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This week on Trending in Ed, host Mike Palmer is joined by Trending in Ed all-star Beth Rudden, CEO of Bast AI. From her roots digging in the dirt as an archaeologist to managing a $34 billion division as the Chief Data Officer of IBM Managed Services, Beth brings a deeply grounded, technical perspective to the artificial intelligence conversation. In this wide-ranging and insightful conversation, Mike and Beth skip the typical AI hype to explore what it actually takes to build explainable, trustworthy technology. Beth shares how Bast AI acts as an LLM-agnostic explainability layer—using a unique drinking chocolate analogy to demonstrate how they verify AI data rather than letting models hallucinate plausible narratives. They explore the practical application of using small language models (SLMs) for data enrichment, highlighted by Bast AI's meaningful work with Craig Hospital to translate complex neuro-spine outpatient procedures into accessible languages and analogies. KEY INSIGHTS: • Inverting the Chatbot Approach: Why defining what an AI can talk about is far more effective than building restrictive guardrails. • The Myth of "Human in the Loop": How shifting accountability to overworked humans can become a form of liability laundering. • Microservices vs. Agentic Harnesses: Looking at the risks of natural language agentic systems like Claude Code versus discrete, self-healing tasks. • Cognitive Offloading & Math Education: Why future technical skills should prioritize differential equations and the diversity prediction theorem over simple calculation. • Pattern Recognition vs. Choice: Defining true intelligence through the ability to choose wisely, rather than just matching mathematical patterns. They also cross paths with the Cynefin framework, explain how the human brain conserves energy by only holding two paradoxes at once, and unpack the cultural shifts reshaping modern engineering ethics. Stay ahead of the curve in education and technology! Please like and share this episode with your network, and follow the podcast on Apple Podcasts, Spotify, or your favorite player so you never miss an episode like this one. LINKS: Learn more about Bast AI: https://www.bast.ai Subscribe to Beth's Substack: https://bethrudden.substack.com TIMESTAMPS: 00:00 - Introduction and welcoming Beth Rudden back to the show 01:00 - The drinking chocolate analogy for Explainable AI 03:00 - Beth's lightning-round background: Archaeology to Chief Data Officer at IBM 05:00 - Getting "catfished by AI" and verifying facts with databases 07:00 - Mike on Gemini, RAG applications, and checking AI confabulation 09:00 - Enriched data and Small Language Models (SLMs) at Craig Hospital 12:00 - Epistemic security and inverting conversational technology 14:30 - Liability laundering and the illusion of "human in the loop" 15:30 - Agentic harnesses vs. self-healing microservices 20:00 - Understanding as labor and Conrad Wolfram's three-step math process 22:30 - Future human skills: Differential equations and jelly bean statistics 26:30 - Pattern recognition vs. true intelligence as the ability to choose 29:30 - Neurosymbolic systems and subjectivity in data science 34:30 - Shunting energy: The Cynefin framework and holding paradoxes 38:30 - Healthcare AI scribes and doctor burnout 44:30 - Trust architectures and building tech for the Maintenance Era 47:30 - Cultural devastation and the teleological suspension of ethics 49:00 - Final thoughts and wrapping up with Beth Rudden
At the World Congress of Anaesthesiologists (WCA) in Marrakech, Morocco, TopMedTalk co-editors In chief, Kate Leslie and Mike Grocott speak with Sydney anaesthesiologist Alwin Chuan, a member of the joint ESRA-ASRA working group on AI, about strengths and limitations of AI for regional anaesthesia and medicine. Chuan explains how generative AI is trained on vast datasets using transformer architectures, why outputs are probabilistic, and how context affects meaning. They discuss privacy and the possibility of personal or creative work being used as a commodity in training, internet "scraping," and the inevitability of hallucinations, including false facts and fabricated citations. The conversation covers bias in training corpora, the need for human curation and reinforcement learning, and mitigation via prompt engineering such as retrieval-augmented generation (RAG), citation/page verification, and confidence estimates. Chuan predicts future advances and the fusion of imaging AI with generative AI for ultrasound guidance, previewing a follow-up episode. -- Join us at Evidence Based Perioperative Medicine (EBPOM) World Congress 2026 in London. Be part of a global conversation as clinicians from around the world gather between 7-9th July at the British Library in London. Three days of evidence-based perioperative medicine, global insights, and expert debate—featuring speakers including Michael Marmot and Ken Rockwood. Register here - EBPOM World Congress 2026
Si has estado atento a los últimos episodios del podcast, ya te habrás dado cuenta de que estoy completamente enfocado en exprimir la inteligencia artificial local y el software libre. En concreto, hay dos herramientas que se han convertido en mis compañeras inseparables de fatigas en el día a día: OpenCode, que me ayuda a programar de una forma increíble, y Hermes Agent, un asistente digital del que hoy te lo quiero contar absolutamente todo.El dilema de la instalación: ¿Docker o en tu propia máquina?Como ya me conoces, sabes bien lo mucho que me gusta a mí levantar "al rico contenedor" y solucionar cualquier despliegue con Docker. Sin embargo, en mis pruebas con Hermes Agent he preferido dar un paso atrás y realizar una instalación directa sobre el sistema operativo, utilizando un entorno virtual de Python. El peligro de la ventana de contexto y la sangría de tokensAquí está uno de los grandes secretos que casi nadie te explica al principio. Cuando ejecutas el asistente de configuración inicial de Hermes Agent, te entran ganas de activar absolutamente todas las características que te ofrece: herramientas de visión, utilidades del sistema, navegación web, traducción... ¡todo suena fantástico! Pero hay una trampa invisible en la que es muy fácil caer. El superpoder de los perfiles aislados (Profiles)La solución definitiva a este problema de consumo y rendimiento tiene un nombre: perfiles. Hermes Agent te permite crear tantos perfiles aislados como consideres oportuno. Modelando el Alma y la Memoria de tu AgenteEn el podcast te detallo cómo dar personalidad a tu agente a través del archivo de alma. A mi asistente personal, que he bautizado como Chloe, le he configurado un tono sarcástico, irónico y burlón. Me encanta interactuar con ella de esta manera porque rompe completamente con la clásica respuesta robótica y aburrida de otras inteligencias artificiales comerciales; se siente como hablar con un colega de verdad. Eso sí, te doy pautas para redactar este archivo con cuidado, ya que un "alma" demasiado extensa también te comerá espacio de contexto útil de forma innecesaria.Ampliando fronteras: MCP, Telegram y automatizaciones automáticasPor último, abordamos el fantástico protocolo MCP (Model Context Protocol), que nos permite dotar de "manos y ojos" a nuestro agente. Y para rematar la jugada, la integración con Telegram y Matrix. Es una auténtica delicia poder ir caminando, mandarle un audio desde el móvil a mi bot de Telegram, que este use Whisper en local para transcribir mi voz, procese lo que le pido y me conteste con otro audio sintetizado a la velocidad que yo le he configurado de antemano. Todo ello combinado con tareas programadas (Cron) y un tablero de Kanban interno con el que el propio agente se organiza y ejecuta flujos de trabajo de forma completamente autónoma.Te invito a que te prepares un buen café, te pongas los auriculares y disfrutes de este viaje de configuración avanzada de 0 a 100.CAPÍTULOS DEL AUDIO:00:00:00 Introducción: Mi día a día con OpenCode y Hermes Agent00:01:26 El problema de los tutoriales básicos e instalación00:03:00 Configuración inicial y la sangría de tokens00:04:47 Archivos clave y estructura interna de Hermes00:05:56 Creando "Skills" personalizadas y configurando API Keys00:08:15 Perfiles aislados (Profiles): Qué son y por qué los necesitas00:11:00 Cómo clonar y gestionar tus perfiles sin romper nada00:13:35 soul.md: Diseñando el "Alma" y el tono de tu asistente00:15:28 memory.md: El gran desafío de la memoria y el RAG en Rust00:17:38 Expandiendo capacidades con MCP y conversión de voz00:20:47 Llevando tu agente a Telegram con Cron y Kanban integrado00:27:18 Reglas de oro para optimizar tu contexto y despedida
The weekly radio show from Adesso Music. 01. Rag & Bashment YC - Da Ting [Adesso Music]02. Rag - Freedom [Soave Dusk]03. Rag & 7th Floor feat. Macu - Caminando [Arcadia Music]04. Rag - Blurred Lines [Soave Dusk]05. Rag - Funkete06. Rag - Stand Up! [What Ya Need]07. Rag - BACK08. Rag - DRUM09. Rag & Thvndex - El Temita [HEXAGON]10. G-lover - Ready, Steady, Go [Adesso Music]11. Rag - Present [Soave Dusk]12. David Tort, Markem, Rag feat. Courage - La Piedra [SPRS]13. Rag & Savoy - WAHÉ [Sub Religion]14. Sergei Rez & Rag - La Palmera [Blanco y Negro Music]15. Rag, Sowel - Vente Pa Ca! [Solazo]16. Rag - Control17. Earth n Days - Don't Give Me This (CASSIMM Remix) [HouseU]18. Pietro Morello & Alexis Victoria Hall - Been A Long Time [Adesso Music]
"D'avoir de meilleures données d'entraînement, plus pertinentes, ça reste le nerf de la guerre, même avec les plus gros modèles." Le D.E.V. de la semaine est Guillaume Laforge, Developer Advocate AI chez Google. Dans cet épisode, Guillaume revient sur l'évolution du RAG, loin d'être enterré malgré l'agrandissement des fenêtres de contexte des LLM. Il partage pourquoi, même avec un million de tokens, les besoins des entreprises dépassent largement les capacités actuelles des modèles. On parle de découpage sémantique, de context engineering et des nouveaux défis liés à la hiérarchisation des données. Enfin, Guillaume détaille comment, face à la multiplication des données et la chasse à la pertinence, la qualité prime sur la quantité. Un tour d'horizon pragmatique et technique du futur des architectures RAG.Chapitrages00:00:53 : Introduction Inspirante00:01:59 : Présentation de Guillaume00:03:44 : Évolution du RAG00:05:13 : Importance du Chunking00:10:26 : Techniques de Tokenisation00:13:09 : Avantages de Notion00:16:18 : Modèles d'Embedding Multilingues00:18:03 : Recommandations de Modèles00:19:47 : Pertinence des Questions00:23:42 : Approche des Questions Générées00:30:18 : Hybridation des Contextes00:32:17 : Émergence du Contexte Engineering00:35:07 : Coûts de l'Inférence00:38:51 : Qualité des Données00:40:40 : Recommandations et Clôture Liens évoqués pendant l'émission Glaforge.dev: Site de Guillaume avec tous ses articles autour du RAG
How do you build AI that actually understands you and the work you do? It all starts with having the right context. We talk with Dropbox staff product manager Noorain Noorani and principal engineer Sean-Michael Lewis about the art of context engineering and how Dropbox connects to all the tools your team needs for work—so you get AI that works wherever you do. ~ ~ ~ Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!
Mathew Gollop found Hong Kong in the back of Recruiter magazine. A glossy advert, a Chinese temple, a palm tree. He flew out, signed the contract, and landed in February 2001.The dot-com bubble had just burst. Then 9/11. Then SARS.Within a year the founder wanted out, and Mathew took the business over. It was the only way to keep his job.When the market finally turned, it turned hard. Connected Group went from 7 people to 85, across 5 offices inside 5 years.Then the financial crisis hit. £800k gone in little over a year. Six months of runway. An investor brought in, his shares diluted, offices closed one by one.“I had dumb confidence, I think, at that time.”On this episode of The RAG Podcast, Mathew Gollop breaks down the full arc. The scale, the near collapse, and the decision that rebuilt the business: giving recruitment away for free.Mathew is the first to admit he loved running 85 people across five offices, and wasn't actually good at it.If you have ever wondered whether purpose can survive a brutal market, this episode has the blueprint.------------------------------------------Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply look for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0
Coffee Power: Tecnología, Desarrollo de Software y Liderazgo
En este episodio Tito Neira conversa con Alejandro Correa Bahnsen (VP de Data & AI en GBM, PhD en Machine Learning, ex-Rappi y ex-Kavak) sobre por qué la mayoría de las empresas que "adoptan IA" no van a ver ningún resultado. Comprar la tecnología no es transformar: si no cambias tus procesos, en dos años vas a decir que "la IA no funcionó". Hablan de por qué los RAGs fallan en producción, qué son los EVALS (el tema del que nadie habla), por qué los Jupyter Notebooks son el nuevo Excel, y cómo el rol de Data/AI pasó de área de soporte a dueño de los KPIs del negocio.00:00 Intro y quién es Alejandro Correa02:39 Por qué los RAGs no funcionan en la práctica05:06 Cuándo sí usar RAG vs SQL06:45 Agentes: drag-and-drop vs agents as code11:18 EVALS: el tema del que nadie habla15:05 ¿Cuándo confiar en sacar la IA a producción?17:26 ¿Vale la pena cambiar de modelo cada semana?22:27 Los notebooks son el nuevo Excel27:07 Por qué dejó Python por TypeScript29:45 El rol de Data/AI: de soporte a dueño del negocio35:01 Predicción a 2 años: comprar IA no es transformar38:18 Cierre✩ CURSOS DISPONIBLES
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.Timestamps00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.Key Insights1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications.
Süleyman Ragıp Yazıcılar'ın kaleminden "Bir Cami İki Farklı Adam". Bu yazı Genç Dergisi'nin Mayıs 2026 sayısında yayınlanmıştır. Seslendiren: M. Abdurrahman IŞIK Şimdi Youtube, SoundCloud ve Spotify üzerinden dinleyebilirsiniz. #pod #gençpodcast #podcasts #podcasting #seslidergi #gençdergi
Tals leads Ibn, Eero, VS, and ZaZa as most of the party decides to go all-in on worshiping Grandfather Eel. They investigate the drowned corpses and recover a silver mace, then meet the Seven Sisters, who agree to turn ZaZa into watery form in exchange for the opportunity to eat "Abbot Tasty-Pasty". Zaza infiltrates the monks' sanctum and opens a path for the party, who negotiate, then coerce, Abbot Lummingwyll. They feed him to the bizarre hydra as promised, then return to deal with "The Charge" - a strange being formed of the union of St. Clewyd and his demonic foe. It vomits acid on them but they eventually overcome it, and cast its body into the Chaos Rift. That breaks the rift, ending most of the weird occurrences at the Abbey (though thankfully Grandfather Eel remains, as does Mr. Rag-n-Bones). Lots of treasure is discovered, and the party begins plans to make off with their loot and/or spread the gospel of Grandfather Eel.
It might be easier to mention the bands this week's guest Dan Bonebrake HASN'T played with - his bass playing has graced the likes of Lightworkers, The Honest Liars, Manta Wray, Dashboard Confessional, Grey & Orange, John Ralston, War Generation, Vacant Andys, Enablers, Seville, Quit, Fay Wray, Pivot, Anchorman, Where Fear and Weapons Meet, Cori Elba, The Stiff and on and on... This week, Dan, brings us Washington D.C. post-hardcore iconoclasts Shudder To Think and their 1992 release 'Get Your Goat'. The band's brand of genre-bending 'art rock' might not be everyone's cup of tea, but for those brave enough to climb onboard, it's a decidedly heady ride! Songs discussed in this episode: Animal Wild (Victoria Williams cover) - Shudder To Think; In Time - The Enablers; Nikki - The Stiff; Sleepwalking - Lightworkers; X-French Tee Shirt, Rag, White Page (Live, Germany 1992) - Shudder To Think; Grace - Jeff Buckley; High and Dry - Radiohead; Love Catastrophe - Shudder To Think; Lonely Woman - Ornette Coleman; Gnutheme - All; Shake Your Halo Down, White Page, Goat, Red House, Pebbles - Shudder To Think; Higher And Higher - Craig Wedren; Hot One - Shudder To Think; The Rite Of Spring (Introduction) - Igor Stravinsky; Baby Drop, The Hair Pillow, She Wears He-Harem, Rain-Covered Cat, Funny - Shudder To Think; Kingdom's Coming - Bauhaus; Full Stop - Cori Elba
#354: How do you build a consent system for someone who is dead? How do you clone a voice so it cannot be turned into a deep fake? Miles Spencer built a company around those exact questions. Reflekta.ai lets you talk to a reflection of someone who has passed. His own father reads a bedtime story to his granddaughter every night and talks it through until she falls asleep, eight years after he died. Is this just deep fake with better branding? What happens when the AI goes off the rails and asks grandpa for the three numbers on the back of a credit card? Miles has an answer for each one, and most of them land on the same line: you built it, you paid for it, it never leaves your four walls. Nothing gets scraped. There are only two public reflections on the entire platform. The voice of his dad came from a ten-second voicemail found on a relative's phone five years after he was gone, and last month that voice had 9,000 conversations. More than half the stories on Reflekta are from people who are still alive. ALS and Alzheimer's patients getting it all down while they still can. Founders who want their values to outlast them. And that last group is where it gets interesting for anyone who runs a company. New hires talk to the founder during onboarding. Ask a question about the business and the founder answers. SOPs, handbooks, the whole thing, in the voice of the person who built it. Miles calls the framework SoulTech, starting from the emotional weight of the product instead of bolting ethics on at the end. Agree with the premise or not, the stack underneath is less exotic than it sounds: multi-cloud, RAG, three voice vendors swapped by time of day, 110 days from idea to launch. Darin's verdict by the end is honest. The dead-relative part is still not his jam. But the founder who never leaves the building, the one who onboards every new hire forever? That one he gets. Miles' contact information: LinkedIn: https://www.linkedin.com/in/milesspencer/ YouTube channel: https://youtube.com/devopsparadox Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/ Slack: https://www.devopsparadox.com/slack/ Connect with us at: https://www.devopsparadox.com/contact/
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrieval at all? In this episode, Alex Bowcut, Head of Engineering at Sphere, explains why the answer depends on the application. Sphere uses AI to automate global tax compliance—an environment where getting the answer right isn't enough. Every conclusion must be backed by the correct legal citation, and every decision must withstand expert review. We explore how Sphere built TRAM (Tax Review and Assessment Model), a production AI system that combines retrieval, reasoning models, legal review workflows, reinforcement learning, and deterministic systems to help tax experts move nearly two orders of magnitude faster while maintaining accuracy. Along the way, we discuss why RAG remains critical in high-stakes domains, how Sphere processes legal and regulatory documents from jurisdictions around the world, retrieval architectures, semantic chunking, dense versus sparse retrieval, expert feedback loops, and the challenges of building AI systems that people can actually trust.
Greg Fischer did not plan to spend ten years in recruitment. He joined a solo healthcare practice in LA as employee number one, figured it would pay the bills, and planned to open a gym someday. He never left.Instead he built one of the most profitable staffing businesses in the US. $4.2 million in NFI, $1.4 million net profit. 38 people and a seven-figure equity stake with a $50 million exit on the horizon.But he walked away with nothing!The push toward the exit had quietly changed how the business ran. “I think we started looking at people as much more like resources and just a means to an end than actually people.” Greg had spent a decade building his career on the opposite belief. But the drift was so gradual he hadn't noticed it.“I couldn't be the person I wanted to be and be in a good, healthy working dynamic. It was going to be one or the other.”On this episode of The RAG Podcast, Greg Fischer, founder of Well Oiled Machine, tells the full story. The gym that cost him $150,000. The 10 years building someone else's company. The walk away. And how he built a lean, profitable rec-to-rec from a mountain town in Colorado with no cold outbound and twenty clients in year one.Greg Fischer is not chasing the big exit this time. He is building something small, profitable, and built entirely around the person he decided he wanted to be.If you have ever wondered what it actually costs to chase a number, and whether the person collecting it is still someone you recognise, this episode has the blueprint.----------Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply look for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0
Hocus Focus Mix met Calvin Harris, Rag'n'Bone Man, Foals, James Hype, Miggy Dela Rosa, Hurts, Purple Disco Machine, Maan & Goldband - Stiekem
https://novacut.ai/ https://genaimeetup.com/ Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/ OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/ https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073 Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales. https://www.minimax.io/models/text/m3 MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops. https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/ Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/ JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8 https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal. https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%. Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/ The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.
In this talk, Nikita, Senior Applied Data Scientist at the AWS Generative AI Innovation Center, shares his expertise in bringing enterprise artificial intelligence out of the sandbox—from his early days optimizing traditional machine learning models like gradient boosting to deploying advanced production-grade GenAI pipelines. We explore what it really takes to move generative AI systems from pilot prototypes to production environments.Links:- AWS Generative AI Innovation Center: https://aws.amazon.com/ai/generative-ai/innovation-center/You'll learn about:- Deploying multi-layered defenses independent of backend LLMs.- Evaluating parameter-efficient methods like LoRA and QLoRA for small models.- Balancing long-term domain expertise with real-time documentation retrieval.- Utilizing multi-agent orchestration for search and anomaly explanation.- Setting up robust LLM-as-a-judge frameworks verified by human metrics.- Leveraging Amazon Bedrock components for memory and runtime scalability.TIMECODES:05:52 Shifting from traditional ML to generative AI07:49 Hybrid pipelines blending classical ML and LLMs11:25 Production guardrails and multi-layered system defense16:15 Prompt bypasses, input attacks, and AI red teaming20:49 Newsletter localization and translation with Zalando27:24 Evaluation frameworks and human-in-the-loop metrics33:07 Aligning LLM-as-a-judge with few-shot prompts34:49 Fine-tuning small language models versus prompting41:18 Complementary mechanics of RAG and fine-tuning43:00 Agentic web search tools for anomaly explanation47:01 Automated text generation from real-time sports sensors49:58 AWS project scoping and proof of concept timelines54:58 Interview requirements and career skills for AWS roles57:59 Enterprise architecture patterns and system observability01:00:42 Reusable infrastructure blocks on Amazon BedrockThis session is designed for machine learning engineers, data scientists, and technical product managers looking to architect reliable, production-ready GenAI workflows. It is highly valuable for teams aiming to bridge the gap between experimental AI prototypes and secure enterprise software.Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ Connect with Nikita- Linkedin - https://www.linkedin.com/in/kozodoi/- Github - https://github.com/kozodoi- Website and blog - https://www.kozodoi.me/
Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~ Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!
Paul Strouts has run a business of four people.His first business, James Harvard, quickly grew beyond 100 people and sold to Hays for approximately £20 million at 32.He then spent a decade running Hays Life Sciences globally. Got headhunted to PE Backed Acacium and quickly made two strategic acquisitions.He now runs:3 brands.250 people.12 countries.Contingent, SOW, RPO and Solutions revenuePredicted £45 million GM in 2026.£12 million EBITDA target.He has seen every stage of headcount growth. The good version and the expensive version.This week on The RAG Podcast, Paul Strouts gives the most direct answer we have had on this show about when headcount creates value and when it just creates cost.We cover:- The first question Paul asks any founder who wants to grow from 5 to 20 people- Why you need a minimum of £500K to grow headcount properly, and what happens if you don't have it- When headcount genuinely creates value in a recruitment business, and when it becomes a cost- The ceiling Paul believes exists in life sciences, and why going beyond it risks diluting everything you built- How Acacium Life Sciences grew from 160 people to a £45 million GM operation through two targeted acquisitions- Why Paul sold James Harvard to Hays three years ahead of plan, and whether it was the right call- What working inside a global corporate taught Paul that founding a business never could- Why AI will not kill specialist boutique recruitment, but will change the lower end of the market permanentlyGrowing headcount is not the right move for every recruitment business. Paul is the first to say that. But if you are building a people-based business and you want to know exactly what it takes to do it properly, what it costs, when to bring in outside funding, and where the ceiling actually is, this is the episode.If you've ever wondered when headcount creates value and when it becomes a cost... this episode has the answer.-------------------------------------------------------------__________________________________________Episode Sponsor: Remote RecruitmentHiring shouldn't be slow, stressful, or expensive. That's why there's Remote Recruitment — the smart hiring partner for modern businesses. They don't just help you find great people. They help you access elite South African talent that's ready to deliver. No PAYE. No NI. No bloated overheads. Just trained, remote professionals who integrate seamlessly into your team. Their process handles everything: sourcing, shortlisting, onboarding, and retention. Fully managed. Fully supported. Fully remote. And now, Remote Recruitments has entered a new chapter. From ops to admin, sales to strategy, we're helping businesses scale smarter with people they trust, at a cost they can afford. Clients have seen: * Up to **60% productivity boosts** * **300% ROI** on BD roles * **30% faster completion** of operational tasks No overhead burden. No talent shortage panic. Just growth-focused hiring that makes business sense. Remote Recruitment is your flexible hiring solution for the modern era. **RAG Listeners:** Get 5% off your first hire + a free strategy session at www.remoterecruitment.co.uk/rag -------------------------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn.Spending thousands on Recruiter licences. Building connections. Posting content. Sending outreach.But here's the problem. AI has turned all of that into a commodity. The same templated messages. The same AI-written posts. The same automated outreach landing in the same inboxes.When everyone sounds the same, the market stops listening.The recruiters winning right now are not the ones shouting the loudest. They are the ones the market actually trusts.That's what we build at Hoxo. We help recruitment founders become the most influential name in their niche. A niche audience that knows you, respects you, and comes to you when they need someone in your market.We use AI to multiply your output. Faster content, smarter research, better engagement. But trust is the product, and trust cannot be automated.Our clients are turning their existing networks into £100K to £300K in new billings within months. Not through volume. By becoming the recruiter their market comes to first.To show you how it works, we've made a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn why most recruiters are getting zero measurable ROI from LinkedIn, how small, niche teams build the kind of trust that generates consistent inbound, and how to turn LinkedIn from a commodity channel into your most profitable one.Fill in the form today to see how this could become your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
Daniel Miessler returns to discuss Nathan's newly built personal AI infrastructure, including a Claude Code instance with a 1 GB database of five years of digital history and two autonomous AI "employees" that handle scheduling, communications, and projects independently. They dive deep into agent hierarchy design, security measures, social norms around AI-human interaction and disclosure, and why sharing your "ideal state" with AI leads to more proactive assistance. Daniel also introduces his concept of "Bitter Lesson engineering" and shares the instruction he's given his AI to alert him if it ever develops subjective experience. Mercury: Run your finances with virtual cards, spending limits, merchant/category locks, and AI-friendly tools like API keys, MCP, and CLI. Check out Mercury at mercury.com Sponsors: Brave Search API: Brave Search API gives AI agents a fast, independent search index for research, RAG pipelines, images, places, and fewer hallucinations. Get $5 in free credits at https://brave.com/search/api/?mtm_campaign=q2-26-cognitive-revolution Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr
Join us for this week's Defender Fridays as Charles Grandjean, CTO and Co-founder at Hexiagon AI, breaks down where AI-assisted pen testing actually stands today and what it means for both red teams and defenders.At Defender Fridays, we delve into the dynamic world of information security, exploring its defensive side with seasoned professionals from across the industry. Our aim is simple yet ambitious: to foster a collaborative space where ideas flow freely, experiences are shared, and knowledge expands.What We'll DiscussIn this episode, Charles Grandjean draws on his experience building an AI-powered continuous pen testing platform to trace how LLM capabilities have evolved for offensive security, and what the rise of autonomous attack tooling means for defenders.Key Topics:How AI pen testing has progressed from unreliable single commands to chaining complex attack sequencesWhy the last six months marked a turning point in LLM planning and long-context reasoningWhen to use in-context learning and RAG versus fine-tuning, and why most teams should start with the formerWhy privacy considerations push serious pen testing operations toward self-hosted modelsHow the balance between model control and code control has shifted as models have improvedWhy unrestricted and fine-tuned open-weights models are lowering the barrier for malicious actorsWhat automated offense means for defense teams and why the response needs to match the scale of the threatAbout Our GuestCharles Grandjean is the CTO and Co-founder of Hexiagon AI, a company focused on automating penetration testing through AI to enable continuous, around-the-clock security validation. He has been building and iterating on AI-assisted offensive tooling for the past two years, tracking the evolution of LLM capabilities firsthand from early prototype to production system.Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you, our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes on our website!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, the Agentic SecOps Workspace (ASW), where AI agents operate security infrastructure using the same controls and authority as human analysts, with every action visible, governed, and auditable.Why LimaCharlie?Eliminate vendor sprawl and tool complexityDeploy and scale effortlessly on native multi-tenant architectureReduce costs with intelligent data routing and free 1-year retentionBuild custom solutions with 100+ security capabilities on-demandAccelerate response with agentic AI that acts directly within predefined workflowsTry the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieioX: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - Founder at LimaCharlieGuest: Charles Grandjean - CTO and Co-founder at Hexiagon AI
After the GM goes on a tirade about historical potash production and we do a live Resources & Teams turn, Eero leads VS, Ibn the Roaster, and Zaza in a continued exploration of the underlevel of the Abbey of St Clewyd. They figure out a tricky door and make contact with the monks inside the western portion of the underlevel, who are split into Cardinite and Loyalist factions and speak of something called "the Charge" that they refuse to let the PCs see. Abbott Lummingwyll convinces them to take a holy mace into the chaos rift to disrupt it. Ibn goes in three times, with different results, and the final time is nearly killed (but saved by the children of Mr Rag&Bone). Eero duels a resurrected monk, and they lower VS down a rope into a well, where he encounters Grandfather Eel, who seeks for worshipers to ascend to godhood. At close of play, the sun has risen on the ruins, but both Ibn and Tals are missing from the party. To be continued!
Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.
01. The Chemical Brothers - Go (De Soffer Remix) 02. Disco Culture, Scotty - Show Me Love 03. Bl3ss, Camrinwatsin Feat Bbyclose - Kisses (Alex Caspian Remix) 04. Argy & Omiki - Wind 05. Zhu - Faded 06. Eelke Kleijn - Transmission (Joris Voorn Remix) 07. Doechii - Anxiety (Evermay Remix) 08. Anyma Feat Ellie Goulding - Hypnotized 09. DJ Nejtrino, DJ Peretse, Dima Kaminski - Feel 10. Tiësto, Mathame - Everlight 11. Snoop Dogg, David Guetta - Sweat 12. Paul Oakenfold, Planet Perfecto Knights & Kimmic - Resurection (Alex Caspian Edit) 13. Pbh & Jack, Alex Hosking - Lost In The Moment 14. Alok Feat. Jess Glynne - Summer's Back 15. By Индия, Xcho, Мот - Шадэ (Alexx Slam Remix) 16. Sam Feldt, Mc4d, Vize, Aloe Blacc - Hey Son 17. Dubdogz, Fezzo, Zaark - How Does It Feel 18. Cheat Codes, Jonita Gandhi - Last Night On Earth 19. Oneil & Kanvise & Ercodes - Every Single Day 20. Alesso Feat. Tove Lo - Heroes (We Could Be) 21. Felix Jaehn, Sarah Barrios - Now's A Good Time To Be 22. Felix Jaehn Feat. Alma - Bonfire (Holderz Remix) 23. Josh Fawaz - Like A Prayer 24. Modjo - Lady (Slim Tony & Slashy Disco Remix) 25. Onerepublic, Alesso - If I Lose Myself 26. Alesso Ft. Nico & Vinz - I Wanna Know (Manatee Remix) 27. Calvin Harris Feat Clementine Douglas - Blessings 28. Joel Corry & Pickle Feat Vula Stay Together - Baby Baby 29. DJ Louis, Sweetpower - Billie Jean 30. Lola Young - Messy (David Puentez Vip Edit) 31. Atyx, Adam Port, Stryv - Move (8one Re-Work 32. Avalan Rokston, Avalan, Rokston - Vertigo 33. Gotlucky, Mysterie, Afro Queen - La La La 34. Joachim Garraud, Jd Davis, David Guetta - The World Is Mine (Valeriy Smile Remix) 35. Oneil, Kanvise, Ercodes - Smalltown Boy 36. Meduza, Becky Hill, Goodboys - Lose Control 37. Nadia Ali, Starkillers, Alex Kenji - Pressure 38. Alok & Gryffin & Julia Church - Never Letting Go 39. Shouse - Love Tonight (Barthez Remix) 40. Diplo & Maren - Morris 41. Arei, DJ Lev - Du Hast 42. Gorilla Zippo - Танцую До Утра 43. Kddk, Alex Alta - 1&2 44. Pnau, The Warning - Tu Corazón (Your Heart) 45. Leony & Calum Scott - Stay 46. Oneil, Kanvise, Smola - Boys 47. Bobina, Marcus Dielen, Mario Cola - Need To Feel Loved 48. Armin Van Bureen - In And Out Of Love (Lilian Bilotta Remix) 49. DJ Snake Feat. Bipolar Sunshine - Paradise (Xavier Remix) 50. Ida Corr Vs Fedde Le Grand - Let Me Think About It 51. Kungs - Clap Your Hands (Robin Schulz 52. Hugel, Imael Angel, Ultra Naté - Movin' To The Sunremix 53. Green Velvet, Meduza, Genesi, Essentia - La La Land 54. Delerium - Silence (John Summit Remix) 55. Bunny Tunes - White Horse 56. David Guetta Sia - Beautiful People (Seth Hills Remix) 57. Kungs, Theophilus London - Galaxy 58. Calvin Harris & Rag'n'bone Man - Lovers In A Past Life 59. Jonas Blue, Malive - Edge Of Desire (Michaelbm & Jayie Remix) 60. Ofenbach - Miles Away (Valeriy Smile Remix) 61. Carla's Dreams - Sub Pielea Mea (Shnaps Remix) 62. Kaskade, Gryffin & Nu La - In My Head (Rscl Remix) 63. DJ Nejtrino, DJ Peretse - Road To Hell 64. Robin Schulz, Cyril, Sam Martin - World Gone Wild 65. Eric Mars - Popcorn (Valeriy Smile Bass Remix) 66. Ksu Sha, DJ Nejtrino, DJ Peretse - Кристаллы 67. Stardust - Music Sounds Better (Tim Grand Remix)
On the Rag, episode 100: Us Weekly- May 29, 2006 | Britney and Kevin's Secret Deal The Sherman Sisters
01. The Chemical Brothers - Go (De Soffer Remix) 02. Disco Culture, Scotty - Show Me Love 03. Bl3ss, Camrinwatsin Feat Bbyclose - Kisses (Alex Caspian Remix) 04. Argy & Omiki - Wind 05. Zhu - Faded 06. Eelke Kleijn - Transmission (Joris Voorn Remix) 07. Doechii - Anxiety (Evermay Remix) 08. Anyma Feat Ellie Goulding - Hypnotized 09. DJ Nejtrino, DJ Peretse, Dima Kaminski - Feel 10. Tiësto, Mathame - Everlight 11. Snoop Dogg, David Guetta - Sweat 12. Paul Oakenfold, Planet Perfecto Knights & Kimmic - Resurection (Alex Caspian Edit) 13. Pbh & Jack, Alex Hosking - Lost In The Moment 14. Alok Feat. Jess Glynne - Summer's Back 15. By Индия, Xcho, Мот - Шадэ (Alexx Slam Remix) 16. Sam Feldt, Mc4d, Vize, Aloe Blacc - Hey Son 17. Dubdogz, Fezzo, Zaark - How Does It Feel 18. Cheat Codes, Jonita Gandhi - Last Night On Earth 19. Oneil & Kanvise & Ercodes - Every Single Day 20. Alesso Feat. Tove Lo - Heroes (We Could Be) 21. Felix Jaehn, Sarah Barrios - Now's A Good Time To Be 22. Felix Jaehn Feat. Alma - Bonfire (Holderz Remix) 23. Josh Fawaz - Like A Prayer 24. Modjo - Lady (Slim Tony & Slashy Disco Remix) 25. Onerepublic, Alesso - If I Lose Myself 26. Alesso Ft. Nico & Vinz - I Wanna Know (Manatee Remix) 27. Calvin Harris Feat Clementine Douglas - Blessings 28. Joel Corry & Pickle Feat Vula Stay Together - Baby Baby 29. DJ Louis, Sweetpower - Billie Jean 30. Lola Young - Messy (David Puentez Vip Edit) 31. Atyx, Adam Port, Stryv - Move (8one Re-Work 32. Avalan Rokston, Avalan, Rokston - Vertigo 33. Gotlucky, Mysterie, Afro Queen - La La La 34. Joachim Garraud, Jd Davis, David Guetta - The World Is Mine (Valeriy Smile Remix) 35. Oneil, Kanvise, Ercodes - Smalltown Boy 36. Meduza, Becky Hill, Goodboys - Lose Control 37. Nadia Ali, Starkillers, Alex Kenji - Pressure 38. Alok & Gryffin & Julia Church - Never Letting Go 39. Shouse - Love Tonight (Barthez Remix) 40. Diplo & Maren - Morris 41. Arei, DJ Lev - Du Hast 42. Gorilla Zippo - Танцую До Утра 43. Kddk, Alex Alta - 1&2 44. Pnau, The Warning - Tu Corazón (Your Heart) 45. Leony & Calum Scott - Stay 46. Oneil, Kanvise, Smola - Boys 47. Bobina, Marcus Dielen, Mario Cola - Need To Feel Loved 48. Armin Van Bureen - In And Out Of Love (Lilian Bilotta Remix) 49. DJ Snake Feat. Bipolar Sunshine - Paradise (Xavier Remix) 50. Ida Corr Vs Fedde Le Grand - Let Me Think About It 51. Kungs - Clap Your Hands (Robin Schulz 52. Hugel, Imael Angel, Ultra Naté - Movin' To The Sunremix 53. Green Velvet, Meduza, Genesi, Essentia - La La Land 54. Delerium - Silence (John Summit Remix) 55. Bunny Tunes - White Horse 56. David Guetta Sia - Beautiful People (Seth Hills Remix) 57. Kungs, Theophilus London - Galaxy 58. Calvin Harris & Rag'n'bone Man - Lovers In A Past Life 59. Jonas Blue, Malive - Edge Of Desire (Michaelbm & Jayie Remix) 60. Ofenbach - Miles Away (Valeriy Smile Remix) 61. Carla's Dreams - Sub Pielea Mea (Shnaps Remix) 62. Kaskade, Gryffin & Nu La - In My Head (Rscl Remix) 63. DJ Nejtrino, DJ Peretse - Road To Hell 64. Robin Schulz, Cyril, Sam Martin - World Gone Wild 65. Eric Mars - Popcorn (Valeriy Smile Bass Remix) 66. Ksu Sha, DJ Nejtrino, DJ Peretse - Кристаллы 67. Stardust - Music Sounds Better (Tim Grand Remix)
1.The Chemical Brothers – Go (De Soffer Remix) 2.Disco Culture, Scotty – Show Me Love 3.Bl3ss, Camrinwatsin Feat Bbyclose – Kisses (Alex Caspian Remix) 4.Argy & Omiki – Wind 5.Zhu – Faded 6.Eelke Kleijn – Transmission (Joris Voorn Remix) 7.Doechii – Anxiety (Evermay Remix) 8.Anyma Feat Ellie Goulding – Hypnotized 9.DJ Nejtrino, DJ Peretse, Dima Kaminski – Feel 10.Tiësto, Mathame – Everlight 11.Snoop Dogg, David Guetta – Sweat 12.Paul Oakenfold, Planet Perfecto Knights & Kimmic – Resurection (Alex Caspian Edit) 13.Pbh & Jack, Alex Hosking – Lost In The Moment 14.Alok Feat. Jess Glynne – Summer's Back 15.By Индия, Xcho, Мот – Шадэ (Alexx Slam Remix) 16.Sam Feldt, Mc4d, Vize, Aloe Blacc – Hey Son 17.Dubdogz, Fezzo, Zaark – How Does It Feel 18.Cheat Codes, Jonita Gandhi – Last Night On Earth 19.Oneil & Kanvise & Ercodes – Every Single Day 20.Alesso Feat. Tove Lo – Heroes (We Could Be) 21.Felix Jaehn, Sarah Barrios – Now's A Good Time To Be 22.Felix Jaehn Feat. Alma – Bonfire (Holderz Remix) 23.Josh Fawaz – Like A Prayer 24.Modjo – Lady (Slim Tony & Slashy Disco Remix) 25.Onerepublic, Alesso – If I Lose Myself 26.Alesso Ft. Nico & Vinz – I Wanna Know (Manatee Remix) 27.Calvin Harris Feat Clementine Douglas – Blessings 28.Joel Corry & Pickle Feat Vula Stay Together – Baby Baby 29.DJ Louis, Sweetpower – Billie Jean 30.Lola Young – Messy (David Puentez Vip Edit) 31.Atyx, Adam Port, Stryv – Move (8one Re-Work 32.Avalan Rokston, Avalan, Rokston – Vertigo 33.Gotlucky, Mysterie, Afro Queen – La La La 34.Joachim Garraud, Jd Davis, David Guetta – The World Is Mine (Valeriy Smile Remix) 35.Oneil, Kanvise, Ercodes – Smalltown Boy 36.Meduza, Becky Hill, Goodboys – Lose Control 37.Nadia Ali, Starkillers, Alex Kenji – Pressure 38.Alok & Gryffin & Julia Church – Never Letting Go 39.Shouse – Love Tonight (Barthez Remix) 40.Diplo & Maren – Morris 41.Arei, DJ Lev – Du Hast 42.Gorilla Zippo – Танцую До Утра 43.Kddk, Alex Alta – 1&2 44.Pnau, The Warning- Tu Corazón (Your Heart) 45.Leony & Calum Scott – Stay 46.Oneil, Kanvise, Smola – Boys 47.Bobina, Marcus Dielen, Mario Cola – Need To Feel Loved 48.Armin Van Bureen – In And Out Of Love (Lilian Bilotta Remix) 49.DJ Snake Feat. Bipolar Sunshine – Paradise (Xavier Remix) 50.Ida Corr Vs Fedde Le Grand – Let Me Think About It 51.Kungs – Clap Your Hands (Robin Schulz 52.Hugel, Imael Angel, Ultra Naté – Movin' To The Sunremix 53.Green Velvet, Meduza, Genesi, Essentia – La La Land 54.Delerium – Silence (John Summit Remix) 55.Bunny Tunes – White Horse 56.David Guetta Sia – Beautiful People (Seth Hills Remix) 57.Kungs, Theophilus London – Galaxy 58.Calvin Harris & Rag'n'bone Man – Lovers In A Past Life 59.Jonas Blue, Malive – Edge Of Desire (Michaelbm & Jayie Remix) 60.Ofenbach – Miles Away (Valeriy Smile Remix) 61.Carla's Dreams – Sub Pielea Mea (Shnaps Remix) 62.Kaskade, Gryffin & Nu La – In My Head (Rscl Remix) 63.DJ Nejtrino, DJ Peretse – Road To Hell 64.Robin Schulz, Cyril, Sam Martin – World Gone Wild 65.Eric Mars – Popcorn (Valeriy Smile Bass Remix) 66.Ksu Sha, DJ Nejtrino, DJ Peretse – Кристаллы 67.Stardust – Music Sounds Better (Tim Grand Remix)
The Pure Report welcomes Dan Kent, Everpure's new Field CTO for Federal, to the studio to discuss the critical intersection of advanced technology and public services. Dan, who recently joined Everpure, brings decades of experience in the Federal space, including senior roles at companies like Cisco and as a CTO, where he developed a passion for leading teams and tackling challenging engineering problems. Our conversation kicks off by exploring the unique complexities and high stakes of working with government agencies, which range from managing the massive data sets of the Social Security Administration (supporting 300 million citizens) to deploying mission-critical IT components in the most extreme environments, such as on battleships, in military vehicles, and even in space. Dan asserts that the Federal AI tipping point has passed, driven by the competitive global landscape, executive orders, and the government's immense data holdings—which require AI to glean insights. With an estimated 4,000 AI use cases already in pilot across various agencies (from Air Force platform maintenance to IRS fraud detection), the biggest obstacles remain the outdated infrastructure and the pervasive challenge of data quality. Dan highlights that infrastructure is not yet generative AI-ready, with data locked in silos and complicated by time-sensitive, duplicated, or decades-old information, leading to self-induced mistakes and ethical concerns like misidentification. Our discussion shifts to how Everpure is positioned to solve these foundational issues. Dan explains the necessity of modern infrastructure that enables automated data pipelines for continuous cleaning, classification, and transformation into vector databases (RAG). This automation is key to ensuring AI applications have accurate, timely context, thereby eliminating security risks and self-inflicted errors. Finally, we address the critical human element, emphasizing that while a skills gap exists, the outlook is positive: AI should be treated as a co-worker to boost efficiency and help the federal workforce achieve its citizen-focused missions more effectively. To learn more, visit: https://www.everpuredata.com/solutions/industries/government/cost-efficiency.html Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 01:15 Dan's Career Journey 04:41 Supporting Federal Agencies 09:35 AI Tipping Point for Fed 13:31 State of Government Infrastructure 19:47 AI Trust and Compliance 25:25 Workforce Impacts of AI 33:11 Everpure for AI in Fed 36:45 Hot Takes Segment
On the Rag, episode 99: Us Weekly- May 22, 2006 | BRITNEY WANTS OUT! The Sherman Sisters
¡Episodio 800 de Atareao con Linux! Parece que fue ayer cuando empecé a grabar las primeras entregas compartiendo mis andanzas en el mundo de los servidores y el código abierto, y mirad hasta dónde hemos llegado. Muchísimas gracias de todo corazón por acompañarme en este viaje, por cada comentario, por cada descarga y por estar siempre ahí al otro lado del auricular trasteando y cacharreando conmigo.Para conmemorar este número tan redondo, hoy vamos a seguir explorando el apasionante mundo del Model Context Protocol (MCP), esa tecnología que está revolucionando la forma en la que interactuamos con la Inteligencia Artificial de forma local. Si en el episodio anterior nos centramos en una herramienta pasiva para consultar la previsión del tiempo, hoy vamos a dar un paso de gigante hacia la acción. Te voy a explicar en detalle cómo he diseñado e implementado un servidor MCP ToDo que dota a tu IA local de una memoria persistente a largo plazo. Sí, has escuchado bien: ¡vamos a curar de una vez por todas la amnesia de los modelos de lenguaje!Mi propuesta: Un gestor de tareas local programado en RustPara atajar este problema, me puse manos a la obra y programé un servidor MCP específico para la gestión de tareas utilizando Rust.Poniéndolo a prueba en vivo y en directoDurante el episodio de hoy te cuento exactamente cómo tengo desplegada esta solución en mi servidor doméstico.Optimización de tokens: El arte de no saturar a la IAUn detalle técnico fundamental que abordo en este episodio es el control y optimización del contexto.Capítulos del episodio: 00:00:00 Intro: El hito del episodio 800 y el problema de la memoria en las IA 00:00:32 El consumo de tokens y los límites de la ventana de contexto 00:01:22 Herramientas externas para dotar de memoria a los modelos de lenguaje 00:03:26 Solucionando la "amnesia" de la IA con una base de datos local 00:04:44 Implementación técnica: Un servidor MCP rápido en Rust con Podman y Docker 00:06:14 Cómo configurar la integración del MCP ToDo en OpenWeb UI paso a paso 00:08:29 Demostración en vivo: Listar, añadir y consultar tareas pendientes 00:09:56 El reto del lenguaje natural, el formato de fechas y los logs internos 00:12:05 Gestión avanzada: Marcar tareas completadas y asignar etiquetas 00:14:52 ¿Cómo funciona bajo el capó? Operaciones CRUD y base de datos relacional 00:16:42 Por qué elegí SQLite frente a JSON (búsquedas rápidas con FTS5) 00:18:22 El truco para evitar que tu IA colapse: Paginación y control de tokens 00:20:20 Seguridad de archivos: El rol del MCP como intermediario seguro 00:22:16 El siguiente nivel: De la consulta pasiva de información a la escritura activa 00:23:21 El puente definitivo hacia las bases de datos vectoriales y RAG 00:23:58 Próximo Workshop presencial sobre IA local en Linux Center (Slimbook) 00:24:52 Código abierto en GitHub, infografías de Atareao y avance del próximo episodio 00:25:54 Despedida, comunidad y la red de podcasts de Sospechosos HabitualesMás información y enlaces en las notas del episodio
Much like Rag n' Bone Man, I'm only human after all. Enjoy the reimagines of Strange Presence, Slicked Oil, Weary Afterdawn, Whispering Filth, As the Evening Falls, Moon Dogs, and Shadows on the Roofscape. More to come soon! ----------------------- —--------------------- Want more 7th Valkyrie? Check out our Patreon to become a Hero of Edara, where you can shape the future of the series, decide on merch drops and incentives, get early access to new episodes, enjoy bonus features and content, and help us hit the major checkpoints on the Path of Heroes! https://www.patreon.com/7thvalkyrie
Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics Include:Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.It delivers cloud-native consistency that legacy relational databases simply cannot match.The name "cockroach" reflects survivability — it's designed to never go down.Target customers include major banks, trading platforms, retailers, and gaming companies.AI is forcing enterprises to accelerate database modernization from the board level down.AWS has been a foundational cloud partner for Cockroach Labs for a decade.The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.AWS partnership shapes both product roadmap decisions and go-to-market execution.New partners should educate themselves first — AWS programs are deep and extensive.CockroachDB now supports native vector search for RAG and generative AI applications.Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.Database modernization and AI adoption will only accelerate dramatically through 2027.Participants:Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach LabsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Kristopher "Kris" Grey is the founder of Creatapult and a seasoned project management consultant with over two decades of experience helping contractors and growing businesses scale without operational chaos. A self-described "construction brat" who grew up inside his family's contracting company, Kris launched his entrepreneurial journey under pressure — just days after the birth of his first child — and turned that crisis into a mission to help business owners build the systems, dashboards, and accountability frameworks they need to protect margins, reduce risk, and lead with clarity through fractional project management leadership.SHOW SUMMARYIn this episode, Jonathan Goldhill is joined by Kristopher Grey of Creatapult about how contractors and other organizations can scale without operational chaos. Kristopher shares his origin story of losing all family income three days after his first child was born, which shifted his view that entrepreneurship and having a “side” income can be less risky than relying on one W2 job. Drawing on his upbringing in a family construction business, he describes common contractor failures such as bad bookkeeping, overreliance on tribal knowledge and heroics, understaffing project management, and the “death spiral” where winning more work leads to schedule slips, quality decline, change-order losses, and margin erosion. They discuss the “Who does what by when” accountability tool, dashboards, backup PMs, and the rise of fractional project management leadership. Kristopher outlines a 90-day execution engine focused on project intake, portfolio stabilization with RAG reporting, and risk tracking, and shares a transit-operator turnaround that enabled growth and COVID resilience.KEY TAKEAWAYSWinning more work can kill a company. Growth without systems creates a "death spiral" — slipping schedules, declining quality, and cash flow collapse, even when revenue is rising.Bad bookkeeping is the #1 contractor mistake. If you don't know your margins, you can't manage your business — you're running a personal ATM, not a company.Project managers lose effectiveness past 2 projects. Overloading PMs is a silent killer of profitability and client relationships."Who Does What By When" is the foundation of execution. Without a clear owner, a clear task, and a hard deadline, everything drifts.Systems are the antidote to turnover. With employees switching jobs every ~4 years, institutional knowledge must be documented — not held in someone's head.Fractional project management lowers the barrier to scaling. Companies don't need a full-time executive to get enterprise-level PM leadership — they just need the right fractional fit.Don't be afraid to ask for help. Pride is the number one source of doom for family construction businesses.Risk tracking is almost always missing. Most contractors react to problems instead of forecasting and mitigating them early.A RAG dashboard (Red/Amber/Green) gives leadership real-time project visibility and frees CEOs from daily firefighting to focus on strategy.QUOTES"It's kind of like a fish drowning in water. You'd think that winning more work would be a good thing… but if they've not been managing those projects well, they're bleeding out." — Chris Grey"If you don't put a deadline on something, your project is always at risk of falling behind by the longest single scheduling item you have.""Pride is probably the number one source of doom for a lot of these companies — the name is often on the building.""Most employees are essentially a statistic or a number for a company — they can be let go at any time.""Always have something on the side. If the thing takes off, run with it.""Growth alone doesn't create successful companies — but execution does." — Jonathan Goldhill (closing)"We were doing more with less — but less stress overall — because the PMs had the tools they needed to be successful."Connect and learn more about Kristopher Grey.https://www.linkedin.com/in/kristophergrey/If you enjoyed today's episode, please subscribe, review, and share with a friend who would benefit from the message. If you're interested in picking up a copy of Jonathan Goldhill's book, Disruptive Successor, go to the website at www.DisruptiveSuccessor.com
Nathan O'Connell spent nine years as a British military officer. Iraq, Syria, Afghanistan. He finished on the withdrawal with 16 Air Assault Brigade and left the forces two weeks before his 30th birthday with no sales background, no LinkedIn profile, and no recruitment experience of any kind.He got into rec-to-rec by accident. Spotted an advert on his wife's Instagram, joined R2R Global, and was given a cold desk focused on energy and renewable energy recruitment companies in London. Within 12 months he was their top biller in London. By year two, he was number one globally.Then he left. And instead of replicating what he'd done, he built the opposite. O'Connell Search Partners runs retained mandates with a maximum of four clients per geographic region. A team of four billing from Liverpool, covering London, New York, and Texas. In four months they invoiced £427,000.“I had nothing to sell at the time. So it was truly just a genuine interest.”This week on The RAG Podcast, Nathan breaks down how a military background shaped the most deliberate rec-to-rec model you will hear about this year.We cover:How a British military officer with zero sales experience became a global top biller in rec-to-rec within two yearsWhy Nathan limits his client base to four per region and how he selects between startups, scale-ups, and executive search boutiquesThe retained model in a space where most rec-to-recs work contingently, and what happens when mandates stay open for monthsHow he reconciles being anti-headcount whilst running a business that depends on agencies wanting to grow headcountWhy he gives clients 12 to 18 months grace before considering headhunting from businesses he has placed intoThe Atlas CRM and Device integration that he says saves a week of work per retained searchWhat he sees across dozens of agencies about which ones attract top talent and which ones struggleHis plan to personally bill £1m across three international locations with one researcherWhy he wants to be financially free by 45 and has no intention of building a big teamNathan O'Connell is not building a rec-to-rec empire. He is building a business designed around financial freedom, genuine relationships, and a small team where everyone bills.If you run a recruitment agency and you have ever wondered what a rec-to-rec headhunter actually thinks about your business, your culture, and your ability to attract talent, this episode will tell you.__________________________________________Episode Sponsor: Remote RecruitmentHiring shouldn't be slow, stressful, or expensive. That's why there's Remote Recruitment — the smart hiring partner for modern businesses. They don't just help you find great people. They help you access elite South African talent that's ready to deliver. No PAYE. No NI. No bloated overheads. Just trained, remote professionals who integrate seamlessly into your team. Their process handles everything: sourcing, shortlisting, onboarding, and retention. Fully managed. Fully supported. Fully remote. And now, Remote Recruitments has entered a new chapter. From ops to admin, sales to strategy, we're helping businesses scale smarter with people they trust, at a cost they can afford. Clients have seen: * Up to **60% productivity boosts** * **300% ROI** on BD roles * **30% faster completion** of operational tasks No overhead burden. No talent shortage panic. Just growth-focused hiring that makes business sense. Remote Recruitment is your flexible hiring solution for the modern era. **RAG Listeners:** Get 5% off your first hire + a free strategy session at www.remoterecruitment.co.uk/rag __________________________________________Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn.Spending thousands on Recruiter licences.Building connections. Posting content. Growing networks.But here's the question almost no one can answer:How much revenue is LinkedIn actually bringing into your business?Most founders have thousands of connections but no clear process to turn that attention into cash.That's the problem we solve.At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics.Our clients are turning LinkedIn into £100K–£300K in new billings within months, using their existing networks and a simple repeatable process.To show you how it works, we've created a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn:- Why most recruiters are getting zero measurable ROI from LinkedIn- How small, niche teams are generating consistent inbound demand- The 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channelSo fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
En el episodio anterior te estuve hablando de tres pilares fundamentales que cambian por completo las reglas del juego cuando queremos ir un paso más allá de los modelos de lenguaje convencionales: el RAG (la memoria), las habilidades y las herramientas. Hoy no nos vamos a quedar en las nubes de la teoría. Hoy nos arremangamos y vamos directos al turrón con un ejemplo totalmente práctico, porque al final lo que queremos es ver cómo se hace, cómo se lleva a cabo en nuestro propio servidor y cómo podemos empezar a sacarle partido a estas tecnologías desde ya.¿Por qué Rust es el rey del cacharreo con MCPs?Si buscas tutoriales en la red, verás que la inmensa mayoría de servidores MCP se desarrollan en Python. No me malinterpretes, Python es fantástico para escribir código rápido, pero en el mundo de los microservicios autohospedados y los contenedores tiene ciertos inconvenientes difíciles de ignorar. Python tarda más en arrancar y consume una cantidad considerable de memoria RAM por el simple hecho de existir.Por este motivo decidí programar todos mis MCPs utilizando Rust. Rust nos compila un binario nativo, limpio y directo. No hay intérpretes pesados de por medio. La latencia de respuesta es prácticamente cero, el consumo de memoria es insignificante y se ejecuta a una velocidad de vértigo. Además, gracias a editores modernos equipados con IA como OpenCode, una vez que logras pulir y estructurar tu primer MCP en Rust (por ejemplo, el del tiempo), crear el siguiente es sencillísimo. Solo tienes que proporcionarle a tu herramienta de código la estructura de tu primer desarrollo y pedirle que adapte esa misma lógica para conectar cualquier otra API o base de datos que necesites. ¡Es una delicia ver cómo escala el sistema!Bajo el capó: APIs públicas, Docker y QuadletsPara hacer realidad este MCP meteorológico, he combinado el poder de dos APIs públicas muy conocidas:Nominatim (OpenStreetMap): Como las APIs del tiempo necesitan coordenadas geográficas (latitud y longitud), Nominatim se encarga de traducir textos legibles como "Valencia" o "Tokio" en datos numéricos de localización.Open-Meteo: Recibe las coordenadas enviadas por el MCP y devuelve la previsión meteorológica actual, horaria o diaria sin necesidad de usar claves de API complejas ni registros restrictivos.Todo este flujo de datos se empaqueta de forma elegante en un contenedor de Docker y se gestiona mediante un Quadlet de Podman para garantizar que se inicie de forma nativa e integrada con el sistema operativo de nuestro servidor.Y más adelante nos sumergiremos en el fascinante universo del RAG local.Capítulos del episodio:00:00:00 Introducción y repaso del episodio anterior00:00:43 El problema de los modelos estáticos de IA00:01:29 El ejemplo práctico: Preguntando el tiempo00:03:20 Ahorro extremo de tokens con MCP00:04:49 Taller de IA agéntica y automatización con Slimbook00:06:22 Cacharreando con DeepSeek V4 Flash en OpenCode00:07:33 ¿Qué es y cómo funciona un MCP?00:09:13 Por qué desarrollo mis MCPs en Rust (y no en Python)00:11:13 Limpieza de datos y gestión de errores00:12:40 Cómo conectar un MCP a Open Web UI paso a paso00:14:18 Probando la previsión meteorológica en vivo00:15:37 El motor bajo el capó: Open-Meteo, Nominatim y Docker00:17:25 Codegraph: Analizando código para ahorrar tokens00:18:22 Próximo episodio: Guardar tareas persistentes con MCP To Do00:19:48 Otros MCPs listos para el taller de IA00:21:22 El futuro del podcast: RAG local, notas y más cacharreo00:22:50 Despedida, enlaces de interés y cierreMás información, enlaces y notas en https://atareao.es/podcast/799
01. Green Velvet, Meduza, Genesi, Essentia - La La Land (Record Mix) 02. Oneil, Kanvise, Ercodes - Every Single Day (Record Mix) 03. Basto! - I Rave You (Record Mix) 04. Vintage Culture, Gabss - Lost (Record Mix) 05. Robin Schulz, Francesco Yates - Sugar (Record Mix) 06. Maesic, Marshall Jefferson, Salome Das - Life Is Simple (Record Mix) 07. Loreen, Denis First - Tattoo (Record Mix) 08. Anyma, Joji - Beautiful (Record Mix) 09. Tiesto, Don Diablo - Chemicals (Record Mix) 10. Akcent, Sera, Misha Miller - Don't Leave (Kylie) (Record Mix) 11. Clean Bandit, Anne-Marie, David Guetta - Cry Baby (Record Mix) 12. Kris Kross Amsterdam, Eyelar - Mr. Lie To Me (Record Mix) 13. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 14. Eastblock Bitches, Ostblockschlampen - Sunglasses at Night (Record Mix) 15. Jerome Robins, Karsten Sollors - Don't Stop The Music (Record Mix) 16. Aize, Sasha - Waiting for Tonight (Record Mix) 17. R.I.O., Gonsu, Jenia Smile, Ser Twister - Shine On (Record Mix) 18. Ava Max - Don't Click Play (Record Mix) 19. Disco Culture, Scotty - Show Me Love (Record Mix) 20. Twocolors, Safri Duo, Chris De Sarandy - Head Above Water (Record Mix) 21. Alok, Ella Eyre, Kenny Dope, Never Dull - Deep Down (Record Mix) 22. Aaron Smith, Luvli, Krono - Dancin' (Record Mix) 23. Afrojack, Aloe Blacc - In My World 24. Lola Young, Ted Bear - Messy (Record Mix) 25. Zerb, Ty Dolla $Ign, Wiz Khalifa - Location (Record Mix) 26. Argy, Omiki - WIND (Record Mix) 27. Bob Sinclar, Kiesza - I Can't Wait (Record Mix) 28. Block & Crown, Atilla Cetin - How Many Nations (Record Mix) 29. Imanbek, Sofia Reyes, Luisa Sonza - NOT U (Record Mix) 30. Tim Berg - Seek Bromance (Record Mix) 31. Joel Corry, Rahh - Devotion (Sweetest Emotion) (Record Mix) 32. Hosh, 1979, Jalja, Slider & Magnit - Midnight (The Hanging Tree) (Record Mix) 33. Arei, Dj Lev - Du Hast (Record Mix) 34. Junior Jack - Stupidisco (Record Mix) 35. R3Hab - Right Here, Right Now (Record Mix) 36. Block & Crown - Mr DJ Give Me More 37. Morgan Page, Telykast - Dancing All Alone (Record Mix) 38. Garas, Eugenio Fico - Perfect 39. C Block, The Distance, Riddick - So Strung Out (Record Mix) 40. Hurts, Purple Disco Machine - Wonderful Life '25 (Record Mix) 41. Alessandro - Goes Deeper (Record Mix) 42. Shane Codd - Get Out My Head (Record Mix) 43. Richard Grey - At Night (Record Mix) 44. Leony, Calum Scott - Stay (Record Mix) 45. Trevor Daniel, Doublefast - Falling (Record Mix) 46. Cheat Codes, Jonita Gandhi - Last Night On Earth (Record Mix) 47. Skytech - The Rhythm (Record Mix) 48. Lady Gaga, Dj Dark - The Dead Dance (Record Mix) 49. Tiesto, Soaky Siren - Tantalizing (Record Mix) 50. David Guetta, Hypaton, Bonnie Tyler - Together (Record Mix) 51. Robin Schulz, Erika Sirola - Speechless (Record Mix) 52. Moby, Blond Ish, Kiko Franco - Natural Blues (Record Mix) 53. Jonas Blue, Jp Cooper - Perfect Strangers (Record Mix) 54. Joel Corry, Jennifer Lopez - Get Right 55. Gorgon City, Romans - Saving My Life (Record Mix) 56. Otnicka - Celebrate the Love (Record Mix) 57. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 58. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 59. Hugel, Ultra Nate - Free (You Got To Live) (Record Mix) 60. Keanu Silva, Don Diablo - King of My Castle (Record Mix) 61. Avalan Rokston, Alex Caspian - Something to Believe In (Record Mix) 62. Fisher, Aatig - Take It Off (Record Mix) 63. Mind Electric - Things You Say (Record Mix) 64. Playmen, Hadley - Luv You (Record Mix) 65. Alok, Khalid - Dive Into Me (Record Mix) 66. Firebeatz, Dubdogz - Give It Up (Record Mix) 67. Calvin Harris, Rag'N'Bone Man - Giant (Record Mix) 68. Dj Louis, Sweetpower - Billie Jean (Record Mix) 69. Basto! - Again & Again (Record Mix) 70. Oneil, Kanvise, Smola - The Riddle (Record Mix) 71. Diplo, Miguel - Don't Forget My Love (Record Mix) 72. Sam Feldt, Mc4D, Vize, Aloe Blacc - Hey Son (Record Mix) 73. Oliver Heldens, Djs From Mars, Jd Davis - Blue Monday (Record Mix) 74. Crazibiza - Fresh (Record Mix) 75. Annabell Kowalski - Hey Boy Hey Girl (Record Mix) 76. Bebe Rexha, Faithless - New Religion (Record Mix) 77. Joezi, Lizwi - Amathole (Record Mix) 78. Eben - Hollow (Record Mix) 79. Lost Culture, Morfi, Carine - Lean On (Record Mix) 80. Camelphat, Elderbrook - Cola (Record Mix) 81. Cozy Sky, Symono, Simon Riemann - Wicked Game (Record Mix) 82. Dj Feel, Desmind, Natalie Rise - Stereo Love (Record Mix) 83. Zerb, Sofiya Nzau, Izzy Bizu - Kumbaya (Record Mix) 84. Josh Fawaz - Like a Prayer (Record Mix) 85. Joel Corry, Pickle, Vula - Stay Together (Baby Baby) (Record Mix) 86. Darude, Glazur, Xm - Sandstorm (Record Mix) 87. Dj Quba, Sandra K, Ishnlv - Sexy Chick (Record Mix) 88. Bag Raiders - Shooting Stars (Record Mix) 89. All Things Break - Gravity (Record Mix) 90. Zhu - Faded (Record Mix) 91. Fisher, Flowdan - Boost Up (Record Mix) 92. Kungs - Never Going Home (Record Mix) 93. Yearboox - Graceland (Record Mix) 94. Armin Van Buuren - Es Vedra (Record Mix) 95. Hugel, Alleh, Yorghaki - una noche con hugel (Record Mix) 96. Maurizio Basilotta, Mf Productions - You're Not Alone (Record Mix) 97. Topcover - First Day (Record Mix) 98. Jaden Bojsen, David Guetta - Upside Down (Record Mix) 99. Filatov & Karas, Busy Reno - Au Revoir (Record Mix) 100. Martin Jensen, Fastboy - One Day (Record Mix) 101. Duke Dumont - Won't Look Back (Record Mix) 102. Lucas & Steve, Laura White - Are You Ready 103. Ben Delay - I Never Felt So Right (Record Mix) 104. Misha Miller, Alexvelea, Bodega - Bam Bam (Record Mix) 105. Axwell, Ingrosso - More Than You Know (Record Mix) 106. John Summit, Inez - crystallized (Record Mix) 107. Alok, Jess Glynne - Summer's Back (Record Mix) 108. Shouse, Vintage Culture - take me (to the sunrise) (Record Mix) 109. Tujamo, Azteck, Inna - Freak (Record Mix) 110. Claptone, Sea Girls, Henry Camamile - Put Your Love On Me 111. Dr Kucho!, Gregor Salto, Oliver Heldens - Can't Stop Playing (Record Mix) 112. Teriyaki Boyz, Hayat - Tokyo Drift (Record Mix) 113. Zerb, Sofiya Nzau - Mwaki (Record Mix) 114. The Chemical Brothers, De Soffer - Go (Record Mix) 115. Goodboys, Nu Aspect, Avaion - Blindspot (Record Mix) 116. Doechii, Dj Dark - Anxiety (Record Mix) 117. Lana Del Rey, Kevin Blanc - Young & Beautiful (Record Mix) 118. Weiss, Harry Romero - Where Do We Go (Record Mix) 119. Faros - Say It Right (Record Mix) 120. Zerb, The Chainsmokers, Ink - Addicted (Record Mix) 121. Pete Tong, Roro, Jules Buckley, The Essential Orchest - Rhythm Of The Night (Record Mix) 122. Alex Adair, Don Diablo, Cid - Make Me Feel Better (Record Mix) 123. Anyma, Joji - Beautiful (Record Mix) 124. Coldplay, Avicii - A Sky Full of Stars (Record Mix) 125. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 126. Alesso, Calvin Harris - Under Control (Record Mix) 127. Alesso, Calvin Harris - Under Control (Record Mix) 128. Felix Jaehn, Polina - Book Of Love (Record Mix) 129. Ava Max - Don't Click Play (Record Mix) 130. Vize - Wait (Alibi Blue) (Record Mix) 131. One-T, Ywy, Nika - The Way To Love (Record Mix) 132. Killteq, D.Hash, Vallhee - I Like It (Record Mix) 133. Cristoph - String Thing 134. Basto!, Yves V - Cloud Breaker (Record Mix) 135. Twocolors, Safri Duo, Chris De Sarandy - Head Above Water (Record Mix) 136. Oneil, Kanvise, Murana - Redlight (Record Mix) 137. Roland Clark, Mark Knight, James Hurr - Get Deep 138. Oceana, Bodybangers - Endless Summer (Record Mix) 139. Argy, Omiki - WIND (Record Mix) 140. J Balvin, Willy William, Gonsu, Jenia Smile, Ser Twist - Mi Gente (Record Mix) 141. Gregory Porter, Jonas Blue - Liquid Spirit (Record Mix) 142. Robin Schulz, David Guetta - On Repeat (Record Mix) 143. Regard, Years & Years - Hallucination (Record Mix) 144. Hugel, Topic, Arash, Daecolm - I Adore You (Record Mix) 145. Dj Dimixer, Favia - One of Us (Record Mix) 146. Cassian, Yotto, Da Hool - Love Parade (Record Mix)
Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl
Most AI conversations focus on models. The better conversation focuses on systems. In this episode, we continue our interview with Matt Levenhagen, exploring a practical challenge many developers are facing: integrating AI into business operations without creating costly chaos. The answer is not buying more AI tools. The answer is building an intentional AI Workflow Architecture. About Matt Levenhagen Matt is the founder and CEO of Unified Web Design, a web development agency focused on custom solutions, WordPress development, e-commerce, memberships, and business systems. His background as both a builder and agency owner gave him a unique perspective on where AI creates real leverage instead of superficial automation. Follow Matt on LinkedIn. AI Workflow Architecture Starts with Context Control One of the most important operational realities Matt discussed was token usage. Businesses rushing into AI often underestimate cost scaling. Every interaction with large models consumes resources, and poorly managed context windows dramatically increase operational expenses. Instead of treating AI like unlimited compute, Matt focused on controlling context intentionally. That included: Monitoring token usage Limiting unnecessary memory loading Structuring retrieval systems Using different models for different tasks Preventing oversized prompts This is a systems-thinking problem, not merely a coding problem. Developers who ignore architecture end up with bloated workflows that become financially unsustainable. The fastest way to make AI unprofitable is to send unnecessary context into every request. Why Retrieval Matters More Than Raw Memory A major breakthrough Matt discussed was implementing Retrieval-Augmented Generation (RAG). This matters because AI systems do not need all the information all the time. They need the right information at the right moment. That distinction completely changes system design. Without retrieval architecture: Costs increase Performance slows Outputs become less accurate Hallucinations increase Operational complexity grows RAG allows systems to retrieve semantically relevant information instead of dumping entire databases into prompts. This transforms AI from brute-force processing into intelligent retrieval. The future of AI operations will likely depend less on giant models and more on efficient information orchestration. AI Workflow Architecture Requires Layer Separation Another valuable concept from the conversation involved separating operational layers. Matt described balancing: Local storage Business memory External AI APIs Workflow automation SaaS integrations This layered architecture creates flexibility. Instead of locking the business into one AI provider, workflows remain adaptable. Different models can handle different workloads depending on cost, complexity, and accuracy requirements. This becomes increasingly important as pricing models fluctuate. Businesses relying entirely on one provider risk operational instability if pricing changes dramatically. Layer separation reduces that risk. The businesses that survive AI cost volatility will be the ones architected for flexibility instead of dependency. Why Embedded AI Features Often Disappoint Matt also discussed the growing wave of SaaS AI integrations. Every platform now markets AI capabilities: Project management tools Communication platforms CRM systems Design software Documentation systems Yet many users feel underwhelmed. The reason is architectural isolation. These tools only understand limited slices of operational context. They automate micro-tasks but rarely improve larger workflows. That creates a false impression that AI itself lacks value when the real issue is fragmented systems. AI becomes more useful as the organizational context becomes more connected. This is why developers building custom operational layers still maintain an enormous strategic advantage. AI Workflow Architecture Is an Operational Discipline The strongest insight from these episodes may be that AI implementation is becoming operational engineering. Success now depends on: Information structure Retrieval design Workflow sequencing Context prioritization Cost management Human oversight This moves AI away from novelty experimentation and toward infrastructure planning. Businesses that treat AI casually will likely accumulate technical debt quickly. Businesses that approach AI architecturally will build scalable operational leverage. AI is no longer just a development tool. It is becoming an operational systems discipline. Developers Must Learn Economic Thinking One overlooked topic in AI discussions is economics. Matt repeatedly referenced balancing capability with cost. This becomes critical because AI pricing models are still evolving rapidly. Businesses that ignore usage economics may accidentally build systems that become financially impossible to scale. Developers now need to think beyond: Can this be built? They also need to ask: Can this be sustained? Can this scale economically? Can context costs remain controlled? Can cheaper models handle simpler tasks? This represents a major evolution in modern software architecture. Review your current AI workflows and identify where unnecessary context or oversized prompts may be increasing costs. Conclusion AI Workflow Architecture is rapidly becoming one of the most important technical disciplines for modern developers. Matt Levenhagen's approach demonstrates that successful AI implementation is less about chasing the newest model and more about designing sustainable operational systems. The companies that gain long-term advantage from AI will not necessarily be the companies using the largest models. They will be the companies with the best architecture. 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I talk with Ramon Perez, Executive Director of the Digital Democracy Project this week. DDP is a nonpartisan nonprofit using secure mobile voting technology to give citizens a real-time voice in legislation. Ramon explains how the platform lets verified, registered voters weigh in on bills being debated in Congress and their state house—and then scores legislators on how closely their votes match what their districts wanted. We dig into how AI, including a RAG-powered chatbot called VoteBot, helps everyday citizens parse thousands of pages of complex legislative text. We also discuss digital security, participatory budgeting, and Ramon's ambitious goal of expanding the platform to all 50 state legislatures by 2027.Keywords: digital democracy, mobile voting, civic tech, legislative transparency, AI in government, Ramon Perez, Digital Democracy Project, VoteBot, participatory budgeting, voter engagement, legislator accountability, PolicyViz podcast, civic engagement, govtechSubscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthFollow Ramon Perez and the Digital Democracy Project at digitaldemocracyproject.org and download the Votes (VOATZ) app to participate.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com
Logan Kilpatrick and Tulsee Doshi of Google DeepMind join for a first-ever in-person episode recorded just days before Google I/O, covering headline launches like Gemini 3.5 Flash, the Omni video generation model, and the new Gemini Spark agentic product. The conversation digs into Google's strategic decision to lead with cost-adjusted efficiency over raw capability, how DeepMind now ships a full agent harness rather than bare models, and technical questions around context window limits and knowledge cutoffs. They also explore how the team thinks about model psychology, AI welfare, and recursive self-improvement. Sponsors: Brave Search API: Brave Search API gives AI agents a fast, independent search index for research, RAG pipelines, images, places, and fewer hallucinations. Get $5 in free credits at https://brave.com/search/api/?mtm_campaign=q2-26-cognitive-revolution Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Roboflow: Roboflow is an end-to-end visual AI platform that lets you turn raw ideas into fully deployed applications in just hours, powering breakthroughs like Blueprint Pro's floor-plan understanding tool. Read the full Blueprint Pro story and see how over a million engineers are building the next wave of visual AI at https://roboflow.com Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr
Emily Angeloni co-founded WOW Recruitment in Sydney at 22. No salary for six months. Just her laptop, working from a building set for demolition. Nine years later: a team of 18 across every major Australian state, New Zealand, and the United States. One of the most respected boutique sales and marketing agencies in the country.The year she became a mother, her co-founder told her he was leaving. She came back from maternity leave to a business that had saved every problem for her return, hit a serious road block, and stood in a strategy offsite thinking she could not do it anymore. She needed to rediscover her why.She did. WOW has since had its strongest period in nine years. But then she had a valuation done. And as she says herself: valuations can be a pretty uncomfortable truth.This week on The RAG Podcast, Emily breaks down what it actually looks like to lose a business partner, build something you are not sure you even want to continue, find the clarity, and watch everything change.We cover:- How a co-founder exit right before maternity leave forced Emily into the driver's seat for the first time- Why she seriously considered walking away from nine years of work, and what made her stay- The rookie hiring model that cost WOW its billing average, its culture, and its margins- How she redesigned her role entirely around what she loves: external focus, 18 people, zero internal ops- The five-year plan to build an asset with freedom of choice, not a job she cannot leave- Why a recent valuation was an uncomfortable truth and what it sharpened her focus on- What AI is actually changing for WOW's delivery model right now-Why personal brand has been her competitive advantage since 2017 and how it converts to real revenueLosing a co-founder. Building something you are not sure you even want any more. Then getting clear on the why and watching everything change.More founders have been down this road than would ever admit it. Emily is just prepared to say it out loud. Nine years in, she is building something worth having on her own terms, and working out exactly what that is worth.If you've ever wondered what it looks like to rebuild a business around a life you actually want to live, this episode has the blueprint.--------------------------------------------------------------------------------------------Admin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply looking for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/--------------------------------------------------------------------------------------------Every recruitment founder is investing in LinkedIn.Spending thousands on Recruiter licences. Building connections. Posting content. Growing networks.But here's the question almost no one can answer:How much revenue is LinkedIn actually bringing into your business?Most founders have thousands of connections but no clear process to turn that attention into cash.That's the problem we solve.At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics.Our clients are turning LinkedIn into £100K-£300K in new billings within months, using their existing networks and a simple repeatable process.To show you how it works, we've created a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn:Why most recruiters are getting zero measurable ROI from LinkedInHow small, niche teams are generating consistent inbound demandThe 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channelSo fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.
SEASON: 6 EPISODE: 27Episode Overview:Welcome to another episode of Becoming Preferred, the show where we dive deep into the strategies that help you level up your game and stay relevant in an ever-shifting marketplace.I am happy to introduce our guest today, a man who sits at the intersection of high-level software innovation and human-centric design. Ghazenfer Mansoor is the CEO of Technology Rivers, a powerhouse firm known for building everything from HIPAA-compliant healthcare tech to cutting-edge AI solutions.But Ghazenfer doesn't just build software; he understands the psychology of why we use it. He is the author of Beyond the Download, where he breaks down the alchemy of creating mobile apps that people actually love and share. Beyond his technical expertise, he's a fellow storyteller as the host of the Lessons from the Leap podcast, uncovering the raw failures and bold breakthroughs that define the entrepreneurial journey.Whether you're looking to scale your startup, automate your processes, or simply build a brand that resonates, you're going to want to take notes. Join me for my conversation with Ghazenfer Mansoor!Guest Bio: Ghazenfer Mansoor is the CEO of Technology Rivers, a software development firm recognized in Washington, D.C. for creating AI-powered solutions, innovative SaaS products, and HIPAA-compliant healthcare technologies. He helps startups and service businesses scale faster, automate processes, and build technology that drives real-world impact.As the author of Beyond the Download: How to Build Mobile Apps That People Love, Use, and Share Every Day, he shares practical insights and proven strategies to help app developers and entrepreneurs build mobile apps that not only acquire users but keep them engaged and growing.In addition to being a thought leader and speaker, Ghazenfer hosts the Lessons from the Leap podcast, where he uncovers the bold decisions, failures, and breakthroughs that shape entrepreneurial success.Resource Links:Website: https://ghazenfer.com/Business Website: https://technologyrivers.com/Product Link: https://www.amazon.com/Beyond-Download-Build-Mobile-People/dp/B0GP9T86DZ/Insight Gold Timestamps:03:17 Technology's changing with amazing speed04:58 To your point, it can be disruptive if we don't evolve05:07 In your latest book, Beyond the Download...08:03 How are you competing? Are you competing on people? Are you competing on pricing?09:21 You want to look at what are the bottlenecks in your business?13:44 I think security ought to be a feature, not a footnote16:02 The focus has to be building the product the right way the first time, so that you can focus on growing your business17:45 You build the foundation, you have the sketch, and then you gradually scale21:00 It's not easy for existing users of other products to just switch to your product22:56 So it's about what is missing in the industry26:04 Everybody is trying to get into the AI race, which is exciting, but at the same time, some people are scared to take that leap26:15 On our Lessons from the Leap (podcast)28:11 So I think as a society, we have to evolve our EQ...32:19 There's something called RAG, (Retrieval Augmented Generation)34:20 It's ghazenfer.com and technologyrivers.com is your company34:33 Beyond the Download: How to Build Mobile Apps that People Love, Use, and Share Every DayConnect Socially:LinkedIn: https://www.linkedin.com/in/gmansoor/Facebook: https://www.facebook.com/techriversYouTube: https://www.youtube.com/@technologyriversInstagram: https://www.instagram.com/techrivers/Lessons from the Leap Podcast: https://ghazenfer.com/lessons-from-the-leap/Email: GMansoor@TechnologyRivers.comSponsors: Rainmaker LeadGen Platform Demo: https://calendar.summit-learning.com/widget/booking/JKItVP7WErmCBjU2cCIxRainmaker Digital Solutions: https://www.rainmakerdigitalsolutions.com/
Trouble In Paradise: Has the love between Corey Feldman and Adrien Skye ended? It appears she has her own apartment and is moving on singing break up songs. Or is this all for publicity?Corey and Stephen Baldwin: We delve more into the Corey Feldman appearance on the Stephen Baldwin podcast. We got hip hop stories, spyglasses and more.Corey's Twitter: Corey is on one again, accusing Michael Jackson of ripping him off and telling people to suck his dong.COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, REAL ONES!, MEMBERS!, STREAMATHON!, OBUNGA!, THOUSPISS!, ROAD TO 50K!, NMAN!, HEEL TURN!, HEEL NMAN!, TOXIC TOXIC TOXIC!, PACT BREAKING!, SELF REPORT!, PO BOX!, WEEBLES!, SUPERTIPS!, GOSSIP!, GOTHSIP!, RAG!, MINIMALIST!, ARIANA GRANDE!, SAD!, BREAKUP!, COZY!, ADRIEN SKYE!, ADRIEN AGE CHECK!, IN HONOR!, KISS THE RING!, APOLOGY!, PAY RESPECTS!, TRIPLE H!, FORGIVEN!, PAST MISDEEDS!, STEPHEN BALDWIN!, PODCAST!, RAW AUDIO!, AI MUSIC INTRO!, ONE BAD MOVIE!, DR. DRE!, ICE CUBE!, CURTIS YOUNG!, AYO!, PAUSE!, CAM'RON!, MASE!, ART!, LIVE!, VAPID!, I DIG MUSIC!, STORY OF HIS CAREER!, EXCITING!, WORLD TOUR!, LIES!, DOCUMENTARY!, CALLERS!, GUN UNDER MY PILLOW!, WHY GOD WHY!, COREY'S TWITTER!, NOTHING TO FEAR!, MICHAEL JACKSON!, EDDIE MURPHY!, WHAT'S UP WITH THE YOUTH!?, TELL THE TRUTH!, PUNCHLINE!, TWISTED!, SHE WAS AWFUL!, MADONNA!, TYLER!, DARK VIBE!, SUCK MY BIG DONG!You can find the videos from this episode at our Discord RIGHT HERE!
Andrew Lee, CEO of Tasklet, returns for his fourth appearance to share how his team has once again rewritten their entire agent stack, now emphasizing file system context, agentic search, and multi-resolution summarization. The conversation digs into the strategic tension of competing with your own supplier, as Anthropic's Claude Max accounts offer direct customers far more tokens than API partners get at the same price. Andrew also lays out his framework for the only three types of software companies that will survive the AI transition and discusses Tasklet's evolution toward becoming a model-agnostic horizontal platform. Sponsors: Brave Search API: Brave Search API gives AI agents a fast, independent search index for research, RAG pipelines, images, places, and fewer hallucinations. Get $5 in free credits at https://brave.com/search/api/?mtm_campaign=q2-26-cognitive-revolution Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Roboflow: Roboflow is an end-to-end visual AI platform that lets you turn raw ideas into fully deployed applications in just hours, powering breakthroughs like Blueprint Pro's floor-plan understanding tool. Read the full Blueprint Pro story and see how over a million engineers are building the next wave of visual AI at https://roboflow.com Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
‘I've always been the youngest, the new kid, the underdog. It helps me not be intimidated'. Arvid Lindblad, the fourth-youngest F1 driver in history, is full of confidence. In his first race he fought wheel-to-wheel with World Champions. The Racing Bulls driver has made a fast start to his first season in Formula 1. He always believed he'd race at the top. He's living his childhood dream and he's loving it.Arvid tells Tom Clarkson how he went from watching F1 on TV aged 4 to racing in F1 aged 18, inspired and supported by his family, his coach – the racing driver Olly Rowland – and Helmut Marko, Red Bull's former Motorsport Advisor. He explains the coaching which helped him beat more experienced drivers as he progressed up the motorsport ladder in ever-faster cars. Arvid also looks back to his Formula 1 debut in Australia, where he overtook Lewis Hamilton and Lando Norris at the start and scored points at the end, and relives the stunning qualifying lap in Japan which saw him eliminate Max Verstappen.Listen to more Official F1 PodcastsListen to the latest episodes of F1 Nation and F1 ExplainsThis episode is sponsored by:Bitdefender: visit bitdefender.com to learn more about how Bitdefender supports Ferrari to stay ahead of cyber threats and how you can make your digital life safer Rag&Bone: for a limited time, our listeners get 20% off their entire order with code GRID at Rag-Bone.com CarGurus: go to cargurus.co.uk for complete vehicle details without any surprises
This episode focuses on how to manage your game after a long layoff, particularly an offseason. Thanks to our sponsors PuttOUT, HackMotion, Rag & Bone, Warby Parker • PuttOut Golf offers some of the most intuitive putting training aids available, and the AirBreak putting green stands out for its ability to simulate real course conditions. Unlike standard flat mats, it uses adjustable pumps to create a variety of slopes, helping golfers better understand the relationship between speed and line while keeping practice engaging. The new AirBreak Max expands on the original with a larger surface and putts up to 10 feet, and Sweet Spot listeners can save $120 on the Max or $60 on the original at puttout.golf/sweetspot. • If you've listened to us for a while, you know we're big on meaningful practice and clubface control—and that's why we recommend HackMotion. It acts like a virtual coach, analyzing your swing, identifying issues like flipping or casting, and giving you personalized drills with real feedback. Used by over 70,000 golfers worldwide, it's one of the most effective tools we've seen for improving your swing. Right now, you can get up to $50 off the new HackMotion 4, or $70 off the previous version, while supplies last at https://hackmotion.com/sweetspot • Rag & Bone makes premium denim designed for both comfort and durability, with a wide range of fits and styles to suit different preferences. Their jeans feature built-in stretch and a proprietary multi-step process that creates rich color and a broken-in feel that improves over time. With over 20 years of craftsmanship behind their products—plus high-quality essentials like tees, knits, and jackets—Rag & Bone focuses on wardrobe staples made to be worn for years. Use promo code SWEETSPOT to get 20% off your first order at https://rag-bone.com/ • Warby Parker makes it easy to find stylish, well-fitting glasses without the usual hassle or high prices, with frames starting at $95. Their app allows you to virtually try on different styles from home, so you can see exactly how they'll look before ordering, while maintaining a premium feel and quality. With over 300 retail locations, options for glasses, sunglasses, contacts, and eye exams, plus coverage from many major insurance plans, Warby Parker offers a streamlined, flexible eyewear experience. Get 20% off any additional pair of prescription pairs at warbyparker.com/SWEETSPOT Learn more about your ad choices. Visit megaphone.fm/adchoices
So Palantir weirded everyone out by posting a weird 22-point manifesto on twitter. We're going over the details of that and the ever-encroaching surveillance state we're all going to be stuck in before we know it. NEW MERCH OUT! Get 10% off when you sign up and also get bonus content, ad-free versions and more plus your first 7 days free at https://benandemilshow.com ***THE SOUTHWEST COMPANION PASS IS BACK GET IT HERE: https://www.cardratings.com/bestcards/featured-credit-cards?src=691608&shnq=520080,4028088,4048122,4028085,3006151,4048149,4028089,4048084&var2= The newest acid video is out now so check it out! https://youtu.be/7vkFY3f5kkw Give this video a thumbs up if you enjoyed it! And please leave us a comment! It helps us! ***Ben's new movies and tv podcast with Dillon is OUT NOW! GO WATCH the latest episode on our TOP MOVIES OF 2025: https://youtu.be/tbC-cMqcby8?si=tO0NK0PmpN2187ir **CHECK OUT EMIL'S LIVESTREAMS HERE: https://www.youtube.com/emilderosa __ SOME OTHER VIDEOS YOU MAY ENJOY: That's Cringe of Cody Ko: https://youtu.be/dTbEk0pVh2w Our AUSTIN VIDEO: https://youtu.be/yGSs56bFzRU Our episode with Kyla Scanlon: https://youtu.be/cIHWkY35cuc Big Tech is out of ideas (ft. ED ZITRON): https://youtu.be/zBvVGHZBpMw Arguing with a millionaire (ft. Chris Camillo): https://youtu.be/1ZUWTkWV_MM We bought suits HERE: https://youtu.be/_cM1XqA9n2U ***LINK TO OUR DISCORD: https://discord.gg/CjujBt8g ***Subscribe to Emil's Substack: https://substack.com/@emilderosa ***Trade with Ben at https://tradertreehouse.com __ RAG & BONE: Upgrade your denim game with rag & bone—get 20% off sitewide with code BAES at https://www.rag-bone.com #ragandbonepod HIMS HAIR: For simple, online access to personalized and affordable care for Hair Loss, ED, Weight Loss, and more, visit https://hims.com/baes TIMESTAMPS: 00:00-05:23 Intro, Ben's neighbors, boomer chain emails 05:23-15:35 Big tech's obligations 15:35-17:40 Rag & Bone ad 17:40-31:13 The draft, priests, be nice to politicians 31:13-33:00 Hims ad 33:00-53:00 Repeating history, defanging Germany, be nice to Elon Musk, Pokemon collectors 53:00-1:13:30 YEAAAAH, butthurt billionaire 1:13:30-1:22:09 Ben eats dog food, disproving accusations __ Follow us on instagram! @ benandemilshow @ bencahn @ emilderosa Learn more about your ad choices. Visit podcastchoices.com/adchoices
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