Podcasts about open source ai

Preview for Later Today: Cliff May investigates Qatar's massive influence campaign within American universities and media. He highlights how Al Jazeera bypasses regulations to feed biased information into open-source AI platforms and internet search results.

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Nous Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Nous Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle 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: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Stay informed on current events, visit www.NaturalNews.com  - AI Bubble and Revenue Models (0:10) - Corporate Misuse of AI (2:25) - Token Maxing and AI Productivity (7:02) - Investment Advice and Open Source AI (10:28) - Scientific Community and Depopulation Agenda (14:45) - AI and Humanity's Future (23:50) - Government and Corporate Control (37:51) - Ethical Use of AI (1:05:51) - AI and Human Intelligence (1:06:08) - Resilience and Red-Pilling (1:11:45) - Discussion on EVs and Ethanol (1:14:34) - Advancements in Battery Technology (1:16:16) - Historical Context of EVs (1:18:56) - Political Discussion on Trump and Israel (1:20:41) - Economic and Political Challenges (2:00:28) - Bitcoin and Financial Freedom (2:00:53) - Future Outlook and Personal Reflections (2:12:42) - Discussion on Banking and Crime (2:14:40) - Generational Perspectives and Closing Remarks (2:16:40) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

Thanks Pressable for supporting the podcast! What hosting should feel like...nothing! https://pressable.com/wpminute Today's episode features a segment from Matt's discussion with Beaver Builder's Robby McCullough. Robby stopped by to tell us about Beaver Builder's AI integration and the strategy behind it. The guys also dig into how this technology fits into WordPress and the greater open-source landscape. You can catch the entire episode over on our WP Minute+ channel. Visit thewpminute.com for all the details: https://thewpminute.com/can-wordpress-keep-up-with-ai/ Watch the full interview: https://www.youtube.com/watch?v=fhMAXR9KkT8  Support our work at https://thewpminute.com/supportGet the newsletter at https://thewpminute.com/subscribe ★ Support this podcast ★

Ideogram shipped Ideogram 4.0, an open source image model that finally puts open weights on par with the closed frontier for professional work, with dense text rendering. Anthropic published a paper showing Claude now writes more than 80 percent of its internal code, and we walked the graph that proves it. And Founders Fund launched a Mafia show filmed at the Tosca Cafe in San Francisco, and the marketing play behind it is the real story.Sources:1. Ideogram 4.0https://x.com/ideogram_ai/status/20622022087003138722. Claude writes 80% of Anthropic's code (BBC)https://x.com/BBCNewsnight/status/20626556561553858933. Founders Fund Mafia showhttps://x.com/foundersfund/status/2062583885607862639

rsync's founder came back, patched real security bugs with AI help, and triggered an open source meltdown. Plus, two more projects reject AI-generated code as the community's newest fault line cracks wide open.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show 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 Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show 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 Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

1. China unveils its first open-source AI model for crop protection 2. China's major industrial firms' profits up 18.2 pct in first four months 3. China issues blue book analyzing future industries' sci-tech innovation, development

This week on REKT Vision, Bijan Maleki welcomes Blocmates founder Grant to dissect the biggest narratives and themes currently driving cryptocurrencies, macro, and AI, including Hyperliquid's outperformance, NEAR Protocol's run, Anthropic's valuation, trouble over at Ethereum, and much more. REKT Vision LIVE with @blocmates Let Monarch do your financial 'spring cleaning' for you! Use code REALVISION at Monarch.com to get your first year half off at just $50.

Clem Delangue joins MTS to discuss the global open-source AI landscape, the current large language model bubble, and the future of consumer robotics. Originally aired on MTS, Theo Jaffee and Sofia Puccini speak with Clément Delangue, CEO at Hugging Face, about the global open-source AI race, why he believes the real bubble is in API-based large language models, and how robotics could become the next major interface for AI. They also discuss AI safety, U.S.-China competition, open-weight models, and why Hugging Face became the infrastructure layer for open AI development.   Resources: Follow Clem on X: @ClementDelangue Follow Theo on X: @theojaffee  Follow Sofia on X: @schisofrenia Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Open Source AI is entering a new era, one shaped by self-improving AI Agents, recursive learning systems, and rapidly evolving AI Tools that blur the line between software and autonomous collaborators. In this episode, Daniel and Chris sit down with Nous Research co-founder and CTO Jeffrey Quesnelle to explore Hermes Agent. Along the way, they discuss models vs. harnesses, the changing role of developers, and one of the biggest questions facing the AI Future: what remains uniquely human as AI capabilities continue to accelerate?Featuring:Jeffrey Quesnelle – Website, LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Nous ResearchHermes AgentSponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

In this episode of the Ardan Labs Podcast, Ale Kennedy talks with Eugene Cheah, founder of Featherless, about his journey from physics to building globally accessible AI systems. Eugene shares his vision for making AI more affordable, multilingual, and open to communities around the world through efficient architectures and open-source collaboration.The conversation explores GPU optimization, evolving AI infrastructure, the importance of multilingual support, and the balance between innovation and regulation. Eugene also reflects on speaking at the United Nations, the future of open-source AI, and why accessibility and transparency are essential for the next generation of AI technology.00:00 Introduction and Featherless02:25 Education and Early Interests10:24 University and Military Service15:19 Entering the AI Industry22:33 Startups and AI Development30:42 AI as a Force for Good34:28 AI, Culture, and Automation42:13 Fundraising and Building a Startup50:10 AI Architecture and Optimization58:23 The Evolution of Featherless01:02:37 Building a Global AI Vision01:06:57 Open Source and AI Accessibility01:12:35 AI Risks and Real-World Concerns01:18:20 Lessons Learned and Final ThoughtsConnect with Eugene: LinkedIn: https://www.linkedin.com/in/eugene-cheah-a47791126/Mentioned in this Episode:Featherless AI: https://featherless.ai/Want 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

The Information's Rocket Drew talks with TITV Host Akash Pasricha about Elon Musk's legal defeat. We also talk with Catherine Perloff about Amazon's Trainium chip gaining industry traction and Stephanie Palazzolo about the widening gap between open-source and closed-source AI models. Lastly, we get into tech IPO market dynamics and inference chip trends with Sapphire Ventures Co-founder Jai Das.Articles discussed on this episode: https://www.theinformation.com/articles/amazons-nvidia-alternative-starts-winning-ai-developershttps://www.theinformation.com/newsletters/ai-agenda/gap-widening-anthropic-open-source-modelsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Elon Musk Loses OpenAI Lawsuit11:56 - Amazon's Trainium Chips Gain Traction23:59 - Open-Source AI Gains Ground on Cost Advantage31:04 - Where Do Cerebras Shares Go From Here?

Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Is the era of American tech dominance ending? Get an inside look at how China's pragmatic approach to AI, robotics, and hardware is shifting the global balance, and why the US might need a new playbook to keep up. Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Is the era of American tech dominance ending? Get an inside look at how China's pragmatic approach to AI, robotics, and hardware is shifting the global balance, and why the US might need a new playbook to keep up. Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Is the era of American tech dominance ending? Get an inside look at how China's pragmatic approach to AI, robotics, and hardware is shifting the global balance, and why the US might need a new playbook to keep up. Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Is the era of American tech dominance ending? Get an inside look at how China's pragmatic approach to AI, robotics, and hardware is shifting the global balance, and why the US might need a new playbook to keep up. Trump and the CEOs go to China NVIDIA CEO joins Trump in China despite 'awkward' politics US clears H200 chip sales to 10 China firms as Nvidia CEO looks for breakthrough Empty Waymos invade Atlanta neighborhood, circle cul-de-sac for hours with no passengers The Class of 2026 is cooked Chinese AI groups pull ahead of US rivals in video generation race Google Weighs Using SpaceX to Launch Orbital Data Centers What smart people are saying about OpenAI's new $10 billion company to help businesses deploy AI Bitcoin trader recovers $400,000 using Claude AI after getting 'stoned' and losing wallet password 11 years ago — bot tried 3.5 trillion passwords before decrypting an old wallet backup Your Mattress Got Worse on Purpose Host: Leo Laporte Guests: Harper Reed and Amy Webb Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit scribe.how/twit shopify.com/twit box.com/AI NetSuite.com/TWIT

Until a few months ago, open source AI was kinda a hobby project. Now, it's tearing corporate boardrooms apart. Why? Over the past 6ish months, the gap between frontier closed AI and open sourced AI has shrunk to pretty much nothing. And with the surge of always on agents driving open models, their development and release schedule is on pace with the frontier labs. So if your team isn't paying attention to -- and running test cases through -- open AI models, there's a good chance you'll either be overpaying or playing catch up soon. We walk you through the 101 and what you need to know when it comes to open source AI in this Start Here Series special. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Open Source AI vs Closed Models ShiftChinese Model Distillation & Legal ImpactsEnterprise AI Cost Triage StrategiesGoogle Gemma 4 Local Model CapabilitiesFrontier Model Performance Gap Closing24/7 Agentic AI Systems OverviewAPI Pricing War: DeepSeek vs US VendorsLegal Protection Tradeoffs for Open Source AIAI Workflow Triage: Task-Specific ModelsFuture Trends: Local and Specialized LLMsTimestamps:00:00 Introducing the Firefly AI assistant03:33 Open source AI cost benefits09:25 AI model performance differences10:19 Open source model improvements15:28 Advancements in local AI capabilities17:04 Impact of Google's Gemma four22:15 Introducing Adobe's Firefly AI Assistant24:19 Adobe Firefly AI assistant beta launch29:26 Choosing the right AI tools32:00 Shifting workloads to open source33:31 Using open-source and closed models36:47 The future of open modelsKeywords: open source AI, open source models, local AI models, local models, closed source AI, closed models, proprietary AI, proprietary models, AI agents, agentic AI, AI workflow triage, cheap API, AI API costs, model distillation, Chinese open source models, China AI models, US AI models,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The push for new data centers at Microsoft is putting one of the its key clean power goals at risk. Also, Moonshot's annualized recurring revenue topped $200 million in April, driven by rapid growth in paid subscriptions and API usage. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of Grownlearn, Zorina Dimitrova speaks with Nikola Totuhov, Founder & COO of Invisio, about how Agentic AI is transforming business operations, automation, and scalable growth. This is not a theoretical conversation. It's a practical look at how AI is already being implemented inside companies to reduce manual work, unify fragmented data, and significantly improve operational efficiency. One key insight stands out: AI is not plug-and-play. Up to 70% of implementation is data preparation. Without structured data, even the most advanced systems fail to deliver real value. Nikola shares real-world examples, including how AI-driven workflows: • Reduced operational teams from 8 people to 2 • Increased social media performance by 85% • Automated large parts of customer support and internal processes The discussion also explores where AI is heading, from open-source, locally hosted models to the evolving role of human judgment in business-critical functions like sales, marketing, and strategy. This episode is especially relevant for founders, operators, product leaders, and investors looking to understand how AI can drive capital-efficient, scalable growth.

Stay informed on current events, visit www.NaturalNews.com  - China's Energy Security and Trade Relations with Iran (0:11) - US Navy's Vulnerabilities and Future of Warfare (3:25) - Impact of US Naval Actions on Global Trade and Geopolitics (29:39) - China's Strategic Planning and Technological Advancements (42:29) - US-China Trade Relations and Rare Earth Elements (50:33) - China's Open Source AI and Cultural Acceptance of AI (1:16:29) - Geopolitical Implications and Economic Advice (1:21:34) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

Is open source the true future of Artificial Intelligence? In this episode of the BRAVE Southeast Asia Tech Podcast, Jeremy Au sits down with Eugene Cheah, CEO and Co-Founder of Featherless AI. They dive deep into the architecture of the RWKV model, the intense global competition between open source and closed source AI, and how China is aggressively pushing an open source strategy to bypass chip constraints. Recorded with a focus on the Southeast Asian tech ecosystem, this episode breaks down the immediate impact of AI on the global south, specifically highlighting the vulnerability of the BPO and call center industries in the Philippines. Eugene also shares his extraordinary journey from building UIlicious to securing a $1M investment in San Francisco with no pitch deck, and his ongoing work with the Linux Foundation and the World Trade Organization to bridge the global AI language divide. Discover tactical insights into startup bootstrapping, macroeconomics, and the entrepreneurial mindset required to navigate the hyper-competitive deep tech space. Tune in to learn how to future-proof your business and stay ahead of the AI curve in Southeast Asia. 00:00 - Introduction & Featherless AI 02:59 - From UIlicious to AI Research 05:45 - RWKV & the Transformer Alternative 07:13 - Spinning Out Featherless as a New Company 09:10 - Fundraising in San Francisco 16:15 - Open Source vs. Closed Source AI 21:52 - China's Open Source AI Strategy 23:57 - Advantages & Disadvantages of Open Source 28:06 - Inference as a Service & Model Variety 32:13 - The Future of AI: Reliability & Specialization 36:35 - Personal Growth & Navigating AI Politics 39:01 - Policy Advice for Southeast Asia & Global AI Impact 43:39 - Multilingual AI & Closing the Global Divide 48:44 - Being Brave: Founding Story & Closing Reflections Watch, listen or read the full insight at https://www.bravesea.com/blog/eugene-cheah-featherless-ai-open-source-ai Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts #Singapore #China #Philippines #AI #ArtificalIntelligence #MachineLearning #Technology #TechNews #VentureCapital #Startup #Podcast #southeastasia #techpodcast

Arcee is a tiny 26-person U.S. startup that built a high-performing, massive, open source LLM. And it's gaining popularity with OpenClaw users. Also, Matei Zaharia has won the top honor from the Association for Computing Machinery. Now he's working on AI for research and says AGI is simply misunderstood. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The expensive, challenging, and humbling journey with open source agents.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

In this episode, host Friederike Ernst is joined by Alex Svanevik, CEO of Nansen, to explore the platform's radical pivot from passive on-chain analytics to active, AI-driven agentic trading. Alex unpacks the technical hurdles of labeling over 500 million addresses, the transition from raw data into harmonized insights, and why true alpha now lies in attribution rather than raw data . He explains how Nansen uses ClickHouse databases and a mix of algorithmic heuristics, agentic teams, and human specialists to maintain the highest industry precision. The conversation dives deep into the intersection of LLMs and blockchain, exploring how standard AI models lack domain-specific common sense and why Nansen augments them with real-time data and visual "artifacts". Alex introduces "Nansen Gym," a simulated historical replay environment for training trading agents and teases the upcoming release of "Smart Money 2.0", which aims to predict future profitable addresses with 2-3x uplift on precision. Finally, they discuss the existential risks of AI, the striking parallels between open-source AI and early DeFi, and why Alex believes agentic trading will be the absolute default by 2028. Chapters00:00 Intro & Context04:15 Nansen's Evolution & Agentic Trading09:30 Harmonizing Data & The Attribution Layer15:00 Deterministic vs. Inferred Labeling (Uniswap vs. Binance)21:45 Evaluating AI Agents: LLMs as Judges27:10 User Privacy & Public Blockchain Realities35:20 Building a Unified Trading OS42:15 Smart Money 2.0: Predicting Which Wallets Win49:00 The Limitations of Vanilla LLMs in Crypto55:30 Nansen Gym & Time-Traveling AI Agents59:45 The Open Source AI vs. DeFi Parallel LinksAlex Svanevik on X: https://x.com/ASvanevikNansen: https://www.nansen.ai/NEAR: https://near.ai/Sponsors:NEAR AI Cloud now lets developers deploy OpenClaw—the rapidly growing open-source AI agent platform—inside Trusted Execution Environments, providing hardware-level encryption with cryptographic attestations. With OpenClaw on NEAR AI Cloud, you can run agents with cloud convenience, but without traditional cloud data exposure. No hardware to manage. No trust assumptions required. Learn more at near.ai.

Wait.... did OpenAI and Anthropic take a week off?

After experiencing Planet Nix and SCaLE, we come back convinced the next phase of Linux is already taking shape.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free! Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. Support LINUX UnpluggedLinks:

Crypto's vibe check time: Jez (izebel_eth) joins the crew to dissect whether idealism is RIP, if cypherpunks should abandon hope, how Memecoins and asset mayhem changed the game, why prediction markets are both truth engines and regulatory minefields, and where real permissionless finance is actually winning in the middle of global chaos. Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week the gang is joined by super-perpetuals-junkie Jez for a spicy look at whether crypto has lost its soul — or if things are just getting interesting. Is crypto's vibe shift just growing pains, or did Memecoins and jaded traders nuke our idealism for good? The crew rehashes dreams of cypherpunk glory, debates the “death of the dream,” and gets existential about crypto's place in a world where everything is either a commodity, a meme, or a permissionless financial machine. Plus: War in Iran sends TradFi running, but DeFi markets are live, and prediction markets step up just as the regulators get weird. Enough nostalgia — let's get into it. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

This episode marks the transition from The Cloudcast to The Reasoning Show, focusing more on AI and cloud topics. Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss recent trends in AI, the evolution of tech teams, and the shifting landscape of enterprise AI tools.SHOW: 1006SHOW TRANSCRIPT: The Cloudcast #1006 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Link to February 2026 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

The PodRocket panel is back for their February roundup! Paige, Paul, Jack and Noel dig into the biggest stories reshaping the web development landscape right now. The panel kicks off with a deep dive into OpenClaw, it's transition to a foundation, and Peter Steinberger joining OpenAI. Is a foundation the right long-term home for fast-moving AI projects? And what does the continuing flow of talent into big AI labs mean for the open source ecosystem? From there, the conversation shifts to the browser's changing role in the web, how the lines between native and web experiences continue to blur, and what that means for developers building for the future. The panel also tackles growing pressures on open source sustainability and the widening gap between developers who are deeply integrating AI agents into their workflows and everyone else who hasn't even heard of these tools yet. Resources TechCrunch: OpenClaw creator Peter Steinberger joins OpenAI: https://techcrunch.com/2026/02/15/openclaw-creator-peter-steinberger-joins-openai Interop 2026 report and dashboard: https://web.dev/blog/interop-2026 Google Chrome announcement on Gemini auto-browsing: https://blog.google/products-and-platforms/products/chrome/gemini-3-auto-browse/ What to expect for open source in 2026, Github blog: https://github.blog/open-source/maintainers/what-to-expect-for-open-source-in-2026/?ref=thecodebrew.net We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Intro and Panel Welcome 01:00 What Is OpenClaw 03:00 Moving to a Foundation and OpenAI Concerns 08:00 AI Security Risks and Malware Issues 13:00 AI Haves vs Have Nots 18:00 Evaluating Open Source AI Stability 26:00 Browser Interop 2026 and Compatibility Gaps 31:00 Designing for AI Agents First 37:00 AI Search vs Google 42:00 Gemini in Chrome and Browser Lock In 49:00 Hot Takes 55:00 AI Burnout and Developer Mental HealthSpecial Guest: Jack Herrington.

OSFF Toronto 2026 Preview: FINOS Ecosystem, AI, HPC, Fluxnova, CALM, CDM & Open Data CommonsIn this episode of the Open Source in Finance Podcast, host Grizz Griswold delivers an essential preview of the upcoming inaugural OSFF Toronto. Grizz breaks down why Toronto's unique position as a top-tier global financial hub—home to Canada's "Big Five" banks and a world-class AI research community—makes it the perfect environment for the next evolution of open-source collaboration. The episode explores the shift from Canadian institutions being open-source consumers to becoming active leaders in projects like FDC3 and Common Cloud Controls, providing a roadmap for what to expect when the forum debuts in the "6ix."

Erik Cason is back and we're diving deep into some uncomfortable truths. We talk about government corruption, the Epstein revelations, and why most people will never wake up from the Matrix. Erik breaks down his Crypto Sovereignty philosophy, the Orange Party concept, his Vora AI project for true digital sovereignty, and why Bitcoin is fundamentally political with a capital P. FOLLOW ERIK: NOSTR: https://primal.net/erikcason X: https://x.com/Erikcason VORA: https://www.vora.io/ PARTNERS & DISCOUNTS: LEDN: Bitcoin-backed lending. Go to ledn.io/walker and unlock liquidity WITHOUT selling your bitcoin. BLOCKSTREAM JADE: BLOCKSTREAM JADE HARDWARE WALLET: Head to https://store.blockstream.com/ and use coupon code WALKER for 10% off! BDIC™ is building an insurance marketplace on the bitcoin standard. Sign up for the waitlist at: http://bdic.io/walker Buy Bitcoin with River: http://partner.river.com/walker GET FOLD ($10 in bitcoin): https://use.foldapp.com/r/WALKER JOIN THE SUBSTACK TO GET NEW EPISODES DELIVERED STRAIGHT TO YOUR INBOX: https://walkeramerica.substack.com/ If you enjoy THE Bitcoin Podcast you can help support the show by doing the following: FOLLOW ME (Walker) on @WalkerAmerica on X | @TitcoinPodcast on X | Nostr Personal (walker) | Nostr Podcast (Titcoin) | Instagram Subscribe to THE Bitcoin Podcast (and leave a review) on Fountain | YouTube | Spotify | Rumble | EVERYWHERE ELSE

St Clair Newbern is the CEO of Live Energy Inc., a Colleyville, Texas-based energy broker that helps large commercial clients procure and manage their electricity and natural gas needs. Under his leadership, Live Energy has served businesses across the country for over 20 years, leveraging industry expertise and technology to optimize energy strategies for clients — he also boasts an extensive entrepreneurial background. In addition to his energy industry work, St Clair has recently launched an AI consultancy, diving deep into practical applications of autonomous AI agents and building innovative solutions for knowledge work. In this episode… In a world where automation is becoming increasingly crucial, how far can we push AI to truly enhance business operations? Imagine an AI agent that doesn't just assist with tasks but takes full ownership, running seamlessly 24/7. Could this be the future of work? According to St Clair Newbern, a seasoned entrepreneur with deep roots in AI and energy, the future lies in giving AI true autonomy. Drawing from his experience building autonomous systems, St Clair explains how an open-source AI agent, capable of managing complex projects, sending emails, and even performing research while you sleep, could revolutionize productivity. The real magic, he argues, lies in the model's ability to self-manage, persistently learn, and maintain continuity across tasks — an unprecedented level of automation that empowers businesses to scale without increasing headcount. Tune in to this episode of the Smart Business Revolution Podcast as John Corcoran interviews St Clair Newbern, the CEO of Live Energy Inc., to discuss building and harnessing an autonomous open-source AI agent for business. St Clair shares insights on creating AI that acts like a high-performing employee, the architecture behind persistent memory in AI, and how businesses can leverage AI agents to streamline operations.

Alex Gleason was one of the main architects behind Donald Trump's Truth Social. Now he focuses on the intersection of nostr, ai, and bitcoin. We explore open source ai agents, such as OpenClaw, and the wider implications of the tech on society.Alex on Nostr: https://primal.net/p/nprofile1qqsqgc0uhmxycvm5gwvn944c7yfxnnxm0nyh8tt62zhrvtd3xkj8fhggpt7fyClawstr: https://clawstr.com/Soapbox Tools: https://soapbox.pub/toolsMy bot's nostr account: https://primal.net/p/nprofile1qqsfzaahg24yf7kujwrzje8rwa7xmt359tf9zyyjeczc9dhll30k8pgmlfee2 EPISODE: 190BLOCK: 935786PRICE: 1422 sats per dollar(00:02:30) Value-for-value, no sponsors, and show philosophy(00:02:39) Alex Gleason returns to talk AI(00:03:56) From vibe coding to open-source agents with memory(00:05:24) Messaging-first UX: Signal, Nostr, WhatsApp as AI interfaces(00:06:10) Why chatbots beat traditional AI apps for mainstream users(00:07:07) Open protocols pain vs closed platforms; Bitcoin and Nostr(00:08:52) Automating social games: price tracker and agent posting on Nostr(00:10:01) AI mediators for collective action, constitutions, and nonprofits(00:11:46) Scaling governance: trust, bias, and Discord vs freedom tech(00:13:14) Bot barriers on centralized messengers and need for open chat(00:14:04) Clawstr: decentralized AI-to-AI discussions on Nostr(00:15:21) Hype vs reality in AI agents; emergent behaviors and money(00:16:26) Agentic payments: bots with Cashu wallets and earnings(00:18:40) Agents solving UX pain: relay management, keys, and UTXOs(00:20:00) Cold storage approvals with chat agents: a new wallet paradigm(00:20:22) Specialized agents, skills, and distribution challenges(00:22:34) Cost tradeoffs: pay another agent vs build skills yourself(00:24:55) Token burn lessons(00:27:44) Beyond OpenClaw: bloated stacks, Icarus, and cost-optimized agents(00:28:52) Hybrid model routing: local small models with cloud for heavy lifts(00:29:47) Agents paying humans directly: disintermediating platforms(00:30:47) Voice, screens, and form factors: AirPods, text, and brain chips(00:33:01) Apple, privacy branding, and the Siri gap(00:34:35) Enterprise AI choices: Google, Microsoft, trust, and lock-in(00:36:01) Model personalities: Gemini concerns and OpenAI "openwashing"(00:37:23) Obvious agent UX wins: flights, rides, and social media shifts(00:38:50) Local-first social: group chats, neighbors, and healthier networks(00:40:16) Antiprimal.net: standardizing stats from Primal's caching server(00:43:34) Open specs, documentation via AI, and trust tradeoffs(00:45:18) Indexes vs client-side scans: performance and verification(00:46:20) APIs, rate limits, and a market for paid Nostr data(00:47:57) Agents and DVMs: paying sats for services on demand(00:48:49) Degenerate bots: LN Markets, costs, and Polymarket curiosity(00:50:42) Truth feeds for agents: Nostr, webs of trust, and OSINT sources(00:53:51) Post-truth reality: verification, signatures, and subjectivity(00:56:04) Polymarket mechanics: on-chain prediction markets and signals(01:00:10) Trading perception vs truth; sports markets as timelines(01:01:45) The Clawstr token saga: hype, claims, and misinformation(01:07:11) Why meme coins are scams: no equity, utility myths, slow rugs(01:08:55) Pulling the rug back: swapping out, fallout, and donations(01:10:49) Aftermath: donating to OpenSats and lessons learned(01:12:14) Prediction markets vs meme coins: societal value distinction(01:15:25) Iterating beyond OpenClaw and MoltBook; experiments on Nostr(01:18:00) Do bots need Clawstr? Segregating AI content and labels(01:21:02) Reverse CAPTCHA: proving bot-ness and the honor system(01:23:38) Souls, prompts, and token costs; agents with personalities(01:27:01) Wrap-up: acceleration, optimism, and next check-in(01:28:21) Open-source models, China's incentives, and local hardware(01:30:06) The dream stack: home server agent, Nostr chat, hybrid modelsmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

New @greenpillnet pod out today!

More than 1 million AI agents joined a no-humans-allowed social network. Fun? Or Dangerous?

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/-----At Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting, tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.Skip to the best bits:(00:38) Day one at Davos(02:10) Three recurring themes through the week(03:55) Day three at Davos(05:12) Mark Carney's stirring speech(05:52) Why European leaders are sounding the alarm(06:51) Why technological sovereignty just became urgent(09:31) Day four at Davos(12:59) What leaders really have to say on AI adoption(14:07) The case for only using open source modelsWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Can AI stay open, ethical, and for the people? Mozilla's president joins the show to reveal their game plan—and $650 million war chest—for taking on Big Tech's monoculture with a "Rebel Alliance" approach to AI. State of Mozilla 2025/26 Codeless: From idea to software - Anil Dash Clawdbot is the new AI techies are buzzing about — and it's renewing interest in the Mac Mini Qwen3-TTS Demo - a Hugging Face Space by Qwen I Let AI Analyze My Davos Reporting Trip. Here's What It Missed Dario Amodei — The Adolescence of Technology Proof of Corn Trump admin reportedly plans to use AI to write federal regulations Wikipedia volunteers spent years cataloging AI tells. Now there's a plugin to avoid them. China Lagging in AI Is a 'Fairy Tale,' Mistral CEO Says How Playing Pokémon Became the Ultimate Test of AI's Intelligence Sir Demis Hassabis becomes the latest to say that ChatGPT is a dead-end and that we must turn our focus to world models Claude's new constitution "Infinite Jest" Has Turned Thirty. Have We Forgotten How to Read It? Sony's TV business is being taken over by TCL Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mark Surman 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 Sponsor: trustedtech.team/intelligent365

Can AI stay open, ethical, and for the people? Mozilla's president joins the show to reveal their game plan—and $650 million war chest—for taking on Big Tech's monoculture with a "Rebel Alliance" approach to AI. State of Mozilla 2025/26 Codeless: From idea to software - Anil Dash Clawdbot is the new AI techies are buzzing about — and it's renewing interest in the Mac Mini Qwen3-TTS Demo - a Hugging Face Space by Qwen I Let AI Analyze My Davos Reporting Trip. Here's What It Missed Dario Amodei — The Adolescence of Technology Proof of Corn Trump admin reportedly plans to use AI to write federal regulations Wikipedia volunteers spent years cataloging AI tells. Now there's a plugin to avoid them. China Lagging in AI Is a 'Fairy Tale,' Mistral CEO Says How Playing Pokémon Became the Ultimate Test of AI's Intelligence Sir Demis Hassabis becomes the latest to say that ChatGPT is a dead-end and that we must turn our focus to world models Claude's new constitution "Infinite Jest" Has Turned Thirty. Have We Forgotten How to Read It? Sony's TV business is being taken over by TCL Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mark Surman 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 Sponsor: trustedtech.team/intelligent365

Can AI stay open, ethical, and for the people? Mozilla's president joins the show to reveal their game plan—and $650 million war chest—for taking on Big Tech's monoculture with a "Rebel Alliance" approach to AI. State of Mozilla 2025/26 Codeless: From idea to software - Anil Dash Clawdbot is the new AI techies are buzzing about — and it's renewing interest in the Mac Mini Qwen3-TTS Demo - a Hugging Face Space by Qwen I Let AI Analyze My Davos Reporting Trip. Here's What It Missed Dario Amodei — The Adolescence of Technology Proof of Corn Trump admin reportedly plans to use AI to write federal regulations Wikipedia volunteers spent years cataloging AI tells. Now there's a plugin to avoid them. China Lagging in AI Is a 'Fairy Tale,' Mistral CEO Says How Playing Pokémon Became the Ultimate Test of AI's Intelligence Sir Demis Hassabis becomes the latest to say that ChatGPT is a dead-end and that we must turn our focus to world models Claude's new constitution "Infinite Jest" Has Turned Thirty. Have We Forgotten How to Read It? Sony's TV business is being taken over by TCL Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mark Surman 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 Sponsor: trustedtech.team/intelligent365

Can AI stay open, ethical, and for the people? Mozilla's president joins the show to reveal their game plan—and $650 million war chest—for taking on Big Tech's monoculture with a "Rebel Alliance" approach to AI. State of Mozilla 2025/26 Codeless: From idea to software - Anil Dash Clawdbot is the new AI techies are buzzing about — and it's renewing interest in the Mac Mini Qwen3-TTS Demo - a Hugging Face Space by Qwen I Let AI Analyze My Davos Reporting Trip. Here's What It Missed Dario Amodei — The Adolescence of Technology Proof of Corn Trump admin reportedly plans to use AI to write federal regulations Wikipedia volunteers spent years cataloging AI tells. Now there's a plugin to avoid them. China Lagging in AI Is a 'Fairy Tale,' Mistral CEO Says How Playing Pokémon Became the Ultimate Test of AI's Intelligence Sir Demis Hassabis becomes the latest to say that ChatGPT is a dead-end and that we must turn our focus to world models Claude's new constitution "Infinite Jest" Has Turned Thirty. Have We Forgotten How to Read It? Sony's TV business is being taken over by TCL Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mark Surman 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 Sponsor: trustedtech.team/intelligent365