude, accès aux modèles d'OpenAI, Anthropic, Mistral, etc. Modèles locaux supportés : Utilisation possible de modèles via Ollama ou Docker Model Runner. Améliorations des outils : Création d'outils à partir d'instances d'objets, meilleur support asynchrone et contrôle des boucles d'exécution. Logique et mémoire avancées : Ajout de callbacks en chaîne et de nouvelles options pour la gestion de la mémoire et le RAG (Retrieval-Augmented Generation). Build simplifié : Introduction d'un POM parent et du Maven Wrapper pour un processus de construction cohérent. JUnit 6 est sorti https://docs.junit.org/6.0.0/release-notes/ :sparkles: Java 17 and Kotlin 2.2 baseline :sunrise_over_mountains: JSpecify nullability annotations :airplane_departure: Integrated JFR support :suspension_railway: Kotlin suspend function support :octagonal_sign: Support for cancelling test execution :broom: Removal of deprecated APIs JGraphlet, une librairie Java sans dépendances pour créer des graphes de tâches à exécuter https://shaaf.dev/post/2025-08-25-think-in-graphs-not-just-chains-jgraphlet-for-taskpipelines/ JGraphlet: Bibliothèque Java légère (zéro-dépendance) pour construire des pipelines de tâches. Principes clés: Simplicité, basée sur un modèle d'exécution de graphe. Tâches: Chaque tâche a une entrée/sortie, peut être asynchrone (Task) ou synchrone (SyncTask). Pipeline: Un TaskPipeline construit et exécute le graphe, gère les I/O. Modèle Graph-First: Le flux de travail est un Graphe Orienté Acyclique (DAG). Définition des tâches comme des nœuds, des connexions comme des arêtes. Support naturel des motifs fan-out et fan-in. API simple: addTask("id", task), connect("fromId", "toId"). Fan-in: Une tâche recevant plusieurs entrées reçoit une Map (clés = IDs des tâches parentes). Exécution: pipeline.run(input) retourne un CompletableFuture (peut être bloquant via .join() ou asynchrone). Cycle de vie: TaskPipeline est AutoCloseable, garantissant la libération des ressources (try-with-resources). Contexte: PipelineContext pour partager des données/métadonnées thread-safe entre les tâches au sein d'une exécution. Mise en cache: Option de mise en cache pour les tâches afin d'éviter les re-calculs. Au tour de Microsoft de lancer son (Microsoft) Agent Framework, qui semble être une fusion / réécriture de AutoGen et de Semnatic Kernel https://x.com/pyautogen/status/1974148055701028930 Plus de détails dans le blog post : https://devblogs.microsoft.com/foundry/introducing-microsoft-agent-framework-the-open-source-engine-for-agentic-ai-apps/ SDK & runtime open-source pour systèmes multi-agents sophistiqués. Unifie Semantic Kernel et AutoGen. Piliers : Standards ouverts (MCP, A2A, OpenAPI) et interopérabilité. Passerelle recherche-production (patterns AutoGen pour l'entreprise). Extensible, modulaire, open-source, connecteurs intégrés. Prêt pour la production (observabilité, sécurité, durabilité, "human in the loop"). Relation SK/AutoGen : S'appuie sur eux, ne les remplace pas, simplifie la migration. Intégrations futures : Alignement avec Microsoft 365 Agents SDK et Azure AI Foundry Agent Service. Sortie de Jackson 3.0 (bientôt les Jackson Five !!!) https://cowtowncoder.medium.com/jackson-3-0-0-ga-released-1f669cda529a Jackson 3.0.0 a été publié le 3 octobre 2025. Objectif : base propre pour le développement à long terme, suppression de la dette technique, architecture simplifiée, amélioration de l'ergonomie. Principaux changements : Baseline Java 17 requise (vs Java 8 pour 2.x). Group ID Maven et package Java renommés en tools.jackson pour la coexistence avec Jackson 2.x. (Exception: jackson-annotations ne change pas). Suppression de toutes les fonctionnalités @Deprecated de Jackson 2.x et renommage de plusieurs entités/méthodes clés. Modification des paramètres de configuration par défaut (ex: FAIL_ON_UNKNOWN_PROPERTIES désactivé). ObjectMapper et TokenStreamFactory sont désormais immutables, la configuration se fait via des builders. Passage à des exceptions de base non vérifiées (JacksonException) pour plus de commodité. Intégration des "modules Java 8" (pour les noms de paramètres, Optional, java.time) directement dans l'ObjectMapper par défaut. Amélioration du modèle d'arbre JsonNode (plus de configurabilité, meilleure gestion des erreurs). Testcontainers Java 2.0 est sorti https://github.com/testcontainers/testcontainers-java/releases/tag/2.0.0 Removed JUnit 4 support -> ups Grails 7.0 est sortie, avec son arrivée à la fondation Apache https://grails.apache.org/blog/2025-10-18-introducing-grails-7.html Sortie d'Apache Grails 7.0.0 annoncée le 18 octobre 2025. Grails est devenu un projet de premier niveau (TLP) de l'Apache Software Foundation (ASF), graduant d'incubation. Mise à jour des dépendances vers Groovy 4.0.28, Spring Boot 3.5.6, Jakarta EE. Tout pour bien démarrer et développer des agents IA avec ADK pour Java https://glaforge.dev/talks/2025/10/22/building-ai-agents-with-adk-for-java/ Guillaume a partagé plein de resources sur le développement d'agents IA avec ADK pour Java Un article avec tous les pointeurs Un slide deck et l'enregistrement vidéo de la présentation faite lors de Devoxx Belgique Un codelab avec des instructions pour démarrer et créer ses premiers agents Plein d'autres samples pour s'inspirer et voir les possibilités offertes par le framework Et aussi un template de projet sur GitHub, avec un build Maven et un premier agent d'exemple Cloud Internet cassé, du moins la partie hébergée par AWS #hugops https://www.theregister.com/2025/10/20/aws_outage_amazon_brain_drain_corey_quinn/ Panne majeure d'AWS (région US-EAST-1) : problème DNS affectant DynamoDB, service fondamental, causant des défaillances en cascade de nombreux services internet. Réponse lente : 75 minutes pour identifier la cause profonde; la page de statut affichait initialement "tout va bien". Cause sous-jacente principale : "fuite des cerveaux" (départ d'ingénieurs AWS seniors). Perte de connaissances institutionnelles : des décennies d'expertise critique sur les systèmes AWS et les modes de défaillance historiques parties avec ces départs. Prédictions confirmées : un ancien d'AWS avait anticipé une augmentation des pannes majeures en 2024. Preuves de la perte de talents : Plus de 27 000 licenciements chez Amazon (2022-2025). Taux élevé de "départs regrettés" (69-81%). Mécontentement lié à la politique de "Return to Office" et au manque de reconnaissance de l'expertise. Conséquences : les nouvelles équipes, plus réduites, manquent de l'expérience nécessaire pour prévenir les pannes ou réduire les temps de récupération. Perspective : Le marché pourrait pardonner cette fois, mais le problème persistera, rendant les futurs incidents plus probables. Web React a gagné "par défaut" https://www.lorenstew.art/blog/react-won-by-default/ React domine par défaut, non par mérite technique, étouffant ainsi l'innovation front-end. Choix par réflexe ("tout le monde connaît React"), freinant l'évaluation d'alternatives potentiellement supérieures. Fondations techniques de React (V-DOM, complexité des Hooks, Server Components) vues comme des contraintes actuelles. Des frameworks innovants (Svelte pour la compilation, Solid pour la réactivité fine, Qwik pour la "resumability") offrent des modèles plus performants mais sont sous-adoptés. La monoculture de React génère une dette technique (runtime, réconciliation) et centre les compétences sur le framework plutôt que sur les fondamentaux web. L'API React est complexe, augmentant la charge cognitive et les risques de bugs, contrairement aux alternatives plus simples. L'effet de réseau crée une "prison": offres d'emploi spécifiques, inertie institutionnelle, leaders choisissant l'option "sûre". Nécessité de choisir les frameworks selon les contraintes du projet et le mérite technique, non par inertie. Les arguments courants (maturité de l'écosystème, recrutement, bibliothèques, stabilité) sont remis en question; une dépendance excessive peut devenir un fardeau. La monoculture ralentit l'évolution du web et détourne les talents, nuisant à la diversité essentielle pour un écosystème sain et innovant. Promouvoir la diversité des frameworks pour un écosystème plus résilient et innovant. WebAssembly 3 est sortie https://webassembly.org/news/2025-09-17-wasm-3.0/ Data et Intelligence Artificielle UUIDv4 ou UUIDv7 pour vos clés primaires ? Ça dépend… surtout pour les bases de données super distribuées ! https://medium.com/google-cloud/understanding-uuidv7-and-its-impact-on-cloud-spanner-b8d1a776b9f7 UUIDv4 : identifiants entièrement aléatoires. Cause des problèmes de performance dans les bases de données relationnelles (ex: PostgreSQL, MySQL, SQL Server) utilisant des index B-Tree. Inserts aléatoires réduisent l'efficacité du cache, entraînent des divisions de pages et la fragmentation. UUIDv7 : nouveau standard conçu pour résoudre ces problèmes. Intègre un horodatage (48 bits) en préfixe de l'identifiant, le rendant ordonné temporellement et "k-sortable". Améliore la performance dans les bases B-Tree en favorisant les inserts séquentiels, la localité du cache et réduisant la fragmentation. Problème de UUIDv7 pour certaines bases de données distribuées et scalables horizontalement comme Spanner : La nature séquentielle d'UUIDv7 (via l'horodatage) crée des "hotspots d'écriture" (points chauds) dans Spanner. Spanner distribue les données en "splits" (partitions) basées sur les plages de clés. Les clés séquentielles concentrent les écritures sur un seul "split". Ceci empêche Spanner de distribuer la charge et de scaler les écritures, créant un goulot d'étranglement ("anti-pattern"). Quand ce n'est PAS un problème pour Spanner : Si le taux d'écriture total est inférieur à environ 3 500 écritures/seconde pour un seul "split". Le hotspot est "bénin" à cette échelle et n'entraîne pas de dégradation de performance. Solutions pour Spanner : Principe clé : S'assurer que la première partie de la clé primaire est NON séquentielle pour distribuer les écritures. UUIDv7 peut être utilisé, mais pas comme préfixe. Nouvelle conception ("greenfield") : ▪︎ Utiliser une clé primaire non-séquentielle (ex: UUIDv4 simple). Pour les requêtes basées sur le temps, créer un index secondaire sur la colonne d'horodatage, mais le SHARDER (ex: shardId) pour éviter les hotspots sur l'index lui-même. Migration (garder UUIDv7) : ▪︎ Ajouter un préfixe de sharding : Introduire une colonne `shard` calculée (ex: `MOD(ABS(FARM_FINGERPRINT(order_id_v7)), N)`) et l'utiliser comme PREMIER élément d'une clé primaire composite (`PRIMARY KEY (shard, order_id_v7)`). Réordonner les colonnes (si clé primaire composite existante) : Si la clé primaire est déjà composite (ex: (order_id_v7, tenant_id)), réordonner en (tenant_id, order_id_v7). Cela aide si tenant_id a une cardinalité élevée et distribue bien. (Un tenant_id très actif pourrait toujours nécessiter un préfixe de sharding supplémentaire). RAG en prod, comment améliorer la pertinence des résultats https://blog.abdellatif.io/production-rag-processing-5m-documents Démarrage rapide avec Langchain + Llamaindex: prototype fonctionnel, mais résultats de production jugés "subpar" par les utilisateurs. Ce qui a amélioré la performance (par ROI): Génération de requêtes: LLM crée des requêtes sémantiques et mots-clés multiples basées sur le fil de discussion pour une meilleure couverture. Reranking: La technique la plus efficace, modifie grandement le classement des fragments (chunks). Stratégie de découpage (Chunking): Nécessite beaucoup d'efforts, compréhension des données, création de fragments logiques sans coupures. Métadonnées à l'LLM: L'injection de métadonnées (titre, auteur) améliore le contexte et les réponses. Routage de requêtes: Détecte et traite les questions non-RAG (ex: résumer, qui a écrit) via API/LLM distinct. Outillage Créer un serveur MCP (mode HTTP Streamable) avec Micronaut et quelques éléments de comparaison avec Quarkus https://glaforge.dev/posts/2025/09/16/creating-a-streamable-http-mcp-server-with-micronaut/ Micronaut propose désormais un support officiel pour le protocole MCP. Exemple : un serveur MCP pour les phases lunaires (similaire à une version Quarkus pour la comparaison). Définition des outils MCP via les annotations @Tool et @ToolArg. Point fort : Micronaut gère automatiquement la validation des entrées (ex: @NotBlank, @Pattern), éliminant la gestion manuelle des erreurs. Génération automatique de schémas JSON détaillés pour les structures d'entrée/sortie grâce à @JsonSchema. Nécessite une configuration pour exposer les schémas JSON générés comme ressources statiques. Dépendances clés : micronaut-mcp-server-java-sdk et les modules json-schema. Testé avec l'inspecteur MCP et intégration avec l'outil Gemini CLI. Micronaut offre une gestion élégante des entrées/sorties structurées grâce à son support JSON Schema riche. Un agent IA créatif : comment utiliser le modèle Nano Banana pour générer et éditer des images (en Java, avec ADK) https://glaforge.dev/posts/2025/09/22/creative-ai-agents-with-adk-and-nano-banana/ Modèles de langage (LLM) deviennent multimodaux : traitent diverses entrées (texte, images, vidéo, audio). Nano Banana (gemini-2.5-flash-image-preview) : modèle Gemini, génère et édite des images, pas seulement du texte. ADK (Agent Development Kit pour Java) : pour configurer des agents IA créatifs utilisant ce type de modèle. Application : Base pour des workflows créatifs complexes (ex: agent de marketing, enchaînement d'agents pour génération d'assets). Un vieil article (6 mois) qui illustre les problèmes du format de fichier YAML https://ruudvanasseldonk.com/2023/01/11/the-yaml-document-from-hell YAML est extrêmement complexe malgré son objectif de convivialité humaine. Spécification volumineuse et versionnée (YAML 1.1, 1.2 diffèrent significativement). Comportements imprévisibles et "pièges" (footguns) courants : Nombres sexagésimaux (ex: 22:22 parsé comme 1342 en YAML 1.1). Tags (!.git) pouvant mener à des erreurs ou à l'exécution de code arbitraire. "Problème de la Norvège" : no interprété comme false en YAML 1.1. Clés non-chaînes de caractères (on peut devenir une clé booléenne True). Nombres accidentels si non-guillemets (ex: 10.23 comme flottant). La coloration syntaxique n'est pas fiable pour détecter ces subtilités. Le templating de documents YAML est une mauvaise idée, source d'erreurs et complexe à gérer. Alternatives suggérées : TOML : Similaire à YAML mais plus sûr (chaînes toujours entre guillemets), permet les commentaires. JSON avec commentaires (utilisé par VS Code), mais moins répandu. Utiliser un sous-ensemble simple de YAML (difficile à faire respecter). Générer du JSON à partir de langages de programmation plus puissants : ▪︎ Nix : Excellent pour l'abstraction et la réutilisation de configuration. Python : Facilite la création de JSON avec commentaires et logique. Gros binz dans la communauté Ruby, avec l'influence de grosses boîtes, et des pratiques un peu douteuses https://joel.drapper.me/p/rubygems-takeover/ Méthodologies Les qualités d'une bonne documentation https://leerob.com/docs Rapidité Chargement très rapide des pages (préférer statique). Optimisation des images, polices et scripts. Recherche ultra-rapide (chargement et affichage des résultats). Lisibilité Concise, éviter le jargon technique. Optimisée pour le survol (gras, italique, listes, titres, images). Expérience utilisateur simple au départ, complexité progressive. Multiples exemples de code (copier/coller). Utilité Documenter les solutions de contournement (workarounds). Faciliter le feedback des lecteurs. Vérification automatisée des liens morts. Matériel d'apprentissage avec un curriculum structuré. Guides de migration pour les changements majeurs. Compatible IA Trafic majoritairement via les crawlers IA. Préférer cURL aux "clics", les prompts aux tutoriels. Barre latérale "Demander à l'IA" référençant la documentation. Prêt pour les agents Faciliter le copier/coller de contenu en Markdown pour les chatbots. Possibilité de visualiser les pages en Markdown (ex: via l'URL). Fichier llms.txt comme répertoire de fichiers Markdown. Finition soignée Zones de clic généreuses (boutons, barres latérales). Barres latérales conservant leur position de défilement et état déplié. Bons états actifs/survol. Images OG dynamiques. Titres/sections lienables avec ancres stables. Références et liens croisés entre guides, API, exemples. Balises méta/canoniques pour un affichage propre dans les moteurs de recherche. Localisée Pas de /en par défaut dans l'URL. Routage côté serveur pour la langue. Localisation des chaînes statiques et du contenu. Responsive Excellents menus mobiles / support Safari iOS. Info-bulles sur desktop, popovers sur mobile. Accessible Lien "ignorer la navigation" vers le contenu principal. Toutes les images avec des balises alt. Respect des paramètres système de mouvement réduit. Universelle Livrer la documentation "en tant que code" (JSDoc, package). Livrer via des plateformes comme Context7, ou dans node_modules. Fichiers de règles (ex: AGENTS.md) avec le produit. Évaluations et modèles spécifiques recommandés pour le produit. Loi, société et organisation Microsoft va imposer une politique de Return To Office https://www.businessinsider.com/microsoft-execs-explain-rto-mandate-in-internal-meeting-2025-9 Microsoft impose 3 jours de présence au bureau par semaine à partir de février 2026, débutant par la région de Seattle Le CEO Satya Nadella explique que le télétravail a affaibli les liens sociaux nécessaires à l'innovation Les dirigeants citent des données internes montrant que les employés présents au bureau "prospèrent" davantage L'équipe IA de Microsoft doit être présente 4 jours par semaine, règles plus strictes pour cette division stratégique Les employés peuvent demander des exceptions jusqu'au 19 septembre 2025 pour trajets complexes ou absence d'équipe locale Amy Coleman (RH) affirme que la collaboration en personne améliore l'énergie et les résultats, surtout à l'ère de l'IA La politique s'appliquera progressivement aux 228 000 employés dans le monde après les États-Unis Les réactions sont mitigées, certains employés critiquent la perte d'autonomie et les bureaux inadéquats Microsoft rattrape ses concurrents tech qui ont déjà imposé des retours au bureau plus stricts Cette décision intervient après 15 000 licenciements en 2025, créant des tensions avec les employés Comment Claude Code est né ? (l'histoire de sa création) https://newsletter.pragmaticengineer.com/p/how-claude-code-is-built Claude Code : outil de développement "AI-first" créé par Boris Cherny, Sid Bidasaria et Cat Wu. Performance impressionnante : 500M$ de revenus annuels, utilisation multipliée par 10 en 3 mois. Adoption interne massive : Plus de 80% des ingénieurs d'Anthropic l'utilisent quotidiennement, y compris les data scientists. Augmentation de productivité : 67% d'augmentation des Pull Requests (PR) par ingénieur malgré le doublement de l'équipe. Origine : Commande CLI simple évoluant vers un outil accédant au système de fichiers, exploitant le "product overhang" du modèle Claude. Raison du lancement public : Apprendre sur la sécurité et les capacités des modèles d'IA. Pile technologique "on distribution" : TypeScript, React (avec Ink), Yoga, Bun. Choisie car le modèle Claude est déjà très performant avec ces technologies. "Claude Code écrit 90% de son propre code" : Le modèle prend en charge la majeure partie du développement. Architecture légère : Simple "shell" autour du modèle Claude, minimisant la logique métier et le code (suppression constante de code superflu). Exécution locale : Privilégiée pour sa simplicité, sans virtualisation. Sécurité : Système de permissions granulaire demandant confirmation avant chaque action potentiellement dangereuse (ex: suppression de fichiers). Développement rapide : Jusqu'à 100 releases internes/jour, 1 release externe/jour. 5 Pull Requests/ingénieur/jour. Prototypage ultra-rapide (ex: 20+ prototypes d'une fonctionnalité en quelques heures) grâce aux agents IA. Innovation UI/UX : Redéfinit l'expérience du terminal grâce à l'interaction LLM, avec des fonctionnalités comme les sous-agents, les styles de sortie configurables, et un mode "Learning". Le 1er Café IA publique a Paris https://www.linkedin.com/pulse/my-first-caf%25C3%25A9-ia-paris-room-full-curiosity-an[…]o-goncalves-r9ble/?trackingId=%2FPHKdAimR4ah6Ep0Qbg94w%3D%3D Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 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) 5-6 novembre 2025 : Tech Show Paris - Paris (France) 5-6 novembre 2025 : Red Hat Summit: Connect Paris 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 6 novembre 2025 : Agile Tour Aix-Marseille 2025 - Gardanne (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) 19 novembre 2025 : SREday Paris 2025 Q4 - Paris (France) 19-21 novembre 2025 : Agile Grenoble - Grenoble (France) 20 novembre 2025 : OVHcloud Summit - Paris (France) 21 novembre 2025 : DevFest Paris 2025 - Paris (France) 24 novembre 2025 : Forward Data & AI Conference - Paris (France) 27 novembre 2025 : DevFest Strasbourg 2025 - Strasbourg (France) 28 novembre 2025 : DevFest Lyon - Lyon (France) 1-2 décembre 2025 : Tech Rocks Summit 2025 - Paris (France) 4-5 décembre 2025 : Agile Tour Rennes - Rennes (France) 5 décembre 2025 : DevFest Dijon 2025 - Dijon (France) 9-11 décembre 2025 : APIdays Paris - Paris (France) 9-11 décembre 2025 : Green IO Paris - Paris (France) 10-11 décembre 2025 : Devops REX - Paris (France) 10-11 décembre 2025 : Open Source Experience - Paris (France) 11 décembre 2025 : Normandie.ai 2025 - Rouen (France) 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 31 mars 2026 : ParisTestConf - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 17 juin 2026 : Devoxx Poland - Krakow (Poland) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 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/

Send us a textStart with a simple truth: when the platform breaks, your clever architecture won't save you. We dig into the AWS US‑East‑1 outage where DynamoDB's role in DNS planning for load balancers collided with a race condition, leaving empty records and stalled EC2 instances. Forget the finger‑wagging about “well‑architected” apps—this was a platform failure with limited customer escape routes. We weigh multi‑region and multi‑cloud trade‑offs with a sober look at cost, complexity, and operational burden.Security took center stage with two high‑risk stories you need to act on. First, a critical WSUS flaw enabling remote unauthenticated code execution against the very servers meant to protect fleets. If WSUS is still live, patch immediately or take it offline until you can. Then, the F5 source code theft: not a cloning threat, but a blueprint for discovering subtle bugs and crafting precise exploits. Attribution points toward Chinese state‑sponsored actors, which means targeted, quiet use rather than noisy mass exploitation. The risk isn't gone when headlines fade; it's just harder to see.We connect this to rising exploitation of vSock across hypervisors like VMware ESXi. With public PoCs and active abuse, vSock opens covert channels from host to guest, making segmentation and management plane isolation non‑negotiable. Patch aggressively, gate access through jump hosts, enforce MFA, and consider disabling vSock where viable on QEMU stacks. These are concrete steps that cut real risk.Then we turn to the elephant in the data center: AI ROI. Vendors keep shipping agentic assistants and copilots, but few can show durable returns outside a subsidized token economy. We share a pragmatic lens for measuring value—cycle time, MTTR, defect rates—while acknowledging the dot‑com‑style arc ahead: hype, correction, then durable wins that prioritize efficiency. As AI demand drives massive new builds, the physical footprint of the cloud is showing up in local power grids and skylines. Infrastructure choices now carry community and energy implications leaders can't ignore.Subscribe, share with a colleague who owns platform reliability or security, and leave a review with your biggest takeaway or question—what will you patch, segment, or measure first?Purchase Chris and Tim's book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

Es näselt leicht im Podcast - die herbstliche Erkältungswelle macht auch vor "Passwort" nicht halt. Trotzdem haben sich Sylvester und Christopher einiges vorgenommen. Sie sprechen über den AWS- und Azure-Ausfall der letzten Wochen, denn auch Verfügbarkeit ist Teil der IT-Sicherheit. Die kritische Sicherheitslücke im Windows-Updateserver WSUS kommt ebenso zur Sprache wie eine trickreiche Malware, die eine wenig bekannte UTF8-Funktion zu ihrem Vorteil nutzt. Und endlich gibt es wieder ein PKI-Thema: Wie eine kroatische CA widerrechtlich Zertifikate für Cloudflare ausstellte, erzählt Christopher dem Publikum und seinem Co-Host. - Online Themenabend: https://aktionen.heise.de/heise-themenabend - AWS' Ausfallanalyse: https://aws.amazon.com/de/message/101925/ - Meredith Whittaker von Signal zur Notwendigkeit der Hyperscaler: https://mastodon.world/@Mer__edith/115445701583902092 - SAP spielt CVSS-Würfeln: https://services.nvd.nist.gov/rest/json/cvehistory/2.0?cveId=CVE-2025-30012 - Microsoft warnt Entwickler vor SoapFormatter: https://learn.microsoft.com/en-us/dotnet/standard/serialization/binaryformatter-security-guide - Koi über GlassWorm: https://www.koi.ai/blog/glassworm-first-self-propagating-worm-using-invisible-code-hits-openvsx-marketplace - QWAC mit Soße (+): https://www.heise.de/select/ct/2023/29/2332409110101310744 - Diskussion um FINA im Bugzilla: https://bugzilla.mozilla.org/show_bug.cgi?id=1986968 - Folgt uns im Fediverse: * @christopherkunz@chaos.social * @syt@social.heise.de

工程師都宅宅的不太會講話？ 其實工程師的幹話多到你聽不下去！ ------ 加入粉絲團留言互動！ https://www.facebook.com/%E5%B7%A5%E7%A8%8B%E5%B8%AB%E8%81%8A%E4%BB%80%E9%BA%BC-109229084578194 ------ softwaretalkthreesmall@gmail.com -- Hosting provided by SoundOn

Aproveitamos a AWS fora do ar e o linkedin cheio de especialistas para falar um pouco sobre DevOps e SRE: o que realmente falhou, como dependências globais amplificam incidentes e por que comunicação e telemetria mudam o jogo quando o provedor está cambaleando. Falamos de estratégias de resiliência multi‑região, desenho de failure domains e decisões pragmáticas de RTO/RPO. Discutimos feature flags para degradar funcionalidades com graça, circuit breakers e backoff nos clientes, priorização de runbooks e exercícios de caos que realmente medem MTTR. Também passamos por impactos colaterais em serviços gerenciados (EKS, IAM, KMS, DynamoDB), observabilidade em modo de guerra e os limites do “gerenciado”.Fechamos com lições acionáveis para times de produto e plataforma: desde budget de disponibilidade e custos até testes de recuperação orientados a cenários. Dois tópicos‑chave que destacamos: resiliência multi‑região na prática e como treinar a organização para incidentes de baixa probabilidade e alto impacto.Por fim, damos um giro de lições aprendidas para equipes de produto e plataforma: feature flags para fallback de integrações, rotas alternativas para planos de controle, circuit breakers em clientes, e playbooks para comunicação com stakeholders. Dois tópicos que merecem atenção especial neste papo: resiliência multi-região na prática e como preparar sua organização para incidentes “quase improváveis”.#Links Importantes:- Lucas Azevedo - https://www.linkedin.com/in/lazevedo-devops/- Comunidade DevOps no Discord - https://discord.com/invite/k6wPagw4tV- João Brito - https://www.linkedin.com/in/juniorjbn/- Assista ao FilmeTEArapia - https://youtu.be/M4QFmW_HZh0?si=HIXBDWZJ8yPbpflM## Hashtags#DevOps #SRE #AWS #Outage #DNS #DynamoDB #AltaDisponibilidade #Resiliencia #Observabilidade #ChaosEngineering #IncidentResponse #Runbooks #FeatureFlags #CircuitBreaker #RTO #RPO #Kubernetes #DevSecOps #Kubicast #Containers #GetupO Kubicast é uma produção da Getup, empresa especialista em Kubernetes e projetos open source para Kubernetes. Os episódios do podcast estão nas principais plataformas de áudio digital e no YouTube.com/@getupcloud.

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

10 月 20 號星期一，亞馬遜雲端服務 AWS 的核心區域 us-east-1 爆出一個 Race Condition，導致 DynamoDB 的 DNS 被清空，結果連帶拖垮了 113 項內部與外部服務。從社群平台、交易所、航空公司、政府單位，甚至英超足球聯盟，全都中標。這場十五小時的大當機，不只是 AWS 的災難，更是「雲端集中化」的一次警訊。這集我們就來聊聊：☁️ 為什麼 us-east-1 這麼關鍵？⚙️ Race Condition 到底怎麼讓 DNS 全毀？

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

AWS Morning Brief for the week of October 27th, with Corey Quinn. Links:Streamline in-place application upgrades with Amazon VPC LatticeBuild a proactive AI cost management system for Amazon Bedrock – Part 2 -Overview and best practices of multithreaded replication in Amazon RDS for MySQL, Amazon RDS for MariaDB, and Amazon Aurora MySQL AWS announces Nitro Enclaves are now available in all AWS RegionsAmazon CloudWatch Synthetics now supports bundled multi-check canaries Amazon U7i instances now available in Europe (London) RegionAmazon Connect now supports automated follow-up evaluations triggered by initial evaluation resultsHow the Wildlife Conservation Society uses AWS to accelerate coral reef monitoring worldwideAmazon MQ is now available in AWS Asia Pacific (New Zealand) Region Amazon CloudWatch introduces interactive incident reportingAWS Secret-West Region is now availableCharting the life of an Amazon CloudFront request

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

News and Updates: Amazon outage knocks out half the internet: A faulty DNS update in Amazon's DynamoDB caused a massive AWS outage, crippling services like Zoom, Alexa, Slack, and major financial platforms. Over 8 million users were affected globally, delaying flights, halting trades, and disrupting daily life. Analysts estimate losses could reach billions, reigniting calls for multi-cloud resilience—and even breaking up Big Tech. Microsoft issues urgent Windows 11 fix: An October update broke Windows Recovery Environment tools, disabling USB keyboards and mice during recovery. Microsoft rushed out patch KB5070773 to restore functionality. Users are urged to install immediately to regain recovery access and avoid potential boot or repair issues. Meta shuts down Messenger desktop apps: Messenger for Windows and macOS will shut down Dec. 15. Users must switch to web access or lose functionality. Messages will remain on Facebook accounts if secure storage is enabled. Meta gave no reason for the change, though declining desktop usage likely drove the decision. GM phases out CarPlay and Android Auto across all models: CEO Mary Barra confirmed GM will remove phone projection from all future vehicles—gas and electric—by 2028. The company is transitioning to a new unified computing platform with Google Gemini AI and in-house apps, part of its push toward a fully integrated infotainment system. GM unveils AI assistant and eyes-off driving system: At its “GM Forward” event, the automaker announced a 2028 launch for its next-gen platform featuring Google Gemini AI, hands-free “eyes-off” driving, and energy systems with home battery leasing. GM calls it a “new era of mobility,” aiming to transform vehicles into intelligent assistants. Apple slashes iPhone Air production amid weak demand: Apple is “drastically” cutting iPhone Air output to near shutdown levels after poor sales and “virtually no demand,” per Nikkei. Customers favor the iPhone 17 Pro lineup for better cameras and battery life. The ultra-thin $999 iPhone Air failed to generate excitement despite its sleek 5.6mm design.

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

以下のようなトピックについて話をしました。 01. H3ロケット7号機、新型補給機HTV-X1打ち上げ成功 JAXA（宇宙航空研究開発機構）は2025年10月26日午前9時00分15秒、種子島宇宙センターから「H3」ロケット7号機の打ち上げを成功させました。このロケットには、新型宇宙ステーション補給機1号機「HTV-X1」が搭載されており、発射から14分4秒後に軌道投入が確認されました。 HTV-X1は、2020年まで運用されていた「HTV（こうのとり）」の後継機として開発された無人補給機です。従来機と比較して大幅に性能が向上しており、貨物搭載能力は質量4トンから5.82トンへ、容積は49立方メートルから78立方メートルへと約1.5倍に拡大されています。打ち上げ時の総質量は約16トンです。 最大の特徴は、ISS（国際宇宙ステーション）での物資補給任務完了後も、最長1.5年間の単独飛行が可能な点です。これにより、軌道上実証プラットフォームとして技術実証や実験ミッションを継続できます。 HTV-X1には、生命維持に必要な窒素・酸素・水の補給タンクや宇宙食、実験機器などの与圧カーゴに加え、船外曝露カーゴとして中型曝露実験アダプタ「i-SEEP」や超小型衛星放出システム「H-SSOD」などの先端技術機器が搭載されています。 分離後のHTV-X1は太陽電池パドルの展開に成功し、10月30日午前0時50分頃にJAXAの油井亀美也宇宙飛行士によってISSにキャッチされる予定です。 02. AWS大規模障害の原因と影響分析 2025年10月20日に発生したAWS大規模障害について、その原因と影響を詳しく解説した記事の要約です。 障害の概要 US-EAST-1リージョンで約4時間にわたる大規模障害が発生。DynamoDBのDNSエンドポイント解決が機能しなくなり、DynamoDB及び依存サービスが連鎖的にダウンしました。 根本原因 AWSのDNS自動更新システムにおける「隠れパターン」の競合が原因でした。このシステムは、DNS PlannerとDNS Enactorの2つのコンポーネントで構成され、高可用性のため3つのAZで独立したインスタンスが同じ機能を実行していました。しかし、異常に長いリトライ処理中に新しいDNSプランが複数生成され、古いプランが適用された直後にクリーンアッププロセスが作動し、現行DNSプランを削除してしまいました。結果、Route 53にDynamoDBエンドポイントのレコードが存在しない「真空」状態が発生しました。 影響範囲 EC2、NLB、Lambdaなどのコアサービスに波及。特に新規インスタンスの起動やLambda関数の作成・更新が不可能になりました。 教訓と今後 筆者は、完全なマルチクラウド化は現実的でないとし、巨大ベンダーへの依存は避けられない一方通行の道だと指摘。しかし責任分散により社会全体でリスクを分担する現在のシステムは合理的だと評価しています。開発者としては、選択した道を信じて進むしかないと結論づけています。 03. GitHub Copilotエージェントの自律開発支援機能 GitHub Copilot コーディング エージェントは、GitHub Copilot Pro、Business、Enterpriseプランで利用可能な自律的なAI開発支援機能です。従来のIDEでのAIアシスタントとは異なり、バックグラウンドで独立して動作し、issueの割り当てやpull request作成依頼を通じてタスクを完了できます。 主な機能として、バグ修正、新機能実装、テストカバレッジ向上、ドキュメント更新、技術的負債への対処が可能です。GitHub Actions環境で動作し、ブランチ作成からコミット、PR作成まで自動化できるため、開発者は複雑な作業により集中できます。 セキュリティ面では、制限された開発環境での動作、copilot/で始まるブランチのみへのアクセス、書き込み権限を持つユーザーのみへの応答など、多層的な保護機能を備えています。また、外部コラボレーターとして扱われ、Actions実行前には承認が必要です。 ただし制限もあり、単一リポジトリでの作業、一度に一つのPR作成のみ、署名済みコミット非対応、セルフホステッドランナー非対応などがあります。現在はClaude Sonnet 4モデルを使用しており、GitHub Actions分とPremiumリクエストを消費します。この機能により、開発チームの生産性向上と作業の効率化が期待できます。 本ラジオはあくまで個人の見解であり現実のいかなる団体を代表するものではありません ご理解頂ますようよろしくおねがいします

In Italia e in Europa, l'energia nucleare rappresenta uno degli argomenti più complessi e dibattuti, eppure spesso la percezione pubblica diverge notevolmente dalla realtà dei fatti. Per comprendere come la tecnologia nucleare si sia evoluta negli ultimi decenni e quali sistemi di sicurezza la caratterizzano oggi, vogliamo ripercorrere gli incidenti storici che hanno plasmato questa industria, da Three Mile Island a Chernobyl, fino a Fukushima, analizzando cosa abbiamo imparato e come le moderne centrali di terza e quarta generazione rispondono a queste lezioni. Proviamo anche a capire cos'è il principio della "difesa in profondità" e come i livelli di protezione ridondanti e indipendenti rendono i reattori straordinariamente sicuri.Nella sezione delle notizie parliamo dei disservizi di Amazon AWS, del debutto italiano di Prime Vision, la modalità di visione per il calcio che integra realtà aumentata e intelligenza artificiale e infine di un impianto di vetrificazione dei rifiuti radioattivi.--Indice--00:00 - Introduzione00:59 - I disservizi di Amazon AWS (IlSole24Ore.com, Luca Martinelli)02:20 - Prime Vision debutta in Italia (DDay.it, Matteo Gallo)03:32 - Una nuova gestione dei rifiuti radioattivi (HDBlog.it, Matteo Gallo)04:59 - La sicurezza nelle centrali nucleari (Matteo Gallo)17:57 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S7E43#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Immagini--• Foto copertina: Wirestock su Freepik--Brani--• Ecstasy by Rabbit Theft• Falling For You by SouMix & Bromar

ChatGPT has launched a browser It's only for Mac though. I tried it and was a little underwhelmed. The first two things I tried to get it to do it failed at. I asked it to get headlines from CNN and the NYTimes but those sites are restricted -- which may be a common issue as you start using it for your every day. Publishers and Apps are thinking about their AI access strategies after being burned from giving so much of their content to Google. I then asked it to draft an email and get it ready in Gmail - but it wasn't any faster than copy and pasting from ChatGPT directly. Not sure there is enough benefit just yet! The big outage earlier the week shows just how reliant we are on AWS Oh boy.. that was a day. 14 hours of downtime with spotty services as different Amazon web services were online and offline. It turned out to be a DNS issue. The ip addresses of the DynamoDB servers were wiped - effectively making them invisible to the internet. It would be like removing all the phone numbers from the phone book - it doesn't make the phone book very useful. Only trouble is that the cloud servers and other AWS services people use rely on that phone book to operate and connect to the internet. It meant they were all taken offline and exposed a bad failure point. It really showed that some companies don't have adequate failovers, or proper multi-cloud implementations. But, it didn't hurt them - their stock actually popped on the day of the outage and they have ended the week up ~5%. LISTEN ABOVESee omnystudio.com/listener for privacy information.

An airhacks.fm conversation with Philipp Page (@PagePhilipp) about: early computing experiences with Windows XP and Intel Pentium systems, playing rally car games like Dirt with split-screen multiplayer, transitioning from gaming to server administration through Minecraft, running Minecraft servers at age 13 with memory limitations and out-of-memory exceptions, implementing caching mechanisms with cron jobs and MySQL databases, learning about SQL injection attacks and prepared statements, discovering connection pooling advantages over PHP approaches, appreciating type safety and Object-oriented programming principles in Java, the tendency to over-abstract and create unnecessary abstractions as junior developers, obsession with avoiding dependencies and implementing frameworks from scratch, building custom Model-View-Controller patterns and dependency injection systems, developing e-learning platform for aerospace industry using PHP Symfony framework, implementing time series forecasting in pure Java without external dependencies, internship and employment at AWS Dublin in Frontier Networking team, working on AWS Outposts and Ground Station hybrid cloud offerings, using python and rust for networking control plane development, learning to appreciate Python despite initial resistance to dynamically typed languages, joining AWS Lambda Powertools team as Java tech lead, maintaining open-source serverless development toolkit, providing utilities for observability including structured JSON logging with Lambda-specific information, implementing metrics and tracing for distributed event-driven architectures, mapping utilities to AWS Well-Architected Framework serverless lens recommendations, caching parameters and secrets to improve scalability and reduce costs, debate about AspectJ dependency and alternatives like Micronaut and quarkus approaches, providing both annotation-based and programmatic interfaces for utilities, newer utilities like Kafka consumer avoiding AspectJ dependency, comparing Micronaut's compiler-based approach and Quarkus extensions for bytecode generation, AspectJ losing popularity in enterprise Java projects, preferring Java standards over external dependencies for long-term maintainability, agents in electricity trading simulations for renewable energy scenarios, comparing on-premise Java capabilities versus cloud-native AWS features, default architecture pattern of Lambda with S3 for persistent storage, using AWS Calculator for cost analysis before architecture decisions, event-driven architectures being native to AWS versus artificially created in traditional Java projects, everything in AWS emitting events naturally through services like EventBridge, filtering events rather than creating them artificially, avoiding unnecessary microservices complexity when simple method calls suffice, directly wiring API Gateway to DynamoDB without Lambda for no-code solutions, using Java for CDK infrastructure as code while minimizing runtime dependencies, maximizing cloud-native features when in cloud versus on-premise optimization strategies, starting with simplest possible architecture and justifying complexity, blue-green deployments and load balancing handled automatically by Lambda, internal AWS teams using Lambda for orchestration and event interception, Lambda as foundational zero-level service across AWS infrastructure, preferring highest abstraction level services like Lambda and ECS Fargate, only dropping to EC2 when specific requirements demand lower-level control, contributing to Powertools for AWS Lambda Python repository before joining team, compile-time weaving avoiding Lambda cold start performance impacts, GraalVM compilation considerations for Quarkus and Micronaut approaches, customer references available on Powertools website, contrast between low-level networking and serverless development, LinkedIn as primary social media platform for professional connections, Powertools for AWS Lambda (Java) Philipp Page on twitter: @PagePhilipp

An airhacks.fm conversation with Ronald Dehuysser (@rdehuyss) about: JobRunner evolution from open source to processing 1 billion jobs daily, carbon-aware job processing using European energy grid data ( ENTSO-E ) for scheduling jobs during renewable energy peaks, correlation between CO2 emissions and energy prices for cost optimization, JobRunner Pro vs Open Source features including workflows and multi-tenancy support, bytecode analysis using ASM for lambda serialization, JSON serialization for job state persistence, support for relational databases and MongoDB with potential S3 and DynamoDB integration, distributed processing with master node coordination using heartbeat mechanism, scale-to-zero architecture possibilities using AWS EventBridge Scheduler, Java performance advantages showing 35x faster than python in benchmarks, cloud migration patterns from on-premise to serverless architectures, criticism of kubernetes complexity and lift-and-shift cloud migrations, cost-driven architecture approach using AWS Lambda and S3, quarkus as fastest Java runtime for cloud deployments, infrastructure as code using AWS CDK with Java, potential WebAssembly compilation for Edge Computing, automatic retry mechanisms with exponential backoff, dashboard and monitoring capabilities, medical industry use case with critical cancer result processing, professional liability insurance for software errors, comparison with executor service for non-critical tasks, scheduled and recurring job support, carbon footprint reduction through intelligent scheduling, spot instance integration for cost optimization, simplified developer experience with single JAR deployment, automatic table creation and data source detection in Quarkus, backwards compatibility requirements for distributed nodes, future serverless edition possibilities Ronald Dehuysser on twitter: @rdehuyss

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf's 76,000+ customers scale their device management operations.Topics Include:Sam Johnson introduces himself as Chief Customer Officer from Jamf companyJamf's 23-year mission: help organizations succeed with Apple device managementCompany manages 33+ million devices for 76,000+ customers worldwide from MinneapolisJamf has used AI since 2018 for security threat detectionReleased first customer-facing generative AI Assistant just last year in 2024Presentation covers why, how they built it, use cases, and future plansJamf serves horizontal market from small business to Fortune 500 companiesChallenge: balance powerful platform capabilities with ease of use and adoptionAI could help get best of both worlds - power and simplicityAI also increases security posture and scales user capabilities significantlyCustomers already using ChatGPT/Claude but wanted AI embedded in productBuilt into product to reduce "doorway effect" of switching digital environmentsCreated small cross-functional team to survey land and build initial trailRest of engineering organization came behind to build the production highwayTeam needed governance layer with input from security, legal, other departmentsEvaluated multiple providers but ultimately chose Amazon Bedrock for three reasonsAWS team support, large community, and integration with existing infrastructureUses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementationAI development required longer training/validation phase than typical product featuresReleased "AI Assistant" with three skills: Reference, Explain, and Search capabilitiesParticipants:Sam Johnson – Chief Customer Officer, JamfFurther Links:Jamf.comJamf on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

There are over 60 new updates that your hosts Simon, Jillian and Shruthi take you through this week!

I speak with Daniela Miao of Momento to discuss her journey from DynamoDB at AWS to creating an alternative caching platform for large scale real time applications.Try the best git GUI for macOS and WindowsGrapple git without the grief and try Tower, the best graphical interface for git on macOS and Windows.go.chrischinchilla.com/tower For show notes and an interactive transcript, visit chrischinchilla.com/podcast/To reach out and say hello, visit chrischinchilla.com/contact/To support the show for ad-free listening and extra content, visit chrischinchilla.com/support/

AWS Morning Brief for the week of April 14th, with Corey Quinn.Links:Amazon Route 53 adds public authoritative DNS service to AWS GovCloud (US) RegionsCost Optimization Hub supports DynamoDB and MemoryDB reservation recommendationsLoad Balancer Capacity Unit Reservation for Gateway Load BalancersAnnouncing new AWS Elemental MediaTailor pricing model with lower VOD ad insertion costsHow AWS and Intel make LLMs more accessible and cost-effective with DeepSeekAnnouncing up to 85% price reductions for Amazon S3 Express One ZoneOptimize Amazon VPC Flow Logs analysis with Cribl Stream samplingExploring Data Transfer Costs for AWS Network Load Balancers

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about stackgres as a complete database cloud solution for PostgreSQL, kubernetes as an abstraction layer over infrastructure providing a programmable API, Stackgres offering high availability with primary and replica nodes using patroni, integrated connection pooling with PgBouncer, kubernetes operators and Custom Resource Definitions (CRDs) as a powerful way to extend Kubernetes, day two operations automated through CRDs including benchmarks and version upgrades, Stackgres supporting sharding with Citus for horizontal scaling similar to DynamoDB, Change Data Capture capabilities using embedded debezium, failover mechanisms taking typically 30 seconds with DNS updates, synchronous vs asynchronous replication options affecting data loss during failover, Stackgres being implemented in Java using quarkus, ContainerD as a programmable container runtime that can be used without Kubernetes, Stackgres offering multiple interfaces including CRDs, REST API, and a web console, considerations for running databases on Kubernetes vs cloud-managed services, the advantages of containerization for infrastructure, the challenges of multi-leader setups in PostgreSQL requiring conflict resolution, the value of Kubernetes for on-premises deployments vs cloud environments Alvaro Hernandez on twitter: @ahachete

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

"Le dernier auditeur pensait que tout avait été codé par la même personne" Le D.E.V. de la semaine est Simon Parisot, CEO et cofondateur de Blank. Simon a fait un pari, un peu fou, au début de l'aventure Blank : avoir un environnement 100% serverless ! Lambda, DynamoDB, S3, &hellip il connait tous les services AWS, mais n'utilise pas une seule EC2 !! Il vient nous raconter comment il a construit cette plateforme, et surtout pourquoi ! Il nous explique aussi les changements que cela a sur le travail des dev (le dev en local est compllqué), les impératifs de qualité du code que cela implique et aussi comment le recrutement doit s'adapter à ce choix technique.Liens évoqués pendant l'émissionIFTTD avec Olivier Dupuis - Faites entrer le hackeurFramework serverless 🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |

AWS Morning Brief for the week of December 16th, 2024, with Corey Quinn. Links:Amazon Bedrock Guardrails reduces pricing by up to 85%Amazon CloudWatch now provides centralized visibility into telemetry configurationsAmazon EC2 F2 instances, featuring up to 8 FPGAs, are generally availableAmazon SES now offers Global Endpoints for multi-region sending resilienceAWS Toolkit for Visual Studio Code now includes Amazon CloudWatch Logs Live TailAccelerate your AWS Graviton adoption with the AWS Graviton Savings DashboardCapture data changes while restoring an Amazon DynamoDB tableUnderstand the benefits of physical replication in Amazon RDS for PostgreSQL Blue/Green DeploymentsHow AWS sales uses Amazon Q Business for customer engagementAWS Network Firewall Geographic IP Filtering launchIssue with DynamoDB local - CVE-2022-1471

Hoje é dia de sobre carreira! No episódio de estreia da série especial do podcast, conversamos com Erika Nagamine, Golden Jacket da AWS, sobre a sua trajetória, sobre as suas decisões, e sobre o poder que a curiosidade teve para lhe impulsionar ao longo de toda a sua carreira. Vem ver quem participou desse papo: Paulo Silveira, o host que gosta de certificação André David, o cohost que está rolando até agora Erika Nagamine, Arquiteta de Soluções Especialista em Dados & AI - Analytics na AWS

Simon is joined by Stephen Liedig to discuss the evolution of serverless technology and its impact on application development, exploring benefits like scalability, cost optimization, and faster dev cycles. They delve into key services and concepts in serverless design, including state machines, event-driven architectures, and observability, highlighting the flexibility and optimization opportunities offered by serverless architecture. - Get started with AWS Serverless (https://aws.amazon.com/serverless) and Application Integration (https://aws.amazon.com/products/application-integration) on the AWS website. - Visit Serverless Land (https://serverlessland.com/) to get the latest information, blogs, videos, code, and learning resources for AWS Serverless. Learn to use and build apps that scale automatically on low-cost, fully-managed serverless architecture. - Implement Serverless best practices and increase your developer velocity with Powertools for AWS (https://powertools.aws.dev/) - Learn by doing! Check out the Serverless Patterns Workshop (https://catalog.workshops.aws/serverless-patterns) to build your first serverless microservice to retrieve data from DynamoDB with Lambda and API Gateway. - Dive even deeper with the Serverless Developer Experience workshop (https://catalog.workshops.aws/serverless-developer-experience) to get hands on experience leveraging serverless application integration patterns, event-driven architectures and orchestration!

In this episode of Startup Project, we chat with Khawaja Shams, Co-founder and CEO of Momento, a serverless caching and messaging service built for interactive applications at scale. Host: Nataraj (Investor at Incisive VC, angel investor, and Senior Product Manager) Guest: Khawaja Shams (Co-founder and CEO of Momento) Website: ⁠Momento Website⁠ LinkedIn: ⁠Nataraj's LinkedIn⁠ | ⁠Khawaja's LinkedIn⁠ [0:00 - 2:00] Khawaja shares his incredible journey—from working on image processing for Mars rovers and communications for interplanetary missions at NASA to building crucial infrastructure at Amazon Web Services (AWS) and ultimately starting Momento. [2:00 - 6:00] Khawaja provides an in-depth look at his early days at NASA, where he was inspired by the company's mission and the potential of cloud computing. He discusses how he prototyped using public datasets on his personal credit card and the challenges of onboarding Amazon as a vendor in the early days of AWS. [6:00 - 10:00] We discuss Khawaja's experience at Amazon, where he witnessed the company's rapid growth and customer obsession firsthand. He details his roles in AWS product engineering and leading key teams, including DynamoDB and Elemental Technologies. [10:00 - 16:00] Khawaja talks about the inspiration behind Momento and how the need for a better caching solution for interactive applications became clear. He explains how Momento addresses the pain points of traditional caching solutions and simplifies development for users. [16:00 - 20:00] We dive deeper into Momento's target customer base and the importance of focusing on verticals like media, gaming, and fintech. Khawaja shares valuable insights on identifying the right customers and building strong design partnerships. [20:00 - 25:00] Khawaja discusses product-market fit and how Momento validated its solution through numerous successful customers. He emphasizes the need for coherence in customer asks and how that provides confidence in the product's direction. [25:00 - 30:00] We talk about B2B growth and marketing strategies, specifically how Momento leverages its existing customer base and focuses on finding similar companies. Khawaja stresses the importance of operational excellence and customer obsession in building trust and advocacy. [30:00 - 35:00] Khawaja shares his thoughts on Amazon's leadership principles and how Momento has cultivated its own unique culture focused on customer centricity and psychological safety. [35:00 - 40:00] We explore the challenges of attracting top talent in a startup environment. Khawaja emphasizes the importance of finding a team you enjoy working with and tackling a problem you believe in. [40:00 - 45:00] Khawaja shares his current consumption habits, including his favorite books and podcasts. He also highlights the importance of mentorship and staying connected with people you admire. [45:00 - 50:00] Khawaja discusses the importance of focus in a startup environment and how prioritizing a few key goals can lead to greater success. [50:00 - 55:00] We finish with a discussion about AI and how Momento plays a crucial role in enabling interactive applications powered by real-time data. #Startup #TechPodcast #Serverless #CloudComputing #AWS #InteractiveApps #B2BMarketing #Entrepreneurship #Leadership #AI #Fintech #MediaTech #GamingTech #ProductMarketFit #Caching #CustomerObsession #FoundersJourney

In this episode of the Modern Web Podcast, Rob Ocel, Danny Thompson, and Adam Rackis talk with Michael Liendo, Senior Developer Advocate at AWS, about building practical AI applications and tackling challenges like scalability, multimodal functionality, and cloud infrastructure choices. Michael shares insights on tools like AWS Amplify and DynamoDB, discusses strategies for managing cloud costs, and explores the evolving role of prompt engineering. Michael previews his upcoming talks at AWS re:Invent on AI and scalable B2B SaaS applications. Chapters 00:00 - Introduction and Guest Welcome 01:30 - Talking Weather and Life in the Midwest 03:00 - Exploring Generative AI and Practical Applications 06:45 - Navigating Cloud Costs and Scalability Considerations 08:30 - Maintaining Creativity and Customization with AI 11:00 - Managed Services vs. On-Prem Infrastructure Debate 15:30 - Choosing a Tech Stack for Side Projects and Startups 18:45 - Learning Cloud: Paths for Full-Stack Cloud Development 22:30 - The Role of Cloud Certifications in Today's Market 26:00 - Preview of Michael's Upcoming Talks at AWS re:Invent 32:00 - Where to Find Michael Online Follow Michael Liendo on Social Media Twitter: https://x.com/focusotter Linkedin: https://www.linkedin.com/in/focusotter/ Sponsored by Wix Studio: wix.com/studio

This week, co-host Conor Bronsdon sits down with Daniela Miao, co-founder and CTO of Momento, to discuss her journey from DynamoDB at AWS to founding the real-time data infrastructure platform Momento. Daniela covers the importance of observability, the decision to rebuild Momento's stack with Rust, and how observability can speed up development cycles. They also explore strategies for aligning technical projects with business objectives, building team trust, and the critical role of communication in achieving success. Tune in for valuable insights on leadership, technical decision-making, and startup growth.Topics:02:01 Why is observability often treated as an auxiliary service?06:14 Making a push for observability13:32 Picking the right metrics to observe and pay attention to15:49 Has the technical shift to Rust paid off?19:23 How did you create trust and buy in from your team to make a switch?26:31 What could other teams learn from Momento's move to Rust?38:15 Advice would you give for other technical founders?Links:Daniela MiaoThe Momento BlogMomento: An enterprise-ready serverless platform for caching and pub/subUnpacking the 2023 DORA Report w/ Nathen Harvey of Google CloudGoogle SRERust Programming LanguageSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

Hoje é dia de falar de nuvem! Neste episódio, exploramos a surpreendente relação entre a AWS e a Amazon Brasil, e as importantes questões ligadas a dimensionamento, escalabilidade e, é claro, segurança quando o assunto é nuvem. Vem ver quem participou desse papo: André David, o host que fica ligado em palavrinhas-chave Vinny Neves, co-host e Tech Lead na UsTwo Bruno Toffolo, Principal Software Development Engineer na Amazon Gaston Perez, Principal Solutions Architect na AWS

Simon is joined by Jason Hunter, AWS Principal Specialist Solutions Architect, do dive super-deep into how to make the most of DynamoDB. Whether you are new to DynamoDB, or have been using it for years - there is something in this episode for everyone! Shownotes: Jason's Blog Posts: https://aws.amazon.com/blogs/database/author/jzhunter/ The Apache Iceberg blog: https://aws.amazon.com/blogs/database/use-amazon-dynamodb-incremental-export-to-update-apache-iceberg-tables/ Traffic spikes (on-demand vs provisioned): https://aws.amazon.com/blogs/database/handle-traffic-spikes-with-amazon-dynamodb-provisioned-capacity/ Cost-effective bulk actions like delete: https://aws.amazon.com/blogs/database/cost-effective-bulk-processing-with-amazon-dynamodb/ A deep dive on partitions: https://aws.amazon.com/blogs/database/part-1-scaling-dynamodb-how-partitions-hot-keys-and-split-for-heat-impact-performance/ Global tables prescriptive guidance (the 25 page deep dive): https://docs.aws.amazon.com/prescriptive-guidance/latest/dynamodb-global-tables/introduction.html

This is a recap of the top 10 posts on Hacker News on May 20th, 2024.This podcast was generated by wondercraft.ai(00:36): Statement from Scarlett Johansson on the OpenAI "Sky" voiceOriginal post: https://news.ycombinator.com/item?id=40421225&utm_source=wondercraft_ai(01:48): ICC prosecutor seeks arrest warrants against Sinwar and Netanyahu for war crimesOriginal post: https://news.ycombinator.com/item?id=40414329&utm_source=wondercraft_ai(03:32): 3M executives convinced a scientist forever chemicals in human blood were safeOriginal post: https://news.ycombinator.com/item?id=40414316&utm_source=wondercraft_ai(05:04): Migrating Uber's ledger data from DynamoDB to LedgerStoreOriginal post: https://news.ycombinator.com/item?id=40413891&utm_source=wondercraft_ai(06:46): EnlightenmentwareOriginal post: https://news.ycombinator.com/item?id=40419856&utm_source=wondercraft_ai(08:31): pg_timeseries: Open-source time-series extension for PostgreSQLOriginal post: https://news.ycombinator.com/item?id=40417347&utm_source=wondercraft_ai(10:07): How a 64k intro is made (2017)Original post: https://news.ycombinator.com/item?id=40414565&utm_source=wondercraft_ai(11:42): CVE-2024-4367 – Arbitrary JavaScript execution in PDF.jsOriginal post: https://news.ycombinator.com/item?id=40414718&utm_source=wondercraft_ai(13:21): Rethinking Text Resizing on WebOriginal post: https://news.ycombinator.com/item?id=40418591&utm_source=wondercraft_ai(15:13): Grothendieck's use of equalityOriginal post: https://news.ycombinator.com/item?id=40414404&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Evelyn Osman, Principal Platform Engineer at AutoScout24, joins Corey on Screaming in the Cloud to discuss the dire need for developers to agree on a standardized tool set in order to scale their projects and innovate quickly. Corey and Evelyn pick apart the new products being launched in cloud computing and discover a large disconnect between what the industry needs and what is actually being created. Evelyn shares her thoughts on why viewing platforms as products themselves forces developers to get into the minds of their users and produces a better end result.About EvelynEvelyn is a recovering improviser currently role playing as a Lead Platform Engineer at Autoscout24 in Munich, Germany. While she says she specializes in AWS architecture and integration after spending 11 years with it, in truth she spends her days convincing engineers that a product mindset will make them hate their product managers less.Links Referenced:LinkedIn: https://www.linkedin.com/in/evelyn-osman/TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. My guest today is Evelyn Osman, engineering manager at AutoScout24. Evelyn, thank you for joining me.Evelyn: Thank you very much, Corey. It's actually really fun to be on here.Corey: I have to say one of the big reasons that I was enthused to talk to you is that you have been using AWS—to be direct—longer than I have, and that puts you in a somewhat rarefied position where AWS's customer base has absolutely exploded over the past 15 years that it's been around, but at the beginning, it was a very different type of thing. Nowadays, it seems like we've lost some of that magic from the beginning. Where do you land on that whole topic?Evelyn: That's actually a really good point because I always like to say, you know, when I come into a room, you know, I really started doing introductions like, “Oh, you know, hey,” I'm like, you know, “I'm this director, I've done this XYZ,” and I always say, like, “I'm Evelyn, engineering manager, or architect, or however,” and then I say, you know, “I've been working with AWS, you know, 11, 12 years,” or now I can't quite remember.Corey: Time becomes a flat circle. The pandemic didn't help.Evelyn: [laugh] Yeah, I just, like, a look at that the year, and I'm like, “Jesus. It's been that long.” Yeah. And usually, like you know, you get some odd looks like, “Oh, my God, you must be a sage.” And for me, I'm… you see how different services kind of, like, have just been reinventions of another one, or they just take a managed service and make another managed service around it. So, I feel that there's a lot of where it's just, you know, wrapping up a pretty bow, and calling it something different, it feels like.Corey: That's what I've been low-key asking people for a while now over the past year, namely, “What is the most foundational, interesting thing that AWS has done lately, that winds up solving for this problem of whatever it is you do as a company? What is it that has foundationally made things better that AWS has put out in the last service? What was it?” And the answers I get are all depressingly far in the past, I have to say. What's yours?Evelyn: Honestly, I think the biggest game-changer I remember experiencing was at an analyst summit in Stockholm when they announced Lambda.Corey: That was announced before I even got into this space, as an example of how far back things were. And you're right. That was transformative. That was awesome.Evelyn: Yeah, precisely. Because before, you know, we were always, like, trying to figure, okay, how do we, like, launch an instance, run some short code, and then clean it up. AWS is going to charge for an hour, so we need to figure out, you know, how to pack everything into one instance, run for one hour. And then they announced Lambda, and suddenly, like, holy shit, this is actually a game changer. We can actually write small functions that do specific things.And, you know, you go from, like, microservices, like, to like, tiny, serverless functions. So, that was huge. And then DynamoDB along with that, really kind of like, transformed the entire space for us in many ways. So, back when I was at TIBCO, there was a few innovations around that, even, like, one startup inside TIBCO that quite literally, their entire product was just Lambda functions. And one of their problems was, they wanted to sell in the Marketplace, and they couldn't figure out how to sell Lambda on the marketplace.Corey: It's kind of wild when we see just how far it's come, but also how much they've announced that doesn't change that much, to be direct. For me, one of the big changes that I remember that really made things better for customers—thought it took a couple of years—was EFS. And even that's a little bit embarrassing because all that is, “All right, we finally found a way to stuff a NetApp into us-east-1,” so now NFS, just like you used to use it in the 90s and the naughts, can be done responsibly in the cloud. And that, on some level, wasn't a feature launch so much as it was a concession to the ways that companies had built things and weren't likely to change.Evelyn: Honestly, I found the EFS launch to be a bit embarrassing because, like, you know, when you look closer at it, you realize, like, the performance isn't actually that great.Corey: Oh, it was horrible when it launched. It would just slam to a halt because you got the IOPS scaled with how much data you stored on it. The documentation explicitly said to use dd to start loading a bunch of data onto it to increase the performance. It's like, “Look, just sandbag the thing so it does what you'd want.” And all that stuff got fixed, but at the time it looked like it was clown shoes.Evelyn: Yeah, and that reminds me of, like, EBS's, like, gp2 when we're, like you know, we're talking, like, okay, provision IOPS with gp2. We just kept saying, like, just give yourself really big volume for performance. And it feel like they just kind of kept that with EFS. And it took years for them to really iterate off of that. Yeah, so, like, EFS was a huge thing, and I see us, we're still using it now today, and like, we're trying to integrate, especially for, like, data center migrations, but yeah, you always see that a lot of these were first more for, like, you know, data centers to the cloud, you know. So, first I had, like, EC2 classic. That's where I started. And I always like to tell a story that in my team, we're talking about using AWS, I was the only person fiercely against it because we did basically large data processing—sorry, I forget the right words—data analytics. There we go [laugh].Corey: I remember that, too. When it first came out, it was, “This sounds dangerous and scary, and it's going to be a flash in the pan because who would ever trust their core compute infrastructure to some random third-party company, especially a bookstore?” And yeah, I think I got that one very wrong.Evelyn: Yeah, exactly. I was just like, no way. You know, I see all these articles talking about, like, terrible disk performance, and here I am, where it's like, it's my bread and butter. I'm specialized in it, you know? I write code in my sleep and such.[Yeah, the interesting thing is, I was like, first, it was like, I can 00:06:03] launch services, you know, to kind of replicate when you get in a data center to make it feature comparable, and then it was taking all this complex services and wrapping it up in a pretty bow for—as a managed service. Like, EKS, I think, was the biggest one, if we're looking at managed services. Technically Elasticsearch, but I feel like that was the redheaded stepchild for quite some time.Corey: Yeah, there was—Elasticsearch was a weird one, and still is. It's not a pleasant service to run in any meaningful sense. Like, what people actually want as the next enhancement that would excite everyone is, I want a serverless version of this thing where I can just point it at a bunch of data, I hit an API that I don't have to manage, and get Elasticsearch results back from. They finally launched a serverless offering that's anything but. You have to still provision compute units for it, so apparently, the word serverless just means managed service over at AWS-land now. And it just, it ties into the increasing sense of disappointment I've had with almost all of their recent launches versus what I felt they could have been.Evelyn: Yeah, the interesting thing about Elasticsearch is, a couple of years ago, they came out with OpenSearch, a competing Elasticsearch after [unintelligible 00:07:08] kind of gave us the finger and change the licensing. I mean, OpenSearch actually become a really great offering if you run it yourself, but if you use their managed service, it can kind—you lose all the benefits, in a way.Corey: I'm curious, as well, to get your take on what I've been seeing that I think could only be described as an internal shift, where it's almost as if there's been a decree passed down that every service has to run its own P&L or whatnot, and as a result, everything that gets put out seems to be monetized in weird ways, even when I'd argue it shouldn't be. The classic example I like to use for this is AWS Config, where it charges you per evaluation, and that happens whenever a cloud resource changes. What that means is that by using the cloud dynamically—the way that they supposedly want us to do—we wind up paying a fee for that as a result. And it's not like anyone is using that service in isolation; it is definitionally being used as people are using other cloud resources, so why does it cost money? And the answer is because literally everything they put out costs money.Evelyn: Yep, pretty simple. Oftentimes, there's, like, R&D that goes into it, but the charges seem a bit… odd. Like from an S3 lens, was, I mean, that's, like, you know, if you're talking about services, that was actually a really nice one, very nice holistic overview, you know, like, I could drill into a data lake and, like, look into things. But if you actually want to get anything useful, you have to pay for it.Corey: Yeah. Everything seems to, for one reason or another, be stuck in this place where, “Well, if you want to use it, it's going to cost.” And what that means is that it gets harder and harder to do anything that even remotely resembles being able to wind up figuring out where's the spend going, or what's it going to cost me as time goes on? Because it's not just what are the resources I'm spinning up going to cost, what are the second, third, and fourth-order effects of that? And the honest answer is, well, nobody knows. You're going to have to basically run an experiment and find out.Evelyn: Yeah. No, true. So, what I… at AutoScout, we actually ended up doing is—because we're trying to figure out how to tackle these costs—is they—we built an in-house cost allocation solution so we could track all of that. Now, AWS has actually improved Cost Explorer quite a bit, and even, I think, Billing Conductor was one that came out [unintelligible 00:09:21], kind of like, do a custom tiered and account pricing model where you can kind of do the same thing. But even that also, there is a cost with it.I think that was trying to compete with other, you know, vendors doing similar solutions. But it still isn't something where we see that either there's, like, arbitrarily low pricing there, or the costs itself doesn't really quite make sense. Like, AWS [unintelligible 00:09:45], as you mentioned, it's a terrific service. You know, we try to use it for compliance enforcement and other things, catching bad behavior, but then as soon as people see the price tag, we just run away from it. So, a lot of the security services themselves, actually, the costs, kind of like, goes—skyrockets tremendously when you start trying to use it across a large organization. And oftentimes, the organization isn't actually that large.Corey: Yeah, it gets to this point where, especially in small environments, you have to spend more energy and money chasing down what the cost is than you're actually spending on the thing. There were blog posts early on that, “Oh, here's how you analyze your bill with Redshift,” and that was a minimum 750 bucks a month. It's, well, I'm guessing that that's not really for my $50 a month account.Evelyn: Yeah. No, precisely. I remember seeing that, like, entire ETL process is just, you know, analyze your invoice. Cost [unintelligible 00:10:33], you know, is fantastic, but at the end of the day, like, what you're actually looking at [laugh], is infinitesimally small compared to all the data in that report. Like, I think oftentimes, it's simply, you know, like, I just want to look at my resources and allocate them in a multidimensional way. Which actually isn't really that multidimensional, when you think about it [laugh].Corey: Increasingly, Cost Explorer has gotten better. It's not a new service, but every iteration seems to improve it to a point now where I'm talking to folks, and they're having a hard time justifying most of the tools in the cost optimization space, just because, okay, they want a percentage of my spend on AWS to basically be a slightly better version of a thing that's already improving and works for free. That doesn't necessarily make sense. And I feel like that's what you get trapped into when you start going down the VC path in the cost optimization space. You've got to wind up having a revenue model and an offering that scales through software… and I thought, originally, I was going to be doing something like that. At this point, I'm unconvinced that anything like that is really tenable.Evelyn: Yeah. When you're a small organization you're trying to optimize, you might not have the expertise and the knowledge to do so, so when one of these small consultancies comes along, saying, “Hey, we're going to charge you a really small percentage of your invoice,” like, okay, great. That's, like, you know, like, a few $100 a month to make sure I'm fully optimized, and I'm saving, you know, far more than that. But as soon as your invoice turns into, you know, it's like $100,000, or $300,000 or more, that percentage becomes rather significant. And I've had vendors come to me and, like, talk to me and is like, “Hey, we can, you know, for a small percentage, you know, we're going to do this machine learning, you know, AI optimization for you. You know, you don't have to do anything. We guaranteed buybacks your RIs.” And as soon as you look at the price tag with it, we just have to walk away. Or oftentimes we look at it, and there are truly very simple ways to do it on your own, if you just kind of put some thought into it.Corey: While we want to talking a bit before this show, you taught me something new about GameLift, which I think is a different problem that AWS has been dealing with lately. I've never paid much attention to it because it is the—as I assume from what it says on the tin, oh, it's a service for just running a whole bunch of games at scale, and I'm not generally doing that. My favorite computer game remains to be Twitter at this point, but that's okay. What is GameLift, though, because you want to shining a different light on it, which makes me annoyed that Amazon Marketing has not pointed this out.Evelyn: Yeah, so I'll preface this by saying, like, I'm not an expert on GameLift. I haven't even spun it up myself because there's quite a bit of price. I learned this fall while chatting with an SA who works in the gaming space, and it kind of like, I went, like, “Back up a second.” If you think about, like, I'm, you know, like, World of Warcraft, all you have are thousands of game clients all over the world, playing the same game, you know, on the same server, in the same instance, and you need to make sure, you know, that when I'm running, and you're running, that we know that we're going to reach the same point the same time, or if there's one object in that room, that only one of us can get it. So, all these servers are doing is tracking state across thousands of clients.And GameLift, when you think about your dedicated game service, it really is just multi-region distributed state management. Like, at the basic, that's really what it is. Now, there's, you know, quite a bit more happening within GameLift, but that's what I was going to explain is, like, it's just state management. And there are far more use cases for it than just for video games.Corey: That's maddening to me because having a global session state store, for lack of a better term, is something that so many customers have built themselves repeatedly. They can build it on top of primitives like DynamoDB global tables, or alternately, you have a dedicated region where that thing has to live and everything far away takes forever to round-trip. If they've solved some of those things, why on earth would they bury it under a gaming-branded service? Like, offer that primitive to the rest of us because that's useful.Evelyn: No, absolutely. And honestly, I wouldn't be surprised if you peeled back the curtain with GameLift, you'll find a lot of—like, several other you know, AWS services that it's just built on top of. I kind of mentioned earlier is, like, what I see now with innovation, it's like we just see other services packaged together and releases a new product.Corey: Yeah, IoT had the same problem going on for years where there was a lot of really good stuff buried in there, like IOT events. People were talking about using that for things like browser extensions and whatnot, but you need to be explicitly told that that's a thing that exists and is handy, but otherwise you'd never know it was there because, “Well, I'm not building anything that's IoT-related. Why would I bother?” It feels like that was one direction that they tended to go in.And now they take existing services that are, mmm, kind of milquetoast, if I'm being honest, and then saying, “Oh, like, we have Comprehend that does, effectively detection of themes, keywords, and whatnot, from text. We're going to wind up re-releasing that as Comprehend Medical.” Same type of thing, but now focused on a particular vertical. Seems to me that instead of being a specific service for that vertical, just improve the baseline the service and offer HIPAA compliance if it didn't exist already, and you're mostly there. But what do I know? I'm not a product manager trying to get promoted.Evelyn: Yeah, that's true. Well, I was going to mention that maybe it's the HIPAA compliance, but actually, a lot of their services already have HIPAA compliance. And I've stared far too long at that compliance section on AWS's site to know this, but you know, a lot of them actually are HIPAA-compliant, they're PCI-compliant, and ISO-compliant, and you know, and everything. So, I'm actually pretty intrigued to know why they [wouldn't 00:16:04] take that advantage.Corey: I just checked. Amazon Comprehend is itself HIPAA-compliant and is qualified and certified to hold Personal Health Information—PHI—Private Health Information, whatever the acronym stands for. Now, what's the difference, then, between that and Medical? In fact, the HIPAA section says for Comprehend Medical, “For guidance, see the previous section on Amazon Comprehend.” So, there's no difference from a regulatory point of view.Evelyn: That's fascinating. I am intrigued because I do know that, like, within AWS, you know, they have different segments, you know? There's, like, Digital Native Business, there's Enterprise, there's Startup. So, I am curious how things look over the engineering side. I'm going to talk to somebody about this now [laugh].Corey: Yeah, it's the—like, I almost wonder, on some level, it feels like, “Well, we wound to building this thing in the hopes that someone would use it for something. And well, if we just use different words, it checks a box in some analyst's chart somewhere.” I don't know. I mean, I hate to sound that negative about it, but it's… increasingly when I talk to customers who are active in these spaces around the industry vertical targeted stuff aimed at their industry, they're like, “Yeah, we took a look at it. It was adorable, but we're not using it that way. We're going to use either the baseline version or we're going to work with someone who actively gets our industry.” And I've heard that repeated about three or four different releases that they've put out across the board of what they've been doing. It feels like it is a misunderstanding between what the world needs and what they're able to or willing to build for us.Evelyn: Not sure. I wouldn't be surprised, if we go far enough, it could probably be that it's just a product manager saying, like, “We have to advertise directly to the industry.” And if you look at it, you know, in the backend, you know, it's an engineer, you know, kicking off a build and just changing the name from Comprehend to Comprehend Medical.Corey: And, on some level, too, they're moving a lot more slowly than they used to. There was a time where they were, in many cases, if not the first mover, the first one to do it well. Take Code Whisperer, their AI powered coding assistant. That would have been a transformative thing if GitHub Copilot hadn't beaten them every punch, come out with new features, and frankly, in head-to-head experiments that I've run, came out way better as a product than what Code Whisperer is. And while I'd like to say that this is great, but it's too little too late. And when I talk to engineers, they're very excited about what Copilot can do, and the only people I see who are even talking about Code Whisperer work at AWS.Evelyn: No, that's true. And so, I think what's happening—and this is my opinion—is that first you had AWS, like, launching a really innovative new services, you know, that kind of like, it's like, “Ah, it's a whole new way of running your workloads in the cloud.” Instead of you know, basically, hiring a whole team, I just click a button, you have your instance, you use it, sell software, blah, blah, blah, blah. And then they went towards serverless, and then IoT, and then it started targeting large data lakes, and then eventually that kind of run backwards towards security, after the umpteenth S3 data leak.Corey: Oh, yeah. And especially now, like, so they had a hit in some corners with SageMaker, so now there are 40 services all starting with the word SageMaker. That's always pleasant.Evelyn: Yeah, precisely. And what I kind of notice is… now they're actually having to run it even further back because they caught all the corporations that could pivot to the cloud, they caught all the startups who started in the cloud, and now they're going for the larger behemoths who have massive data centers, and they don't want to innovate. They just want to reduce this massive sysadmin team. And I always like to use the example of a Bare Metal. When that came out in 2019, everybody—we've all kind of scratched your head. I'm like, really [laugh]?Corey: Yeah, I could see where it makes some sense just for very specific workloads that involve things like specific capabilities of processors that don't work under emulation in some weird way, but it's also such a weird niche that I'm sure it's there for someone. My default assumption, just given the breadth of AWS's customer base, is that whenever I see something that they just announced, well, okay, it's clearly not for me; that doesn't mean it's not meeting the needs of someone who looks nothing like me. But increasingly as I start exploring the industry in these services have time to percolate in the popular imagination and I still don't see anything interesting coming out with it, it really makes you start to wonder.Evelyn: Yeah. But then, like, I think, like, roughly a year or something, right after Bare Metal came out, they announced Outposts. So, then it was like, another way to just stay within your data center and be in the cloud.Corey: Yeah. There's a bunch of different ways they have that, okay, here's ways you can run AWS services on-prem, but still pay us by the hour for the privilege of running things that you have living in your facility. And that doesn't seem like it's quite fair.Evelyn: That's exactly it. So, I feel like now it's sort of in diminishing returns and sort of doing more cloud-native work compared to, you know, these huge opportunities, which is everybody who still has a data center for various reasons, or they're cloud-native, and they grow so big, that they actually start running their own data centers.Corey: I want to call out as well before we wind up being accused of being oblivious, that we're recording this before re:Invent. So, it's entirely possible—I hope this happens—that they announce something or several some things that make this look ridiculous, and we're embarrassed to have had this conversation. And yeah, they're totally getting it now, and they have completely surprised us with stuff that's going to be transformative for almost every customer. I've been expecting and hoping for that for the last three or four re:Invents now, and I haven't gotten it.Evelyn: Yeah, that's right. And I think there's even a new service launches that actually are missing fairly obvious things in a way. Like, mine is the Managed Workflow for Amazon—it's Managed Airflow, sorry. So, we were using Data Pipeline for, you know, big ETL processing, so it was an in-house tool we kind of built at Autoscout, we do platform engineering.And it was deprecated, so we looked at a new—what to replace it with. And so, we looked at Airflow, and we decided this is the way to go, we want to use managed because we don't want to maintain our own infrastructure. And the problem we ran into is that it doesn't have support for shared VPCs. And we actually talked to our account team, and they were confused. Because they said, like, “Well, every new service should support it natively.” But it just didn't have it. And that's, kind of, what, I kind of found is, like, there's—it feels—sometimes it's—there's a—it's getting rushed out the door, and it'll actually have a new managed service or new service launched out, but they're also sort of cutting some corners just to actually make sure it's packaged up and ready to go.Corey: When I'm looking at this, and seeing how this stuff gets packaged, and how it's built out, I start to understand a pattern that I've been relatively down on across the board. I'm curious to get your take because you work at a fairly sizable company as an engineering manager, running teams of people who do this sort of thing. Where do you land on the idea of companies building internal platforms to wrap around the offerings that the cloud service providers that they use make available to them?Evelyn: So, my opinion is that you need to build out some form of standardized tool set in order to actually be able to innovate quickly. Now, this sounds counterintuitive because everyone is like, “Oh, you know, if I want to innovate, I should be able to do this experiment, and try out everything, and use what works, and just release it.” And that greatness [unintelligible 00:23:14] mentality, you know, it's like five talented engineers working to build something. But when you have, instead of five engineers, you have five teams of five engineers each, and every single team does something totally different. You know, one uses Scala, and other on TypeScript, another one, you know .NET, and then there could have been a [last 00:23:30] one, you know, comes in, you know, saying they're still using Ruby.And then next thing you know, you know, you have, like, incredibly diverse platforms for services. And if you want to do any sort of like hiring or cross-training, it becomes incredibly difficult. And actually, as the organization grows, you want to hire talent, and so you're going to have to hire, you know, a developer for this team, you going to have to hire, you know, Ruby developer for this one, a Scala guy here, a Node.js guy over there.And so, this is where we say, “Okay, let's agree. We're going to be a Scala shop. Great. All right, are we running serverless? Are we running containerized?” And you agree on those things. So, that's already, like, the formation of it. And oftentimes, you start with DevOps. You'll say, like, “I'm a DevOps team,” you know, or doing a DevOps culture, if you do it properly, but you always hit this scaling issue where you start growing, and then how do you maintain that common tool set? And that's where we start looking at, you know, having a platform… approach, but I'm going to say it's Platform-as-a-Product. That's the key.Corey: Yeah, that's a good way of framing it because originally, the entire world needed that. That's what RightScale was when EC2 first came out. It was a reimagining of the EC2 console that was actually usable. And in time, AWS improved that to the point where RightScale didn't really have a place anymore in a way that it had previously, and that became a business challenge for them. But you have, what is it now, 2, 300 services that AWS has put out, and out, and okay, great. Most companies are really only actively working with a handful of those. How do you make those available in a reasonable way to your teams, in ways that aren't distracting, dangerous, et cetera? I don't know the answer on that one.Evelyn: Yeah. No, that's true. So, full disclosure. At AutoScout, we do platform engineering. So, I'm part of, like, the platform engineering group, and we built a platform for our product teams. It's kind of like, you need to decide to [follow 00:25:24] those answers, you know? Like, are we going to be fully containerized? Okay, then, great, we're going to use Fargate. All right, how do we do it so that developers don't actually—don't need to think that they're running Fargate workloads?And that's, like, you know, where it's really important to have those standardized abstractions that developers actually enjoy using. And I'd even say that, before you start saying, “Ah, we're going to do platform,” you say, “We should probably think about developer experience.” Because you can do a developer experience without a platform. You can do that, you know, in a DevOps approach, you know? It's basically build tools that makes it easy for developers to write code. That's the first step for anything. It's just, like, you have people writing the code; make sure that they can do the things easily, and then look at how to operate it.Corey: That sure would be nice. There's a lack of focus on usability, especially when it comes to a number of developer tools that we see out there in the wild, in that, they're clearly built by people who understand the problem space super well, but they're designing these things to be used by people who just want to make the website work. They don't have the insight, the knowledge, the approach, any of it, nor should they necessarily be expected to.Evelyn: No, that's true. And what I see is, a lot of the times, it's a couple really talented engineers who are just getting shit done, and they get shit done however they can. So, it's basically like, if they're just trying to run the website, they're just going to write the code to get things out there and call it a day. And then somebody else comes along, has a heart attack when see what's been done, and they're kind of stuck with it because there is no guardrails or paved path or however you want to call it.Corey: I really hope—truly—that this is going to be something that we look back and laugh when this episode airs, that, “Oh, yeah, we just got it so wrong. Look at all the amazing stuff that came out of re:Invent.” Are you going to be there this year?Evelyn: I am going to be there this year.Corey: My condolences. I keep hoping people get to escape.Evelyn: This is actually my first one in, I think, five years. So, I mean, the last time I was there was when everybody's going crazy over pins. And I still have a bag of them [laugh].Corey: Yeah, that did seem like a hot-second collectable moment, didn't it?Evelyn: Yeah. And then at the—I think, what, the very last day, as everybody's heading to re:Play, you could just go into the registration area, and they just had, like, bags of them lying around to take. So, all the competing, you know, to get the requirements for a pin was kind of moot [laugh].Corey: Don't you hate it at some point where it's like, you feel like I'm going to finally get this crowning achievement, it's like or just show up at the buffet at the end and grab one of everything, and wow, that would have saved me a lot of pain and trouble.Evelyn: Yeah.Corey: Ugh, scavenger hunts are hard, as I'm about to learn to my own detriment.Evelyn: Yeah. No, true. Yeah. But I am really hoping that re:Invent proves me wrong. Embarrassingly wrong, and then all my colleagues can proceed to mock me for this ridiculous podcast that I made with you. But I am a fierce skeptic. Optimistic nihilist, but still a nihilist, so we'll see how re:Invent turns out.Corey: So, I am curious, given your experience at more large companies than I tend to be embedded with for any period of time, how have you found that these large organizations tend to pick up new technologies? What does the adoption process look like? And honestly, if you feel like throwing some shade, how do they tend to get it wrong?Evelyn: In most cases, I've seen it go… terrible. Like, it just blows up in their face. And I say that is because a lot of the time, an organization will say, “Hey, we're going to adopt this new way of organizing teams or developing products,” and they look at all the practices. They say, “Okay, great. Product management is going to bring it in, they're going to structure things, how we do the planning, here's some great charts and diagrams,” but they don't really look at the culture aspect.And that's always where I've seen things fall apart. I've been in a room where, you know, our VP was really excited about team topologies and say, “Hey, we're going to adopt it.” And then an engineering manager proceeded to say, “Okay, you're responsible for this team, you're responsible for that team, you're responsible for this team talking to, like, a team of, like, five engineers,” which doesn't really work at all. Or, like, I think the best example is DevOps, you know, where you say, “Ah, we're going to adopt DevOps, we're going to have a DevOps team, or have a DevOps engineer.”Corey: Step one: we're going to rebadge everyone with existing job titles to have the new fancy job titles that reflect it. It turns out that's not necessarily sufficient in and of itself.Evelyn: Not really. The Spotify model. People say, like, “Oh, we're going to do the Spotify model. We're going to do skills, tribes, you know, and everything. It's going to be awesome, it's going to be great, you know, and nice, cross-functional.”The reason I say it bails on us every single time is because somebody wants to be in control of the process, and if the process is meant to encourage collaboration and innovation, that person actually becomes a chokehold for it. And it could be somebody that says, like, “Ah, I need to be involved in every single team, and listen to know what's happening, just so I'm aware of it.” What ends up happening is that everybody differs to them. So, there is no collaboration, there is no innovation. DevOps, you say, like, “Hey, we're going to have a team to do everything, so your developers don't need to worry about it.” What ends up happening is you're still an ops team, you still have your silos.And that's always a challenge is you actually have to say, “Okay, what are the cultural values around this process?” You know, what is SRE? What is DevOps, you know? Is it seen as processes, is it a series of principles, platform, maybe, you know? We have to say, like—that's why I say, Platform-as-a-Product because you need to have that product mindset, that culture of product thinking, to really build a platform that works because it's all about the user journey.It's not about building a common set of tools. It's the user journey of how a person interacts with their code to get it into a production environment. And so, you need to understand how that person sits down at their desk, starts the laptop up, logs in, opens the IDE, what they're actually trying to get done. And once you understand that, then you know your requirements, and you build something to fill those things so that they are happy to use it, as opposed to saying, “This is our platform, and you're going to use it.” And they're probably going to say, “No.” And the next thing, you know, they're just doing their own thing on the side.Corey: Yeah, the rise of Shadow IT has never gone away. It's just, on some level, it's the natural expression, I think it's an immune reaction that companies tend to have when process gets in the way. Great, we have an outcome that we need to drive towards; we don't have a choice. Cloud empowered a lot of that and also has given tools to help rein it in, and as with everything, the arms race continues.Evelyn: Yeah. And so, what I'm going to continue now, kind of like, toot the platform horn. So, Gregor Hohpe, he's a [solutions architect 00:31:56]—I always f- up his name. I'm so sorry, Gregor. He has a great book, and even a talk, called The Magic of Platforms, that if somebody is actually curious about understanding of why platforms are nice, they should really watch that talk.If you see him at re:Invent, or a summit or somewhere giving a talk, go listen to that, and just pick his brain. Because that's—for me, I really kind of strongly agree with his approach because that's really how, like, you know, as he says, like, boost innovation is, you know, where you're actually building a platform that really works.Corey: Yeah, it's a hard problem, but it's also one of those things where you're trying to focus on—at least ideally—an outcome or a better situation than you currently find yourselves in. It's hard to turn down things that might very well get you there sooner, faster, but it's like trying to effectively cargo-cult the leadership principles from your last employer into your new one. It just doesn't work. I mean, you see more startups from Amazonians who try that, and it just goes horribly because without the cultural understanding and the supporting structures, it doesn't work.Evelyn: Exactly. So, I've worked with, like, organizations, like, 4000-plus people, I've worked for, like, small startups, consulted, and this is why I say, almost every single transformation, it fails the first time because somebody needs to be in control and track things and basically be really, really certain that people are doing it right. And as soon as it blows up in their face, that's when they realize they should actually take a step back. And so, even for building out a platform, you know, doing Platform-as-a-Product, I always reiterate that you have to really be willing to just invest upfront, and not get very much back. Because you have to figure out the whole user journey, and what you're actually building, before you actually build it.Corey: I really want to thank you for taking the time to speak with me today. If people want to learn more, where's the best place for them to find you?Evelyn: So, I used to be on Twitter, but I've actually got off there after it kind of turned a bit toxic and crazy.Corey: Feels like that was years ago, but that's beside the point.Evelyn: Yeah, precisely. So, I would even just say because this feels like a corporate show, but find me on LinkedIn of all places because I will be sharing whatever I find on there, you know? So, just look me up on my name, Evelyn Osman, and give me a follow, and I'll probably be screaming into the cloud like you are.Corey: And we will, of course, put links to that in the show notes. Thank you so much for taking the time to speak with me. I appreciate it.Evelyn: Thank you, Corey.Corey: Evelyn Osman, engineering manager at AutoScout24. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, and I will read it once I finish building an internal platform to normalize all of those platforms together into one.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started.