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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…
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…
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,…
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…
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…
On episode 9 of Third Loop, the Progressive Delivery team explores the complicated relationship between AI, automation, and human creativity. Kim Harrison, Adam Zimman, and Heidi Waterhouse discuss AI’s ability to lower technical barriers and reduce toil, along with its tendency to strip away context, reinforce sameness, and confidently produce answers it does not understand. Along the way, they…
On episode 3 of Lab Notes, Amir Zohrenejad sits down with Qizheng Zhang to explore one of the fastest-moving areas of AI research: recursive self-improvement. Together, they discuss Meta-Harness, context engineering, and why the future of AI may depend as much on the software surrounding models as the models themselves. The conversation also examines evaluation, agentic systems, and the limits of…
On episode 57 of Generationship, Rachel Chalmers sits down with Navneet Kaur, founder of FemTech India and TechThrive Ventures. Together they explore why women's health innovation must extend beyond Western markets, how AI is reshaping femtech, and what founders need to understand about trust, culture, and personalization when building global healthcare companies.
On episode 8 of Third Loop, the Progressive Delivery team sits down with Melinda Fekete to discuss FeatureOps, developer experience, and modern software delivery. The conversation covers feature flag lifecycles, runtime control, experimentation, chaos engineering, and why progressive delivery is becoming even more important in the age of AI-assisted development.
On episode 92 of o11ycast, Ray Myers joins Ken and Jess to explore how observability, reliability engineering, and formal software engineering practices are becoming even more important as AI coding agents take on larger roles in development. Rather than viewing AI as a replacement for established engineering disciplines, Ray argues that techniques like continuous delivery, testing,…
On episode 2 of Lab Notes, Amir Zohrenejad sits down with Hanchen Li to explore the systems that make modern AI agents faster, more efficient, and better at learning from experience. They discuss KV Cache optimization, long-context inference, prompt learning, continual learning, and why better benchmarks may be just as important as larger models in advancing AI research.
