Open||Source||Data

• Multi-Agent Systems and Human-Agent Collaboration | Rodrigo Nader

  In this episode, Charna Parkey welcomes Rodrigo Nader, the founder of Langflow, an open-source, low-code app builder for multi-agent AI systems. Rodrigo and Charna dive into his beginnings in a small Brazilian town to the future of AI and the emergence of multi-agent systems. Discover how these systems will enable human-agent collaboration, increase productivity, and solve complex problems across various industries.---TIMESTAMPS00:01:00 Introduction to Rodrigo Nader, CEO and founder of Langflow, and an overview of Langflow's mission and recent developments.00:03:00 - Rodrigo Nader's background and journey into open-source, data science, and machine learning, including his early experiences with MIT OpenCourseWare and Kaggle.00:06:00 - Rodrigo's work at Bitvore Corp, focusing on structuring financial data using machine learning, and his introduction to the open-source AI ecosystem.00:10:00 -  The inspiration behind Langflow, including the idea of connecting multiple AI models to create a more powerful, trainable system.00:15:00 - Discussion on the evolution of AI agents, their decision-making capabilities, and the future of multi-agent systems.00:18:00 -The role of agents in AI development, the democratization of AI tools, and the potential for community-driven innovation.00:22:00 -The importance of multi-agent collaboration and the future of human-AI interaction in productivity and task management.00:26:00 - Common use cases for Langflow, including language model pipelines, RAG (Retrieval-Augmented Generation), and agentic systems.00:30:00 - Challenges in AI development, particularly debugging and prompt engineering, and the need for better tools to visualize and monitor AI systems.00:34:00 - Predictions for the future of AI in 2025, including the rise of specialized agents and the importance of human feedback in AI training.00:38:00 - Rodrigo's personal interests outside of AI, particularly his fascination with physics, quantum mechanics, and the concept of time.00:42:00 - Final thoughts on the democratization of AI tools, the importance of community contributions, and advice for aspiring developers and AI enthusiasts.00:46:00 - Reflections with executive producer Leo Godoy, discussing the impact of Langflow, the differences between traditional and AI development, and the rapid pace of AI evolution.QuotesCharna Parkey"For any developer who has sort of avoided the soft skills, the managerial skills, et cetera, you should go listen to some of those courses. You are now going to be managing this AI workforce that you really do need to treat like a team of interns that you're delegating work to, that you're giving feedback on, and all of those skills of sort of like more senior-level engineering of design reviews, code reviews, feedback, like that's gonna be more central than actually writing a line of code yourself."Rodrigo Nader"We're going to see millions and millions more agents than humans very soon, right? So we don't think that these agents are going to emerge from, one, only developers, meaning like hard-code developers, neither from big companies creating solutions that will suddenly solve all the problems."

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• Why AI Can’t Scale Without Infrastructure Fixes | Darrick Horton

  From energy bottlenecks to proprietary GPU ecosystems, the CEO of TensorWave, Darrick Horton explains why today’s AI scale is unsustainable—and how open-source hardware, smarter networking, and nuclear power could be the fix.QUOTESDarrick Horton“The energy crisis is getting worse every day. It’s very hard to find data center capacity—especially capacity that can scale. Five years ago, 10 or 20 megawatts was considered state-of-the-art. Now, 20 is nothing. The real hyperscale AI players are looking at 100 megawatts minimum, going into the gigawatt territory. That’s more than many cities combined just to power one cluster.”Charna Parkey“We’re still training models in a very brute-force way—throwing the biggest datasets possible at the problem and hoping something useful emerges. That’s not sustainable. At some point, we have to shift toward smarter, more intentional training methods. We can’t afford to be wasteful at this scale.”TIMESTAMPS[00:00:00] Introduction[00:01:00] Founding TensorWave[00:04:00] AMD as a Viable Alternative[00:08:00] Open Source as a Startup Enabler[00:09:30] Launching ScalarLM[00:12:00] ScalarLM Impact and Reception[00:14:30] Roadmap for 2025[00:16:00] Technical Advantages of AMD[00:18:00] Emerging Open Source Infrastructure[00:20:00] Broader Societal Issues AI Must Address[00:22:00] AI’s Impact on Global Energy[00:26:00] Fundamental Hardware vs. Human Efficiency[00:30:00] Data Center Density Evolution[00:34:00] Advice to Founders and Tech Trends[00:38:00] AI Energy Challenges[00:44:00] AI’s Rapid Impact vs. Internet[00:46:00] Monopoly vs. Democratization in AI[00:50:00] Close to Season Wrap Discussion and Predictions

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• Building Open-Source LLMs with Philosophy | Anastasia Stasenko

  Join Charna Parkey as she welcomes Anastasia Stasenko, CEO and co-founder of pleias, through her unique journey from philosophy to building open-source, energy-efficient LLMs. Discover how pleias is revolutionizing the AI landscape by training models exclusively on open data and establishing a precedent for ethical and socially acceptable AI. Learn about the challenges and opportunities in creating multilingual models and contributing back to the open-source community. QUOTES[00:00:00] Introducing Anastasia and pleias[00:02:00] From Philosophy to AI[00:06:00] The Problem of Generic Models[00:10:00] Open Weights vs. Open Source vs. Open Science[00:14:00] Why Open Data Matters[00:18:00] High-Quality, Specialized Models[00:22:00] Multilingual Challenges[00:26:00] Global Inclusion Requires Small Models[00:30:00] Using and Contributing to Wikidata[00:38:00] The Future: Specialized Models[00:48:00] Advice for Newcomers[00:54:00] Cultural Sensitivity and Data Representation[00:50:00] Leo’s Takeaways[00:52:00] Charna on Ethical, Verifiable AI[00:54:00] Representation vs. Exclusion[00:56:00] Letting People Be More Human[00:57:30] Applied, Transformative AIQUOTESCharna:"If you didn’t make it represented in the data, then we’re leaving another culture behind... So which one are you wanting to do, misrepresent them or just completely leave them behind from this technical revolution?"Anastasia:"The real issue now is that the lack of diversity in the current AI labs leads to the situation where all LLMs look alike."Anastasia:"Being able to design, to find, and also to create the appropriate data mix for large language models is something that we shouldn't really forget about when we talk about the success of what large language models are."

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• Democratizing Cloud Infrastructure | Kevin Carter

  Discover how Rackspace Spot is democratizing cloud infrastructure with an open-market, transparent option for cloud servers. Kevin Carter, Product Director at Rackspace Technology, discusses Rackspace Spot's hypothesis and the impact of an open marketplace for cloud resources. Discover how this novel approach is transforming the industry. TIMESTAMPS[00:00:00] – Introduction & Kevin Carter’s Background[00:02:00] – Journey to Rackspace and Open Source[00:04:00] – Engineering Culture and Pushing Boundaries[00:06:00] – Rackspace Spot and Market-Based Compute[00:08:00] – Cognitive vs. Technical Barriers in Cloud Adoption[00:10:00] – Tying Spot to OpenStack and Resource Scheduling[00:12:00] – Product Roadmap and Expansion of Spot[00:16:00] – Hardware Constraints and Power Consumption[00:18:00] – Scrappy Startups and Emerging Hardware Solutions[00:20:00] – Programming Languages for Accelerators (e.g., Mojo)[00:22:00] – Evolving Role of Software Engineers[00:24:00] – Importance of Collaboration and Communication[00:28:00] – Building Personal Networks Through Open Source[00:30:00] – The Power of Asking and Offering Help[00:34:00] – A Question No One Asks: Mentors[00:38:00] – The Power of Educators and Mentorship[00:40:00] – Rackspace’s OpenStack and Spot Ecosystem Strategy[00:42:00] – Open Source Communities to Join[00:44:00] – Simplifying Complex Systems[00:46:00] – Getting Started with Rackspace Spot and GitHub[00:48:00] – Human Skills in the Age of GenAI - Post Interview Conversation[00:54:00] – Processing Feedback with Emotional Intelligence[00:56:00] – Encouraging Inclusive and Clear Collaboration QUOTESCHARNA PARKEY“If you can’t engage with this infrastructure in a way that’s going to help you, then I guarantee you it’s not up to par for the direction that we’re going. [...] This democratization — if you don’t know how to use it — it’s not doing its job.”KEVIN CARTER“Those scrappy startups are going to be the ones that solve it. They’re going to figure out new and interesting ways to leverage instructions. [...] You’re going to see a push from them into the hardware manufacturers to enhance workloads on FPGAs, leveraging AVX 512 instruction sets that are historically on CPU silicon, not on a GPU.”

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• AI and the Future of Media Consumption | Pete Pachal

   In this episode of Open Source Data, Charna Parkey interviews Pete Pachal, founder of The Media Copilot. With over two decades of experience covering technology, Pete shares his insights on how AI is transforming media, journalism and discusses  how journalists can embrace AI as a tool to enhance their work to adapt and thrive in this new environment. QUOTESPETE PACHAL: AI is something that you control. I know, it feels like it's a wave that's coming over that it's unstoppable, inevitable. And that's true to a large extent. But at the same time, it's not, there's no there, right? There's no spark, there's no intent. (...) Never relinquish your role as the ultimate creator and person responsible for what's coming out of this thing.CHARNA PARKEY: I think that there was a point where I found myself shifting more away from media and towards individual curated newsletters because like subject matter experts in that area, I could be like maybe they're going to summarize it incorrectly, et cetera. But at least I know my theory of mind of that individual. And then when I expand that to media, I don't know who's writing what and who's shadow writing what for who.TIMESTAMPS00:00:00 - Introduction of Pete Pachal and his background in journalism and AI.00:02:00 - Pete’s career journey, including his work at CoinDesk and founding The Media Copilot.00:04:00 - AI training for media professionals (journalists, PR, marketers).00:06:00 - Evolution of AI in journalism: From skepticism to ethical frameworks.00:08:00 - AI in content pipelines: Idea generation vs. post-production tasks.00:10:00 - Open-source builders needing to cater to domain experts (e.g., journalists).00:12:00 - Meta’s removal of fact-checking and its implications.00:16:00 - Public tolerance for AI errors (e.g., Apple’s AI summaries).00:18:00 - Consumer trust shifts away from platforms like Facebook/X.00:22:00 - Ghostwriting vs. authenticity in AI-generated content.00:24:00 - Preference for human-curated newsletters over AI summaries.00:26:00 - AI in news digests (e.g., Perplexity, Alexa).00:28:00 - Publisher AI experiments (Washington Post chatbot, TIME summaries).00:32:00 - AI’s impact on click-through rates and publisher economics.00:34:00 - AI-written articles (e.g., ESPN’s use case) and copyright issues.00:36:00 - Legal battles over AI training data (NYT vs. OpenAI).00:38:00 - Copyright concerns with AI-generated outputs.00:40:00 - AI search tools (Perplexity, ChatGPT) and publisher licensing deals.00:46:00 - The unhealthy impact of social media trends on journalism.00:48:00 - Post-interview discussion: Accountability in AI and media.00:56:00 - Leo’s perspective as a journalist on AI adoption.00:58:00 - Closing thoughts on balancing AI innovation with industry needs.

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