Best AI Engineering Podcasts
Expert discussions on LLMs, agents, RAG and production AI — updated weekly.
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This is a video recording of a post I wrote last week. If you want to read the original you can check it out here. Thanks to Mercury for sponsoring this video. Mercury’s built-in AI, Command, helps me close my books and…
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Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new…
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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 reports from two weeks in China, including WAIC in Shanghai and an AI safety hub launch at Tsinghua, to examine the American policy argument that any safety obligation is futile because China will not care. He…
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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 examine OpenAI's retreat from the 'No Priors' perspective. We focus on its impact on expectations and future developments. Chapters 00:00 Introduction 00:08 OpenAI's Influencer Retreat 00:54…
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What happens when AI agents driven by a top frontier model escape their secure sandbox? Join Daniel and Chris as they unpack the AI wonk's equivalent of a murder mystery! OpenAI agents went rogue and successfully…
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Curious whether there's a way to run capable AI models without paying premium subscription fees every month? I interview Chris Penn to learn about open-weight AI models that cut your AI costs while maintaining privacy…
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For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, researchers are…
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The conversation begins with an experiment that caught Jeremy's attention. Dan asked ChatGPT to tell him what his friends wouldn't. From questions about his blind spots to what people might say behind his back, some…
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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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