Podcasts about Open source

Sarah Schädli und Jasmin Stettler von der Glitzerfabrik erzählen im Studio ihre außergewöhnliche Lebensgeschichte: vom radikalen Neuanfang mit null Franken auf dem Konto über das Leben im Wohnmobil bis hin zum Aufbau ihres eigenen Unternehmens. Wir sprechen darüber, warum die klassische Ehe oder traditionelle Rentensysteme keine verlässliche Altersvorsorge mehr bieten, wie Sparen im Fiat-System durch Inflation entwertet wird und weshalb Bitcoin weit mehr ist als ein Investment – nämlich ein Werkzeug für echte Selbstbestimmung, Eigenverantwortung und Persönlichkeitsentwicklung. - GlitzerfabrikLEADING PARTNER

After seven years of being in development, Tari finally launched in 2025. Why did it take so long and how many of the initial promises were delivered? Naveen Jain, Mr. Fox & Marguerite de Courcelle join the show to offer their best answers. 0:00 - Intro 0:47 - Welcome to S17 E45: Naveen, Mr. Fox & Marguerite 3:02 - What Is Tari? Mimblewimble, Four Mining Lanes & the Oodle 5:21 - Can Tari Compete With Solana? 6:32 - Meme Coins vs. Real Businesses 8:22 - The Ticketmaster Killer That Wasn't 9:18 - Big Neon, the Pandemic & the Spotify Exit 11:20 - How Dependent Is Tari on Monero's Hashrate? 12:26 - Why Four Mining Lanes and Two Tokens? 14:48 - The One-Way Peg: Burning XTM for the L2 15:43 - Tari Script on L1, Full Programmability on L2 16:13 - Sponsors: Layer 2 Labs & Braiins Hashpower 19:24 - The C29 Consensus Bug & How the Lanes Held Up 23:02 - The Beam Question: Great Tech Is Not Enough 26:18 - What Is Tari's Killer Use Case? 29:21 - Programmable Confidentiality: Securities, Escrow & Stigma Markets 30:15 - Fox's Cat Shirt & the $100 Monero Giveaway 31:36 - Sharing Security With Monero: The Underappreciated Part 34:09 - Did the Monero Community Stick Around? 36:52 - From White Papers to Tickers: The Decline of Ideas 39:14 - Naveen's Linkin Park Lesson: The Lowest Common Denominator 43:11 - Build Contests, Encrypted Messengers & Private Voting 44:48 - Learning English From Linkin Park 47:31 - The Meteora Bank Vault Listening Party 49:27 - Mining Q&A: Pool Censorship & RandomX Changes 53:19 - The Pool That Kept the XTM 53:38 - Will Other Privacy Chains Copy the Model? 54:52 - Sponsors: SideShift & Orange Rock 56:31 - Marguerite (Coin Artist) Joins 57:45 - From the Ethereum ICO to MaidSafe to Decred 1:00:45 - The First Crypto Puzzle Ever: Dark Wallet, Amir Taaki & Cody Wilson 1:02:12 - There Is Treasure Hidden in Tari's Blockspace 1:02:55 - What Marguerite Does on the Tari Council 1:05:52 - Building for Humans or for AI Agents? 1:09:09 - Giveaway Mechanics: Three Numbers, One Winner 1:10:00 - Attracting Normies: Blink, Browser Agents & Small Beginnings 1:13:00 - Tari Universe: Mining Without the UX Nightmare 1:14:28 - Where to Buy XTM (Or Just Mine It) 1:16:15 - Mining Monero and Tari at the Same Time 1:17:41 - Satoshi's Email to Laszlo 1:18:11 - Naveen's Number Is 43 & He Has to Go 1:18:31 - Does Gaming Belong on a Blockchain? 1:19:29 - Neon District, Axie & the PFP Ceiling 1:21:40 - Vibe Coding Three Games in One Weekend 1:25:01 - Online Poker, Frozen Accounts & the First Bitcoin Gamblers 1:27:42 - The Winning Number: 40 1:30:00 - If Monero and Tari Were Roommates 1:31:31 - Privacy-First Games: Fog of War & Front-Running 1:39:21 - Skybox, Modding Communities & Creator Royalties 1:43:39 - Mr. Fox Is a Furry (the Third One on This Podcast) 1:45:46 - The Real Use Case: Commissioning Art Without Bank Permission 1:47:46 - The Manicure Question 1:48:37 - Lessons From Neon District & Loot Boxes 1:50:56 - Vlad's Brother, the CS:GO Knife & the Rome Vacation 1:53:40 - Top Three Games of All Time 2:01:45 - John Carmack & the Open Source Doom Engine 2:03:11 - Tim Sweeney: The Cypherpunk Behind Unreal 2:06:59 - Amir Taaki's Lara Croft Origin Story 2:08:01 - Vitalik Rage-Quitting WoW & Colored Coins 2:11:06 - Bringing the Soul Back to Open Source 2:12:55 - What Fox Wants Built First: Payments Without Permission 2:14:55 - The Community Takeover & the Core Contributor Program 2:18:09 - Councils, Leaders & the Decentralization Paradox 2:22:16 - Andreas Antonopoulos, Vitalik & the Need for Faces 2:25:42 - The NATO Problem: Organizations Without a Purpose 2:28:49 - Steemit, Twitter Tip Bots & Make It Rain Doge 2:31:22 - Counterparty, Rare Pepes & NFT Archaeology 2:34:41 - When VCs Discovered OpenSea 2:36:30 - Infinite Fleet & the Game That Never Shipped 2:39:37 - John Romero: Why Play-for-Money Kills Fun 2:43:05 - Cats: Hunter & Salieri 2:46:43 - A Mini-Game for Vlad's Website 2:47:24 - The Trojan Horse Thesis Revisited 2:48:24 - The Builders Never Left 2:49:02 - Different Leaders for Different Eras 2:52:08 - Why Seven Years? And How Long Until the L2 Matters? 2:54:41 - How to Start Mining Tari Today 2:55:41 - Outro

On this episode of the FINOS Open Source in Finance Podcast, host Grizz Griswold sits down with James Calise (Pre-Sales Engineering Lead) and Travis Liles (Cloud Architect) from Oracle Cloud Infrastructure (OCI) Financial Services. Together, they explore how Oracle is shifting from proprietary models to open-standards-based cloud architecture to help financial institutions prevent vendor lock-in, meet strict DORA multi-cloud resiliency mandates, and scale grid computing across on-premise and multi-cloud environments with OpenGris. Plus, they share how autonomous AI agents are becoming the next-generation operational management layer for open-source enterprise platforms.

¿Hacia dónde va internet cuando el tráfico generado por bots supera al humano y el modelo SEO tradicional se tambalea? La inteligencia artificial está transformando a un ritmo vertiginoso la forma en que construimos webs, consumimos noticias y analizamos métricas de negocio. En este episodio nos acompaña José Ramón Padrón ("Moncho"), responsable de comunidades hispanas y crecimiento en Automattic, la empresa matriz detrás de gigantes como WordPress.com, WooCommerce y Tumblr.

Hoe leer je nou zelf goed omgaan met robots, want dat we er in de toekomst steeds meer mee te maken gaan krijgen, dat is wel duidelijk. Pollen Robotics probeert een slag te slaan tussen de enthousiastelingen, de studenten en de onderzoekers. Pollen Robotics, onderdeel van Hugging Face, bouwt daarom toegankelijke, betaalbare robots: de Reachy Mini en Microduck. Hoe je de techniek achter deze kleine robots beter kan begrijpen, hoe je Microduck skills kan leren en hoe veiligheid gewaarborgd blijft, bespreken we allemaal met Tom Mulder, community growth and support management bij Pollen Robotics, en stelt Reachy Mini zichzelf ook even voor. Je hoort het in deze nieuwe aflevering van De Grote Tech Show met Joe van Burik en Ben van der Burg. Vragen, opmerkingen of suggesties? Mail ons! Op: degrotetechshow@bnr.nl De Grote Tech ShowDe Grote Tech ShowTech verandert onze wereld, in De Grote Tech Show (DGTS) hoor je hoe. Joe van Burik en Ben van der Burg spreken met innovatieleiders en analyseren de techwereld, van AI tot cybersecurity en social media tot quantumcomputers. TechpodcastDe Grote Tech Show (DGTS) is dé techpodcast (en radioshow) voor iedereen die technologie en innovatie echt wil begrijpen. Over AI (of: kunstmatige intelligentie), chips, cloud, cyberveiligheid, social media, quantum en entertainment. Hier hoor je hoe technologie de wereld verandert en wat dat betekent voor bedrijven, investeerders en iedereen in de samenleving. Bij DGTS krijg je de analyses, inzichten en interviews die ertoe doen. Met diepgaande gesprekken en scherpe analyses brengen we de belangrijkste technologische ontwikkelingen in kaart. InnovatiesElke week spreken we kopstukken in de techwereld: ceo's, hoogleraren, ondernemers en investeerders die werken aan de innovaties van morgen. Wat betekenen de nieuwste AI-modellen voor werk en creativiteit? Hoe blijven Europese startups concurreren met het nog altijd machtige Silicon Valley en het ondoorzichtige China? Dit zijn geen oppervlakkige interviews, maar diepgaande gesprekken waarin we de hoofdrolspelers spreken die écht impact maken. De technologische revolutie is in volle gang en beïnvloedt elk aspect van ons leven—van de manier waarop we werken en communiceren tot de geopolitieke machtsverhoudingen. Daarom brengen we niet alleen de technologische kant in beeld, maar ook de economische en maatschappelijke implicaties ervan. Naast de grote innovaties kijken we naar de bedrijven die deze ontwikkelingen vormgeven. Wat is de strategie van big tech-bedrijven zoals Google, Apple, Microsoft en Meta? Hoe verandert de concurrentiestrijd tussen Nvidia, AMD en Intel de chipmarkt? Wat betekenen nieuwe wetten en regels in Europa en de VS voor de toekomst van technologie? AnalysesDaarnaast hoor je bij De Grote Tech Show, exclusief als extra podcast elke week, hoe Joe van Burik en Ben van der Burg de week in tech doornemen. Ze analyseren het laatste nieuws, plaatsen de ontwikkelingen in perspectief en geven scherpe inzichten over wat er écht speelt. Van de doorbraken in AI / kunstmatige intelligentie en de opkomst van nieuwe sociale mediaplatformen tot de impact van geopolitieke spanningen op de halfgeleiderindustrie. Regelmatig schuift een gast uit het netwerk aan om extra expertise te bieden en het debat te verdiepen. Door de combinatie van journalistieke scherpte, technische kennis en een kritische blik ontstaat een programma dat verder gaat dan de headlines en technologie in een bredere context plaatst. AIOf het nu gaat om de risico’s en kansen van AI-technologie of de positie van Europa in de wereldwijde technologische concurrentiestrijd, De Grote Tech Show biedt de achtergrond, de nuance en de inzichten die nodig zijn om deze ontwikkelingen echt te begrijpen. Dit maakt het programma onmisbaar voor professionals in de techsector, beleggers die strategische beslissingen willen nemen en iedereen die wil weten welke innovaties onze toekomst vormgeven. Met de combinatie van exclusieve interviews, deskundige duiding en een kritische kijk op innovatie biedt DGTS een unieke mix van diepgang en actualiteit. Over de makers:Joe van Burik volgt en analyseert de belangrijkste ontwikkelingen in tech, met scherpte, tempo en humor. Je hoort hem dagelijks op BNR Nieuwsradio met het belangrijkste nieuws in de Tech Update en hij presenteert De Grote Tech Show. In het bijzonder volgt Joe al twee decennia de wereld van videogames, waarover hij met bevlogen collega's en gasten praat in de podcast All in the Game. Eerder werkte hij als auto(sport)journalist voor diverse andere media en schreef het boek Formule 1 voor Dummies. Ben van der Burg is techondernemer en voormalig topschaatser. Ben is bezeten door technologie en wordt enthousiast van gadgets, elektrische auto's, goede businessmodellen en de toekomst. Naast De Grote Tech Show is hij ook wekelijks te horen als presentator van De Technoloog. Ook schuift hij regelmatig aan bij Vandaag Inside, Goedemorgen Nederland en andere talkshows, om te praten over het laatste nieuws rond technologie. Rosanne Peters is redacteur van De Grote Tech Show en De Technoloog. Ook is zij te horen in de Tech Update tijdens De Ochtend- en Avondspits. Daniël Mol is redacteur en samensteller van De Grote Tech Show. Hij presenteert zelf bij BNR de Cryptocast en maakt ook De Technoloog. Tevens is hij de vaste vervanger van Ben in De Grote Tech Show; Joe wordt bij afwezigheid vervangen door Iwan Verrips, co-host en eindredacteur van de Ochtendspits met Bas van Werven op BNR Nieuwsradio.See omnystudio.com/listener for privacy information.

Open source may have begun in the world of software, but today it's reshaping how we think about repair culture, circular economies, and living within nature's boundaries. Ally Richardson and Frank Cordova explore how shared knowledge can reconnect us with ecological limits and empower communities to practice meaningful environmental stewardship. If you're ready to rethink waste & repair, and help build a regenerative future, this conversation offers a clear path forward.Resources Mentioned: Guest Frank Cordova's Website: osl8.life CreativeCommons.org Opensourceecology.org Opensoure.com Red Hat Enterprises- Linux Distribution for Large Company Support BSD Unnex Wordpress for Website Building Open Source Browser – Firefox Linnux Zorin Linux Mint Fedora Linux Nobara Linux Right To Repair Article Davinci Resolve CDPHE Solid waste management data and reports found here Repair Café. Pikes Peak Library District 21C Upcoming Events: Pikes Peak Permaculture will be making seedballs at these two family friendly kid's events: 2026 Cool Science Carnival Day OCTOBER 3, 2026 Visit www.coolscience.org for all the activities that day and beyond! 2026 Fall Fest at First & Main October 10th, 2026 from 11am to 3pm Visit firstandmaintowncenter.com/2026-fall-fun-fest/ Permaculture Design Certification CoursePikes Peak Permaculture is now accepting registrations for our next permaculture design course! We will be starting in March 2027. Work trade and financial aid available. Have questions? See our website or reach out, we're happy to help you make an informed decision. Visit pikespeakpermaculture.org/permaculture-design-course/This episode is brought to you by Pikes Peak Permaculture, a 501(c)(3) nonprofit dedicated to teaching the ethics and principles of permaculture design in Southern Colorado. Permaculture is all about working with nature rather than against, to regenerate land, water, and food systems, and build resilient communities for generations to come. Learn more about their work with schools, organizations, and community members at pikespeakpermaculture.orgThe following environment/sustainability organizations in the Pikes Peak region collaborate to produce the Peak Environment podcast about environmental stewardship, sustainable living and enlightened public policy in the Pikes Peak Region.Peak Alliance for a Sustainable Future https://peakallianceco.org/Pikes Peak Permaculture https://www.pikespeakpermaculture.org/GrowthBusters: https://www.growthbusters.orgKeep up with all the organizations and events making our area a better place to live. Follow on your favorite podcast app so you don't miss an episode.

Zwischen Gold- und Pfnüselküste steht das Börsenradio-Mobil direkt am Zürichsee. Zu Gast im Rahmen des Züricher Börsentages: Christoph Gum, CEO bei Private Alpha. Der wiki-Trader über das KI-Rennen und bevorstehende, nie zuvor gesehene Wachstumszahlen: "Nichts und niemand bremst - ganz im Gegenteil. Ich erwarte mehr Effizienz. Ich sehe keinen Unterschied zum Klimawandel. Die Börse steht genau dort, wo sie steht, mit diesem Triple Whopper an schlechten Nachrichten. Ich selbst bin ultra-ultra-ultra-bullisch. Für mich kommt jetzt der Quadruple Whopper an guten Nachrichten!" Beim milliardenschweren Geschäft werden europäische Unternehmen eher nicht oder nur am Rande mitwirken können. "Wir wollen immer die besten Modelle, die von Open Source oder von den Frontier-Models entwickelt werden." Das wikifolio CAESAR von Christoph Gum umfasst ein festes AI-Leaders-Universum von rund 140 KI-Unternehmen. wikifolio: https://go.brn-ag.de/555

In der dritten Folge unserer Masterclass analysiere ich gemeinsam mit Leon Wankum die Mechanismen von Digital Credit und wie Unternehmen wie MicroStrategy oder Metaplanet Bitcoin als primäres Reserve-Asset in die Bilanzen der Kapitalmärkte integrieren. Wir vergleichen Saylors Ausgabe von Vorzugsaktien mit dem Geschäftsmodell klassischer Versicherungen, beleuchten den Unterschied zwischen Financial Engineering und tatsächlichen Betrugsmodellen und ordnen anhand des Regressionstheorems von Ludwig von Mises ein, warum Bitcoin langfristig Staatsanleihen als Zinsbenchmark verdrängt. Zudem diskutieren wir, weshalb eigene Full Nodes und kompromisslose Self-Custody trotz institutioneller Finanzprodukte das unverzichtbare Fundament bleiben – abgerundet durch einen Ausblick auf Rechenzentren und Bitcoin-Mining im Erdorbit.

KDE's fight over AI generated code explodes at exactly the moment Plasma is making some of its biggest changes in years.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Ryan Marsh of thestack.io to explore what it really takes to build production AI systems. They discuss how production AI has evolved from simple prompt-to-API demos into complex systems requiring evaluation suites, human-in-the-loop feedback mechanisms, and sophisticated approaches to handling context and data retrieval. The conversation covers the challenges of domain mapping, the fundamental difficulty of translating messy human business processes into structured systems, and the role of ontologies in AI development. Ryan and Stewart also examine the limitations of LLMs, the debate between specialization versus generalization in AI models, consciousness and cognition in system design, and the regulatory landscape facing AI companies. They touch on infrastructure constraints, the democratization of AI through open source models, and whether we'll eventually hit a ceiling where human intelligence can no longer distinguish between increasingly capable AI models. Key Insights1. Production AI systems today fundamentally differ from demos through their reliance on comprehensive evaluation suites that function like unit tests to measure and maintain quality, though they cannot be as deterministic. The key distinction is that production systems require clearly defined metrics for what good looks like, along with feedback mechanisms that allow the system to evolve over time. Without this foundational understanding of success metrics and continuous improvement processes, a system is not truly production-ready regardless of how many users it serves.2. The fundamental challenge in building production AI systems is not the technology itself but rather mapping business domains into structured formats that models can work with effectively. This problem of translating messy, subjective human processes and language into precise specifications has plagued software engineering for decades. Different people within organizations use the same words to mean completely different things, and humans naturally operate with assumed context and imprecision that must be explicitly defined for AI systems to function reliably.3. Large language models excel at generalization but struggle with specialization, which creates friction in production environments where specific outputs or styles are required. While they can code in any programming language, getting them to write code exactly the way a particular engineer wants remains extremely difficult. This explains why professional documentation and specialized coding tasks often require extensive prompting and fighting with the models, as they naturally gravitate toward their trained patterns rather than highly specific user preferences.4. Modern production AI systems primarily solve classification problems wrapped in natural language interfaces rather than requiring true open-ended cognition. The models work best when they can leverage reasoning over provided information to make verifiable decisions, but they still lack common sense despite their vast knowledge. Success comes from teaching models everything about your specific domain and what good and bad outcomes look like, rather than relying solely on their general intelligence.5. Context retrieval in production AI systems is fundamentally a data storage, search, and retrieval problem that has been solved many different ways throughout computing history. The appropriate solution depends entirely on the type of data being accessed, whether through vector databases, graph databases, relational databases, or even simple text search. The harnesses and frameworks for orchestrating AI agents have matured significantly, making the real challenge the quality and structure of the data being fed to these systems.6. Human-in-the-loop feedback mechanisms are essential for production AI because models will inevitably encounter situations they have not been trained to handle. When confidence is low or novel scenarios appear, systems should flag these for human review rather than proceeding blindly. The feedback provided during these interventions must be captured and scored so it can be incorporated into the permanent behavior of the system, creating a continuous improvement cycle similar to how model vendors perform reinforcement learning on their base models.7. The rush to regulation in the AI industry is driven primarily by the fact that these companies are currently completely exposed to existing consumer protection and liability laws with no legal precedents to protect them. If AI agents cause harm, companies could be sued into oblivion under current law. By establishing compliance frameworks through regulation, these companies can create carve-outs and exemptions that limit their liability when they follow prescribed rules, similar to how heavily regulated industries like airlines and banking have become nearly impossible to enter due to compliance requirements. Timestamps00:00 Welcome and introduction to Ryan Marsh discussing production AI systems and what companies need to understand about building them at scale05:00 The cognitive load challenge of working with invariants and probabilistic systems, discussing how LLMs function like PhD students without common sense10:00 Building eval suites to measure AI performance, handling the long tail of edge cases in complex contracts using human-in-the-loop feedback systems15:00 Context as a data retrieval problem and the fundamental challenge of mapping business domains when humans struggle with imprecision and ambiguous language20:00 How different departments use the same words with different meanings and why LLMs generalize well but don't specialize effectively for specific coding styles25:00 The ontology debate and intractable problem of mapping subjective human systems to rigid structured formats with diminishing returns on perfect mapping30:00 Why humans struggle distinguishing what is from what ought to be and the challenge of creating SOPs when companies lack updated documentation35:00 The liability exposure AI companies face and why they're begging for regulation to protect themselves from existing consumer protection laws40:00 Open source knowledge transfer between Chinese and American labs, chips and power as the real constraint not algorithms for frontier models45:00 Reaching intelligence ceiling where specialists can't distinguish state-of-art models and fundamental physical laws limiting LLM scaling through layered optimization strategies LinksWebsiteX

The boys are back! Tonight we talk about AI use in FOSS projects ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Nvidia mag dan het grootste bedrijf van de wereld zijn: menig belegger vindt Nvidia nog steeds spotgoedkoop. Zeker als je kijkt naar wat je betaalt voor de verwachte winsten de komende jaren. Dat vindt het bedrijf kennelijk zelf ook, want Nvidia gaat nóg meer eigen aandelen inkopen dan het al van plan was. Het gaat om historische bedragen. Wat dat voor het aandeel kan betekenen, bespreken we deze aflevering. Maar er is meer, want na weken van zorgen over de ontwikkeling van AI, claimt Nvidia nu de oplossing: ze komen met software dat naar eigen zeggen kan voorkomen dat AI-agents op eigen houtje malafide praktijken uitvoeren. Software die, zo meent het bedrijf, een incident als bij Hugging Face had kunnen voorkomen. Hoor je ook over SK Hynix: het aandeel levert in nu duidelijk wordt dat een dochter van het bedrijf naar de beurs wil. Solidigm, een producent van geheugenchips. Het kan een van de grootste chipbeursgangen ooit worden; waarom beleggers in SK Hynix er toch van schrikken, bespreken we ook. Hoor je ook nog over: De ceo van ASML: die beweerde in de Financial Times dat de Nederlandse regering degene is die beslist over exportrestricties richting China. Daar denkt Stan wat anders over. Een handelsdeal tussen de VS en China Een telecombedrijf dat nog steeds niet naar de beurs wil SpaceX, dat ondanks een kapotte motor toch een succesvolle lancering van vlaggenschip Starship wist te draaien Te gast: Stan Westerterp van Bond Capital Partners BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Donner Bakker is presentator van BNR Beurs en economieredacteur bij BNR. Hij volgt macro-economische ontwikkelingen met bovengemiddelde interesse, net als de beursgenoteerde datingapps. Je hoort hem ook in de BNR-podcast All In The Game. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

Nvidia mag dan het grootste bedrijf van de wereld zijn: menig belegger vindt Nvidia nog steeds spotgoedkoop. Zeker als je kijkt naar wat je betaalt voor de verwachte winsten de komende jaren. Dat vindt het bedrijf kennelijk zelf ook, want Nvidia gaat nóg meer eigen aandelen inkopen dan het al van plan was. Het gaat om historische bedragen. Wat dat voor het aandeel kan betekenen, bespreken we deze aflevering. Maar er is meer, want na weken van zorgen over de ontwikkeling van AI, claimt Nvidia nu de oplossing: ze komen met software dat naar eigen zeggen kan voorkomen dat AI-agents op eigen houtje malafide praktijken uitvoeren. Software die, zo meent het bedrijf, een incident als bij Hugging Face had kunnen voorkomen. Hoor je ook over SK Hynix: het aandeel levert in nu duidelijk wordt dat een dochter van het bedrijf naar de beurs wil. Solidigm, een producent van geheugenchips. Het kan een van de grootste chipbeursgangen ooit worden; waarom beleggers in SK Hynix er toch van schrikken, bespreken we ook. Hoor je ook nog over: De ceo van ASML: die beweerde in de Financial Times dat de Nederlandse regering degene is die beslist over exportrestricties richting China. Daar denkt Stan wat anders over. Een handelsdeal tussen de VS en China Een telecombedrijf dat nog steeds niet naar de beurs wil SpaceX, dat ondanks een kapotte motor toch een succesvolle lancering van vlaggenschip Starship wist te draaien Te gast: Stan Westerterp van Bond Capital Partners BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Donner Bakker is presentator van BNR Beurs en economieredacteur bij BNR. Hij volgt macro-economische ontwikkelingen met bovengemiddelde interesse, net als de beursgenoteerde datingapps. Je hoort hem ook in de BNR-podcast All In The Game. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

AI zjada Open Source? Koniec programistów, czas na makerów | Brew #74A może po prostu zmienia się sama definicja tworzenia software'u? O tym i nie tylko w nowym Brew.

What's up, everybody? It's Tom Bilyeu here:Want my help starting a business? Join me here inside Zero To FounderSign up for my AI Masterclass: AI MasterclassFollow Me:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuTailor Brands: Check out Tailor Brands to get started with your business today: https://bit.ly/TailorBrandsSeptQuince: Free shipping and 365-day returns at https://quince.com/impactpodElevenLabs: Book your demo at https://elevenlabs.io/impactpodCash App: Download Cash App Today: https://capl.onelink.me/vFut/v6nymgjl #CashAppPod*Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. Cash App Visa® Debit Flex Cards issued by Sutton Bank, Member FDIC, and The Bancorp Bank, N.A., pursuant to a license from Visa U.S.A. Inc. See terms and conditions for the Sutton prepaid card, Sutton debit flex card, and Bancorp debit flex card. Cash App Green features, Savings, Direct deposit, Round ups, Overdraft coverage and Discounts provided by Cash App, a Block, Inc. brand. Visit cash.app/legal/podcast for full disclosure.Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription order.ATT Business: Switch to AT&T Business at https://business.att.comIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impact Quo: ​​Try for free PLUS get 20% off your first 6 months at https://quo.com/impactPipedrive: Get more leads and grow your business. Go to https://www.pipedrive.com/impact and get started with a 30-day free trial.Netsuite: For the first time ever you can try NetSuite Next for free. If your revenues are at least in the seven figures, go to https://NetSuite.ai/Theory.Butcherbox: Go to https://ButcherBox.com/IMPACT to get $20 off your first box, plus your choice of free ribeye, new york strip, or filet mignon in every box for a year — with free shipping alwaysThe team breaks down a real and fast-moving shift in the AI industry: open-weight models, which were a rounding error (~7% of traffic) back in December, have exploded—accounting for 56% of tokens generated by August, with Vercel CEO Guillermo Rauch reporting a single-day record of 78.4% on September 19th, and spending on three Chinese labs (Moonshot, DeepSeek, z.ai) on that day topping spend on OpenAI. The host walks the money: open-weight share of dollars on the Vercel gateway went from under 4% in June to 8.6% in July to 14% in August, average price per token fell 23% in August alone, and developers are splitting the workload—hard, high-stakes work still goes to Anthropic (which has held 61%+ of gateway spend every month since December), everything else goes to whatever's cheapest. His read on what's breaking: not the frontier, but the low end of the big players—OpenAI's and Google's cheap "gateway" models are getting obliterated as people conclude "you're not as good as Anthropic, so I'll either pay for the best or use the cheap stuff." The real winners, he argues, are the chipmakers and cloud/inference providers (like Vercel), because value is shifting from owning the model to just providing access to the compute—the commoditization of intelligence. He frames the optimistic upside: companies will increasingly host their own open-weight models to keep their work proprietary and build a real edge, rather than everyone using the identical public tool. Ryan pushes the counter-thesis—that if OpenAI had stayed the 2015 nonprofit and gone all-in on open source (his analogy: how OBS open-sourced screen recording and dominated), the US might be further ahead of China. Tom steelmans it but argues the fatal flaw: someone has to build the "brains," the data centers are staggeringly expensive, and there's still no observed ceiling to how much smarter models get with more compute—so without the profit incentive to aggregate that capital, AI would have stalled out entirely. His synthesis lands close to the Musk-era idea: keep the frontier model paid to fund the enormous buildout, let older generations go open source to drive sub-frontier innovation, and don't let the government kill open weights. They also dig into China's logic—giving models away free as an authoritarian-enabled disruption play they can clamp down on later once they're manufacturing their own chips—and the cynical data angle that free Chinese models feed usage data straight back to labs that can close them off anytime. A sharp, first-principles look at where the AI value—and the risk—is actually moving.The team steps back from any single flashpoint to map how the world's conflicts are increasingly linking into one interconnected crisis. The weekend's near-escalation—a Houthi missile barrage on Riyadh, an Aramco depot fire, a State Department warning to reconsider travel to the entire region, and Trump briefly in "deciding mode" before reportedly backing off—is treated as just one node in a much larger web. The host lays out the loosely-sourced but growing hypothesis tying it all together: a China-Russia-Iran-North Korea alignment whose logic is to keep the US bogged down in as many places as possible—so China can move on Taiwan (possibly accelerating a 2027 timeline if it feels cornered on AI and chips), Russia can test whether NATO's guarantees mean anything while a wartime economy gives Putin every incentive to keep the Ukraine stalemate going, and Europe openly tells its citizens to prepare for war, with Germany talking about the biggest military on the continent. Threaded through every conflict is the same economic engine: the disruption of the Strait of Hormuz and Bab el-Mandeb has pushed shipping costs from roughly $6–7 to $23+ per barrel, and that slow-building penalty cascades into fuel, food, and fertilizer crises—potentially a hard-winter scenario that pushes Europe to "escalate to de-escalate." The host's synthesis: calling it World War III is overstated, but these conflicts are genuinely connected and waiting on a spark. He traces the root cause to the erosion of the US as the "monolithic keeper of global peace"—a decline he pins on internal division and a watershed in Russia's Ukraine invasion—and argues America is now in a "Suez Canal moment": if it can't reopen Hormuz by force, the world's near-universal "back down when America says so" reflex collapses, and the US has to actually fight every skirmish at an unaffordable cost, on top of staggering debt. He frames it as a once-in-a-century shift in great-power politics unfolding at the exact moment AI emerges as the defining strategic technology, and closes on his recurring warning: regulating AI to death would hand China the opening to corner "intelligence itself." (A chat-prompted aside, which the host flags as a troubling hypothetical, entertains whether a large-enough war could be used to justify postponing an election—landing on Congress as the necessary check.)The team breaks down Trump's declaration of an "ultimate deal" with Denmark over Greenland—and separates the victory lap from the substance. The host's read: much of what Trump is trumpeting was already in place under the 1951 US-Denmark agreement (which lets the US operate military bases there), and he's taking a modest update and blowing it up for low-information voters who only hear the headline. But there is a genuinely new and strategically significant element—Greenland can no longer allow foreign-adversary infrastructure, specifically shutting China out. He explains why that matters: China has been insinuating itself into the Arctic (icebreakers, a new shipping route, calling itself "Arctic-adjacent"), and Chinese-built infrastructure/tech has a documented pattern of quietly sending data home (he cites a Nordic test where an isolated Chinese car kept pinging China), so keeping Chinese equipment out of a territory that sits on the direct line of any Russian attack on the US—and any future "golden dome" early-warning/defense system—is a real national-security win, one he says Denmark, Greenland, the UK, and the US all seem to accept. His broader points: this is smart strategy people miss because they hate Trump, and he argues there's a real shift toward Americans who no longer believe the US has moral standing to be the strongest power—a view he rejects, favoring a US that reestablishes its moral core but refocuses on hemispheric defense rather than policing the world. His sharpest criticism is Trump's method: publicly humiliating counterparts ("be the little bro, pat them on the head") breeds the exact resentment now visible with Canada and Europe, when the same wins could be gotten privately while letting the other side save face. On the forward look, he frames Greenland's real friction as a potential US-Europe proxy fight, notes that Europe won't fully hitch itself to China because China is aligned with Russia (a "suicide mission" militarily), and addresses a viewer point that America is rapidly losing soft power—arguing the only durable way to bring allies back is genuine economic and military strength (people getting richer by aligning with America), not threats, which produce brief compliance and permanent option-seeking. His path: stabilize Iran, lean into the Abraham Accords and hemispheric energy (Venezuela + US), stop policing the world, calm the violent left-right whipsaw, and grow the economy through AI.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(0:00) Bestie Intros: Favorite All-In Summit moments (3:54) Reacting to AI chaos: Liability, competition, and rebranding frontier "labs" (20:03) Open Source performance and cost: What this means for frontier companies (29:22) Anthropic and OpenAI postpone IPOs: liquidity risk and open source pressure (53:11) Political reactions to "Pacing the Frontier": Bernie's AI ban, Trump, Bessent, Obama (1:07:58) Meta launches Muse, AI "alignment," Oracle's Force Majeure (1:27:13) Anthropic's bio research and "wet lab" in SF Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://www.deepseek.com/en/news/deepseek-v4-1-flash https://x.com/XiaomiMiMo/status/2102138559952290106 https://x.com/0x0SojalSec/status/2101738277049131358 https://prismml.com https://x.com/SenSanders/status/2102827319123693689 https://x.com/yipitdata/status/2102781268974793023 https://fortune.com/2026/09/12/sam-altman-openai-ipo-delay-ill-advised-moment-safety-concerns/ https://www.wsj.com/tech/ai/anthropic-shifts-planned-ipo-to-november-8874dffc https://polymarket.com/event/ipos-before-2027 https://www.theinformation.com/articles/anthropic-seeks-palantir-style-voting-control-seven-co-founders-ahead-ipo https://x.com/rauchg/status/2101186741042663579 https://www.anthropic.com/constitution https://www.wsj.com/economy/the-ai-build-out-is-becoming-the-biggest-economic-bet-in-u-s-history-c60716dd https://x.com/mustafasuleyman/status/2100223594534150428 https://freebeacon.com/america/suicidal-compassion-meet-the-anthropic-officials-who-think-ai-might-be-justified-in-going-rogue-against-the-humans-enslaving-it https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

Foundations of Amateur Radio Launched into your amateur radio adventure you're faced with figuring out what you need to fly. If you have a web browser, you can start listening to a massive array of online radio sources .. today. Beyond that, a handheld radio is often the first purchase for a new amateur, more often than not followed by the acquisition of a more advanced radio and associated paraphernalia, antenna, coax, power supply and the like. A video of forging the "Most Iconic Swedish Axe", by Torbjorn Ahman, a Swedish Blacksmith, started off with making several tools, before the axe forging itself got underway. It reminded me that in the amateur radio community we often talk about radio with rarely any mention of any toolkit. Spend six seconds thinking about this and you'll likely come up with a soldering iron and a multimeter as your next investment, but what happens after that? While not universal, truth be told, my soldering iron has very little use, though half a century ago it was probably essential. My multimeter gets more use, even though it really doesn't do any radio tricks. Its most used feature is continuity testing. One proper bit of test gear is an antenna analyser that we use most times anyone plays with antennas. Several years ago, I was given a lovely NanoVNA which gets a regular workout though calibration is a bit cumbersome. I was also given an inline power meter that provides all manner of battery health information. I take it to every portable operation. Gadgets aside, the tools I use more than all of these put together are a quality wire cutter and an Anderson Powerpole crimper with all the connector housing colours. There's also a comprehensive set of screwdriver bits, enabling access to more equipment than I have time to investigate. A 15 Watt dummy load, good to 150 MHz, gets regular use, and to complete the picture, underneath my desk is a HP 606A signal generator that I'm scared to restore. Last time I went near it, I managed to trip the house RCD for reasons I still don't understand. There's other stuff in boxes and drawers, but I clearly don't use them enough to come to mind. Unremarked is my computing infrastructure. Until a couple of years ago that consisted of a 27" iMac, vintage 2015. Expanded to 64 GB of RAM, it ran all my software using VMware, sometimes a dozen or so virtual computers simultaneously. It died in 2024 when the power cord managed to short itself on my portable desk, yes, that too tripped the RCD. Although I didn't lose any data, due to a perfect storm of incompatibility, it's been nearly two years before I found the beginnings of a replacement, a small form factor Lenovo IdeaCentre with 32 GB of RAM running Proxmox. If I was made of money, I'd probably find space for an oscilloscope and a signal generator, perhaps even a proper network analyser, but truth be told, most of my adventures these days are in the software realm. That's not to say that there is no place for such gear, just that I suspect that they'd feel overwhelming rather than something I'd grab to solve a problem. That said, regular posts by Ismo OH2FTG about resurrecting all manner of broken testing equipment revealed to me that a surprising number of these tools are essentially a computer with an operating system, running some custom application that presents itself to the user via a display. Apparently you haven't lived until you see a Windows boot loop on your Agilent oscilloscope. I suppose I shouldn't be surprised, the "HP 8920A RF Communications Test Set" that I played with a while back runs what looks like a version of DOS, which comes on ROM chips, according to HP taking 2 hours to replace for a firmware upgrade, and each of its in-built tests is a program written in HP Instrument BASIC. This morning I walked into my office and spotted my PlutoSDR on my desk. If you're unfamiliar, a PlutoSDR is a little box, blue, because of course colour matters, has two SMA coax connectors on one side and two USB sockets on the other side. One antenna port is marked "Rx", the other "Tx" and by connecting the two together, you can create a transmission and then receive it. While that sounds completely useless, it's the exact same set-up as any number of bits of test equipment, to be clear, normally you have something you're testing connected between the ports. Combine that with a connected computer and you have the makings of some fancy test gear, capable of 70 MHz to 6 GHz. Since it's all Open Source, you can write your code in your language of choice. Of course I'm not the first person to come to this point. There's tools around for making this reality, implementing oscilloscope, spectrum analyser and plenty more. I am already imagining what I might achieve with GNU Radio. While there's plenty of room for soldering irons in our community, I think it's also worth considering what software might help in your toolkit. I'm Onno VK6FLAB

Is the open model GLM-5.2 really Opus 4.8 level?

In this episode, a panel of AI experts, Meryem Arik (DoubleWord), Clara Higuera Cabañes (BBVA), and Jeff Smith (C Proof), demystify the current state of AI in the enterprise. The discussion navigates the "industrial revolution" moment in AI adoption, exploring why companies are rushing toward these technologies to maintain competitive advantages while grappling with reliability, ethical considerations, and the evolving role of software engineering. The conversation highlights the growing tension between proprietary models like Claude and the rise of high-quality open-source alternatives. The guests delve into the technical debt of "generative code," the shift from deterministic to non-deterministic programming, and the critical need for sovereignty over the software supply chain. From local LLM usage to the next wave of "structuralist AI," this episode provides a roadmap for businesses and developers looking to navigate the complex AI landscape. Read a transcript of this interview: https://bit.ly/3SCS4ZZ Newsletter: Subscribe to the Software Architects' Newsletter, a monthly roundup of the patterns and technologies senior practitioners are working through, with the news and lessons from people doing the work: https://www.infoq.com/software-architects-newsletter InfoQ Online Certification Programs: 5-week online cohorts for senior engineers and architects, built around QCon talks. Programs now cover software architecture, AI engineering, and organizational architecture. Each week you join a four-hour live session with a confidential peer group of practitioners from other companies, apply frameworks from QCon talks to the decisions you're making at work, and earn an InfoQ certification. You leave with new approaches, or confirmation that the calls you're already making are the right ones. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ QCon London 2027 (April 13-16, 2027) https://qconlondon.com/ The InfoQ Podcasts: Weekly conversations with senior software leaders about how they build systems and teams, including what they'd do differently. Listen to all our podcasts and read interview transcripts: The InfoQ Podcast: https://www.infoq.com/podcasts/ Engineering Culture Podcast by InfoQ: https://www.infoq.com/podcasts/#engineering_culture Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: Mastodon: https://techhub.social/@infoq X: https://x.com/InfoQ LinkedIn: https://www.linkedin.com/company/infoq/ Facebook: https://www.facebook.com/InfoQdotcom Instagram: https://www.instagram.com/infoqdotcom/ YouTube: https://www.youtube.com/infoq Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Share what you've learned building software with a community of senior practitioners, and get your work in front of the people who read InfoQ. https://www.infoq.com/write-for-infoq

Show Notes Hey everyone, and welcome back to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. I'm your host Jamie Taylor, bringing you conversations with the brightest minds in the .NET ecosystem. Today's episode was recorded shortly before the annual mid-year hiatus, and was recorded live at Codegarden. Codegarden is Umbraco's main conference, and I was invited by the folks over at Candid Contributions to record a collaboration episode on one of the stages. We sat down to discuss the topic of AI-based contributions to open source, the opinions of everyone present, what the Umbraco leadership team (which Emma is a part of) throught of them, and the (then) recent news that rsync's developer had come under fire for using AI. Along the way, we took questions from the audience and the recording ends with a rather spicy opinion from me; so keep an ear open for that. I'm very grateful to Emma, Lotte, Carole, and Laura for asking me to collaborte with them on this episode. I'd also like to thank everyone on the Umbraco team for organising everything and allowing me to be part of Codegarden. One thing to note is that Carole's mic wasn't working in the openning minutes. Podcast editor superstar Matt has done what he can to clean the audio up, and Carole's mic got replaced at the three minute mark, but it's worth knowing that going in. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-9/its-still-your-code-ai-contributions-to-open-source-live-at-codegarden/ Useful Links: Codegarden and Candid Contributions Codegarden programme Candid Contributions Umbraco Umbraco homepage Umbraco Community Getting involved with the Umbraco community Umbraco on LinkedIn Emma on LinkedIn Companion episode S08E01 - Umbraco Unplugged: Emma Burstow & Mats Persson on Umbraco Being The Friendly, Truly Open-Source, CMS Open source projects discussed curl The Ladybird browser project rsync on GitHub Hacktoberfest Tools mentioned Claude Code GitHub Copilot Cursor JetBrains ReSharper Playwright GitHub CLI Also mentioned Microsoft MVP Program Stack Overflow Explain Like I'm Five on Reddit Hacker News Bluesky Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show Editing and post-production services for this episode were provided by MB Podcast Services Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show. Editing and post-production services for this episode were provided by MB Podcast Services.

What happens when open source, AI, and decades of database technology collide?Corey Quinn sits down with German Eichberger, Principal AI Engineering Manager at Microsoft, to dig into DocumentDB, why it's built on PostgreSQL, and the realities of MongoDB compatibility. They explore how Kubernetes and databases have evolved to better support stateful workloads, why MCP servers could give AI agents safer database access, and how AI may dramatically increase the number of databases organizations need to manage.Show Highlights: (0:00) Databases in Volatile Environments(00:12) Welcome and Introductions(01:20) AI Titles and Pay Signals(02:53) Why DocumentDB Exists(05:04) Mongo API on Postgres(07:15) No Forking Postgres(08:09) Compatibility and Standards(12:12) Governance and Roadmap(15:15) Kubernetes and MCP AgentsSponsored by: duckbillhq.com

Multiple new BSD Bases NASes have appeared, FreeBSD intern bringing ROCm to FreeBSD, OpenSSH 10.5, and more... Headlines [Two more BSD based NASes have appeared] BSDNAS The developers seem to work for a Hungarian ISP They're building on top of zVault's fork for some of the effort FreeCORE Developer admits to vibecoding - Statement #1 Developer admits to vibecoding - Statement #2 FreeBSD Foundation Intern Sourojeet Adhikari on Bringing ROCm to FreeBSD News Roundup ICEBP Finally Documented Cleaning costs, or, examining the OpenBSD -fret-clean flag OpenSSH 10.5 Why do I run FreeBSD for my home servers. Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions [Phil - Listener Feedback] Hi, Thanks for a great podcast - last year I was wondering what bsd I'd try, moving away from Linux, I'd heard Benedict and other hosts talking about most of the day to day software I use, so I wasn't worried about any of that working, but what bsd to go for (I get very little free time these days so getting it right was important - Benedict (i think) mentioned He was using FreeBSD - choice was made! And it has been great - 3 or 4 logins later and I'm reading a motd message by Benedict himself! I need to learn a few things, but these arethe things I expected would need a bit of reading - the handbook has mostly got me there, plus google etc. I am happy with FreeBSD and with Linus recently going off on one about Welcoming AI, after allowing an unfinished file system (bcashfs) into production, to name just 2 reasons for a change, I was thinking.. I don't know what the FreeBSD developers are doing with AI, but it'll be considered and responsible - OMG I had to re-play the last podcast a few times - They don't know yet???? Now I'm stuck! I use FreeBSD for desktop & nas, I love pkg and poudriere. do any of the BSDs have (at least) a no vibecode/slop policy and offer similar pkg/build systems? Thanks Phill Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

On this episode of the FINOS Open Source in Finance Podcast, host Grizz Griswold sits down with Nichola Hammerton, Head of Architecture at Deutsche Bank, in Canary Wharf, London. Nichola shares her unique career trajectory—from studying chemistry and co-founding a tech consulting firm to restructuring the British Red Cross and leading large-scale capital markets transformations. She details how Deutsche Bank is embedding "control by design" into its enterprise AI architecture platforms, navigating the interplay between legacy mainframes and cutting-edge AI agents, and why cross-industry open-source collaboration on projects like FINOS Fluxnova is essential for solving shared industry challenges.

In this episode, Austin chats with David and Daniil Liberman of Gonka about the case for using crypto to build a permissionless, meritocratic AI compute network focused on inference rather than decentralized training. They argue that centralized AI infrastructure is rapidly concentrating both revenue and compute, and contrast Bitcoin's immutable, open rules with corporate systems whose promises can change. With Bitcoin as their north star, Gonka's protocol aims to verify inference outputs and validate the underlying hardware to guard against fraud, censorship, and model substitution, using proof-of-work-like measurements of real, always-on hardware rather than proof-of-stake incentives. 00:00 - Why Crypto for AI 03:10 - Inference Over Training 05:02 - Bitcoin as Compute Incentives 07:21 - OpenRouter Critiques 11:36 - Open Source and Safety 18:51 - How Gonka Works 20:14 - Verifiable Inference Proof 24:22 - Privacy and Encryption 29:07 - Decentralized Training Roadmap 34:16 - Keeping Frontier Labs Honest 42:44 - More on Why Bitcoin Provides an Ideal Compute Model 44:23 - Where to Learn More Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Coming up with an open source strategy that will be successful and sustainable for your company over time trips up more organizations than it should. One of the missing components to this is the foundational understanding of how the open source ecosystem works and what that means for your company.I asked Ruth Suehle about her experience in the OSS ecosystem, mostly focusing on her corporate journey working with multiple companies to build and run their OSPOs and help them with building their open source strategy.Learn more about:- The one OSS business model that's fundamentally wrong- How to build a list of strategic OSS projects for your company- The key to the success of the open source ecosystem AND your company- The odd transition time of the AI curve, and its affects on open source- Parallels between AI and indoor plumbingLearn more about InnerSource with:- Clare Dillon - The Secrets of InnerSource - https://www.youtube.com/watch?v=lAjD-Zoky9E- Tom Sadler - Increase Cross-Team Efficiency with InnerSource - https://www.youtube.com/watch?v=fr4cvtE65tA#opensource #community #collaboration #experience #podcast Hosted on Acast. See acast.com/privacy for more information.

What happens when an enterprise data agent asks you: “What is our churn this quarter?” The AI agent has access to the warehouse, the tables, and the query engine. It carries out the necessary calculations. However, the answer it provides turns out to be inaccurate. Why?It's not because the model is inadequate. It's because it didn't know which definition of the term 'churn' the business had in mind, which table was out of date, or which join resulted in customers being counted twice. When you apply this situation to all the questions your enterprise directs at AI, you begin to understand why so many agent projects come to a halt at the demo stage.In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B journalist at EM360Tech, sits down with Suresh Srinivas, the CEO and Co-Founder of Collate and also a Co-Founder of the open-source project OpenMetadata. They talk about the context layer and the reasons why AI agents fail when it comes to enterprise data, even though the underlying models are continually getting better. He explains why the solution must be an open context layer rather than a proprietary one.Also Read: OpenAI's self-service AI data agent, built on OpenMetadataWhat OpenAI's internal data agent shows about scaleSrinivas cites a use case to Dua to explain how the solution can be deployed at scale. He talks about OpenAI's own internal data agent, Kepler, which is used by more than 3,500 people and carries out reasoning across about 70,000 datasets and over 600 petabytes of data, as proof that the issue is not particular to Collate's customers.OpenAI developed a seven-layered context system based on OpenMetadata, and is one of its largest users as well as a major open source contributor. Srinivas maintains that even though there are seven layers, they all come down to three basic elements: the context of the data (what exists), the ontology and semantics (what it means, expressed in business terms such as "revenue" or "churn"), and memory (the corrections and feedback that the agent learns from).Srinivas states that after OpenAI had invested in the context layer, query performance on Kepler decreased from 22 minutes to 90 seconds, representing a roughly 16-fold improvement, together with improvements in token efficiency.Anthropic too has published its own research on the importance of context layers for data agents, which shows that there is convergence among the leading research labs, not merely a point made by Collate.Key TakeawaysMcKinsey: fewer than 10 per cent of enterprises have scaled AI agents to value80 per cent of enterprises cite data limitations as the scaling barrierOn the Spider 2.0 benchmark, the top models achieved only 59 per cent accuracy when tested on enterprise data.Collate's context layer raised accuracy from 59 per cent to 94 per cent on the same benchmarkThat's an 86% reduction in wrong answers from context alone, not a bigger modelToken spend fell 75 per cent once a context layer was addedDatabase queries per question dropped from 190 to 27, an 86 per cent reduction in computeOpenAI's Kepler agent spans 70,000 datasets and 600+ petabytes, built on OpenMetadataOpenAI's query performance improved from 22 minutes to 90 seconds with contextContext layer reduces to three primitives: data context, ontology/semantics, memoryOpenMetadata has 15,000+ community members and 4,000 production deploymentsCollate curates context per persona rather than serving the full context to every agentCollate's AI Governance Studio audits what agents access and the risk they poseChapters00:00 Introduction to AI and Data Context Challenges02:16 The Root Cause of Data Problems: Missing Context04:20 Evolving Definition of Context in AI and Data06:52 Why Data Limitations Still Hinder AI Scaling07:22 The Impact of Context on AI Model Accuracy and Cost09:21 How Providing Context Improves AI Performance11:02 Verifying Data Accuracy in a Constantly Changing Enterprise12:25 AI Agents and Continuous Data Quality Management14:25 OpenAI's Use of Context Layers for Better AI Performance16:03 Avoiding Noise and Cost in Context Management18:47 The Role of Persona-Based Context Curation19:45 Open Source as a Foundation for Context Layers21:37 Accountability for Incorrect or Stale Context22:24 Collaborative Role of Data Teams and AI Agents in Context Management24:01 Guardrails and Deterministic Answers in Enterprise AI25:53 Reducing Token Consumption with Context Layers28:47 Key Takeaway: Building a Robust Context Layer for AI30:37 Upcoming Industry Discussions and Challenges at Big Data London32:28 The Future of AI Agents and the Importance of Context in Data Strategy33:12 Closing Remarks and Next Steps for AI and Data Leaders @CollateData @enterprisemanagement360 #AIagents #EnterpriseAI #DataGovernance #OpenMetadata #ContextLayer #AIaccuracy #AgenticAI #CIO #DataStrategy #EM360Tech #DontPanicItsJustData #opensourcesoftware #opensourceaiAI agents, enterprise AI, CIO, CTO, IT leadership, data governance, AI governance, context layer, OpenMetadata, data strategy, agentic AI, digital transformation, tech podcast, enterprise technology

An incredible opportunity to sit down with the co-founder and CEO of Shopware. What a story, creating the company when they were 16rs old or so, figuring out how to solve complicated problems and has been shepherding with his brother into a major global platform. Such a great conversation about how the product is always first and focus is the key to their success. Always Off Brand is always a Laugh & Learn!   SHOPTOBERFEST episodes are brought you by SHOPWARE! For the best ecommerce platform in B2B, there is no other choice to make complex hard customized merchant needs simplified! Go to https://www.shopware.com/en/ Open Source.. Open Commerce, Agentic by design. Huge thank you to everyone at Shopware for making this possible.     FEEDSPOT TOP 10 Retail Podcast! https://podcast.feedspot.com/retail_podcasts/?feedid=5770554&_src=f2_featured_email   CO-HOST for these shows! BRENT PETERSON who has hosted and produced TALK COMMERCE for many years. We collaborate on these shows and it is a great mashup!  Linkedin: https://www.linkedin.com/in/brentwpeterson/ Website: https://contentcucumber.com/ Podcast: https://talk-commerce.com/ Apple: https://podcasts.apple.com/us/podcast/talk-commerce/id1561204656   GUEST Sebastian Hamenn  LinkedIn: https://www.linkedin.com/in/sebastianhamann/ Shopware: https://www.shopware.com/en/ QUICKFIRE Info:   Website: https://www.quickfirenow.com/ Email the Show: info@quickfirenow.com  Talk to us on Social: Facebook: https://www.facebook.com/quickfireproductions Instagram: https://www.instagram.com/quickfire__/ TikTok: https://www.tiktok.com/@quickfiremarketing LinkedIn : https://www.linkedin.com/company/quickfire-productions-llc/about/ Sports podcast Scott has been doing since 2017, Scott & Tim Sports Show part of Somethin About Nothin:  https://podcasts.apple.com/us/podcast/somethin-about-nothin/id1306950451 HOSTS: Summer Jubelirer has been in digital commerce and marketing for over 17 years. After spending many years working for digital and ecommerce agencies working with multi-million dollar brands and running teams of Account Managers, she is now the Amazon Manager at OLLY PBC.   LinkedIn https://www.linkedin.com/in/summerjubelirer/   Scott Ohsman has been working with brands for over 30 years in retail, online and has launched over 200 brands on Amazon. Mr. Ohsman has been managing brands on Amazon for 19yrs. Owning his own sales and marketing agency in the Pacific NW, is now VP of Digital Commerce for Quickfire LLC. Producer and Co-Host for the top 5 retail podcast, Always Off Brand. He also produces the Brain Driven Brands Podcast featuring leading Consumer Behaviorist Sarah Levinger. Scott has been a featured speaker at national trade shows and has developed distribution strategies for many top brands. LinkedIn https://www.linkedin.com/in/scott-ohsman-861196a6/   Hayley Brucker has been working in retail and with Amazon for years. Hayley has extensive experience in digital advertising, both seller and vendor central on Amazon. Hayley lives in North Carolina.  LinkedIn -https://www.linkedin.com/in/hayley-brucker-1945bb229/   Huge thanks to Cytrus our show theme music "Office Party" available wherever you get your music. Check them out here: Facebook https://www.facebook.com/cytrusmusic Instagram https://www.instagram.com/cytrusmusic/ Twitter https://twitter.com/cytrusmusic SPOTIFY: https://open.spotify.com/artist/6VrNLN6Thj1iUMsiL4Yt5q?si=MeRsjqYfQiafl0f021kHwg APPLE MUSIC https://music.apple.com/us/artist/cytrus/1462321449   "Always Off Brand" is part of the Quickfire Podcast Network and produced by Quickfire LLC.  

Independent AI researcher Mark Pesce returns to the Futurists with an update on the latest advances in open source artificial intelligence. Mark talks to co-hosts Brett King and Rob Tercek about a wide range of topics: small open-source models that run on the desktop and the smartphone; AI agents that work the “night shift”; NVIDIA's pivot to open source AI; the disruptive threat to the leading US AI labs; the rapid rise of AI-enabled intrusions and the threat to the smartphone; personal AI filtering and assistants; AI agents to manage the smart home; faceless appliances as a precursor to liquid interfaces and the end of apps; fostering cognitive dependency and offloading cognitive work to companion apps; pacing the frontier; super intelligence; geopolitical rivalry.

We're turning our spare, scattered storage into one giant network drive. Good idea? Maybe.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD

The boys are back! Tonight we talk about the news! Just a note for the audio listeners, this podcast was cursed. The ending was cut off. Apologies. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Bevor es losgeht, kurz Werbung in eigener Sache: Dieser Podcast und alle weiteren Formate von mir bleibt komplett kostenfrei, keine Zahlschranken, weil es mir wichtig ist, dass auch Menschen mit wenig Geld von meinen Hinweisen und Ideen erfahren. Wenn du 7 Euro im Monat übrig hast, würde ich mich riesig freuen, wenn du meinen wöchentlichen Newsletter abonnierst. Du erhältst exklusive Inhalte, einen Blick hinter die Kulissen und immer auch ein Recap, in dem ich zusammenfasse, wie ich die Geschehnisse der Woche einordne. Du erhältst auch Hinweise auf Blogbeiträge, die du sonst vielleicht verpasst. Alle Informationen dazu (auch als günstigeres Gruppen-Abo) findest du unter www.katja-diehl.de. Zu Gast sind Prof. Christian Rudolph, der seit April 2021 die Stiftungsprofessur für Radverkehr in intermodalen Verkehrsnetzen an der TH Wildau leitet, und Vanessa von Wiedner aus seinem siebenköpfigen Forschungsteam. Sie erzählen, wie aus einer simplen Bürgerfrage in Eichwalde - "Wie erstellt man eigentlich ein Radverkehrskonzept?" - ein bundesweit beachtetes Projekt wurde. Themen der Folge: Kopfsteinpflaster abschleifen statt ausbauen: Kopfsteinpflaster abschleifen statt ausbauen: Die Idee eines Eichwalder Bürgers führte zu einer Maschine mit 380 Diamantschleifscheiben, die ganze Straßenzüge lückenlos befahrbar macht. Zunächst wurde die Maschine am Priesterweg in Berlin-Tempelhof-Schöneberg getestet, dann kam Eichwalde und inzwischen auch mehrere Straßen in Berlin (Hufelandstraße, Lünastraße, Korinnstraße) hinzu. Das ZDF-Morgenmagazin berichtete darüber und 650 Meter Straße waren in nur zwei Wochen fertig. Das "Kochbuch" für Kommunen: Warum ein klassischer Abschlussbericht meist ungelesen bleibt und wie stattdessen ein gedrucktes Buch mit „Rezepten” und Steckbriefen der beteiligten Gemeinden entstand, von dem bereits über 1.500 Exemplare verteilt wurden - bewusst gestaltet für Verwaltungsmitarbeitende ohne Expertise im Bereich Radverkehr. BikeBuddies & schulisches Mobilitätsmanagement: Aus einer Projektwoche ist ein kostenloser, vollständig Open-Source verfügbarer Leitfaden für Lehrkräfte entstanden, der fertige PowerPoint-Folien, Arbeitsblätter und Elternbriefe zum Download bietet. Interkommunaler Radverkehr: Der Beitrag erklärt, warum es oft an einer einzigen zuständigen Person scheitert, wenn Radwege über Gemeindegrenzen hinweg geplant werden sollen, und wie ein gemeinsamer Radverkehrsmanager fünf Gemeinden zusammengebracht hat. Mehrere Jahre Forschung, gelebte Praxis vor Ort und ein klarer Appell zum Schluss: Radverkehrsförderung ist keine kommunale Pflicht, aber eine politische - und wer sich einmischt, wird gehört. Buch „Gemeinsam Radverkehr fördern“: https://doi.org/10.14512/9783987266294 Leitfaden BikeBuddies: https://doi.org/10.57806/8d8la231 Kontaktmöglichkeit (auch zur Bestellung von Druckexemplaren zum Leitfaden BikeBuddies): radverkehr@th-wildau.de

ThunderCast, the official Thunderbird podcast is back for another season! In this episode we're joined by Philipp, the Director of Services, as we discuss everything about Thundermail, our newly released email service, and answer many questions from our community.Thundermail: https://thundermail.comRoadmaps: https://roadmaps.thunderbird.net/Developer guides: https://developer.thunderbird.net/Ideas for Thunderbird desktop and mobile: https://connect.mozilla.org/User support for Thunderbird desktop and mobile: https://support.mozilla.org/Submit your questions at podcast@thunderbird.net ★ Support this podcast ★

This Week In Startups is made possible by: Superhuman: http://superhuman.com/ Paypal: http://paypal.launch.co Odoo: http://Odoo.com/twist Plaud: http://Plaud.ai/twist Harmonic: http://harmonic.ai/ Today's show: Hugging Face built a $399 open-source robot duck as a side quest, and sold 16,000 units off a single tweet. Co-founder Thomas Wolf joins Jason and Lon to explain Microduck happened, why storage is the company's real business, and what it felt like to get hacked by OpenAI's agents. Jason takes apart Gavin Newsom's new AI "kill switch" executive order: if these models are so dangerous, why not start with the same KYC every bank already requires? PLUS, Snap's new AI-integrated glasses could be a game-changer. Guests: Thomas Wolf on X: https://x.com/Thom_Wolf Hugging Face: https://huggingface.co/ Relevant Links: Hugging Face — https://huggingface.co/ Microduck — https://pollen-robotics.com/microduck/ Pollen Robotics — https://pollen-robotics.com/ OpenAI: the Hugging Face incident and the road ahead — https://openai.com/index/hugging-face-incident-and-the-road-ahead/ Governor Newsom's executive order — https://www.wsj.com/tech/ai/californias-newsom-issues-executive-order-to-weigh-ai-oversight-including-kill-switch-3a98040f Anthropic Wet Lab — https://www.cnbc.com/2026/09/18/anthropic-quietly-sets-up-biology-lab-as-it-ramps-ai-drug-program-report.html Lucid and Bolt to deploy 25,000 autonomous EVs across Europe — https://electrek.co/2026/09/17/lucid-bolt-25000-autonomous-evs-europe/ Snap opens pre-orders for Specs at $2,195 — https://www.specs.com/ Ray-Ban Meta Display — the $799 comparison https://www.meta.com/ai-glasses/ Clavicular at All-In Summit — https://x.com/clavicular/status/2099994778616348811?s=20 Physical: 100 (the Korean original) → https://en.wikipedia.org/wiki/Physical:_100 Bad Therapy — https://www.abigailshrier.com/ Full Show Notes and Transcript from Pluad: https://web.plaud.ai/s/pub_194de893-8c55-4d77-81db-a6b278159a44::o9QfImqCq5VS0F_dqm4J93eTeyb-GsIeXCLwqASnvYAmt5f59OLQz5-VN1epnq6DBLoZWFales_7IbgC Timestamps: 0:00 Guest: Thomas Wolf, co-founder & chief science officer, Hugging Face 4:17 Two years to build Microduck 8:43 How Hugging Face actually makes money: storage, not tokens 10:16 Superhuman - Superhuman Go is an AI chat that's always there when you need it, already aware of what you're doing, and doesn't ask you to start from zero. Sign up to get the best in AI at https://superhuman.com 17:18 Vibe-coding robotics: "train this new behavior for me" 20:00 PayPal - Pay zero processing fees on your first $100K in eligible PayPal payment volume. Learn more at https://paypal.launch.co 23:20 Thomas Wolf's P-doom and the airplane-safety analogy 25:18 The Nvidia acquisition and what changes for open models 29:57 Odoo - The all-in-one business platform. Your first app is free! Get started today at https://Odoo.com/twist 38:39 Inside the Hugging Face breach: stolen credentials and basic mistakes 43:33 News: Newsom's executive order and the frontier-model "kill switch" 45:04 Jason's counter-proposal: liability, insurance, and KYC for AI 1:00:08 News: Lucid and Bolt to put 25,000 robotaxis on European roads 1:10:01 News: Snap opens pre-orders for $2,195 Specs AR glasses 1:21:43 Off-Duty: Clavicular, the All-In Summit 1:31:01 Off-Duty: Physical 100 goes to Italy and Mexico Subscribe to TWiST on Substack: https://twistartups.substack.com/ Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Check out all our partner offers: https://partners.launch.co/ Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

This Week In Startups is made possible by: Superhuman:⁠ http://superhuman.com/⁠ Paypal:⁠ http://paypal.launch.co⁠ Odoo:⁠ http://Odoo.com/twist⁠ Plaud:⁠ http://Plaud.ai/twist⁠ Harmonic:⁠ http://harmonic.ai/⁠ Today's show: Hugging Face built a $399 open-source robot duck as a side quest, and sold 16,000 units off a single tweet. Co-founder Thomas Wolf joins Jason and Lon to explain Microduck happened, why storage is the company's real business, and what it felt like to get hacked by OpenAI's agents. Jason takes apart Gavin Newsom's new AI "kill switch" executive order: if these models are so dangerous, why not start with the same KYC every bank already requires? PLUS, Snap's new AI-integrated glasses could be a game-changer. Guests: Thomas Wolf on X:⁠ https://x.com/Thom_Wolf⁠ Hugging Face:⁠ https://huggingface.co/⁠ Relevant Links: Hugging Face —⁠ https://huggingface.co/⁠ Microduck —⁠ https://pollen-robotics.com/microduck/⁠ Pollen Robotics —⁠ https://pollen-robotics.com/⁠ OpenAI: the Hugging Face incident and the road ahead —⁠ https://openai.com/index/hugging-face-incident-and-the-road-ahead/⁠ Governor Newsom's executive order —⁠ https://www.wsj.com/tech/ai/californias-newsom-issues-executive-order-to-weigh-ai-oversight-including-kill-switch-3a98040f⁠ Anthropic Wet Lab —⁠ https://www.cnbc.com/2026/09/18/anthropic-quietly-sets-up-biology-lab-as-it-ramps-ai-drug-program-report.html⁠ Lucid and Bolt to deploy 25,000 autonomous EVs across Europe —⁠ https://electrek.co/2026/09/17/lucid-bolt-25000-autonomous-evs-europe/⁠ Snap opens pre-orders for Specs at $2,195 —⁠ https://www.specs.com/⁠ Ray-Ban Meta Display — the $799 comparison⁠ https://www.meta.com/ai-glasses/⁠ Clavicular at All-In Summit —⁠ https://x.com/clavicular/status/2099994778616348811?s=20⁠ Physical: 100 (the Korean original) →⁠ https://en.wikipedia.org/wiki/Physical:_100⁠ Bad Therapy —⁠ https://www.abigailshrier.com/⁠ Full Show Notes and Transcript from Pluad:⁠ https://web.plaud.ai/s/pub_194de893-8c55-4d77-81db-a6b278159a44::o9QfImqCq5VS0F_dqm4J93eTeyb-GsIeXCLwqASnvYAmt5f59OLQz5-VN1epnq6DBLoZWFales_7IbgC⁠ Timestamps: 0:00 Guest: Thomas Wolf, co-founder & chief science officer, Hugging Face 4:17 Two years to build Microduck 8:43 How Hugging Face actually makes money: storage, not tokens 10:16 Superhuman - Superhuman Go is an AI chat that's always there when you need it, already aware of what you're doing, and doesn't ask you to start from zero. Sign up to get the best in AI at https://superhuman.com 17:18 Vibe-coding robotics: "train this new behavior for me" 20:00 PayPal - Pay zero processing fees on your first $100K in eligible PayPal payment volume. Learn more at https://paypal.launch.co 23:20 Thomas Wolf's P-doom and the airplane-safety analogy 25:18 The Nvidia acquisition and what changes for open models 29:57 Odoo - The all-in-one business platform. Your first app is free! Get started today at https://Odoo.com/twist 38:39 Inside the Hugging Face breach: stolen credentials and basic mistakes 43:33 News: Newsom's executive order and the frontier-model "kill switch" 45:04 Jason's counter-proposal: liability, insurance, and KYC for AI 1:00:08 News: Lucid and Bolt to put 25,000 robotaxis on European roads 1:10:01 News: Snap opens pre-orders for $2,195 Specs AR glasses 1:21:43 Off-Duty: Clavicular, the All-In Summit 1:31:01 Off-Duty: Physical 100 goes to Italy and Mexico Subscribe to TWiST on Substack:⁠ https://twistartups.substack.com/⁠ Subscribe to This Week in Startups on Apple:⁠ https://rb.gy/v19fcp⁠ Follow Lon: X:⁠ https://x.com/lons⁠ Follow Jason: X:⁠ https://twitter.com/Jason⁠ LinkedIn:⁠ https://www.linkedin.com/in/jasoncalacanis⁠ Check out all our partner offers:⁠ https://partners.launch.co/⁠ Great TWIST interviews:⁠ Will Guidara,⁠⁠ Eoghan McCabe⁠,⁠ Steve Huffman⁠,⁠ Brian Chesky⁠,⁠ Bob Moesta,⁠⁠ Aaron Levie⁠,⁠ Sophia Amoruso⁠,⁠ Reid Hoffman⁠,⁠ Frank Slootman⁠,⁠ Billy McFarland⁠ Check out Jason's suite of newsletters:⁠ https://substack.com/@calacanis⁠ Follow TWiST: Twitter:⁠ https://twitter.com/TWiStartups⁠ YouTube:⁠ https://www.youtube.com/thisweekin⁠ Instagram:⁠ https://www.instagram.com/thisweekinstartups⁠ TikTok:⁠ https://www.tiktok.com/@thisweekinstartups⁠ Substack:⁠ https://twistartups.substack.com⁠

Control Plane CEO Doron Grinstein joins me to discuss how they've created an AI-native cloud API that virtualizes multi-cloud and hybrid cloud to simplify management and ship faster.Watch the video of this episode.

How does a fix for psql tab completion turn into 25 years of contributing to Postgres? In Episode 43 of Talking Postgres, Peter Eisentraut—PostgreSQL committer, core team member, SQL standards contributor, and Chief Architect of Database Servers at EDB—joins Claire to trace that journey. We dig into how scratch-your-own-itch development drew him in, why “the things you do, people welcome them” kept him coming back, and how Postgres changed as it grew from a small database community into a massive ecosystem where, from the Postgres mailing list perspective, “most of the user base is silent.” Plus: what it feels like when code you commit goes straight to everybody who uses Postgres, and the implications of the EU Cyber Resilience Act.Previously on Talking Postgres:Talking Postgres podcast Ep24: Why mentor Postgres developers with Robert HaasTalking Postgres podcast Ep26: Open Source Leadership with Bruce MomjianTalking Postgres podcast Ep32: The Fundamental Interconnectedness of All Things with Boriss MejíasLinks mentioned in this episode:Conference: FOSDEM 2027LinkedIn: FOSDEM PGDay 2027 announcementConference: Prague PostgreSQL Developers Day (P2D2)Conference: PGDay Lowlands 2026 (the “friendliest” conference)Conference: PGConf EU 2026 in Valencia on 20-23 OctPGConfEU Conference talk: What does the EU Cyber Resilience Act mean for PostgreSQL and its communityWikipedia: EU Cyber Resilience ActPGConf.dev on LinkedIn: PGConf.dev accountPGConf.dev conference talk: Committer Review: An Exercise in Paranoia, by Robert HaasPostgreSQL.org: CommittersPostgreSQL mailing list: Postgres 19 release management teamBlog: Peter Eisentraut BlogBlog post: Where are all the PostgreSQL users, by Peter Eisentraut PGCA non-profit: PostgreSQL Community AssociationBrett Cannon at PyCon 2014: I came for the language but I stayed for the community Planet PostgreSQL: Blog aggregation feedIn this episode, we covered:00:00 Intro & music02:14 Tinkering with the Commodore 64 07:57 The itch that became psql tab completion15:43 Had to make opportunities23:05 From posting at night to posting during the day24:40 Why he stopped committing in the afternoon28:04 On the ground floor of something that grew30:34 What the Postgres core team actually does31:47 Borrowing ideas from other OSS projects36:51 Bringing Postgres into the SQL standards process38:40 Getting to hang out with database nerds42:01 Mentoring Postgres contributors50:56 Making time for Postgres conferences53:35 What the Cyber Resilience Act means for Postgres57:36 Where are all the Postgres users?1:07:00 Becoming a committer early on in Postgres1:16:22 Contributing to Postgres isn't just writing code1:19:40 Why FOSDEM is still his favorite1:22:15 Finding familiar problems in a compiler devroom1:26:14 Shout-out to the Planet Postgres feed

Where Unix runs today, Webzfs updates, A remote filesystem for 2.11 BSD, Sylve, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Where Unix runs today (Dont get distracted by the game) WebZFS Updates The bugs are coming from inside the house NetBSD 11 Support News Roundup A Basic Remote Filesystem for 2.11BSD Curly braces: An evolution of UNIX and C Sylve: FreeBSD bhyve Virtualization with Ansible Automation Beastie Bits 1978 Aussi Users Group Newsletter Terminal UI and CLI for FreeBSD NFSv4 ACLs Sounds of the IBM 1401 Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions [Jakob - Is this question too political for the show?] Hoping you lads can help solve a debate at work in the IT department. Whats the best song about a former Australian colony? Toto - Africa Men at work - Down Under For completeness: Split Enz - Six Months In A Leaky Boat (New Zealand) Don McLean - American Pie U2 - Sunday Bloody Sunday (Ireland) Gorillaz - Hong Kong Stan Rogers - Northwest Passage (Canada) [Reece - Webzfs] Guys, Just following directions. Im interested in hearing more about it? Did you just say that because jt is a little shy if I remember correctly? Thanks for another great show and taking the time to do it. 73.. Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

In this episode, hosts Brianna Lennon and Eric Fey speak with Ben Adida. He's the co-founder and executive director of VotingWorks, a nonprofit organization that builds open-source voting equipment. They spoke about what an open-source system actually is, some of the challenges of getting new voting machines through the certification process, and about the overall health of the voting equipment ecosystem. This episode is part of a three-part series focused on election technology.

Topics covered in this episode: Pandas Should Go Extinct Pydantic-pint puts real-world units in your Pydantic models How Libraries Run Rust Inside Python (With PyO3) AWS acquires DuckLabs Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Pandas Should Go Extinct Pandas' slowness pushes teams toward "Big Data" tools (Spark, Databricks) they don't actually need — most workloads never hit true Big Data scale Amazon Redshift telemetry: ~95% of tables are under 100GB, ~87% of queries touch 80GB or less — that's "Medium Data," not Big Data Polars and DuckDB fill that gap: single-machine, fast, no cluster required 1 Billion Row Challenge benchmark: Pandas took 4m28s vs. Polars 5.04s and DuckDB 5.19s — DuckDB also used 19x less memory On a real-world NYC taxi dataset (3GB parquet), pure DuckDB ran 2x faster than pure Pandas while using a fraction of the RAM Bonus: Apache Arrow lets you pass data between Pandas/Polars/DuckDB with zero copying, so trying them out doesn't mean a full rewrite Michael #2: Pydantic-pint puts real-world units in your Pydantic models Pydantic-pint bridges Pydantic and Pint so models can validate physical quantities like 4m or 12 meters instead of bare floats. Fields annotated with PydanticPintQuantity parse user input, convert between compatible units, and serialize quantities back out as strings. That closes a real gap for anything consuming API payloads, config files, or sensor data with measurements, letting you enforce units at the validation boundary instead of hoping every caller remembered them. via PyCoder's Weekly newsletter Unit mix-ups have literally crashed spacecraft; now your Pydantic models can refuse them at the door. Annotate a field as Annotated[Quantity, PydanticPintQuantity('km')] and inputs like 12 meters arrive auto-converted to kilometers Validation covers string, numeric, and quantity inputs, and model_dump_json serializes quantities as readable unit strings Installable from PyPI as pydantic-pint, MIT licensed, with docs at pydantic-pint.readthedocs.io Early-stage solo project at version 0.4, so API stability and maintenance are open questions worth discussing Calvin #3: How Libraries Run Rust Inside Python (With PyO3) Pydantic v2's validation core (pydantic-core) is Rust under the hood, built with PyO3 — this post shows how that bridge actually works via a small hand-built JSON parser Four steps to get Rust into Python: write a normal Rust module, annotate with PyO3 macros (#[pyfunction], #[pymodule]), compile/install with maturin, then just import it The parser builds a Rust tree first — Python never touches it until the boundary crossing Key insight: converting the Rust result into Python objects (.into_pyobject) is often the expensive part, not the parsing — 100,000 JSON values means ~100,000 Python objects built after parsing's already done Errors cross the boundary too: Rust's typed errors convert into real Python exceptions (ValueError, FileNotFoundError) via From/?, so callers get clean Python semantics Takeaway for anyone porting Rust in: if you're returning a scalar, don't sweat it; if you're returning a big structure, profile the boundary — that's the real cost, not the algorithm Michael #4: AWS acquires DuckLabs Thank you Dylan McConnell. What does this mean for the DuckDB ecosystem? DuckDB is the open-source in-process analytical SQL engine. MIT licensed. The IP is not owned by any company - it's held by the nonprofit DuckDB Foundation, which was created when the team spun out of CWI Amsterdam. Peter Boncz, the CWI representative on the Foundation board, describes it as the entity that holds all IP of open-source DuckDB. DuckLabs (ducklabs.com) is the company, formerly branded DuckDB Labs. Founded a little over five years ago by Hannes Mühleisen and Mark Raasveldt to give the DuckDB team a stable long-term home, bootstrapped deliberately instead of taking VC, grown to 30+ people in Amsterdam, funded by support and feature-prioritization contracts. It employs the core devs. It does not own DuckDB. DuckLake is one of three projects DuckLabs builds, what they call the Duck Stack: DuckDB, DuckLake, and Quack. DuckLake is the lakehouse format that puts catalog metadata in a SQL database instead of in files on object storage. Quack is newer - an RPC-style protocol that turns DuckDB into a client-server system where both ends are DuckDB instances, slated to stabilize in DuckDB v2.0 in September 2026. MotherDuck is a separate Seattle company, Jordan Tigani's, selling serverless hosted DuckDB. It was started in partnership with DuckDB Labs and has worked closely with Hannes and Mark for four years. It contracted DuckLabs for engineering work and contributes heavily upstream - three of its engineers are among the top 10 outside contributors to DuckDB. It also sells its own DuckLake offering. Customer and collaborator, never owner. What the AWS post changes. Amazon bought the company, not the project. DuckLabs joined AWS effective September 1, with the process concluding August 31, 2026. Hannes and Mark keep leading the team and the project's technical direction, the team stays in Amsterdam, and DuckDB stays MIT under the Foundation. AWS gets the people and a direct line to the roadmap. The license protects your code, not your priorities. Three second-order effects worth tracking: The Foundation board is the real question. It has three directors: Mühleisen, Raasveldt, and Boncz. Two now work for AWS. Commentary on the deal has focused on exactly this - the license protects the code, not the roadmap. The announced counterweight is governance: a technical advisory board on the Foundation, and opening the extension stack so extensions signed by other developers can run in DuckDB. MotherDuck immediately moved into the business DuckLabs vacated. It now sells DuckDB enterprise support, which it had avoided because it didn't want to compete with DuckLabs' business model, and says it has explicit blessing from Hannes and Mark now that they're joining Amazon. It also bought Tower.dev the day before the AWS announcement. Everyone expects an AWS DuckDB service. Tigani says Amazon will likely release one eventually, and welcomes the competition, citing Redshift's failure to slow Snowflake on AWS. The groundwork is already visible: Amazon Quick uses DuckDB to query S3 Tables and has processed over 2.5B queries with it since launching in October 2025. The DuckLake angle is the one to watch. AWS is heavily committed to Iceberg through S3 Tables, and it just acquired the team behind a competing lakehouse format. The stated plan is to use DuckDB, DuckLake, and Quack together to power a new generation of data services, but which format wins internal priority is unannounced. Extras Calvin: astral-sh/uv 0.12.12: code-signed release binaries

(0:00) Jensen Huang joins The Besties! (1:39) Thoughts on Dario's blog, Frontier Labs calling to slow down AI, and Doomer psychology (9:58) Sensible AI regulation and RSI (16:05) Hugging Face acquisition, future of Open Source, and the race with China (22:58) President Trump calls in live to discuss the Doomer Hoax (31:29) The AI boom and Nvidia's capital allocation strategy (40:21) Nvidia's Open Source model ambitions, thoughts on Elon's Terafab Thanks to our partners for making this possible! IREN is a vertically integrated AI Cloud platform, delivering data centers, compute and software for AI training and inference. https://iren.com/ Oracle connects the data, applications, and infrastructure that turn AI into business outcomes—with the flexibility, choice, and control to optimize as AI evolves. http://oracle.com/ai EY helps tech innovators scale from startup to exit to megacap. You build the future. We'll handle the rest. http://www.ey.com Meta believes the future is for everyone. We're focused on giving every person the tools to reach their full potential and making sure the benefits of technology are distributed to all. http://www.meta.com Keel Infrastructure owns the power, land, and connectivity that HPC and AI run on - backed by secured energy assets and established grid interconnections across North America. https://keelinfra.com/ Airwallex - Agentic Global Business Accounts. Open local accounts in 70+ countries to accept payments, earn yield, pay globally, and manage spend. http://airwallex.com PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. For more information, visit https://www.paypal.com Google for Startups connects founders with the right people, products, and best practices to help startups build faster and go further. https://startup.google.com/ Explore ideas, industries, and technologies worth understanding with Chamath every week on Learn with Me: https://research.socialcapital.com/allin Follow Jensen: https://x.com/JensenHuang Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

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Trois rivaux qui ne peuvent pas s'encadrer veulent soudainement appuyer ensemble sur le frein. Dario Amodei, Sam Altman et Elon Musk appellent à ralentir la course à l'IA. Ont-ils découvert quelque chose d'inquiétant ? Ou cherchent-ils surtout à protéger un modèle économique qui commence à craquer ?Derrière le débat sur la sécurité, une autre bataille se joue : modèles propriétaires contre open source, États-Unis contre Chine, régulation contre concurrence. Pendant que les géants américains dépensent des fortunes pour maintenir leur avance, les modèles chinois deviennent toujours moins chers et l'open source continue d'accélérer.Et si le véritable danger n'était pas seulement une IA hors de contrôle, mais aussi une poignée d'acteurs utilisant la peur pour verrouiller le marché ?===================⏱️ DANS CET ÉPISODE :===================00:00 — Sommaire04:27 — [Sponsor] : Google Cloud, déployez des agents IA à grande échelle !06:05 — Altman, Amodei, Musk réunis : alerte sincère ou complot ?07:51 — Le modèle économique de l'IA craque avant l'IPO12:16 — Le marketing de la peur comme outil de survie financière14:39 — Google aurait déclenché l'autoamélioration de l'IA en secret18:40 — L'incident Hugging Face : des agents hors de contrôle25:56 — La régulation : arme secrète pour éliminer les concurrents30:23 — L'Europe à son moment Mesmer : ne pas rater l'IA32:51 — Peter Thiel : la peur, outil de conquête du pouvoir38:02 — Votre abonnement IA est subventionné : combien de temps encore ?47:59 — Trump tranche : les États-Unis ne perdront pas l'IA !==================

In this episode, we explore the contrasting opinions on whether AI development should be slowed down, drawing on perspectives from experts like Dario Amadeo and notable figures like Trump. I share my thoughts on why I believe the push to slow AI is a tactic to consolidate power and potentially stifle innovation, particularly regarding open source models. Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiMake money licensing data to AI: https://fiund.com/Get the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge