Ep. #3, Give It a Name: Why Software Needs a Third Loop

On episode 11 of Data Renegades, CL Kao sits down with Michael Stonebraker, legendary database pioneer and creator of Ingres and Postgres. They explore the origins of modern relational databases, why specialized systems outperform one-size-fits-all platforms, and what today’s AI hype gets wrong about enterprise data. Stonebraker also shares hard-earned lessons on startups, research, and staying relevant over five decades in tech.

On episode 8 of High Leverage, Joe Ruscio sits down with Scott Hanselman for a conversation that goes far beyond prompts and productivity gains. They talk about craftsmanship, learning by doing, the long-term talent pipeline for engineering teams, and how AI could either free people to do more meaningful work or simply accelerate existing inefficiencies. From pair programming to Star Trek economics, this episode examines the bigger human questions behind the current AI boom.

On episode 36 of Open Source Ready, Brian Douglas and John McBride sit down with Jesse Vincent. They explore how Jesse’s Superpowers project turns AI coding tools into structured, reliable development systems. The conversation dives into agent orchestration, prompt engineering, and what it takes to make AI behave like a capable software engineer.

In this episode, the hosts unpack the thinking behind the name Third Loop and what it represents. Building on ideas from their book Progressive Delivery, they explore the gap between shipping software and having it truly adopted by users. The conversation dives into feedback loops, user agency, and why modern software requires a closer relationship between builders and consumers.

On episode 54 of Generationship, Rachel Chalmers sits down with Vishakha Gupta to explore the hidden infrastructure challenges behind modern AI. They unpack why multimodal data systems are still fragmented, how graph and vector approaches can be unified, and what it takes to build production-ready AI pipelines. This conversation offers a systems-level perspective on scaling AI beyond prototypes.

On episode 50 of The Kubelist Podcast, Marc Campbell and Benjie De Groot sit down with Ivan Burazin to explore the rise of sandbox environments for AI agents, how Daytona enables instant, stateful compute, and why traditional infrastructure models fall short. Ivan also shares lessons from building early cloud IDEs and finding product-market fit in the AI era.

On episode 89 of o11ycast, Ken Rimple and Charity Majors are joined by Bryan Cantrill. They dive into the origins of observability, the realities behind AI productivity gains, and the tension between cloud convenience and infrastructure control. The discussion highlights how major tech shifts often look obvious only in hindsight.

On episode 10 of Data Renegades, CL Kao and Dori Wilson sit down with Toby Mao. They explore how real-world data problems at Netflix and Airbnb led to the creation of SQLGlot and SQLMesh, and why data modeling remains the hardest challenge in data engineering. The conversation also dives into how AI is reshaping engineering workflows, without replacing the need for strong architectural thinking.

On episode 2 of Third Loop, Kim, Heidi, and Adam sit down with Kent Beck. They explore how Progressive Delivery extends ideas from Agile and Extreme Programming by focusing on safer releases, feature flags, reversibility, and observability in production. The conversation also dives into AI-assisted coding, experimentation, and what it takes to ship software users can actually trust.

On episode 34 of Open Source Ready, Brian Douglas and John McBride sit down with Sunil Dhaliwal to explore how AI is reshaping open source and startup building. They discuss why technical founders still have an edge, how taste is becoming a critical differentiator, and what it takes to find product-market fit in a world of abundant software. The conversation also dives into monetization challenges and the evolving role of open source.