Podcasts about MySQL

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

En este episodio de "atareao con Linux", abordamos una frustración común: la sobrecarga de complejidad en el mundo del blogging. Si has intentado usar WordPress y te has cansado de gestionar plugins, temas y vulnerabilidades, o si las soluciones de Static Site Generator (SSG) te parecen excesivas para simplemente publicar notas y código, Noet es la solución que has estado buscando.Noet es una plataforma de blogging de código abierto con una filosofía clara: priorizar la escritura. Su diseño se basa en quitar todo lo que se interpone entre tú y la publicación de tu contenido. Es, esencialmente, un editor de texto avanzado que guarda posts en una base de datos y los sirve como un sitio web limpio y legible.La verdadera magia de Noet reside en su simplicidad técnica, lo cual lo hace perfecto para nuestro entorno Linux (VPS, Raspberry Pi, o tu servidor local):Single Binary (Go): Todo el backend se compila en un único ejecutable (escrito en Go), lo que facilita enormemente el despliegue y el mantenimiento en cualquier plataforma Linux.SQLite para la Gestión de Datos: En lugar de depender de bases de datos externas como MySQL o PostgreSQL, Noet usa SQLite. Esto significa que todos tus posts y configuraciones se almacenan en un solo archivo, noet.db. Esta característica es fundamental para una gestión de datos eficiente y para realizar copias de seguridad de forma increíblemente sencilla.Despliegue con Docker: Fieles a nuestro estilo práctico, te mostramos el archivo docker-compose.yaml necesario para poner Noet en marcha en cuestión de minutos. Si ya usas Docker para servicios como Traefik, Syncthing o tus bases de datos [cite: 2025-07-15], añadir Noet a tu stack es trivial.Para el escritor técnico o el power user de Linux, Noet brilla en su editor:Soporte Markdown Nativo: Usa la sintaxis que ya conoces.Código y LaTeX: El editor soporta resaltado de sintaxis para bloques de código y permite incrustar ecuaciones matemáticas con LaTeX/KaTeX. Es ideal para documentar tus proyectos o publicar tutoriales avanzados.Auto-guardado: No pierdas ni una línea de lo que escribes.Sencillez en Imágenes: Arrastra y suelta para subir imágenes y gestiona su tamaño con un clic.Si buscas mejorar tu productividad, simplificar tu infraestructura y tener un blog que se sienta tan ligero y moderno como Neovim u Obsidian [cite: 2025-07-15] pero listo para publicar en la web, tienes que probar Noet.Escucha el episodio para obtener todos los comandos, el archivo docker-compose y los mejores consejos de uso.Más información y enlaces en las notas del episodio

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

AWS Morning Brief for the week of October 20th, with Corey Quinn. Links:Amazon Location Service Introduces New Map Styling Features for Enhanced CustomizationAWS Resource Explorer launches immediate resource discovery within a Region AWS SAM CLI adds Finch support, expanding local development tool options for serverless applicationsSimplified model access in Amazon BedrockAmazon EC2 now supports CPU options optimization for license-included instancesIntroducing Amazon EBS Volume Clones: Create instant copies of your EBS volumesOptimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inferenceIntroducing URL and host header rewrite with AWS Application Load BalancersNew Amazon EKS Auto Mode features for enhanced security, network control, and performanceMonitor, analyze, and manage capacity usage from a single interface with Amazon EC2 Capacity ManagerPerformance optimization strategies for MySQL on Amazon RDSAWS re:Invent 2025: Reimagining customer experience with Amazon AWS Deprecates Two Dozen Services (Most of Which You've Never Heard Of)A FinOps Guide to Comparing Containers and Serverless Functions for ComputeAnnouncing vector search for Amazon ElastiCache

Back in May, the Remix cofounders revealed they were reimagining Remix v3 from the ground up, and this past week at Remix Jam, they gave a sneak peek of it. It's fair to say this new framework shouldn't be called Remix at all because it's departed so far from its origins: devs manually update state, it uses signals, routes are defined in a TS doc, and it will ship with a component library, for starters. Will it catch on, who knows?Not to be outdone by React v19.2 last week, Next.js 16 beta debuted (with support for React 19.2 included). In addition to the latest version of React, Next.js 16 has also declared Turbopack, RSC support, and React Compiler all stable, and improved its caching system as well.And Bun is back in the news with the release of Bun 1.3, and it's a doozy of a minor version release. Bun wants to be a full-stack JavaScript runtime as it now includes a full-stack dev server, built in support for MySQL and Redis DBs, routing, and the ability to package an entire project into one executable for cross-platform support. Well done, Bun team!Chapter Markers:01:14 - Remix v310:38 - Next.js 16 beta17:35 - Bun 1.324:42 - Firefox 144 released w/view transition support25:19 - HBO changes TV channel names28:00 - W3C has a new logo31:25 - What's making us happyNews:Paige - Bun 1.3Jack - Remix v3TJ - Next.js 16 betaLightning News:Firefox 144 released w/view transition supportW3C has a new logo and the Gavin Belson signature from Silicon Valley HBO changes TV channel namesWhat Makes Us Happy this Week:Paige - The Gilded Age TV seriesJack - KPop Demon HuntersTJ - Madison, WIThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.com

This show has been flagged as Explicit by the host. Part I - Lee talks about: Cyber - Capture the flag, providing OAuth, Secure design and static typing Databases - SQL Server, MySQL and SQLite Test Frameworks Generative AI for coding Hardware (as in IoT, not as in computers) Part II - A ramble about neurdivergence In academia and work Accommodation vs Encouraging work styles that fit the task Remote working Unusual career paths Technical communication Some personal code projects Url to Markdown Konsole extension Epub in a terminal Markdown table generator MySQL output formatter Resources of note Report on Changing the Workplace (2022) - about disability and remote working Model Context Protocol - A way to give AI chat bots access to software systems to increase their relevant knowledge and abilities Secure by Design book No chatbots were harmed in the making of this episode Provide feedback on this episode.

Michael and Jake open with retro arcade serendipity (a Mortal Kombat cabinet sighting!) and tumble into family bowling, kid-approved card games, and why tactile gadgets are back in style.Then they pivot hard into dev-mode: shadcn/ui (and shadcn-vue), Inertia, React-ish forms, and the age-old tradeoff between “batteries-included” simplicity and modern real-time UX.Highlights:Mortal Kombat cabinet & mini arcades, gift ideas for Laracon AUDuckpin bowling explainer and family bowling stories (plus UNO, Yahtzee, Taco Cat Goat Cheese Pizza)The “analog is cool again” thread: mechanical keyboards, a Keychron board, and a retro 3D-printed mouse shell for a Logitech M185Dev deep-dive: shadcn docs, Inertia forms, partial reloads vs full refresh, Livewire/Alpine, and real-time updates with Pusher/ReverbShow linksRetroPie / Arcade1UpLaracon AUDuckpin bowlingKeychron keyboard3D-printed retro mouse shell for Logitech M185Taco Cat Goat Cheese PizzaInertia.jsshadcn/uishadcn-vueLivewireAlpine.jsPusherLaravel ReverbAxiosfetch

"Escolha uma área e fique ali. É o tempo que vai dar espaço para a multidisciplinariedade. Tente criar algo com aquilo" - Felipe Nunes No sexto episódio do Hipsters.Talks, PAULO SILVEIRA , CVO do Grupo Alun, conversa com FELIPE NUNES, senior sales engineer da NEO4J, sobre bancos de dados de grafos e como eles estão revolucionando a forma de trabalhar com dados. Uma conversa sobre como os grafos democratizam o acesso aos dados e potencializam a inteligência artificial. Prepare-se para um episódio cheio de conhecimento e inspiração! Espero que aproveitem :) Sinta-se à vontade para compartilhar suas perguntas e comentários. Vamos adorar conversar com vocês!

Jake and Michael dive into a wide range of topics, from coding practices in Laravel to the evolving role of AI in software development. They kick things off with daylight savings and weekend updates before moving into technical discussions on authorization, policies, and form requests in Laravel.The conversation expands to cover recent changes in middleware and controller patterns, contextual attributes in the service container, and practical approaches to request validation.Later, the focus shifts toward AI tools like Claude, Grok, and Cursor, including their strengths, frustrations, and industry-wide adoption pressures. We reflect on the uneasy balance between developer control and AI assistance, wrapping up with thoughts on productivity, value, and what it means to let machines write code.Show linksLawn HubArcade 1UpRetroPieMortal Kombat cabinetNuno's authorization on form requestsContextual AttributesGrok Code Fast 1

Sam Lambert, my former boss at PlanetScale, talks to me about PlanetScale moving from a MySQL company to now also having a Postgres offering. Sam shares why PlanetScale decided to move to Postgres, how MySQL and Postgres are different at a technical level, and how the change has impacted the company culture. Stay to the end for a special surprise!PlanetScale Metal Episode: https://youtu.be/3r9PsVwGkg4Join the waitlist to be notified of the MySQL for Developers release on Database School: https://databaseschool.com/mysqlFollow Sam: PlanetScale: https://planetscale.comTwitter: https://twitter.com/isamlambertFollow Aaron:Twitter:  https://twitter.com/aarondfrancis LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today)Chapters:00:00 - Inaugural episode on this channel01:46 - Introducing Sam Lambert and his background03:04 - How PlanetScale built on MySQL and Vitess06:10 - Explaining the layers of PlanetScale's architecture09:57 - Node lifecycles, failover, and operational discipline12:02 - How Vitess makes sharding work14:21 - PlanetScale's edge network and resharding19:02 - Why downtime is unacceptable at scale20:04 - From Metal to Postgres: the decision process23:06 - Why Postgres vibes matter for startups27:04 - How PlanetScale adapted its stack for Postgres34:38 - Entering the Postgres ecosystem and extensions41:02 - Permissions, security, and reliability trade-offs45:04 - Building Ni: a Vitess-style system for Postgres53:33 - Why PlanetScale insists on control for reliability1:02:05 - Competing in the broader Postgres landscape1:08:33 - Why PlanetScale stays “just a database”1:12:33 - What GA means for Postgres at PlanetScale1:17:43 - Call to action for new Postgres users1:18:49 - Surprise!1:22:21 - Wrap-up and where to find Sam

In this episode, Michael and Jake catch up on life and code. They talk about fatigue, seasonal shifts, lawn adventures, and the return of hay fever.We dive into replacing a legacy Salesforce integration with Saloon, frustrations with mocks, and how Saloon fakes have improved testing workflows. Michael walks through his experiments with AI tools like Claude and opencode to prototype fake gateways - treating AI as a “junior dev” pair. The discussion covers gateway patterns, middleware, registry-based response handling, and strategies for testing Salesforce without polluting production environments.From weeds and soil temps to software fakes and AI-driven dev, this one's a mix of everyday life and practical engineering insights.Show linksLawnHub – Michael's lawn care supplierSaloon (by Sam Carré) – Laravel/HTTP client packageSalesforce – CRM platform discussed in the episodeMockery – PHP mocking frameworkopencode – terminal tool for AI coding (by SST's Dax and Adam, Terminal Coffee)Claude – AI model used for coding explorationGitHub Copilot – AI coding assistantStripe test cards – referenced in gateway fake analogy

Tony Cardella is a seasoned software engineer based in Houston, Texas. With a robust background in enterprise development, Tony brings deep expertise in the .NET Framework (C#), Python, and cloud platforms including Microsoft Azure and Amazon Web Services. His technical repertoire spans both relational databases — such as SQL Server, MySQL, and PostgreSQL — and NoSQL solutions like Azure Cosmos DB.   Tony is a strong advocate for developer productivity tools, frequently leveraging JetBrains products including ReSharper, DataGrip, PyCharm, and Rider, as well as Visual Studio. Outside the world of code, Tony is equally passionate about strength training, whether he's lifting weights himself or coaching others in the discipline.   Topics of Discussion: [1:34] Tony shares his career journey, starting with a consulting company that reached out to him while he was job hunting. [3:17] NCrunch is an automated testing tool that runs unit tests continuously, focusing on impacted tests. [5:08] Challenges and benefits of NCrunch, and why would we need to use it? [7:44] Tony shares his approach to unit testing, focusing on covering 80% of the code with minimal effort and addressing the remaining 20% as needed. [8:51] The importance of not over-investing in unit tests that may not provide significant value. [11:47] Tony explains how Ncrunch provides code coverage metrics and visual indicators of covered and uncovered code. [12:59] The tool's ability to show exactly where unit tests are failing, without needing to dive into stack traces. [13:51] Distributed processing and integration tests. [27:44] The challenges of running integration tests with external dependencies, such as databases. [29:18] Exploratory testing and code quality. [32:34] Tony emphasizes the value of unit tests in codifying tribal knowledge and ensuring code quality.   Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Tony Cardella Lightning Talks! The Code Gorilla Survey: Fixing Bugs Stealing Time from Development NCrunch   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 23/08 a 29/08.☕​ Que tal um café com desconto?Veroo Café: https://codigofonte.click/veroocafeCupom: CODIGOFONTE - Plano anual com brinde especial!

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 23/08 a 29/08.☕​ Que tal um café com desconto?Veroo Café: https://codigofonte.click/veroocafeCupom: CODIGOFONTE - Plano anual com brinde especial!

In this episode, Jake and Michael catch up on life, family, and tech.Michael shares proud stories about his son Eli turning into a “soccer terrorist” on the field, while Jake recounts his own stint as a stand-in soccer coach. They dive into Laracon AU updates — from speaker announcements and Road to Laracon podcasts, to quiz night and swag planning.Other highlights include experiments with AI-generated artwork, Bruce's new social media adventures, sponsor promotion, and even a tangent on coding tools like PHPStan and how AI can help fix issues in the background.Show linksLaracon AURoad to LaraconBruce on XLaravel Live DenmarkBoost

Matt Hamann knew he was going to be in tech way back in his younger days. His Dad worked for IBM, so there were always fun things to talk about and play with. He got his first family computer when he was 4 years old, and started programming BASIC when he was 8. Eventually, they got dialup through AOL - and he took off building websites with PHP & MySQL. Outside of tech, he is married with 3 kids. He loves to travel and spend time with his family. He also plays several instruments, including the piano and pipe organ, and enjoys tinkering with smart home devices.Right around the time of the pandemic, Matt and his co-founder were pitching a new company idea in Y Combinator, around data privacy. After receiving the feedback that there wasn't a big market for the original idea, they started to jam on ideas on how to pivot - and quickly landed on how cool it would be to have password-less authentication.This is the creation story of Rownd.SponsorsPaddle.comSema SoftwarePropelAuthPostmanMeilisearchMailtrap.TECH Domains (https://get.tech/codestory)Linkshttps://rownd.com/https://www.linkedin.com/in/matthamann/Support this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

MariaDB is a name with deep roots in the open-source database world, but in 2025 it is showing the energy and ambition of a company on the rise. Taken private in 2022 and backed by K1 Investment Management, MariaDB is doubling down on innovation while positioning itself as a strong alternative to MySQL and Oracle. At a time when many organisations are frustrated with Oracle's pricing and MySQL's cloud-first pivot, MariaDB is finding new opportunities by combining open-source freedom with enterprise-grade reliability. In this conversation, I sit down with Vikas Mathur, Chief Product Officer at MariaDB, to explore how the company is capitalising on these market shifts. Vikas shares the thinking behind MariaDB's renewed focus, explains how the platform delivers similar features to Oracle at up to 80 percent lower total cost of ownership, and details how recent innovations are opening the door to new workloads and use cases. One of the most significant developments is the launch of Vector Search in January 2023. This feature is built directly into InnoDB, eliminating the need for separate vector databases and delivering two to three times the performance of PG Vector. With hardware acceleration on both x86 and IBM Power architectures, and native connectors for leading AI frameworks such as LlamaIndex, LangChain and Spring AI, MariaDB is making it easier for developers to integrate AI capabilities without complex custom work. Vikas explains how MariaDB's pluggable storage engine architecture allows users to match the right engine to the right workload. InnoDB handles balanced transactional workloads, MyRocks is optimised for heavy writes, ColumnStore supports analytical queries, and Moroonga enables text search. With native JSON support and more than forty functions for manipulating semi-structured data, MariaDB can also remove the need for separate document databases. This flexibility underpins the company's vision of one database for infinite possibilities. The discussion also examines how MariaDB manages the balance between its open-source community and enterprise customers. Community adoption provides early feedback on new features and helps drive rapid improvement, while enterprise customers benefit from production support, advanced security, high availability and disaster recovery capabilities such as Galera-based synchronous replication and the MacScale proxy. We look ahead to how MariaDB plans to expand its managed cloud services, including DBaaS and serverless options, and how the company is working on a “RAG in a box” approach to simplify retrieval-augmented generation for DBAs. Vikas also shares his perspective on market trends, from the shift away from embedded AI and traditional machine learning features toward LLM-powered applications, to the growing number of companies moving from NoSQL back to SQL for scalability and long-term maintainability. This is a deep dive into the strategy, technology and market forces shaping MariaDB's next chapter. It will be of interest to database architects, AI engineers, and technology leaders looking for insight into how an open-source veteran is reinventing itself for the AI era while challenging the biggest names in the industry.

In this episode, Michael and Jake reflect on their recent time at Laracon US 2025 in Denver - catching up in person after six years, reconnecting with the Laravel community, and sharing behind-the-scenes stories from the conference floor.They also cover:Why this Laracon felt like a true “homecoming”Building Laravel meetups and fostering communityThe book (and tv show) Station Eleven (and how different things might have been)The value of attending conferences, particularly as a non-speakerContinued discussion on the complexities of handling roles and permissionsThe episode weaves together community highlights, technical challenges, and personal reflections.

In this episode, I speak with Peter Zaitsev of Percona about the history of MySQL, his history with the venerable database, and his history with Percona.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.https://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/

In this episode, Michael and Jake kick things off with some Laracon travel talk, sharing their hotel plans, coffee quests, and even jokes about pillow fights at the conference hotel. Michael reveals his precise coffee scouting for the Vib by Best Western hotel, determined not to survive three days on Starbucks alone.Should you define middleware in a controller's constructor? Michael explains why he avoids it - preferring to keep all middleware in route definitions for better visibility and maintainability. Jake explores the pros and cons and why he's still tempted to use it for certain edge cases.Dynamic permissions vs. static definitions: We switch gears to talk about the balance between flexibility and clarity when defining permissions for applications, especially when it comes to handling user roles, teams, and complex business rules.Mentioned in this episode:Laracon US travel plansVib by Best Western (the hotel coffee and tacos!)Laravel middleware usagePermission handling in appsTravel gear for developers on the go

In this episode, Jake and Michael discuss the nuance of being “busy”, saying no to features (and why), handling user feedback early, Laravel-powered static views with dynamic data, and building tools that stand the test of time.

In this episode, Jake and Michael reflect on parenting, discuss Apple's new Liquid Glass UI, finding smarter ways to use video on the web, plus share thoughts on AI overload, Laracon prep, and why Wistia might be your next favourite video tool.In this episode:- Apple's Liquid Glass UI- Kit.com and Wistia for video- Reflections on AI, tech bubbles, and accessibility- Laracon US and vox pop interviews- The emotional ride of watching your kids grow up

Welcome to episode 306 of The Cloud Pod – where the forecast is always cloudy!  This week, we have a bunch of announcements concerning the newest offering from Anthropic – Claude Sonnet 4 and Opus 4, plus container security, Azure MySQL Maintenance, Vertex AI, and Mistral AI. Plus, we've got a Cloud Journey installment AND an aftershow – so get comfy and get ready for a trip to the clouds! Titles we almost went with this week: ECS Failures Now Have 4x the Excuses Nailing Down Your Container Security, One Patch at a Time HashiCorp’s New Recipe: Terraform, AI, and a Pinch of MCP Teaching an Old DNS New IPv6 Tricks Dash-ing through the Klusters, in an AWS Console Google’s Generative AI Playground Gets a Glow-Up Vertex AI Studio: Now with 200% More Darkness! Like our souls Claude Opus 4 Strikes a Chord on Google Cloud Sovereign-teed to Please: Google Cloud’s Royal Treatment Google’s Cloud Kingdom Expands its Borders Shall I Compare Thee to a Summer’s AI? Anthropic Drops Sonne(t) 4 Knowledge on Vertex Mistral AI Chats Up a Storm on Google Cloud Google Cloud’s Vertex AI Gets a Dose of Mistral Magic .NET Aspire on Azure: The App Service Strikes Back Default Outbound Access Retires, Decides Florida Isn’t for Everyone  AI Is Going Great – or How ML Makes Money  01:52 Introducing Claude 4 Claude has launched the latest models in Claude Opus 4 and Claude Sonnet 4, setting new standards for coding, advancing reasoning and AI agents. Maybe they'll actually follow instructions when told to shut down? (Looking at you, ChatGPT.) Claude Opus 4 is “the world's best coding model” with sustained performance on complex, long-running tasks and agent workflows.  Opus 4 has 350 billion parameters, making it one of the largest publicly available language models.  It demonstrates strong performance on academic benchmarks, including research.  Sonnet 4 is a smaller 10 billion parameter model optimized for dialogue, making it well-suited for conversational AI applications.  Alongside the models, they are also announcing: Extended thinking with tool use (beta): Both models can use tools – like web search – during extended thinking, allowing Claude to alternate between reasoning and tool use to improve its responses. New Model Capabilities: Both models can use tools in parallel, follow instructions more precisely, and when given access to local files by developers — demonstrate significantly improved memory capabilities, extracting and saving key facts maintain continuity and build tacit knowledge over time Claude code is now generally available: After receiving extensive positive feedback during our research preview, they are expanding how developers can collaborate with Claude.  Claude code now supports background tasks via gith

In this episode, Jake and Michael discuss Jake's new stealth grill, his eldest son's takeover of the state finals (and metric's takeover of measurement), and Michael goes through the process of refining over 150 talk submissions down to the final Laracon AU schedule.

The Disturbing Reality of AI-Generated AbuseA new study reveals nearly 35,000 publicly downloadable AI models capable of generating deepfake pornography—often targeting women and celebrities. These "model variants" can be tweaked to create millions of non-consensual intimate images, fuelling a growing crisis in digital exploitation. Researchers identified 35,000 deepfake model variants in public repositories, with each model being used to generate countless AI-generated explicit images of real people. While 35,000 models may seem small compared to the vast internet, each one represents a potential weapon for harassment. Experts warn that without stricter regulation, deepfake abuse could spiral further out of control. The study was led by Will Hawkins from the Oxford Internet Institute. He joins Gareth and Ghislaine on the show.    MySQL at 30: The Unsung Hero Powering Facebook, the Web & Your Data - The Database That Quietly Runs the InternetYou've probably never heard of it—but your data lives on MySQL. Celebrating its 30th anniversary this year, this open-source database is the invisible backbone of Facebook, countless websites, and nearly every major online service. It's an open-source success story, which relies on simplicity and speed and it's easy to set up. We spoke with Peter Zaitsev, who joined MySQL in 2002 and later co-founded Percona, a leading database consultancy.  Next time you log into Facebook or book a flight, remember - there's a 30-year-old database working behind the scenes. Not bad for a tech "underdog."   The programme is presented by Gareth Mitchell and the studio expert is Ghislaine Boddington. More on this week's stories: Dramatic rise in publicly downloadable deep fake image generators 1995-2025: MySQL at 30!   Production Manager: Liz Tuohy Editor: Ania Lichtarowicz For the PodExtra version of the show please subscribe via this link: https://somewhere-on-earth-the-global-tech-podcast-the-podextra-edition.pod.fan/ Follow us on all the socials: Join our Facebook group Instagram BlueSky   If you like Somewhere on Earth, please rate and review it on Apple Podcasts or Spotify   Contact us by email: hello@somewhereonearth.co  Send us a voice note via WhatsApp: +44 7486 329 484   Find a Story + Make it News = Change the World Learn more about your ad choices. Visit megaphone.fm/adchoices

Aprende Grafana y domina el monitoreo de datos en tiempo real

Aprende Grafana y domina el monitoreo de datos en tiempo real

In this episode, Jake and Michael discuss using interfaces as a dictionary of constants, working with and testing inputs passed down multiple layers of the application, and refactoring legacy code with PHP's ArrayAccess interface.

In this episode, Jake and Michael discuss the ramp up of Laracon AU planning, touch base on Jake's unorthodox usage of Laravel Horizon, and Michael finally coming around to using AI.

In this episode, Dave Rubinstein interviews Heikki Linnakangas, co-founder of Neon, a company that provides Postgres solutions. They discuss: The factors that have contributed to adoption of PostgreSQLWhy PostgreSQL has leapfrogged over MySQL in popularityWhat to expect in PostgreSQL 18

Kevin Weil is the chief product officer at OpenAI, where he oversees the development of ChatGPT, enterprise products, and the OpenAI API. Prior to OpenAI, Kevin was head of product at Twitter, Instagram, and Planet, and was instrumental in the development of the Libra (later Novi) cryptocurrency project at Facebook.In this episode, you'll learn:1. How OpenAI structures its product teams and maintains agility while developing cutting-edge AI2. The power of model ensembles—using multiple specialized models together like a company of humans with different skills3. Why writing effective evals (AI evaluation tests) is becoming a critical skill for product managers4. The surprisingly enduring value of chat as an interface for AI, despite predictions of its obsolescence5. How “vibe coding” is changing how companies operate6. What OpenAI looks for when hiring product managers (hint: high agency and comfort with ambiguity)7. “Model maximalism” and why today's AI is the worst you'll ever use again8. Practical prompting techniques that improve AI interactions, including example-based prompting—Brought to you by:• Eppo—Run reliable, impactful experiments• Persona—A global leader in digital identity verification• OneSchema—Import CSV data 10x faster—Where to find Kevin Weil:• X: https://x.com/kevinweil• LinkedIn: https://www.linkedin.com/in/kevinweil/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Kevin's background(04:06) OpenAI's new image model(06:52) The role of chief product officer at OpenAI(10:18) His recruitment story and joining OpenAI(17:20) The importance of evals in AI(24:59) Shipping quickly and consistently(28:34) Product reviews and iterative deployment(39:35) Chat as an interface for AI(43:59) Collaboration between researchers and product teams(46:41) Hiring product managers at OpenAI(48:45) Embracing ambiguity in product management(51:41) The role of AI in product teams(53:21) Vibe coding and AI prototyping(55:55) The future of product teams and fine-tuned models(01:04:36) AI in education(01:06:42) Optimism and concerns about AI's future(01:16:37) Reflections on the Libra project(01:20:37) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com/• The AI-Generated Studio Ghibli Trend, Explained: https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained/• Introducing 4o Image Generation: https://openai.com/index/introducing-4o-image-generation/• Waymo: https://waymo.com/• X: https://x.com• Facebook: https://www.facebook.com/• Instagram: https://www.instagram.com/• Planet: https://www.planet.com/• Sam Altman on X: https://x.com/sama• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• OpenAI evals: https://github.com/openai/evals• Deep Research: https://openai.com/index/introducing-deep-research/• Ev Williams on X: https://x.com/ev• OpenAI API: https://platform.openai.com/docs/overview• Dwight Eisenhower quote: https://www.brainyquote.com/quotes/dwight_d_eisenhower_164720• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• StackBlitz: https://stackblitz.com/• Claude 3.5 Sonnet: https://www.anthropic.com/news/claude-3-5-sonnet• Anthropic: https://www.anthropic.com/• Four-minute mile: https://en.wikipedia.org/wiki/Four-minute_mile• Chad: https://chatgpt.com/g/g-3F100ZiIe-chad-open-a-i• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Figma: https://www.figma.com/• Julia Villagra on LinkedIn: https://www.linkedin.com/in/juliavillagra/• Andrej Karpathy on X: https://x.com/karpathy• Silicon Valley CEO says ‘vibe coding' lets 10 engineers do the work of 100—here's how to use it: https://fortune.com/2025/03/26/silicon-valley-ceo-says-vibe-coding-lets-10-engineers-do-the-work-of-100-heres-how-to-use-it/• Cursor: https://www.cursor.com/• Windsurf: https://codeium.com/windsurf• GitHub Copilot: https://github.com/features/copilot• Patrick Srail on X: https://x.com/patricksrail• Khan Academy: https://www.khanacademy.org/• CK-12 Education: https://www.ck12.org/• Sora: https://openai.com/sora/• Sam Altman's post on X about creative writing: https://x.com/sama/status/1899535387435086115• Diem (formerly known as Libra): https://en.wikipedia.org/wiki/Diem_(digital_currency)• Novi: https://about.fb.com/news/2020/05/welcome-to-novi/• David Marcus on LinkedIn: https://www.linkedin.com/in/dmarcus/• Peter Zeihan on X: https://x.com/PeterZeihan• The Wheel of Time on Prime Video: https://www.amazon.com/Wheel-Time-Season-1/dp/B09F59CZ7R• Top Gun: Maverick on Prime Video: https://www.amazon.com/Top-Gun-Maverick-Joseph-Kosinski/dp/B0DM2LYL8G• Thinking like a gardener not a builder, organizing teams like slime mold, the adjacent possible, and other unconventional product advice | Alex Komoroske (Stripe, Google): https://www.lennysnewsletter.com/p/unconventional-product-advice-alex-komoroske• MySQL: https://www.mysql.com/—Recommended books:• Co-Intelligence: Living and Working with AI: https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X• The Accidental Superpower: Ten Years On: https://www.amazon.com/Accidental-Superpower-Ten-Years/dp/1538767341• Cable Cowboy: https://www.amazon.com/Cable-Cowboy-Malone-Modern-Business/dp/047170637X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

In this episode, Jake and Michael discuss Michael's new recording gear, building integrations with external APIs using Saloon, and configuring Laravel Horizon.

RJJ Software's Software Development Service This episode of The Modern .NET Show is supported, in part, by RJJ Software's Podcasting Services, whether your company is looking to elevate its UK operations or reshape its US strategy, we can provide tailored solutions that exceed expectations. Show Notes "So I've been focused on the code to cloud journey, I like to call it, for the template. And two years ago, my goal was to provide a solution that could take you from code to cloud in 45 minutes or less. So I wanted it to be "file new project" to deploy a solution on Azure—because that's where my main focus is—within 45 minutes."— Jason Taylor Welcome friends to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. We are the go-to podcast for .NET developers worldwide, and I am your host: Jamie "GaProgMan" Taylor. In this episode, Jason Taylor (no relation) joined us to talk about his journey from Classic ASP to .NET and Azure. He also discusses clean architecture's maintainability, and his open-source Clean Architecture Solution template for ASP .NET Core, along with strategies for learning new frameworks and dealing with complexity. "Right now the template supports PostgreSQL, SQLite, and SQL Server. If you want to support MySQL, it's relatively easy to do because there's already a Bicep module or a Terraform module that you can go in and use it. So I went from 45 minutes to now I can get things up and running in like, I don't know, two minutes of effort and 15 minutes of waiting around while I make my coffee"— Jason Taylor Along the way, we talk about some of the complexities involved with creating a template which supports multiple different frontend technologies and .NET Aspire (which was news to me when we recorded), all the while maintaining the goal of being the simplest approach for enterprise development with Clean Architecture. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-7/from-code-to-cloud-in-15-minutes-jason-taylors-expert-insights-and-the-clean-architecture-template/ Jason's Links: Jason's Clean Architecture repo on GitHub Jason's Northwind Traders with Clean Architecture repo on Github Connect with Jason Jason's RapidBlazor repo on GitHub Other Links: C# DevKit for Visual Studio Code Code, Coffee, and Clever Debugging: Leslie Richardson's Microsoft Journey and the C# Dev Kit in Visual Studio Code with Leslie Richardson dotnet scaffold devcontainers .NET Aspire Azure Developer CLI GitHub CLI Obsidian Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back 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 Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

Luca Casanato, member of the Deno core team, delves into the intricacies of debugging applications using Deno and OpenTelemetry. Discover how Deno's native integration with OpenTelemetry enhances application performance monitoring, simplifies instrumentation compared to Node.js, and unlocks new insights for developers! Links https://lcas.dev https://x.com/lcasdev https://github.com/lucacasonato https://mastodon.social/@lcasdev https://www.linkedin.com/in/luca-casonato-15946b156 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Luca Casonato.

In this episode of The Eric Ries Show, I sit down with Marten Mickos, a serial tech CEO who has been at the forefront of some of the most transformative moments in open-source technology. From leading MySQL through its groundbreaking journey to guiding HackerOne as a pioneering bug bounty platform, Marten's career is a masterclass in building innovative, trust-driven organizations.Our wide-ranging conversation explores Marten's remarkable journey through tech leadership, touching on his experiences building game-changing companies and, more recently, his work coaching emerging CEOs. We dive deep into the world of open source, company culture, and the nuanced art of leadership.In our conversation today, we talk about the following topics: • How MySQL revolutionized open-source databases and became Facebook's database• The strategic decision to make MySQL open source and leverage Linux distributions• The art of building a beloved open-source project while creating a profitable business model• How a lawsuit solidified MySQL's position in the open-source database market• The role of transparency and direct feedback in building organizational trust• Why Marten was drawn to HackerOne's disruptive approach to cybersecurity• Marten's transition to coaching new CEOs • Marten's unique "contrast framework" for making complex decisions• And much more!—Brought to you by:• Wilson Sonsini – Wilson Sonsini is the innovation economy's law firm. ⁠⁠Learn more⁠⁠.• Gusto – Gusto is an easy payroll and benefits software built for small businesses. ⁠⁠⁠⁠Get 3 months free⁠⁠⁠⁠.—Where to find Marten Mickos: • LinkedIn: https://www.linkedin.com/in/martenmickos/• Bluesky: https://bsky.app/profile/martenmickos.bsky.social—Where to find Eric:• Newsletter:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericries.carrd.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Podcast:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericriesshow.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • YouTube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@theericriesshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ —In This Episode We Cover:(00:00) Intro(03:15) The first time Eric used MySQL(07:10) The origins of MySQL and how Marten got involved (13:22) Why MySQL pivoted to open source to leverage the power of Linux distros(17:03) Open source vs. closed (18:56) Building profitable open-source companies (24:52) The fearless company culture at MySQL and the Progress lawsuit(29:30) The value of not cutting any corners (33:35) How a dolphin became part of the MySQL logo (35:55) What it was like to build a company of true believers(38:47) Marten's management approach emphasizes kindness and direct feedback (42:12) Marten's hiring philosophy(45:14) Why MySQL sold to Sun Microsystems and tried to avoid Oracle (50:24) How Oracle has made MySQL even better(52:22) Why Marten decided to lead at HackerOne(55:41) An overview of HackerOne(59:31) How HackerOne got started and landed the Department of Defense contract(1:03:19) The trust-building power of transparency(1:08:30) Marten's successor and the state of HackerOne now(1:09:23) Marten's work coaching CEOs(1:14:20) Common issues CEOs struggle with (1:16:45) Marten's contrast framework (1:26:12) The book of Finnish poetry that inspired Marten's love of polarities—You can find the transcript and references at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.ericriesshow.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠—Production and marketing by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Eric may be an investor in the companies discussed.

AWS Morning Brief for the week of March 17th, with Corey Quinn. Links:Amazon Bedrock now supports multi-agent collaborationAmazon RDS for MySQL announces Extended Support minor 5.7.44-RDS.20250213Amazon Route 53 Traffic Flow introduces a new visual editor to improve DNS policy editingApplication Load Balancer announces integration with Amazon VPC IPAMAnnouncing the end of support for Node.js 14.x and 16.x in AWS CDKWatch the recordings from AWS Developer Day 2025How GoDaddy built a category generation system at scale with batch inference for Amazon BedrockFormula 1® unlocks the most competitive season yet with AWSSecure cloud innovation starts at re:Inforce 2025

Today, we have Sam Lambert back on the show! Sam is the CEO of PlanetScale, and if you follow him on X, you know he's one of the sharpest voices in the database space—cutting through the hype with deep experience and a no-nonsense approach. In this episode, we dive into PlanetScale's new Metal offering, which has been battle-tested with PlanetScale's high-scale cloud business partners and is now GA. Sam also shares why staying profitable is crucial—not just for the business but for the stability and reliability it guarantees for customers. While many cloud infrastructure companies chase the next hype cycle, Sam prefers to keep it boring—delivering rock-solid performance with no surprises Finally, we close with Sam's thoughts on other happenings in the database space -- Aurora DSQL, Aurora Limitless, MySQL benchmarks, and multi-region strong consistency. Tune in for a deep dive into databases, cloud infrastructure, and what it takes to build a sustainable, high-performance tech company. Timestamps 01:34 Start 06:42 PlanetScale Metal 11:15 The problem with separation of storage and compute 15:02 EBS Tax 17:32 How does Vitess handle durability 22:58 Metal recommended for all PlanetScale users? 27:20 The hidden expense of IOPS for cloud databases 37:41 Timeline of creating PlanetScale Metal 41:32 Focus on profitability 47:52 Removal of hobby plan 57:45 Deprecation of PlanetScale Boost 01:00:24 DSQL 01:01:51 Aurora Limitless 01:04:15 AWS as a partner 01:07:00 The spectacle of AWS re:Invent 01:12:22 Benchmarks and benchmarketing 01:15:51 AWS Databases + multi-region strong consistency

In this episode, Jake and Michael discuss circles of influence and information, eloquently handling return of single values from the database, and monitoring tools for your applications.

In this episode, Jeremy Maldonado shares his experiences and insights on server management, highlighting the importance of learning from mistakes, the power of automation, and finding balance between Linux and Windows environments. He discusses the challenges and rewards of managing servers, the pivotal role of Ansible in streamlining operations, and the confidence required to maintain a reliable infrastructure. Jeremy encourages listeners to view setbacks as opportunities for growth while reminding us to be kind to ourselves throughout our professional journeys.

Jake and Michael discuss those features you ship that nobody uses but everybody has feedback for, testing a system where the valid state can change based on user input, and compliance auditing and adherence.

AWS Morning Brief for the week of February 17, with Corey Quinn. Links:Amazon DynamoDB now supports auto-approval of quota adjustmentsAmazon Elastic Block Store (EBS) now adds full snapshot size information in Console and APIAmazon RDS for MySQL announces Extended Support minor 5.7.44-RDS.20250103Amazon Redshift Serverless announces reduction in IP Address Requirements to 3 per SubnetAWS Deadline Cloud now supports Adobe After Effects in Service-Managed FleetsAWS Network Load Balancer now supports removing availability zonesAWS CloudTrail network activity events for VPC endpoints now generally availableHarness Amazon Bedrock Agents to Manage SAP InstancesTimestamp writes for write hedging in Amazon DynamoDBUpdating AWS SDK defaults – AWS STS service endpoint and Retry StrategyLearning AWS best practices from Amazon Q in the ConsoleAutomating Cost Optimization Governance with AWS ConfigAmazon Q Developer in chat applications rename - Summary of changes - AWS Chatbot

Databases are hard. Making the right choices early and keeping things running smoothly even when budget pressures and customer requests start piling on — that's the hard part that many solopreneurs and indie founders struggle with.I certainly do.So here's a journey through my learnings and experiences from running SaaS offerings with sizeable —talking about terabytes of data— databases.The blog post: https://thebootstrappedfounder.com/indie-hacking-databases-at-scale/ The podcast episode: https://tbf.fm/episodes/374-indie-hacking-databases-at-scaleCheck out Podscan to get alerts when you're mentioned on podcasts: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

In this episode, Jake and Michael discuss the blockbuster trade of Luka Dončić to the the Los Angeles Lakers in exchange for Anthony Davis, the just-announced Laracon US, and pitch our talks for the very same conference.

News includes the exciting release of Oban Web as open source with newly added MySQL support, nine new ElixirConf 2024 videos have been published, a new full-stack web framework called Hologram that transpiles Elixir to JavaScript was announced, PhoenixTest gained Playwright driver support for enhanced testing capabilities, Protoss reached feature-complete status as it moves to version 1.0, and several Elixir conferences were announced including Code BEAM Lite Stockholm and GigCityElixir, and more! Show Notes online - http://podcast.thinkingelixir.com/238 (http://podcast.thinkingelixir.com/238) Elixir Community News https://oban.pro/articles/oss-web-and-new-oban (https://oban.pro/articles/oss-web-and-new-oban?utm_source=thinkingelixir&utm_medium=shownotes) – Oban Web has been officially released as OpenSource, including MySQL support in Oban v2.19 and Oban Web v2.11. https://www.youtube.com/playlist?list=PLqj39LCvnOWbW2Zli4LurDGc6lL5ij-9Y (https://www.youtube.com/playlist?list=PLqj39LCvnOWbW2Zli4LurDGc6lL5ij-9Y?utm_source=thinkingelixir&utm_medium=shownotes) – Nine new ElixirConf 2024 videos have been published and added to the official YouTube playlist. https://hologram.page/ (https://hologram.page/?utm_source=thinkingelixir&utm_medium=shownotes) – Introduction of Hologram, a new full stack isomorphic Elixir web framework that transpiles Elixir to JavaScript for client-side code. https://github.com/bartblast/hologram (https://github.com/bartblast/hologram?utm_source=thinkingelixir&utm_medium=shownotes) – The GitHub repository for Hologram, currently at version 0.2.0. https://hexdocs.pm/phoenixtestplaywright/PhoenixTest.Playwright.html (https://hexdocs.pm/phoenix_test_playwright/PhoenixTest.Playwright.html?utm_source=thinkingelixir&utm_medium=shownotes) – PhoenixTest now has a Playwright driver, enabling three layers of Phoenix testing with a common assertion layer. https://github.com/ityonemo/protoss (https://github.com/ityonemo/protoss?utm_source=thinkingelixir&utm_medium=shownotes) – Protoss, a library for powerful Elixir protocols, is now feature-complete and moving to version 1.0. Looking for maintainer. https://ashweekly.substack.com/p/ash-weekly-issue-1 (https://ashweekly.substack.com/p/ash-weekly-issue-1?utm_source=thinkingelixir&utm_medium=shownotes) – Launch of Ash Weekly newsletter to keep up with Ash Framework updates and news. https://ash-project.github.io/ash_phoenix/nested-forms.html (https://ash-project.github.io/ash_phoenix/nested-forms.html?utm_source=thinkingelixir&utm_medium=shownotes) – AshPhoenix update featuring improved handling for nested forms. https://sessionize.com/code-beam-lite-stockholm-2025 (https://sessionize.com/code-beam-lite-stockholm-2025?utm_source=thinkingelixir&utm_medium=shownotes) – Call for speakers open until February 20th for Code BEAM Lite Stockholm, happening June 2nd 2025. NervesConf EU and Goatmire Elixir announced for September 10-12 in Varberg, Sweden. https://www.gigcityelixir.com/ (https://www.gigcityelixir.com/?utm_source=thinkingelixir&utm_medium=shownotes) – GigCityElixir conference announced in Chattanooga, TN, May 9-10, preceded by NervesConf on May 8th. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Jake and Michael recap their Christmas and New Year break, talk through lingering packages, Laravel 11 upgrades, and breaking changes in PHPUnit.

On this Screaming in the Cloud Replay, Corey is joined by Nipun Agarwal, Senior Vice President of MySQL HeatWave Development at Oracle, to discuss the release of MySQL HeatWave and how it will benefit users among the sea of database offerings on AWS. Nipun reveals why Oracle decided to develop HeatWave, how HeatWave is providing meaningful cost savings to users, and how HeatWave has been optimized for the cloud. Nipun explains how they've lowered the barriers to entry for new users of HeatWave, and Oracle's focus on implementing customer feedback when developing new offerings.Show Highlights(0:00) Intro(0:55) The Duckbill Group sponsor read(1:28) The significance of HeatWave coming to AWS(2:20) What is MySQL HeatWave?(5:13) What jumped out to Corey during his conversations with Nipun on Oracle(8:40) What's “under the hood” of MySQL HeatWave(14:12) How Oracle built out its pricing for MySQL HeatWave(16:41) Why MySQL HeatWave doesn't show up on AWS bills(21:27) The Duckbill Group sponsor read(22:09) Oracle's historical customer base and the company's credit system(24:30) The point behind MySQL HeatWave(27:51) How MySQL HeatWave runs(33:53) Where you can find more from Nipun and OracleAbout Nipun AgarwalNipun Agarwal is a Senior Vice President, MySQL HeatWave and Advanced Development, Oracle. His interests include distributed data processing, machine learning, cloud technologies and security. Nipun was part of the Oracle Database team where he introduced a number of new features. He has been awarded over 170 patents., Nipun Agarwal is Senior Vice President of MySQL Database & HeatWave Development. He leads a global engineering organization responsible for Oracle's MySQL innovations that enable organizations to use a single database for both transactional and analytical workloads. His interests include data processing, distributed systems, machine learning, cloud computing and security. Prior to his current position, Nipun was with Oracle Labs and the Oracle Database team, where he introduced a number of new features. He has been awarded over 175 patents.LinksOracle: https://oracle.comMySQL HeatWave info: https://www.oracle.com/mysql/ MySQL Service on AWS and OCI login (Oracle account required): https://cloud.mysql.comOriginal Episodehttps://www.lastweekinaws.com/podcast/screaming-in-the-cloud/heatwave-and-the-latest-evolution-of-mysql-with-nipun-agarwal/SponsorThe Duckbill Group: duckbillgroup.com

Join Jake, Michael, David, TJ, and Chris for a merry chat about tech, holiday antics, and the entertaining Wheel of Fortune game!## SocialXhttps://x.com/northsouthaudiohttps://x.com/JacobBennett https://x.com/michaeldyryndahttps://x.com/davidhemphillhttps://x.com/heytjmillerhttps://x.com/cmgmyrBlueskyhttps://bsky.app/profile/northmeetssouth.audiohttps://bsky.app/profile/dyrynda.auhttps://bsky.app/profile/jakebennett.bsky.socialhttps://bsky.app/profile/tjmiller.bsky.socialhttps://bsky.app/profile/chrisgmyr.dev (00:00) - Introductions (00:30) - Holiday Humor (02:28) - Brain Fog Chat (05:25) - Favorite Christmas Movies (08:26) - Significant 2024 Events (15:32) - Wheel of Fortune Game (59:14) - Wrapping Up

In this episode, I'm joined by Jesmar Canol, COO of ProxySQL, to explore the  journey behind the creation of this open source solution that has become a game-changer for database management.  From his early days in IT to addressing the challenges that database administrators (DBAs) face daily, Jesmar shares the story of how ProxySQL evolved from a side project into a vital tool for empowering database teams around the world. We discuss the complexities of managing MySQL and PostgreSQL infrastructures, ProxySQL's unique approach to query routing, load balancing, and its ability to maintain high availability even in the most demanding environments. Jesmar explains why ProxySQL's open-source model is critical in fostering trust and transparency, and how it helps organizations adapt to the growing demands for cloud-native and on-premise database solutions. Jesmar also offers insights into the challenges of running a distributed team, the evolution of database management in an era of increasing automation, and the emerging trends shaping the future of this space. Whether you're a seasoned DBA, a tech leader, or simply curious about the transformative power of open source solutions, this episode is packed with valuable takeaways.

What if your organization could unlock the full potential of AI without ever compromising on privacy or sharing sensitive data? In this episode of Tech Talks Daily, I am joined by Alexander Alten, Co-Founder and CEO of Scalytics, to explore how he is building the next-generation infrastructure layer for AI agents. Alexander brings a wealth of expertise, having led data and product teams at industry giants like Cloudera, Allianz, and Healthgrades. With a background in startups such as X-Warp and Infinite Devices, he has a proven track record of developing customer-centric, data-driven solutions that not only disrupt conventional norms but also fuel measurable growth. During our conversation at the IT Press Tour in Malta,  Alexander introduces Scalytics Connect, a modern AI data platform designed to accelerate insights while preserving privacy. He unpacks the challenges of breaking down data silos and explains why centralizing data may not always be the optimal solution. We also demystify federated learning, shedding light on its potential to empower businesses, particularly in regulated industries, to collaborate on AI models without exposing their data. The discussion extends to the value of open-source technologies and why they often emerge as long-term winners, citing examples like MySQL, Postgres, and WordPress. Alexander shares how Scalytics leverages open-source principles to provide scalable and transparent machine learning solutions for businesses looking to outperform in an increasingly data-driven world. As AI continues to redefine the way we work and innovate, Alexander's insights provide a roadmap for navigating the complexities of decentralized machine learning, privacy-first AI, and scalable technology. Could his approach to AI and data collaboration be the key to unlocking your organization's potential? Tune in to find out, and don't forget to share your thoughts on the future of AI-powered innovation.