Best AI Engineering Podcasts
Expert discussions on LLMs, agents, RAG and production AI — updated weekly.
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Had Ada Palmer back on – this time to talk about Machiavelli, perhaps the most misunderstood thinker of all time. Machiavelli cut his teeth as a high-level diplomat for Florence, a position from which he got to closely…
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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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The AI policy advocacy group Americans for Responsible Innovation, co-founded by Brad Carson, emphasizes the need for restraint in AI development, drawing parallels with the Asilomar Conference on Recombinant DNA in 1975. The organization's stance is rooted in the idea that AI's potential risks can be mitigated through regulation and responsible innovation, rather than relying on the notion that the technology is unstoppable.
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In this episode, we explore the functionalities of MCPs and how they enhance the capabilities of AI tools like Claude and ChatGPT. We also discuss the differences between MCPs and APIs, share practical use cases, and…
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Andreas Stuhlmüller and Jungwon Byun return to discuss how Elicit is building trusted reasoning workflows for scientific research as frontier models grow more powerful but less transparent. They explain process…
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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, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the…
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Have you experimented with building one-size-fits-all AI agents and been disappointed with the results? I interview Keith Moehring to discover how to build a custom system of AI agents that work with your business and…
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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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As AI agents become more capable and autonomous, they also introduce new security challenges. In this 'Fully Connected' episode, Dan and Chris unpack Anthropic’s Zero Trust for AI Agents security framework and what it…
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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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Chantel Prat studies how different brains make sense of the world. Her work starts from a simple idea: every experience leaves a mark. The inputs we consume shape how we think, what we notice, and ultimately who we…
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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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