Best AI Engineering Podcasts
Expert discussions on LLMs, agents, RAG and production AI — updated weekly.
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Adam Brown is back! General relativity is said to be the most beautiful idea the human mind has ever produced. Most of us will never get to fully appreciate its elegance by taking the 20-lecture graduate course Adam…
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Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system…
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We’re announcing AIEWF speakers this week! Take the AI Engineering Survey! Today’s guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine…
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Nathan Labenz and Prakash Narayanan lead this AI:AM highlights episode with a live, hosts-only exploration of Anthropic’s “global workspace” paper, including the J-space and J-lens claims about readable concepts inside…
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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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In this episode, we discuss how the abrupt closure of Atlas took many by surprise. We also examine how MuseSpark could dynamically alter the AI landscape. Chapters 00:00 OpenAI Shuts Down Atlas 03:05 Meta Launches…
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What does it take to move AI agents from demos to reliable production systems? In this episode, Hamza Tahir explores how MLOps principles are shaping the future of generative AI, covering workflows, agent harnesses,…
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In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell,…
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Geoff Woods returns to Beyond the Prompt to discuss the updated edition of The AI-Driven Leader and what has changed over the past 18 months. Rather than focusing on the latest AI models, Geoff argues that leaders need…
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Looking for an agentic AI tool that will pass off multi-step processes to other tools without constant input from you? I interview Kate vanderVoort to discover how to get started using Manus to build agentic workflows…
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Subscribe to AI Agents Podcast Channel: https://link.jotform.com/subscribe-to-podcast In this episode of the AI Agents Podcast, host Demetri Panici sits down with Aidan Mirza, founder and CEO of Fellow, to explore how…
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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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