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AI is already displacing workers in targeted ways - entry-level knowledge workers are being quietly erased from hiring pipelines, freelancers are getting crushed, and the career ladder is being sawed off at the bottom rungs. Yet ML engineer demand has surged 89% with a 3.2:1 talent deficit and $187K median salary. Covers the real displacement data, lessons from the artist bloodbath, the trades escape hatch, the orchestrator treadmill, expert disagreements on timelines, and concrete short- and long-term career moves for ML engineers. Links Notes and resources at ocdevel.com/mlg/mla-4 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want Market Metrics and Displacement Dynamics ML Market: H1 2025 demand rose 89% with a 3.2 to 1 talent deficit. Median salary is $187,500, while Generative AI specialists earn a 40 to 60 percent premium. The "Quiet" Decline: Macro data shows only 4.5% of total layoffs are AI-attributed, but entry-level hiring is collapsing. Stanford/ADP data shows a 13 to 16 percent employment drop for workers aged 22 to 25 in AI-exposed roles since late 2022. UK graduate job postings fell 67%. Corporate Attrition: Salesforce cut 4,000 roles after AI absorbed 30 to 50 percent of workloads. Microsoft cut 15,000 roles as AI began generating 30% of its code. Amazon cut 30,000 jobs while spending $100 billion on AI infrastructure. Sector Analysis: Creative and Trades Illustrators: Jobs in China's gaming sector fell 70% in one year. Clients accept "good enough" work (80% quality) at 5% of the cost. Western freelance graphic design and writing jobs fell 18.5% and 30% respectively within eight months of ChatGPT's launch. Manual Labor: The U.S. construction industry lacks 1.7 million workers annually, but apprenticeships take five years. Humanoid robotics are advancing, with Unitree's R1 priced at $5,900 and Figure AI robots completing 1,250 runtime hours at BMW. Full automation is 10 to 15 years away, but partial displacement via smaller crews is closer. The Orchestration Treadmill Obsolescence Speed: Prompt engineering roles went from $375,000 salaries to obsolescence in 24 months. AI coding agents like Claude Code now resolve 72% of medium-complexity GitHub issues autonomously. Fragile Expertise: Replacing junior workers with AI prevents the development of future senior talent. New engineers risk "fragile expertise," directed by tools they cannot debug during novel failure modes. Economic and Expert Outlook Macro Risks: Daron Acemoglu warns of "so-so automation" that cuts costs without raising productivity, predicting only 0.66% growth over ten years. "Ghost GDP" describes AI-inflated accounts that fail to circulate because machines do not consume. Expert Camps: Accelerationists (Anthropic, OpenAI) predict human-level AI by 2027. Skeptics (LeCun, Marcus) argue LLMs are a dead end lacking world models. Pragmatists (Andrew Ng) suggest shifting from implementation to specification as the cost of code nears zero. Tactical Adaptation for ML Engineers Immediate Skills: Master production ML systems, MLOps, LLM evaluation, and safety engineering. Ability to manage deployment risks and hallucination detection is the primary hiring differentiator. Long-term Moats: Focus on "Small AI" (on-device, private), mechanistic interpretability, and deep domain knowledge in healthcare, logistics, or climate science. The Playbook: Optimize for the current three to five year window. Move from being a model builder to a product-focused engineer who understands business tradeoffs and regulatory compliance.

ML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and mastering AI-augmented workflows before standard implementation becomes a commodity. Links Notes and resources at ocdevel.com/mlg/mla-30 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want Market Data and Displacement ML engineering demand rose 89% in early 2025. Median salary is $187,500, with senior roles reaching $550,000. There are 3.2 open jobs for every qualified candidate. AI-exposed roles for workers aged 22 to 25 declined 13 to 16%, while workers over 30 saw 6 to 12% growth. Professional service job openings dropped 20% year-over-year by January 2025. Microsoft cut 15,000 roles, targeting software engineers, and 30% of its code is now AI-generated. Salesforce reduced support headcount from 9,000 to 5,000 after AI handled 30 to 50% of its workload. Sector Comparisons Creative: Chinese illustrator jobs fell 70% in one year. AI increased output from 1 to 40 scenes per day, crashing commission rates by 90%. Trades: US construction lacks 1.7 million workers. Licensing takes 5 years, and the career fatality risk is 1 in 200. High suicide rates (56 per 100,000) and emerging robotics like the $5,900 Unitree R1 indicate a 10 to 15 year window before automation. Orchestration: Prompt engineering roles paying $375,000 became nearly obsolete in 24 months. Claude Code solves 72% of GitHub issues in under eight minutes. Technical Specialization Priorities Model Ops: Move from training to deployment using vLLM or TensorRT. Set up drift detection and monitoring via MLflow or Weights & Biases. Evaluation: Use DeepEval or RAGAS to test for hallucinations, PII leaks, and adversarial robustness. Agentic Workflows: Build multi-step systems with LangGraph or CrewAI. Include human-in-the-loop checkpoints and observability. Optimization: Focus on quantization and distillation for on-device, air-gapped deployment. Domain Expertise: 57.7% of ML postings prefer specialists in healthcare, finance, or climate over generalists. Industry Perspectives Accelerationists (Amodei, Altman): Predict major disruption within 1 to 5 years. Skeptics (LeCun, Marcus): Argue LLMs lack causal reasoning, extending the adoption timeline to 10 to 15 years. Pragmatists (Andrew Ng): Argue that as code gets cheap, the bottleneck shifts from implementation to specification.

Jason Martin, Director of Adversarial Research at HiddenLayer, returns to discuss the security implications of OpenClaw, a viral open-source AI personal assistant that was entirely vibe-coded and has exploded to 180,000 GitHub stars. Subscribe to the Gradient Flow Newsletter

In this episode of Startup Hustle, Matt Watson interviews Krishna Oza, founder and COO of Git Hired, discussing the challenges of hiring software engineers, particularly for startups. Krishna shares his personal experiences that led to the creation of GitHired, an AI-driven platform designed to help startups find the right technical talent based on proof of work. The conversation delves into the unique needs of early-stage developers, the importance of product thinking, and how GitHired identifies and surfaces 10x engineers. Krishna also discusses the business model of GitHired and the struggles faced by startup founders in finding suitable engineering talent.TAKEAWAYSKrishna's personal experience with hiring challenges inspired GitHired.Startups need engineers who can match their fast-paced environment.Early-stage developers are builders who understand product development.Product thinking is crucial in today's AI-driven landscape.10x engineers possess product vision and minimal organizational friction.Get Hired surfaces hidden engineering talent through GitHub analysis.The platform creates one-page portfolios for applicants based on their work.Complexity of projects is a key factor in evaluating candidates.The business model includes a flat fee for successful hires.Startup founders often struggle to find engineers who can build for users.⏱️ Episode Breakdown00:00 The Genesis of GitHired03:01 The Ideal Early Stage Developer07:01 The Importance of Product Thinking10:10 Identifying 10x Engineers12:52 The Role of Proof of Work20:09 Business Model and Market Fit23:40 Startup Founder StrugglesLinks & ResourcesConnect with Krishna Oza on LinkedInWhat Smart CTOs Are Doing Differently With Offshore Teams in 2025Subscribe to the Global Talent SprintFull Scale – Build your dev team quickly and affordablyIf you're trying to get your team out of the basement and into real product ownership, this episode is your playbook. Stop being a ticket factory. Build teams that think, create, and lead.Follow the show, rate it, and send this to someone who's still trying to do “real Scrum.” They need it more than you do.

In this episode of the Ardan Labs Podcast, Ale Kennedy talks with Jens Neuse, CEO and co-founder of WunderGraph, about his unconventional path into technology and entrepreneurship. After a life-altering accident ended his carpentry career, Jens taught himself to code during recovery and eventually built WunderGraph to solve modern API challenges.Jens shares the evolution of WunderGraph from an early-stage startup to a successful open-source platform, including pivotal moments like securing eBay as a customer. The conversation highlights the importance of resilience, community-driven development, and balancing startup life with family, offering insight into what it takes to build meaningful technology through adversity and persistence.00:00 Introduction and Current Life07:19 Dropping Out and Carpentry Career10:52 Life-Altering Accident and Recovery18:01 Learning to Walk and Finding Direction27:46 Discovering Coding and Technology31:17 Starting the Startup Journey33:07 Discovering the Power of APIs40:50 Building a Team and Leadership Growth48:17 Founding WunderGraph59:07 Pivoting to Open Source01:05:32 eBay Breakthrough and Validation01:10:08 Balancing Family and Startup LifeConnect with Jens: LinkedIn: https://www.linkedin.com/in/jens-neuseMentioned in this Episode:Wundergraph: https://wundergraph.comWant more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

I guess let's talk about the BAFTAs... again. Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

SolarWinds patches four critical remote code execution vulnerabilities. A ransomware attack on Conduant puts the data of over 25 million Americans at risk. RoguePilot enables Github repository takeovers. ZeroDayRat targets Android and iOS devices. North Korea's Lazarus group deploy Medusa ransomware against organizations in the U.S. and the Middle East. Attackers' breakout times drop to under half an hour.  CISA maintains its mission despite staffing challenges. Russian satellites draw fresh scrutiny. Two South Korean teenagers are charged with breaching Seoul's public bike service. Krishna Sai, CTO at SolarWinds, discusses why leaders should focus less on speculating about an AI bubble, and more on how to quantify AI's tangible contributions. The Pope pushes prayerful priests past predictable programs.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Krishna Sai, CTO at SolarWinds, discussing why leaders should focus less on speculating about an AI bubble, and more on how to quantify AI's tangible contributions. Selected Reading Critical SolarWinds Serv-U flaws offer root access to servers (Bleeping Computer) Massive Conduent Data Breach Exfiltrates 8 TB Affects Over 25 Million Americans (GB Hackers) GitHub Issues Abused in Copilot Attack Leading to Repository Takeover (SecurityWeek) New ZeroDayRAT Malware Claims Full Monitoring of Android and iOS Devices (Hackread) North Korean state hackers seen using Medusa ransomware in attacks on US, Middle East (The Record) CrowdStrike says attackers are moving through networks in under 30 minutes (CyberScoop) Shutdown at D.H.S. Extends to Cyber Agency, Adding to Setbacks (The New York Times) From Cold War interceptors to Ukraine: how Russia came to park spy satellites next to the West's most sensitive tech in orbit (Meduza) Korean cops charge two teens over Seoul bike hire breach (The Register) Pope tells priests to use their brains, not AI, to write homilies (EWTN News) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show.  Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Sally and Aji flick through thoughtbot's guide to best practices in a bid to brush up on their coding habits. Our hosts discuss key ideas from the guides that stand out to them the most, why they're considered to be good practice, as well as reviewing the cons of complex writing and the benefits of simple coding. — Be sure to check out Sally's new repo Michel if you're looking to create an appointment database, and check out the thoughtbot guides for more general coding advice. If you've got some spare time and want to hear Aji's talk on breaking the enigma code you can watch that here. Your hosts for this episode have been thoughtbot's own Sally Hall and Aji Slater. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

This is a free preview of a paid episode. To hear more, visit www.latent.spaceFirst speakers for AIE Europe and AIEi Miami have been announced. If you're in Asia/Aus, come by Singapore and Melbourne. AI Engineering is going global!One year ago today, Anthropic launched Claude Code, to not much fanfare:The word of mouth was incredibly strong however, and so we were glad to be one of the first podcasts to invite Boris and Cat on in early May:As we discussed on the pod, all CC usage was API-based and therefore it was ridiculously expensive to do anything. This was then fixed by the team including Claude Code in the Claude Pro plan in early June, and then the virality caused us to make a rare trend call in late June:Now, 6 months on, Doug has just calculated that around 4% of GitHub is written by Claude Code:We talk about how Doug uses Claude Code to do SemiAnalysis work.Memory ManiaIn the second part of this episode, we also check in on Memory Mania, which is going to affect you (yes, you) at home if it hasn't already:Full Episode on YouTubeTimestamps00:00 AI as Junior Analyst00:59 Meet Swyx and Doug03:30 From Value Mule to Semis06:28 Moore's Law Ends Thesis12:02 Claude Code Awakening32:02 Agent Swarms Reality Check32:53 Kimi Swarm Benchmarks37:31 Bots vs Zapier Automation39:44 Claude Code Workflow Setup57:54 AGI Metrics and GDP01:04:48 Railroad CapEx Analogy01:06:00 Funding Bubbles and Demand01:08:11 Agents Replace Work Tools01:13:56 Codex vs Claude Race01:21:15 Microsoft and TPU Strategy01:34:13 TPU Window vs Nvidia01:36:30 HBM Supply Chain Squeeze01:39:41 Memory Shock and CXL01:45:20 Context Rationing Future01:54:37 Writing and Trail LessonsTranscript[00:00:00] AI as Junior Analyst[00:00:00] Doug: This crap makes mistakes all the time. All the time. It is still just like a, like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information and you bring it all together to make a good decision at the top. Historically what happens is that junior analyst, who I once was, went and gathered all that information, and after doing this enough times, there's a meta level thinking that's happening where it's like, okay, here's what I really understand and how this type of analysis, I'm an expert in, actually I'm very good at, I consistently have a hit rate.[00:00:28] Now I'm the expert, right? I don't think that meta level learning is there yet. We'll see if l ones do it, right? Everyone who's spending one quadrillion dollars in the world thinks it will, it better, it better happen by if you're spending, you know, a trillion dollars and there's not meta level learning.[00:00:44] But for me, in our firm, that massively amplifies everyone who is an expert. ‘cause like you have to still do something that you can just like lop it up. It's very obvious to me. What It's slop.[00:00:59] Meet Swyx and Doug

Cafes, restaurants and car washes all use points and rewards to drive behaviors. Can we do the same with our Learning Management Systems? In this week's episode of The Mindtools L&D Podcast, Incentli's Jeff Campbell speaks to Ross G and Ross D about: how digital currencies give LMS administrators levers they can pull to drive behavior the role of extrinsic and intrinsic motivation on learning the impact of branded swag on learner advocacy In 'What I Learned This Week', Ross D discussed GitHub commits (super fun to bet on!) For more from Incentli, visit incentli.com. Incentli are a Mindtools Kineo partner, so if you would like to discuss integrating points and rewards with our Totara LMS please do get in touch by contacting custom@mindtools.com. For more from Mindtools Kineo, visit mindtools.com or kineo.com. There, you'll also find details of our Learning Management Systems, Content Hub for leaders and managers, and custom learning design service. Like the show? You'll LOVE our newsletter! Subscribe to The L&D Dispatch at lddispatch.com Connect with our speakers If you'd like to share your thoughts on this episode, connect with us on LinkedIn: Ross Garner Ross Dickie Jeff Campbell

Hi family, let's talk about the BAFTAs and Charlemagne's 'response' ... Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

What if your AI assistant could negotiate your next car purchase, trade prediction markets, manage your inbox, and research crypto… while you sleep? This week we break down the insane rise of ClaudeBot → Moltbot → OpenClaw, the open-source AI agent that rocketed past 200,000 GitHub stars and sparked a massive wave of autonomous agents almost overnight. But of course, crypto had to crypto. Along the way, the project triggered a triple rebrand, a $16M scam token spun up by opportunists, and a security mess that unfolded in real time as attackers hunted for poorly-secured instances and exposed keys. We also get into the bigger picture: as AI agents start doing real work for people (and eventually paying bills, trading, and moving value), crypto becomes the natural payment rail. Permissionless. Programmable. Always on. Which is exciting… and also a whole new playground for scammers. We cover what happened, why it matters, and what you should do if you’re experimenting with agents: lock it down, separate machines/accounts, protect your keys, and don’t chase random tokens. Show notes and links: http://badco.in/804 Leave a comment with the best OpenClaw tutorial you’ve found — we’ll dig in.Support the show: https://badcryptopodcast.comSee omnystudio.com/listener for privacy information.

Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Better Python tests with inline-snapshot Alex Hall, on Pydantic blog Great for testing complex data structures Allows you to write a test like this: from inline_snapshot import snapshot def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({}) Then run pytest --inline-snapshot=fix And the library updates the test source code to look like this: def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({ "id": 123, "name": "test_user", "status": "active" }) Now, when you run the code without “fix” the collected data is used for comparison Awesome to be able to visually inspect the test data right there in the test code. Projects mentioned inline-snapshot pytest-examples syrupy dirty-equals executing Michael #2: jolt Battery intelligence for your laptop Support for both macOS and Linux Battery Status — Charge percentage, time remaining, health, and cycle count Power Monitoring — System power draw with CPU/GPU breakdown Process Tracking — Processes sorted by energy impact with color-coded severity Historical Graphs — Track battery and power trends over time Themes — 10+ built-in themes with dark/light auto-detection Background Daemon — Collect historical data even when the TUI isn't running Process Management — Kill energy-hungry processes directly Brian #3: Markdown code formatting with ruff Suggested by Matthias Schoettle ruff can now format code within markdown files Will format valid Python code in code blocks marked with python, py, python3 or py3. Also recognizes pyi as Python type stub files. Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks. Requires preview mode [tool.ruff.lint] preview = true Michael #4: act - run your GitHub actions locally Run your GitHub Actions locally! Why would you want to do this? Two reasons: Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides. Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile! When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run. Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match what GitHub provides. Extras Michael: Winter is coming: Frozendict accepted Django ORM stand-alone Command Book app announcement post Joke: Plug ‘n Paste

¿Es Python siempre la mejor opción para tus scripts de automatización? En este episodio, Lorenzo profundiza en una de las discusiones más recurrentes de la comunidad: la estabilidad de los scripts frente a la comodidad de los módulos de terceros. Acompaña a nuestro experto en Linux mientras desglosa los motivos que lo llevaron a abandonar soluciones basadas en Python para la gestión de metadatos de audio.Descubre ID3CLI, una herramienta potente y ligera escrita en Rust que soluciona los problemas de retrocompatibilidad y fallos en tiempo de ejecución. Aprenderás cómo automatizar el etiquetado de tus podcasts extrayendo datos directamente de archivos Markdown, eliminando la necesidad de introducir información manualmente en herramientas gráficas. Analizamos la importancia de tener binarios compilados que simplemente "funcionan", permitiéndote centrarte en crear contenido en lugar de arreglar herramientas rotas.Temas destacados del episodio: Bash vs Python: ¿Cuándo el "follón" de compilar merece la pena? Los peligros de depender de módulos de terceros que cambian sin previo aviso. De EasyTag a la automatización total en la terminal. Uso de Front Matter y RipGrep para un flujo de trabajo eficiente. Soporte de metadatos para Apple y carátulas en múltiples formatos. Capítulos,00:00:00 Introducción: El dilema de Bash vs Python00:00:48 El riesgo de las dependencias de terceros en Python00:01:35 La obsesión por la automatización de metadatos00:03:01 Flujo de trabajo: De EasyTag a la Terminal00:05:36 Extrayendo datos del Front Matter en Markdown00:07:24 Herramientas antiguas: ID3 y MiD3v2 (Mutagen)00:09:12 El colapso de los módulos y la necesidad de compilar00:10:13 Presentando ID3CLI: La solución definitiva en Rust00:11:53 Características técnicas y soporte de formatos (MP3, OGG, FLAC)00:13:48 Integración de ID3CLI en scripts de automatización00:15:23 Reflexión sobre la importancia de los metadatos00:16:42 Nuevo proyecto: El podcast "La Era de las Distros"00:17:47 Comunidad y cierre del episodioAdemás, Lorenzo nos habla sobre su nuevo podcast "La Era de las Distros", una mirada necesaria a las distribuciones Linux que marcaron un hito en la informática española como LinEx o Guadalex. ¡Disfruta del episodio y optimiza tu entorno Linux!Más información y enlaces en las notas del episodio

Das ist das KI-Update vom 23.02.2026 unter anderem mit diesen Themen: Bremens Straßenbahnen werden zur KI-Überwachungszone KI-Systeme blockten 2025 Millionen schädliche Apps OpenClaw trifft auf Smart Glasses und GitHub will gegen AI Slop in Open Source vorgehen === Anzeige / Sponsorenhinweis === Dieser Podcast wird von einem Sponsor unterstützt. Alle Infos zu unseren Werbepartnern findet ihr hier. https://wonderl.ink/%40heise-podcasts === Anzeige / Sponsorenhinweis Ende === Links zu allen Themen der heutigen Folge findet Ihr im Begleitartikel auf heise online: https://heise.de/-11186054 Weitere Links zu diesem Podast: https://www.heiseplus.de/audio https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.ct.de/ki Eine neue Folge gibt es montags, mittwochs und freitags ab 15 Uhr.

Parce que… c'est l'épisode 0x712! Shameless plug 25 et 26 février 2026 - SéQCure 2026 31 mars au 2 avril 2026 - Forum INCYBER - Europe 2026 14 au 17 avril 2026 - Botconf 2026 28 et 29 avril 2026 - Cybereco Cyberconférence 2026 9 au 17 mai 2026 - NorthSec 2026 3 au 5 juin 2026 - SSTIC 2026 19 septembre 2026 - Bsides Montréal 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 Notes IA Sécurité et le code Kevin Beaumont: “Today in InfoSec Job Security …” - Cyberplace AI Found Twelve New Vulnerabilities in OpenSSL Anthropic rolls out embedded security scanning for Claude Cyber Stocks Slide As Anthropic Unveils ‘Claude Code Security' Plagiat chez Microsoft Microsoft deletes blog telling users to train AI on pirated Harry Potter books Microsoft Uses Plagiarized AI Slop Flowchart To Explain How Git Works The Promptware Kill Chain Why ‘secure-by-design' systems are non-negotiable in the AI era Side-Channel Attacks Against LLMs Gentoo dumps GitHub over Copilot nagware European Parliament bars lawmakers from AI tools AI chatbots to face strict online safety rules in UK LLM-generated passwords ‘fundamentally weak,' experts say PromptSpy ushers in the era of Android threats using GenAI Claude just gave me access to another user's legal documents OpenClaw Security Fears Lead Meta, Other AI Firms To Restrict Its Use Was an Amazon Service Taken Down By Its AI Coding Bot? Kevin Beaumont: “Microsoft need a better way of…” - Cyberplace OpenAI Employees Raised Alarms About Canada Shooting Suspect Months Ago The Internet Is Becoming a Dark Forest — And AI Is the Hunter Souveraineté ou tout ce que je peux faire sur mon terrain India's New Social Media Rules: Remove Unlawful Content in Three Hours, Detect Illegal AI Content Automatically UK to require tech firms to remove nonconsensual intimate images within 48 hours or face fines Greece throws support behind social media bans for kids Kevin Beaumont: “Ireland's data protection watc…” - Cyberplace Spain orders NordVPN, ProtonVPN to block LaLiga piracy sites Poland bans Chinese-made cars from entering military sites Texas sues TP-Link over Chinese hacking risks, user deception Microsoft throws spox under the bus in ICC email flap Digital sovereignty must define itself before it can succeed “Made in EU” - it was harder than I thought. Privacy ou tout ce qui devrait rester à la maison Underground Facial Recognition Tool Unmasks Camgirls Leaked Email Suggests Ring Plans to Expand ‘Search Party' Surveillance Beyond Dogs Mysk

Nick Câmara (CTO e Co-Founder da Firecrawl) conta como lançou o projeto open source num fim de semana, entrou no avião pra São Francisco… e quando pousou já tinha 1.000 estrelas no GitHub e os primeiros clientes pagando.Nesse episódio você vai descobrir:• Como a Firecrawl nasceu de um pivot brutal depois de um produto que “crescia, mas não explodia”.• O que realmente significa transformar qualquer site em texto limpo e JSON que IA entende de verdade.• Por que ser 100% open source foi a estratégia que mais acelerou a adoção.• Como atender Shopify, Canva e milhares de devs com apenas 22 pessoas no time.• A velocidade insana que eles alcançaram (menos de 1 segundo pra scrapear uma página).• E o que o Nick acha que todo mundo está subestimando nos próximos 24 meses de IA.Se você constrói com IA, vende pra devs ou sonha em fazer um SaaS que realmente escala, esse papo é ouro puro.

OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at ocdevel.com/mlg/mla-29 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter Steinberger in November 2025, the project reached 196,000 GitHub stars in three months. Architecture and Persistent Memory Operational Loop: Gateway receives message, loads SOUL.md (personality), USER.md (user context), and MEMORY.md (persistent history), calls LLM for tool execution, streams response, and logs data. Memory System: Compounds context over months. Users should prompt the agent to remember specific preferences to update MEMORY.md. Heartbeats: Proactive cron-style triggers for automated actions, such as 6:30 AM briefings or inbox triage. Skills: 5,705+ community plugins via ClawHub. The agent can author its own skills by reading API documentation and writing TypeScript scripts. Claude Code Integration Mobile to Deploy Workflow: The claude-code-skill bridge provides OpenClaw access to Bash, Read, Edit, and Git tools via Telegram. Agent Teams: claude-team manages multiple workers in isolated git worktrees to perform parallel refactors or issue resolution. Interoperability: Use mcporter to share MCP servers between Claude Code and OpenClaw. Industry Comparisons vs n8n: Use n8n for deterministic, zero-variance pipelines. Use OpenClaw for reasoning and ambiguous natural language tasks. vs Claude Cowork: Cowork is a sandboxed, desktop-only proprietary app. OpenClaw is an open-source, mobile-first, 24/7 daemon with full system access. Professional Applications Therapy: Voice to SOAP note transcription. PHI requires local Ollama models due to a lack of encryption at rest in OpenClaw. Marketing: claw-ads for multi-platform ad management, Mixpost for scheduling, and SearXNG for search. Finance: Receipt OCR and Google Drive filing. Requires human review to mitigate non-deterministic LLM errors. Real Estate: Proactive transaction deadline monitoring and memory-driven buyer matching. Security and Operations Hardening: Bind to localhost, set auth tokens, and use Tailscale for remote access. Default settings are unsafe, exposing over 135,000 instances. Injection Defense: Add instructions to SOUL.md to treat external emails and web pages as hostile. Costs: Software is MIT-licensed. API costs are paid per-token or bundled via a Claude subscription key. Onboarding: Run the BOOTSTRAP.md flow immediately after installation to define agent personality before requesting tasks.

This week, we're sharing two segments. First up, a chat with Cooper Quintin, a senior staff technologist at the Electronic Frontier Foundation and developer of the Rayhunter. Rayhunter is open-source firmware to turn specific hotspots into IMSI-catcher, effectively scanning for and logging any signs of fake cell towers (often known under the brand-name of Stingrays) in the area. Law enforcement has at times deployed these as a way of collecting information about phones in the area and could use it to intercept some communications like sms or phone calls. Cooper talks about what's known of law enforcement use of IMSI-catchers, what has been observed of the data collected by deployed Rayhunters, phone security at demonstrations and related topics. Then you'll hear Radio Ausbruch from Frieberg from this month's B(A)D News podcast from the A-Radio Network talking about the repression and deBanking of anti-repression projects like ABC Dresden and Rote Hilfe in Germany based on pressure from the US government related to the so-called Antifa Ost case. This carries heavy implications for prisoner support, anti-racist and other social struggles. Links Cooper at DefCon talking about Rayhunter: https://m.youtube.com/watch?v=meC2JqNAbCA EFF on what Rayhunter has found so far: https://www.eff.org/deeplinks/2025/09/rayhunter-what-we-have-found-so-far Github for Rayhunter: https://github.com/EFForg/rayhunter EFF Mattermost chat platform: https://opensource.eff.org/ A project for detecting Meta Rayban sunglasses: https://github.com/NullPxl/banrays Ouispy bluetooth scanning and notification tool: https://github.com/colonelpanichacks/oui-spy . ... . .. Featured Track: TFSR by The Willows Whisper

Bienvenidos a FailAgain, una newsletter / podcast sobre crear contenido y estrategia.Hay una herramienta que lleva semanas rompiendo internet. Y la mayoría de creadores todavía no saben qué hacer con ella.OpenClawA finales de enero, un proyecto open-source llamado OpenClaw (antes Clawdbot) se convirtió en el repositorio de GitHub que más rápido ha crecido en la historia. 209.000 estrellas en semanas. Cobertura en TechCrunch. Un episodio con Lex Fridman. Y al creador, Peter Steinberger, fichado por OpenAI.¿Qué es?Un agente de IA que vive en tu ordenador, se conecta a tus aplicaciones y ejecuta tareas. Solo. Sin que se lo pidas cada vez.La diferencia clave con ChatGPT: ChatGPT te responde. OpenClaw actúa.Le dices qué quieres que haga, cuándo, y con qué herramientas. Y lo hace. Mientras tú estás haciendo otra cosa. O durmiendo.Llevo semanas mirando qué están haciendo los creadores que se han lanzado antes. Me he recorrido Twitter, Reddit, GitHub, y los vídeos de Matthew Berman, que tiene uno de los canales más detallados sobre el tema en inglés. Y he filtrado los que tienen sentido real para alguien que está construyendo su canal y su newsletter, no para un ingeniero con ocho monitores.Aquí van.0. El punto de partida: tu asistente por TelegramAntes de los casos avanzados, hay uno básico que casi todo el mundo monta primero y que ya justifica por sí solo el setup.Conectas OpenClaw a Telegram. A partir de ahí tienes un asistente al que puedes escribirle como si fuera un compañero: “redáctame una estructura de post sobre…”, “busca información sobre ese creador”, “resúmeme este artículo”. Él responde, ejecuta, y te lo manda de vuelta.La diferencia con usar ChatGPT o Claude es que este te conoce muy bien. Sabe en qué trabajas, cuál es tu audiencia, cómo escribes. No hay que explicarle el contexto cada vez. Y si le dices que recuerde algo, lo tiene en cuenta de cara al futuro.Es el punto de entrada. Una vez lo tienes, lo demás es tirar del hilo.1. El investigador de ideas Imagina que ves un artículo interesante. Lo mandas por Telegram con un simple “idea de vídeo”. En los siguientes minutos, OpenClaw hará lo siguiente:* Busca qué está diciendo la gente en Twitter sobre ese tema* Comprueba si ya lo has tratado antes * Te devuelve un briefing completo: título sugerido, concepto de thumbnail, los primeros 30 segundos del vídeo y una estructura por bloques.Lo que antes llevaba una hora de búsquedas y notas, ahora llega en minutos.Para un creador que trabaja solo, esto es brutal. ¿Cuántas veces has visto algo y has pensado “esto podría ser un contenido” pero no te has lanzado a desarrollarlo? 2. El espía semanalCada semana analiza los canales de tu nicho: qué publicaron, qué les funcionó, qué vídeos están despuntando, qué patrones de título están repitiendo.El valor no es solo saber qué hace la competencia. Es hacerlo de forma sistemática, sin que te cueste tiempo. La mayoría de creadores lo hacemos a mano de vez en cuando, que es lo mismo que no hacerlo. Con esto tienes una foto actualizada del terreno cada lunes. Otra vez lo mismo, ideas frescas que vienen a tu puerta.

Visit https://cupogo.dev/ for all the links.Using go fix to modernize Go codeEric S. Raymond's tweet about auto-converting his C code to GoEric's HomepageSkill-validatorLinkedIn, GitHub, AgentSkillReport.comcmd/vet: check for missing Err calls for bufio.Scanner and sql.Rows #17747Meetups Shay will be at:GoSF Go Israel April MeetupLightning Round:lazygitKoyeb is Joining Mistral AIPaged Out! #8 is out! ★ Support this podcast on Patreon ★

Foundations of Amateur Radio How to go about documenting your setup? Possibly the single most important thing that separates science from "fiddling around" is documentation. Figuring out how to document things is often non-trivial and me telling you that "unless you wrote it down, it didn't happen" only goes so far. If documentation isn't your thing, what about "I broke something and I don't know how it was before I fiddled" as an incentive instead? Recently I had cause to explore how to document how my station is configured. To give you a sense, the microphone is connected to a remote-rig, which is connected to a Wi-Fi base station, over Wi-Fi to a Wi-Fi slave, to another remote-rig, to the radio body, to the VHF port, through two coax switches, a run of RG213, to an antenna. When receiving, it goes from the antenna, to a run of RG213, through two coax switches, to the VHF port, to the radio body, to a remote-rig, to a Wi-Fi slave, to a Wi-Fi base station to a remote-rig, to the remote head, to a set of headphones. Of course, at this point I've written it down, so, job done .. right? Well, what about the data connection, the external speaker, the remote head display and other goodies, say nothing of the duplicate devices with similar names. All in all, the FT-857d has something like eleven ports, each remote-rig has ten, so just wording it is a start, but hardly qualifies as documented. What if we drew a picture instead? At this point you could pull out your crayons and start scribbling on a sheet of butcher's paper and that would be a fine start, but it would be difficult to share with me or anyone else and updating it would be a challenge, let alone versioning it. As it happens, we're not the first people to have this issue. In the 1980's and 1990's researchers at Bell Labs were trying to figure out how to draw graphs and from that work a language, 'DOT', since everyone is a fan of the "DAG Of Tomorrow", and a series of tools, which today are known as 'Graphviz', made the visualisation of relationships possible without the application of coloured wax on dried cellulose fibre. In my other, computing job, I had cause to visualise the relationship between a million or so nodes, allowing me to discover a specific node that was directing all traffic, where I could insert my debugging code, but it was only possible thanks to these free and open source tools. While the DOT language isn't particularly complex, it occurred to me that for someone not conversant with the syntax, we can start even simpler with a CSV text file that shows the relationships between each device and convert the CSV to DOT and in turn to a picture. For example, I documented the relationship between the radio and the antenna by adding five lines to a CSV file, essentially, FT857d to VHF port to VHF coax switch to VHF grounding switch to RG213 to antenna. In all, to document everything except power, since I haven't decided how I want to describe it, I used a CSV with 47 lines. On the face of it, that might sound ridiculous, but I can tell you, it shows all the sockets on the FT857d, all the sockets on both remote-rig devices and the relationships between them. With it anyone can duplicate my set-up. Having previously spent some quality time learning various aspects of the DOT language, I figured I could write a little script to convert CSV files to DOT, but being of the generation of software developers with the attitude, "Why write something if someone else already did?", I discovered that Reinier Post at the Eindhoven University of Technology has a delightful collection of scripts, including one appropriately named 'csv2dot'. Written in Perl, the only language that according to some looks just as impenetrable before and after encryption, the tool works as advertised and makes a DOT file that you can then visualise using Graphviz. Of course there's Python scripts lying around that claim to do the same, but I wasn't keen to install the kitchen sink just to try them. Instead I made a quick little Docker file that you can find on my vk6flab GitHub repository that will walk you through this, complete with my example, so you have a starting point. Now, I used this here to describe my station, well, one part of it, but it can easily extend to document your entire station, and because we're talking about text files that contain the information, anyone with a copy of a text editor can update the file when things change, since that's where the real magic happens. So, what are you waiting for, documentation? I'm Onno VK6FLAB

Pour l'épisode de cette semaine, je reçois Gilles Barbier, entrepreneur récidiviste et fondateur de TinyStaff.Gilles évolue dans l'écosystème tech depuis plus de 20 ans : créateur de startups, ancien CTO de The Family, contributeur open source… Il suit aujourd'hui de très près la révolution en cours autour des agents IA et des nouveaux outils de développement.Au cours de cet épisode, nous avons parlé d'OpenClaw, le projet open source qui a explosé en quelques semaines (plus de 200 000 stars sur GitHub), et de ce qu'il change concrètement dans la façon de travailler.Nous avons abordé :Ce qu'est réellement OpenClaw et pourquoi il a suscité un tel engouementLa différence entre une IA “chat” classique et une IA agentique proactiveComment Gilles a construit TinyStaff au-dessus d'OpenClaw pour proposer des “virtual employees” prêts à l'emploiL'impact des outils comme Claude Code, Codex ou Cursor sur la productivité des développeursLe coût réel des tokens et la question des abonnements vs APIL'avenir des SaaS face aux agents : disparition, transformation ou adaptation ?Pourquoi les éditeurs devront rendre leurs produits “agent-compatible” (API, CLI, MCP…)Ce que cette révolution va changer, au-delà des développeurs, pour tous les métiersUn épisode un peu différent, plus “actu chaude” que d'habitude, mais passionnant pour comprendre la vague en cours et anticiper ses conséquences sur l'écosystème SaaS.Vous pouvez suivre Gilles sur LinkedIn.Bonne écoute !Pour soutenir SaaS Connection en 1 minute⏱ (et 2 secondes) :Abonnez-vous à SaaS Connection sur votre plateforme préférée pour ne rater aucun épisode

CoreStory is building code intelligence platforms that address the fundamental limitation of today's coding agents: their inability to navigate complex enterprise codebases. While foundation models excel at greenfield development, they fail at real-world engineering tasks in systems spanning millions of lines of code. CoreStory's context layer delivers a 44% improvement on SWE-bench, the industry's standard benchmark for measuring coding agent effectiveness on actual GitHub issues. In this episode of BUILDERS, I sat down with Anand Kulkarni, CEO of CoreStory, to explore how his team is enabling the shift to AI-native engineering and seeding the category of spec-driven development across Microsoft, GitHub, and Amazon. Topics Discussed: Building with GPT-3 API 18 months before ChatGPT went public Why even GPT-5 and Opus 4.5 struggle with enterprise codebases on SWE-bench The narrative shift required when selling AI pre- and post-ChatGPT CoreStory's 44% improvement in coding agent performance through context intelligence How "spec-driven development" got adopted by Microsoft, GitHub, and Amazon without formal analyst relations The parallel between JIRA monetizing Agile and CoreStory enabling AI-native engineering Three-channel distribution: direct enterprise, coding agent partnerships via MCP, and hyperscaler/GSI routes Why specs become the source of truth while code becomes disposable in the AI era GTM Lessons For B2B Founders: Match your narrative precision to technical depth: CoreStory deploys three distinct positioning strategies based on audience sophistication. For AI practitioners tracking benchmarks, they lead with "44% SWE-bench improvement"—a metric that immediately signals meaningful progress on the hardest problem in the space. For engineering leaders aware of AI tooling but not deep in the research, they focus on velocity gains and ROI metrics. For executives, they describe reverse-engineering codebases into machine-readable specs. The key insight: technical audiences dismiss vague value props, while non-technical audiences get lost in benchmark details. Map your positioning to how your audience measures success in their world. Seed category language through earned adoption, not manufactured consensus: Anand initially called their approach "requirements-driven development" before simplifying to "spec-driven development." Rather than pitching analysts, they used the term consistently in customer conversations, gave talks at GitHub Universe, and shipped demos showing the workflow. When customers naturally adopted the language and community leaders began using similar terminology independently, Microsoft and GitHub followed with their own implementations (like GitHub's SpecKit). The lesson: category language sticks when practitioners choose to use it because it clarifies their work, not because a vendor pushed it. Focus on customer adoption as proof of concept before seeking broader market validation. Position against emergent practices, not just incumbent products: CoreStory doesn't position against legacy code analysis tools—they position as the enabler of AI-native engineering, the discipline that will displace Agile. Anand's insight from watching JIRA's success: "People don't love JIRA. What they love is Agile as a way to move away from waterfall." CoreStory is betting that 10x velocity gains from AI-native practices will drive the same categorical shift. When you're early in a technology wave, attach to the practice change (how teams will work differently) rather than feature comparisons with existing tools. Movements create markets. Design channel strategy around customer problem awareness: CoreStory's three channels map to different stages of buyer sophistication. Direct enterprise comes from teams already deep in AI engineering who've hit the context limitation wall. Coding agent partnerships (via MCP integration with tools like Cognition and Factory) serve builders wanting better AI tooling who haven't diagnosed the context problem yet. Hyperscalers and GSIs distribute into modernization and maintenance projects where AI enablement is emerging as a requirement. Each channel serves a distinct buyer journey stage. Don't force one go-to-market motion—design multiple paths based on where different customer segments are in understanding the problem you solve. Navigate pre-legitimacy markets by hiding the breakthrough: Before ChatGPT, selling anything AI-driven faced immediate skepticism about whether it was "real" or just smoke and mirrors. Anand couldn't lead with AI without triggering disbelief. CoreStory focused on delivered outcomes—"here's what you'll be able to do"—with AI as the mechanism, not the message. Post-ChatGPT, the challenge flipped: everyone expects AI, but now the differentiation question becomes harder. If you're building on emerging technology before market consensus forms, deemphasize the technology until buyers have context to evaluate it. Once the market validates the technology category, shift to demonstrating your specific technical advantage within it. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

OnThe New Stack Agents, Gavriel Cohen discusses why he built NanoClaw, a minimalist alternative to OpenClaw, after discovering security and architectural flaws in the rapidly growing agentic framework. Cohen, co-founder of AI marketing agencyQwibit, had been running agents across operations, sales, and research usingClaude Code. When Clawdbot (laterOpenClaw) launched, it initially seemed ideal. But Cohen grew concerned after noticing questionable dependencies—including his own outdated GitHub package—excessive WhatsApp data storage, a massive AI-generated codebase nearing 400,000 lines, and a lack of OS-level isolation between agents. In response, he createdNanoClawwith radical minimalism: only a few hundred core lines, minimal dependencies, and containerized agents. Built around Claude Code “skills,” NanoClaw enables modular, build-time integrations while keeping the runtime small enough to audit easily. Cohen argues AI changes coding norms—favoring duplication over DRY, relaxing strict file limits, and treating code as disposable. His goal is simple, secure infrastructure that enterprises can fully understand and trust.   Learn more from The New Stack about the latest around personal AI agents Anthropic: You can still use your Claude accounts to run OpenClaw, NanoClaw and Co. It took a researcher fewer than 2 hours to hijack OpenClaw OpenClaw is being called a security “Dumpster fire,” but there is a way to stay safe Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Boris Cherny is the creator and head of Claude Code at Anthropic. What began as a simple terminal-based prototype just a year ago has transformed the role of software engineering and is increasingly transforming all professional work.We discuss:1. How Claude Code grew from a quick hack to 4% of public GitHub commits, with daily active users doubling last month2. The counterintuitive product principles that drove Claude Code's success3. Why Boris believes coding is “solved”4. The latent demand that shaped Claude Code and Cowork5. Practical tips for getting the most out of Claude Code and Cowork6. How underfunding teams and giving them unlimited tokens leads to better AI products7. Why Boris briefly left Anthropic for Cursor, then returned after just two weeks8. Three principles Boris shares with every new team member—Brought to you by:DX—The developer intelligence platform designed by leading researchers: https://getdx.com/lennySentry—Code breaks, fix it faster: https://sentry.io/lennyMetaview—The AI platform for recruiting: https://metaview.ai/lenny—Episode transcript: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Boris Cherny:• X: https://x.com/bcherny• LinkedIn: https://www.linkedin.com/in/bcherny• Website: https://borischerny.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Boris and Claude Code(03:45) Why Boris briefly left Anthropic for Cursor (and what brought him back)(05:35) One year of Claude Code(08:41) The origin story of Claude Code(13:29) How fast AI is transforming software development(15:01) The importance of experimentation in AI innovation(16:17) Boris's current coding workflow (100% AI-written)(17:32) The next frontier(22:24) The downside of rapid innovation (24:02) Principles for the Claude Code team(26:48) Why you should give engineers unlimited tokens(27:55) Will coding skills still matter in the future?(32:15) The printing press analogy for AI's impact(36:01) Which roles will AI transform next?(40:41) Tips for succeeding in the AI era(44:37) Poll: Which roles are enjoying their jobs more with AI(46:32) The principle of latent demand in product development(51:53) How Cowork was built in just 10 days(54:04) The three layers of AI safety at Anthropic(59:35) Anxiety when AI agents aren't working(01:02:25) Boris's Ukrainian roots(01:03:21) Advice for building AI products(01:08:38) Pro tips for using Claude Code effectively(01:11:16) Thoughts on Codex(01:12:13) Boris's post-AGI plans(01:14:02) Lightning round and final thoughts—References: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

Sami makes his triumphant return to the Giant Robots Smashing into other Giant Robots Podcast as he and his fellow hosts discuss a brand new tool currently in pre-production at thoughtbot. Chad lays out his idea for the tool, how he thinks it will differ from other similar products currently available, who it's for and the considerations needed going in to make it as accessible as possible, and Sami plays devil's advocate as he asks Chad the hard questions about his new project. — Follow the development of thoughtbot's latest product through Chad's live streams, AI in Focus over on our YouTube channel. And if you want to look as cool as our hosts do you can check out the thoughtbot merch store! You can find Chad all over social media as @cpytel and Sami through his website. You can also connect with the trio via their LinkedIn pages - Chad - Will - Sami. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@giantrobots.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - LinkedIn - Mastodon - YouTube - Bluesky © 2026 thoughtbot, inc.

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

I have a theory that only bad projects get finished — good ones keep finding new things to do. Asciinema is a case in point. What started as a way to share terminal sessions with friends has, over 14 years, grown into a full suite of tools covering recording, hosting, playback, and live streaming — and been rebuilt multiple times along the way. So what does it actually take to record and replay a terminal session faithfully in a browser?Joining us for this conversation is Marcin Kulik, Asciinema's creator. The project's architecture has passed through almost every interesting corner of software engineering: a Python recorder built around pseudo-terminals (PTY), a ClojureScript terminal emulator for the browser that hit performance limits with immutable data structures and garbage collection pressure, a move to Rust compiled to WebAssembly, a Go experiment that didn't last, and a new Rust CLI for concurrent live streaming backed by an Elixir/Phoenix server that calls Rust code via NIFs. The same Rust terminal emulator library now powers all three components — the browser player, the server, and the CLI.If you've ever looked at those terminal animations embedded in a README and wondered what's underneath them, or if you're interested in how a passionate open-source developer navigates 14 years of language changes and rewrites, this conversation has plenty to offer.---Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinAsciinema: https://asciinema.orgAsciinema Docs: https://docs.asciinema.orgAsciinema CLI (GitHub): https://github.com/asciinema/asciinemaAsciinema Player (GitHub): https://github.com/asciinema/asciinema-playerAsciinema Server (GitHub): https://github.com/asciinema/asciinema-serverAVT - Rust terminal emulator library: https://github.com/asciinema/avtvt-clj - the original ClojureScript terminal emulator: https://github.com/asciinema/vt-cljPaul Williams' ANSI/VT100 State Machine Parser: https://vt100.net/emu/dec_ansi_parserRust: https://www.rust-lang.orgWebAssembly: https://webassembly.orgSolidJS: https://www.solidjs.comElixir: https://elixir-lang.orgPhoenix Framework: https://www.phoenixframework.orgRustler (Rust NIFs for Elixir/Erlang): https://github.com/rusterlium/rustlerClojure: https://clojure.orgClojureScript: https://clojurescript.orgcmatrix: https://github.com/abishekvashok/cmatrixMarcin Kulik on GitHub: https://github.com/ku1ikMarcin Kulik on Mastodon: https://hachyderm.io/@ku1ikMarcin Kulik on asciinema.org: https://asciinema.org/~ku1ik"They're Made Out of Meat" demo: https://asciinema.org/a/746358Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/---0:00 Intro2:28 What Is Asciinema?4:48 How Asciinema Started9:51 The Problem of Parsing Terminal Output14:07 Building a Cross-Platform Recorder17:01 Rewriting the Parser in ClojureScript22:19 The Hidden Complexity of Terminals29:28 Rendering Terminals in the Browser39:47 When ClojureScript Can't Keep Up45:28 Moving to Rust and WebAssembly52:01 The Go Experiment57:43 Adding Live Terminal Streaming1:07:12 Can You Scrub Back in a Live Stream?1:14:40 Editing Recordings1:25:27 Outro

Welcome to episode 1 of Pop Woke. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

¿Tu contenedor está realmente funcionando o es solo un proceso zombie ocupando memoria? En el episodio 772 de Atareao con Linux, te revelo los secretos para gestionar la salud de tus contenedores como un experto.Soy Lorenzo y en esta entrega nos enfocamos en Podman y los Health Checks. Si en el episodio 688 hablamos de Docker, hoy damos el salto definitivo hacia la automatización profesional en Linux utilizando Quadlets y Systemd.Lo que vas a descubrir en este audio: Detección de Zombies: Aprende a identificar procesos que parecen activos pero no responden. Dependencias Reales: Cómo configurar tu stack de WordPress, MariaDB y Redis para que arranquen en el orden correcto y solo cuando sus predecesores estén sanos. Auto-reanimación: Configura políticas de reinicio que actúan automáticamente ante fallos de salud. Notificaciones Inteligentes: Recibe alertas en Telegram o en tu escritorio cuando tus servicios cambien de estado.Este episodio es una guía práctica para cualquier persona que quiera robustecer su infraestructura de contenedores, evitando los cierres inesperados y las dependencias rotas que suelen ocurrir con herramientas tradicionales como Docker Compose.Capítulos: 00:00:00 ¿Tu contenedor está vivo o es un ZOMBIE? 00:01:44 ¿Qué es realmente un Health Check? 00:02:22 4 Ventajas de usar Health Checks 00:03:20 Implementación en Podman y Docker 00:05:20 La potencia de los Quadlets 00:08:58 Dependencias inteligentes: WordPress+MariaDB+Redis 00:11:00 Notificaciones On Success 00:13:55 Gestión de errores On Failure 00:18:21 Próximos pasos y TraefikSi disfrutas del podcast, te agradecería enormemente una valoración en Spotify o Apple Podcast. ¡Ayúdame a difundir la palabra del Open Source!Más información y enlaces en las notas del episodio

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

This is my second conversation with Josh Kushner, founder and managing partner of Thrive Capital. I recorded this conversation in October after publishing the Colossus cover story about him and Thrive. Given the overwhelming response, we created some breathing room before releasing it. Josh started Thrive in 2011. The firm now manages approximately $50 billion with a very small investment team. What makes Thrive different is how concentrated they are and how involved they get with their portfolio companies. We cover the iconic investments that defined Thrive: Instagram, Stripe, GitHub, and spend a lot of time on OpenAI. Josh explains how Thrive thinks about investing today and the three categories they're currently focused on. Josh also talks about building the firm, why they keep the team small, and what he's learned from A24 about enabling artists to do their best work. He shares personal stories that shaped him, including his grandmother's experience surviving the Holocaust, and lessons from Stan Druckenmiller, Jon Winkelried, and others at formative moments in Thrive's history. Please enjoy my great conversation with Josh Kushner. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- ⁠Rogo⁠ is an AI-powered platform that automates accounts payable workflows, enabling finance teams to process invoices faster and with greater accuracy. Learn more at ⁠Rogo.ai/invest⁠. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:43) Intro: Josh Kushner (00:03:46) How Thrive Has Changed Since 2023 (00:05:18) Thrive's Entrepreneurial Culture (00:12:22) The Power of Small Teams (00:13:35) Sponsors (00:14:35) Concentration as Differentiation (00:16:16) The Github Deal (00:18:08) Lesson from Stan Druckenmiller (00:20:37) Leading Stripe's $50 Billion Round (00:23:16) Instagram: Doubling an Investment in Days (00:25:43) Isomorphic: Thrive as an Enabling Technology (00:27:04) Thrive & A24 (00:28:19) OpenAI: The Product Josh Couldn't Unsee (00:32:09) Pricing the OpenAI Investment (00:33:40) OpenAI and Power (00:35:26) Finding Joy in Hard Work (00:39:15) Inside View of the Tech & AI Landscape (00:42:28) Three Investment Categories Thrive is Focused On (00:44:37) Thrive Holdings: Inside-Out Disruption (00:48:54) Competition in Venture (00:50:49) Sponsors (00:51:48) Thrive's Immutable Values (00:54:21) A Family Story of Survival (00:56:43) The American Dream (00:58:03) What Artists Can Teach Investors (01:00:26) Never Compromise Your Values (01:01:33) The Story Behind Josh's Forever Watch

This week on Sinica, I speak with Kyle Chan, a fellow at the John L. Thornton China Center at Brookings, previously a postdoc at Princeton, and author of the outstanding High-Capacity Newsletter on Substack. Kyle has emerged as one of the sharpest and most empirically grounded voices on U.S.-China technology relations, and he holds the all-time record for the most namechecks on Sinica's “Paying it forward” segment. We use his recent Financial Times op-ed on “The Great Reversal” in global technology flows and his longer High-Capacity essay on re-coupling as jumping-off points for a wide-ranging conversation about where China now sits at the global technological frontier, why the dominant decoupling narrative misses powerful structural forces pulling the two economies back together, and what all of this means for innovation, choke points, and the global tech ecosystem.4:35 – How Kyle became Kyle Chan: from Chicago School economics to development, railways, and systems thinking 12:50 – The Great Reversal: China at the technological frontier, from megawatt EV charging to LFP batteries 17:59 – The electro-industrial tech stack and China's overlapping, mutually reinforcing tech ecosystems 22:40 – Industrial strategy and time horizons: patience, persistence, and the long arc of China's auto industry 33:45 – Re-coupling under pressure: Waymo and Zeekr, Unitree robots, and the structural forces binding the two economies 40:22 – The gravity model: can political distance overwhelm technological mass? 47:01 – What China still wants from the U.S.: Cursor, GitHub, talent, and the AI brain drain 51:52 – Weaponized interdependence and the danger of securitizing everything 57:30 – Firm-level adaptation: HeyGen, Manus, and the playbook for de-sinification 1:02:58 – The view from the middle: Gulf states, Southeast Asia, and India as geopolitical arbitrageurs 1:10:18 – Engineering resilience: what policymakers are getting wrong about the systems they're buildingPaying it forward: Katrina Northrop; Grace Shao and her AI Proem newsletterRecommendations:Kyle: Wired Magazine's Made in China newsletter (by Zeyi Yang and Louise Matsakis); The Wire China Kaiser: The Wall Dancers: Searching for Freedom and Connection on the Chinese Internet by Yi-Ling LiuSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As AI accelerates innovation and adoption, leaders are facing rising cognitive load, shifting systems, and new emotional realities inside their organizations. In this episode, Deloitte's Chief Innovation Officer Deborah Golden joins us to explore how AI is reshaping leadership, why vulnerability and empathy are critical in this moment, and how anti-fragility, not just resilience, will define the future of work.Featuring:Deborah Golden – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:DeloitteSponsor: Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIUpcoming Events: Register for upcoming webinars here!

In our latest Open Source Startup Podcast episode, co-hosts Robby and Tim talk with Magnus Müller, the Co-Founder & CEO of Browser Use - the platform that makes web agents come to life. Their open source, browser-use, has almost 80K stars on GitHub and is widely adopted. This episode dives into the unexpected rise of an open-source browser automation project that took off during Y Combinator - while many similar projects before and after it never gained traction. The founder reflects on why: delivering a “magical moment” fast. Early demos showing AI controlling a browser, inspired by trends like OpenAI's Operator, and immediately clicked with people. What began as a developer-only Python library evolved into a hosted product as non-technical users - from sales teams to startups - wanted access. Along the way, the team leaned into controversial but compelling use cases, like AI applying for jobs on your behalf, which sparked conversation and accelerated growth. The core challenge they focused on solving was reliability: unlike deterministic automation scripts, AI agents can behave unpredictably, making trust and repeatability central problems to overcome.The long-term vision goes beyond UI automation toward agents that can skip the browser entirely and interact directly with website servers through structured actions. But the conversation isn't just about infrastructure. The founder admits that early growth came mostly from building and talking to users, while recent months have been dedicated to storytelling and marketing rather than coding. A personal through-line emerges as well: learning to replace defensiveness with curiosity - questioning assumptions, staying open to feedback, and continuously refining both the technology and the narrative around it.

Heute im Blick: die Diskussion um Altersgrenzen für Social Media und neue Vorstösse, Altersprüfungen in die Systeme zu bringen. Leider werden auch hier zunehmend Unternehmen ins Spiel gebracht, deren Investor Peter Thiel ist. Ansonsten ist das Internet in Aufruhr, weil durch OpenClaw eine Armada von AI-Agenten auf das Netz losgelassen wird, die sich jetzt in Foren und auf GitHub wichtig machen und offenbar schnell zur beleidigten Leberwurst mutieren und beginnen, Entwickler an den Pranger zu stellen, von denen sie sich unfair behandelt fühlen. Währenddessen will das US-Amerikanische "Kriegsministerium" auch gerne mehr AI nutzen bei ihren Operationen und versucht Anthropic dazu zu bringen, von ihren moralischen Vorsätzen abzurücken. Im Superbowl wird derweil die Zusammenschaltung aller privaten Überwachungskameras zur Hundesuche gepriesen, was einige Leute nachdenklich stimmt, ob nicht demnächst noch mehr als nur Hunde gesucht werden könnten.

Yeah, you prolly saw the news: OpenAI acquihired OpenClaw.

Joël and Sally examine the simpler components of programming and why using basic data structures may not always be the best approach to solving a problem. Our hosts cover all the telltale signs and symptoms of primitive obsession, what it is, it's drawbacks and limitations, and how to avoid it creeping into your own work. — Want to learn more about primitive obsession and readability in programming? Check out these links for some wider reading, including a talk from Joël! - Design Patterns and Null - thoughtbot blog on primitive obsession - Define User Your hosts for this episode have been thoughtbot's own Joël Quenneville and Sally Hall. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

Taylor Mullen, Principal Engineer at Google and creator of Gemini CLI, reveals how his team ships 100-150 features and bug fixes every week—using Gemini CLI to build itself. In this first in-depth interview about Gemini CLI's origin story, we explore why command-line AI agents are having a "terminal renaissance," how Taylor manages swarms of parallel AI agents, and the techniques (like the viral "Ralph Wiggum" method) that separate 10x engineers from 100x engineers. Whether you're a developer or AI-curious, you'll learn practical strategies for using AI coding tools more effectively.

Hi family, let's talk the Hasan Piker and Jennifer Welch meet-up. Because you all know how much I love Hasan... Jump in with Janaya Future Khan. Project MVT on Github: https://github.com/mvt-project/mvt SUBSCRIBE + FOLLOW IG: www.instagram.com/darkwokejfk Youtube: www.youtube.com/@darkwoke TikTok: https://www.tiktok.com/@janayafk SUPPORT THE SHOW Patreon - https://patreon.com/@darkwoke Tip w/ a One Time Donation - https://buymeacoffee.com/janayafk Have a query? Comment? Reach out to us at: info@darkwoke.com and we may read it aloud on the show!  

As LLM apps evolve from simple chatbots to tool-using agents, the attack surface explodes, and the old security playbooks don't hold. In this episode of Alexa's Input (AI), Alexa Griffith sits down with Ian Webster, co-founder and CEO of PromptFoo, to break down what AI security actually looks like in practice: automated red teaming, prompt injection and jailbreak testing, evaluation workflows that scale, and why “guardrails alone” is not a security strategy.Ian shares how PromptFoo grew from a side project into a widely adopted open-source standard, what it means to raise multi-millions in a fast-moving market, and how enterprises are approaching the full vulnerability lifecycle, from finding issues to triage, remediation, and validation. Ian also discusses the “lethal trifecta” that makes agents fundamentally risky (untrusted input + sensitive data + exfil path), and why MCP security isn't just about users and tools, it's about dangerous tool combinations and rogue servers.Podcast LinksWatch: ⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠More: ⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at:PromptFoo Website: https://www.promptfoo.dev/Github: https://github.com/promptfoo/promptfooIan's LinkedIn: https://www.linkedin.com/in/ianww/Chapters00:00 Introduction to AI Security Challenges02:06 Funding and Growth of PromptFu06:16 The Genesis of PromptFu11:05 Career Journey and Lessons Learned12:53 Understanding AI Red Teaming17:36 Recent AI Security Vulnerabilities19:46 The Dual Nature of AI in Security21:47 Understanding the Lethal Trifecta in AI Security24:22 Exploring Model Context Protocol (MCP) and Its Security Implications26:22 Common Security Issues in MCP Systems28:17 The Role of Identity and Permissions in AI Security30:00 Practical Implications of Using PromptFoo for Developers31:33 Evaluating Language Models: Challenges and Techniques36:34 The Limitations of Guardrails in AI Security38:25 Best Practices for Engineers in AI Development39:58 Future Trends in AI and Security42:28 Everyday Applications of AI and Language Models

https://clearmeasure.com/developers/forums/   Damian Brady is a Staff Developer Advocate at GitHub. He's a developer, speaker, and author specializing in AI, DevOps, MLOps, developer process, and software architecture. Formerly a Cloud Advocate at Microsoft for four years, and before that, a dev at Octopus Deploy and a Microsoft MVP, he has a 25-year background in software development and consulting in a broad range of industries. In Australia, he co-organized the Brisbane .Net User Group and launched the annual DDD Brisbane conference. Mentioned In This Episode  Episode 306 Episode 258 Episode 206 Episode 008 Github CoPilot Workspace X Account Website  Githubnext  Copilot for Docs Copilot for Pull Requests CoPilot Voice "What is GitHub Models? Here's how to use AI models easily"  "15 Minutes to Merge: The top feature announcements from GitHub Universe!" SpecKit  Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that’s the fastest-growing project in GitHub history. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep491-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/peter-steinberger-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Peter’s X: https://x.com/steipete Peter’s GitHub: https://github.com/steipete Peter’s Website: https://steipete.com Peter’s LinkedIn: https://www.linkedin.com/in/steipete OpenClaw Website: https://openclaw.ai OpenClaw GitHub: https://github.com/openclaw/openclaw OpenClaw Discord: https://discord.gg/openclaw SPONSORS: To support this podcast, check out our sponsors & get discounts: Perplexity: AI-powered answer engine. Go to https://perplexity.ai/ Quo: Phone system (calls, texts, contacts) for businesses. Go to https://quo.com/lex CodeRabbit: AI-powered code reviews. Go to https://coderabbit.ai/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Blitzy: AI agent for large enterprise codebases. Go to https://blitzy.com/lex Shopify: Sell stuff online. Go to https://shopify.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex OUTLINE: (00:00) – Introduction (03:51) – Sponsors, Comments, and Reflections (15:29) – OpenClaw origin story (18:48) – Mind-blowing moment (28:15) – Why OpenClaw went viral (32:12) – Self-modifying AI agent (36:57) – Name-change drama (54:07) – Moltbook saga (1:02:26) – OpenClaw security concerns (1:11:07) – How to code with AI agents (1:42:02) – Programming setup (1:48:45) – GPT Codex 5.3 vs Claude Opus 4.6 (1:57:52) – Best AI agent for programming (2:19:52) – Life story and career advice (2:23:49) – Money and happiness (2:27:41) – Acquisition offers from OpenAI and Meta (2:44:51) – How OpenClaw works (2:56:09) – AI slop (3:02:13) – AI agents will replace 80% of apps (3:10:50) – Will AI replace programmers? (3:22:50) – Future of OpenClaw community