Hanselminutes with Scott Hanselman

Eric Allman, creator of sendmail and early internet engineer who also built syslog, shares stories about the origins of email and the messy realities of building software at planetary scale. He recounts designing sendmail’s rule-based glue, watching the internet evolve from ARPANET roots, and his perspective on early AI versus today’s models.

Scott Hanselman sits down with Allen Stewart, Partner Director of Software Engineering at Microsoft, to explore how AI agents with persistent memory are transforming scientific research and software engineering. Allen explains how his team built an AI system that learns from every investigation turning a 12-day autonomous drug discovery run into reusable knowledge that makes future research exponentially faster. Instead of starting from scratch each time, the AI inherits hypotheses, methodologies, and findings from previous work, saving hundreds of millions of tokens and weeks of effort. 

Don Syme, creator of F# and GitHub engineer working on agentic workflows. He discusses tools that plan and iterate across codebases. They cover agents proposing repo fixes, continuous AI alongside CI/CD, and how to set goals and guardrails for safe automation.

This week on the show, Scott talks to Philip Kiley about his new book, Inference Engineering. Inference Engineering is your guide to becoming an expert in inference. It contains everything that Philip has learned in four years of working at Baseten. This book is based on the hundreds of thousands of words of documentation, blogs, and talks he's written on inference; interviews with dozens of experts from our engineering team; and countless conversations with customers and builders around the world. https://www.baseten.co/inference-engineering/

What does it take to design a programming language from scratch when the target isn’t just CPUs, but GPUs, accelerators, and the entire AI stack? In this episode, I sit down with legendary language architect Chris Lattner to talk about Mojo — his ambitious attempt to rethink systems programming for the machine learning era. We trace the arc from LLVM and Clang to Swift and now Mojo, unpacking the lessons Chris has carried forward into this new language. Mojo aims to combine Python’s ergonomics with C-level performance, but the real story is deeper: memory ownership, heterogeneous compute, compile-time metaprogramming, and giving developers precise control over how AI workloads hit silicon. Chris shares the motivation behind Modular, why today’s AI infrastructure demands new abstractions, and how Mojo fits into a rapidly evolving ecosystem of ML frameworks and hardware backends. We also dig into developer experience, safety vs performance tradeoffs, and what it means to build a language that spans research notebooks all the way down to kernel-level execution.

Peter Steinberger, founder of OpenClaw and creator of local-first AI tooling, discusses building fast, personal AI that lives in your environment. He talks about native apps vs nodes, intentional install complexity, balancing privacy and latency, agent personalities, and practical automations like voice control and file tasks. The conversation digs into security tradeoffs, observability, and making agents feel ambient and helpful.

Steve Yegge, software engineer, blogger, and creator of Gas Town. He explains the 'AI Vampire' idea about AI boosting output while draining human energy. They discuss managing many coding agents, the physical and mental toll of long AI sessions, who captures productivity gains, and whether better agents or stepping away can restore healthier work rhythms.

Code reviews are one of the most powerful tools teams have for maintaining quality — but they're also one of the most emotionally charged parts of the development process. With AI coding agents generating more code than ever, the review bottleneck is growing fast. But what if AI-assisted reviews could not only keep up with the volume, but actually be kinder about it? Scott talks with Nnenna Ndukwe, Developer Relations Lead at Qodo, about how AI code review is evolving beyond glorified linting into something that understands context, catches what matters, and delivers feedback developers actually want to read. They explore what happens when the same AI writes and reviews its own code, and whether thoughtful AI review can make code review culture healthier for everyone...not just faster.https://www.qodo.ai/

Mark Cavage, President and COO of Docker and container tooling leader. He talks about Docker's new sandbox feature, micro-VMs and secure runtimes for running AI agents. They discuss isolation, curated agent images, filesystem visibility, pen-testing escapes, observability, and how sandboxes enable safer automation without exposing host secrets.

AI is moving faster than our collective ability to metabolize it. Between copilots, agents, vibe coding, and the ever-shifting definition of “senior engineer,” developers are asking a deeper question. Where is this all actually going? In this episode, Scott sits down with Gergely Orosz, author of The Pragmatic Engineer and longtime observer of how software gets built inside high-performing teams, to separate signal from hype.They dig into what AI is really doing to day-to-day engineering work. Productivity boosts versus skill atrophy. The changing expectations for junior developers. Whether “AI-first” companies are structurally different or simply marketing-forward. Gergely brings his trademark data-driven pragmatism, grounded in conversations with hundreds of engineering leaders navigating hiring freezes, agent experiments, and the reshaping of career ladders.Scott and Gergely also explore the human side. What happens to craftsmanship when code is abundant. How we teach the next generation to think, not just prompt. Why developer experience may matter more, not less, in an AI-accelerated world. Along the way, they consider whether we are watching a platform shift on the scale of cloud and mobile, or something even bigger.https://www.pragmaticengineer.com/