Best AI Engineering Podcasts
Expert discussions on LLMs, agents, RAG and production AI — updated weekly.
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Joscha Bach is a cognitive scientist and the founder of the California Institute for Machine Consciousness. SPONSOR: --- Parallel is a single platform for all your agentic research: web search built for AI agents, with…
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The Robert Caro interview. Watch on YouTube; read the transcript. Sponsors * Antithesis lets you run thorough simulation tests on every PR your coding agents ship. That’s because Antithesis is FAST. On a recent project,…
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In this AI:AM Highlights episode, Nathan Labenz and Prakash revisit three live mornings with Louis Kirsch and Damon Falck of Inherent Laboratories, Vercel CTO Malte Ubl, Genesis Molecular AI CTO Sergey Edunov, Arm’s…
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MCP (Multi-Cloud Platform) leverages Kubernetes to manage and orchestrate AI-native applications, enabling enterprises to deploy and scale AI agents across multiple cloud environments. ToolHive, an emerging infrastructure, facilitates identity management, agent orchestration, and system architecture to manage entire fleets of AI agents working behind the scenes.
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We're going live on November 11! Henrik will be interviewing Jeremy about his new book, The Human Advantage: What AI Can't Do Without You, followed by a 45-minute AMA. Got questions about AI, entrepreneurship, writing,…
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What would you build if an app only needed to work for you? Aytekin Tank and Demetri Panici share how they use AI to build personal mobile apps and rethink the software they use every day. The conversation includes…
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AI can help scientists write code, analyze data, and draft papers. But can it generate the ideas that lead to new discoveries? Richard Socher, former chief scientist at Salesforce, founder of You.com, and co-founder of…
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Want an always-on AI agent that manages your email, calendar, and tasks without putting your business data at risk? I interview Jonathan Mast to learn how to onboard, secure, and train a persistent AI agent, how to…
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Nathan Labenz and Prakash Narayanan examine whether prevailing AI assumptions survive real-world data across conversations with Anastasios Angelopoulos of Arena, Debo Olaosebikan of Kepler Computing, Samvit Ramadurgam…
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20% of enterprise website traffic is already coming from AI agents rather than humans, and that traffic converts at 38% more revenue per session than human visits. Within a year, Jeff Lunsford expects that number to…
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In this special episode, we bring you highlights from three talks at the Midwest AI Summit. Heidi Porter explores how to build reliable AI-augmented workflows using the right tools, evaluations, and guardrails. Kevin…
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Anthropic faces new scrutiny as the FTC seeks executive testimony in its consumer-harm investigation. Jaeden breaks down the news, the reported $63 billion financing plan for Broadcom chips destined for Anthropic, and…
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What happens when artificial intelligence becomes your marketing department, assistant, operations team, and business analyst all at once? In this episode, we explore the growing world of AI-powered solopreneurs and the…
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The discussion revolves around the inherent contradiction in company structures, where founders prioritize customer impact but legally serve shareholders first, leading to mission drift over time. Governance is treated as a legal formality rather than a design problem, with Eric Ries arguing that AI exacerbates this issue, making it more urgent for companies to prioritize their original mission.
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The Relational Foundation Model (RFM2) utilizes in-context learning over subgraphs to make accurate predictions on new databases and tasks without explicit training. RFM2 benchmarks against RelBench and other multi-table datasets, demonstrating its effectiveness in real-world deployments at companies like Reddit, DoorDash, and Coinbase.
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GPT-5.5 demonstrates improved performance compared to Opus 4.6, with enhanced capabilities in specific domains, but its overall architecture and training data differ significantly from Opus. The discussion highlights the trade-offs between GPT-5.5's strengths and Opus's unique characteristics, underscoring the ongoing evolution of large language models in the AI landscape.
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The podcast discusses the complexities of AI adoption, highlighting its paradoxical nature of being both brilliant and resource-heavy, with a focus on its practical applications and limitations. Key takeaways include AI's potential to create "invisible" economic value, its struggles with simple physical tasks, and the growing importance of energy use, water consumption, and transparency in AI development.
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