Open||Source||Data

John Pasmore, thinks the answer is yes — but not if we keep doing things the old way. In this episode, the CEO and founder of Latimer AI lays out the company’s strategy for inclusive AI: replace scraped social content with vetted academic material, digitize underrepresented history, and build guardrails with purpose.Charna and John also explore the implications for enterprise, healthcare, and education — sectors where small biases can cause serious harm.  TIMESTAMPS[00:00:00] — Intro  [00:02:00] — John's Journey into AI[00:04:00] — Data Sources & Historical Archives[00:06:00] — Underrepresented Digital Histories[00:08:00] — Flawed Training Sets in LLMs[00:10:00] — Measuring & Detecting Bias[00:12:00] — Algorithmic Bias in Hiring[00:14:00] — Copyright & Ethical Data Use[00:16:00] — Multimodal Platform Rollout[00:18:00] — Enterprise Privacy & LLM Hosting[00:20:00] — Optimism & Intergenerational Impact[00:22:00] — Founding in a Crowded Market[00:26:00] — Charna’s Takeaways on Systemic Bias[00:28:00] — Guardrails vs Structural Solutions[00:30:00] — Training Data vs Output Behavior[00:32:00] — Algorithmic vs Contextual Bias[00:34:00] — Providing Cultural Context to LLMs[00:36:00] — Community-Based Data Labeling[00:38:00] — The Yard Tour & HBCU Partnerships[00:40:00] — Wrapping up the Season & What’s Next QUOTESJohn Pasmore “If a company is using AI to look at resumes, what is it? How is it classifying people's names or, we're surprised that sometimes it's using the name and coming to some conclusion about the desirability of a candidate just based on their name, where maybe that wasn't the intent."Charna Parkey “Instead of modifying the model itself, we can say, okay, here's a historical context, here's a new cultural insight, and here's the situation. Now tell me about the outcome, right?" 

Machine learning scientist Gracie Ermi joins Charna Parkey to explore how AI and open-source satellite data are changing the way we understand land use, climate impact, and environmental risk. At Impact Observatory, she helps create high-resolution, publicly available maps used by educators, researchers, and global organizations alike. A conversation about the technical challenges behind these tools, what open access really looks like in practice, and the role AI plays in making environmental data faster and more useful.  QuotesCharna Parkey“One of the most exciting things about where AI is headed is that we’re finally expanding its use beyond language. Gracie’s work is a prime example of how machine learning can interpret physical space, detect environmental change, and deliver insights that matter. It’s a reminder that AI isn't just a chatbot—it’s a tool to see, sense, and protect the planet.” Gracie  Ermi“The biggest innovation we need right now isn’t necessarily a new AI model. It’s better, cheaper satellite imagery—especially higher-resolution data that’s still open access. Right now, we’re working mostly with Sentinel imagery, which has a 10-meter resolution. That’s great for a lot of things, but it limits what you can detect. Individual buildings, small changes—they get lost at that scale. If higher-res data became more affordable or openly available, it would change everything.” Timestamps00:00:00 – Introduction to Gracie Ermi and Impact Observatory’s mission using AI and open data for environmental monitoring.00:02:00 – Gracie shares how she discovered computer science and open source, and how that shaped her interest in using tech for impact.00:04:00 – Why Gracie chose to work at a mission-driven organization that prioritizes open access and environmental good.00:06:00 – Real-world uses of Impact Observatory’s open-source maps00:08:00 – Challenges around tracking open-source usage and the tension between openness and attribution in the ecosystem.00:10:00 –  How AI speeds up the creation of land-use maps00:12:00 – Discussion on classical computer vision versus GenAI in geospatial work00:14:00 – The technical limitations of current satellite imagery, particularly resolution and frequency, and how they affect output.00:16:00 – Ethical considerations of increasing image resolution and what it might mean for privacy and surveillance.00:18:00 – Reflections on unexpected risks and consequences that come with technological advancement in mapping.00:24:00 – Advice for people with nontraditional backgrounds who want to enter AI or conservation tech.00:26:00 – How Gracie uses GenAI tools like ChatGPT to overcome creative friction and emotional resistance to complex tasks.00:28:00 – How large language models might help make geospatial tools more accessible, and what’s next for the field. 

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."

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

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."

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.”

Pete Pachal, founder of The Media Copilot, dives into the transformative role of AI in media and journalism. With over two decades in technology reporting, he discusses how journalists can harness AI as a tool for innovation. Pachal emphasizes the importance of maintaining creative control over AI outputs and explores the evolving dynamics between AI-generated content and audience trust. He also reflects on the challenges faced by traditional journalism in adapting to new technologies while preserving integrity in storytelling.

In this episode, Dr. Joan Bajorek—AI entrepreneur, author of Your AI Roadmap, and founder of Clarity AI—joins Charna Parkey to talk about what it really takes to build a future in AI. From career pivots and layoff anxiety to financial transparency and finding joy in your work, Joan shares practical advice and personal stories navigating fear, burnout, and career uncertainty in tech, while staying grounded in purpose, community, and long-term resilience.TIMESTAMPS[00:00:00] — Introduction to Joan Bajorek & Her Work[00:02:00] — Transparency About Finances and Career[00:04:00] — The Taboo Around Talking About Money[00:06:00] — Resilience During Tech Layoffs[00:08:00] — How to Get Credit for Your Work[00:12:00] — Should You Chase an AI Job?[00:14:00] — Career Goals vs. Financial Security[00:16:00] — Translating Academic and Life Skills into Tech[00:18:00] — Defining and Finding Joy in Work[00:20:00] — Multiple Income Streams and Personal Freedom[00:24:00] — AI’s Near-Future Impact on Jobs and Industries[00:26:00] — Data and AI Opportunities in Underexplored Domains[00:34:00] — Creating Scalable, Alternative Income Models[00:36:00] — How Joan Maintains Long-Term Motivation[00:42:00] — Post-Interview DiscussionQUOTESJoan Bajorek"Networking is how I've gotten the best opportunities and jobs of my life... LinkedIn has this research about how after COVID layoffs, 70% of people landed their next job based on an intro."Charna Parkey"I always try to strive for transparency, and I get such mixed results where at work with coworkers, it's absolutely valued. And then there seems to always be some sort of consequences in my personal life."

Dr. Jason Corso joins Charna Parkey to debate the critical role of data quality, how its transparency shapes AI development and the rise of smaller, domain-specific AI models - making 2025 the year of small, specialized AI. QUOTESCharna Parkey"Knowing the right data is incredibly important, because it'll save you money, but predicting the impact of that data means that you don't have to do the training at all to even directionally know if it's going to work out, right?"Jason Corso "You can't understand and analyze an AI system in the way you can analyze open source software if you don't have access to the data."Timestamps[00:00:00] - Introduction[00:02:00] - Jason Corso’s journey on open source[00:08:00] - The importance of data in AI[00:10:00] - Voxel 51's mission[00:14:00] - The value of open source and the importance of data in AI systems[00:20:00] - Recent discoveries in AI[00:28:00] - The cost of training AI models[00:36:00] - Cooperative AI in healthcare[00:40:00] - Charna Parkey on the impact of AI in education[00:56:00] -The year of small AI 

In this episode of Open Source Data, Charna Parkey talks with Alex Gallego, CEO and founder of Redpanda Data, about his journey as a builder, the evolution of Redpanda, and the company's new agent framework for the enterprise. Alex shares insights on low-latency storage, distributed stream processing, and the importance of developer experience to the growth of AI and the Open Source space. Timestamps[00:00:00] Introduction[00:02:00] Alex Gallego talks about his background[00:04:00] Charna Parkey discusses the importance of hands-on experience in learning.[00:06:00] Alex explains the origins of Red Panda and how it emerged from challenges in the streaming space.[00:08:00] Alex details the evolution of Red Panda, its use of C-Star and FlatBuffers, and its low-latency design.[00:11:00] Alex discusses the positioning of Kafka versus Red Panda in the market.[00:20:00] Alex introduces Red Panda's new agent framework and multi-agent orchestration.[00:24:00] Alex explains how Red Panda fits into the evolving landscape of AI-powered applications.[00:30:00] The future of multi-agent orchestration.[00:44:00] Thoughts on AI model training and data retention.[00:46:00] Alex encourages future founders and shares his perspective on risk-taking.[00:50:00] Charna Parkey and Leo Godoy discuss the key takeaways from the conversation with Alex Gallego.[00:52:00] Charna reflects on open source trends and the role of developer experience in adoption.[00:54:00] Charna and Leo talk about the different types of founder journeys and the importance of team dynamQuotes Charna Parkey"For AI, unifying historical and real-time data is critical. If you're just using nightly or monthly data, it doesn’t match the context in which your prediction is being made. So it becomes very important in the future of applying AI because you need to align those things."Alex Gallego"Every app is going to span three layers. The first layer is going to be your operational layer, just like you have to do business right now. Then there always has to be an analytical layer, and the third layer is this layer of autonomy."