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On episode 6 of Lab Notes, Amir Zohrenejad speaks with James Evans about the surprising finding that reasoning models simulate a society rather than a single brain. They dig into sycophancy and why users trust AI less but perform better when it has a point of view, the Computational Life experiments that evolved self-replicating programs from a primordial soup of code, and James’s argument that diverse novel ideas are about to become the scarcest resource on the planet.

On episode 45 of Open Source Ready, Brian Douglas and John McBride speak with Dotta about Paperclip, an open-source platform for managing teams of AI agents. They explore what happens when agents produce more work than humans can review, why permissions need to extend beyond a separate computer, and how engineering judgment changes when writing code becomes easier.

On episode 13 of Third Loop, the Progressive Delivery team speaks with Jennifer Riggins about why faster code doesn’t automatically mean better software. They explore how AI exposes organizational bottlenecks, why psychological safety matters, and what Spotify’s growing feature set reveals about user value. Jennifer makes the case for smaller experiments, stronger feedback loops, and keeping people at the center of delivery.

On episode 60 of Generationship, Rachel Chalmers speaks with Vasanth Mohan of SambaNova about when faster AI agents actually mean better work. They explore why token consumption and code volume are poor substitutes for business value, how inference latency interrupts productive workflows, and why engineering taste still matters. Sometimes the most valuable thing an engineer can produce is less code.

On episode 44 of Open Source Ready, Brian Douglas and John McBride sit down with Mikyo King to explore why an agent can fail even when its model gets things right. They discuss observing the full agent workflow, using evaluations to uncover unexpected behavior, and testing whether skills actually improve results. Along the way, Mikyo shares how Arize approaches open source collaboration as coding agents reshape the contribution process.

On episode 12 of Third Loop, the Progressive Delivery team speaks with Steph Hippo, Platform Engineering Director at Honeycomb, about what happens when AI accelerates development beyond the pace humans can absorb. They explore safe defaults, evolving code reviews, and the feedback loops that help teams understand what they ship. Steph makes the case for reinvesting saved time in better decisions, software quality, and human connection.

On episode 59 of Generationship, Rachel Chalmers speaks with Emily Long and Alex Zenla, co-founders of Edera, about rethinking the foundations of cloud and AI security. They explain how an IoT side project became an approach to workload isolation built around Xen and Rust, and why AI agents are making stronger security boundaries increasingly urgent. Along the way, they discuss complementary leadership skills, fundraising lessons, and the value of questioning the industry’s technical assumptions.

In episode 5 of Lab Notes, Amir Zohrenejad speaks with Aoden Teo, co-founder and CEO of Miso Labs, about what it will take for voice AI to become truly humanlike. They explore the limitations of today's speech-to-text and text-to-speech pipelines, the promise of full-duplex speech models, and the technical challenges around audio tokenization, turn-taking, emotion, reasoning, and real-time inference.

On episode 11 of Third Loop, the Progressive Delivery team sits down with Chad Fowler to explore regenerative software and what architecture looks like when AI makes code cheap enough to continually replace. They discuss Phoenix Architecture, Pace Layers, observability, architectural constraints, and why the system itself, rather than any individual implementation, may become the real asset in an AI-native future.

On episode 58 of Generationship, Rachel Chalmers sits down with Anastasia Marchenkova. They explore what it will take to move quantum computing from promising hardware to useful, production-ready systems, including better orchestration across quantum, classical, and AI compute. Anastasia also discusses open-source infrastructure, the limits of AI automation, and her larger vision of making powerful computing and scientific tools more broadly accessible.

On episode 4 of Lab Notes, Amir Zohrenejad speaks with Junchen Jiang about why KVCache may be better understood as reusable, AI-native data rather than a temporary inference optimization. They explore how LMCache and CacheBlend can reduce redundant computation, move context across distributed inference systems, and help support increasingly complex AI agents. The conversation also covers multimodal workloads, open-source infrastructure, and the future of AI systems research.

On episode 10 of Third Loop, the Progressive Delivery team speaks with Honeycomb co-founder and CTO Charity Majors about observability, AI, and the changing economics of software development. They explore a future where code is increasingly disposable and regenerable while architecture, constraints, production behavior, and promises to users become the durable artifacts that matter. Along the way, Charity shares her perspective on responsible AI adoption, engineering in nondeterministic systems, and why production remains the ultimate source of truth.

On episode 12 of High Leverage, Joe Ruscio sits down with Dexter Horthy of HumanLayer. They explore the promise and limitations of autonomous coding, including why today’s models excel at bounded programming tasks but struggle to account for the long-term consequences of architectural decisions. The conversation covers dark software factories, code review bottlenecks, program design, technical debt, and the continuing importance of experienced engineers.

In episode 54 of The Kubelist Podcast, Marc and Benjie sit down with David Crawshaw. David shares how a weekend WireGuard experiment became Tailscale, and how a series of unsuccessful developer-tool experiments eventually became exe.dev. The conversation offers a candid look at product discovery, technical failure, cloud economics, and building infrastructure for AI agents.

On episode 41 of Open Source Ready, Brian Douglas and John McBride sit down with Phil Estes. They explore why the definition of a container remains surprisingly fuzzy, how microVM-backed sandboxes could support the next generation of AI agents, and what AI-generated contributions mean for projects like containerd. Phil also explains why the future of open source depends as much on trusted maintainers and healthy communities as it does on code.