Hanselminutes with Scott Hanselman

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.

There’s a new wave of AI tools that don’t just live in the cloud, don’t just autocomplete code, and don’t just sit in a browser tab. They reach into your local environment, understand your context, and act more like a thinking companion than a chatbot. In this episode, I talk with Peter Steinberger, founder of OpenClaw, about the rise of “The Claw” and what it means to build AI that feels fast, personal, and deeply integrated into your workflow.

We explore why OpenClaw is having a moment, how developer expectations are shifting from prompts to agents, and what it takes to design tools that balance power, safety, and usability. Peter shares the architectural choices behind OpenClaw, the tradeoffs between local and cloud inference, and his perspective on privacy, ownership, and latency in a world of ever-larger models.

This is a conversation about control. Who owns your context? Where does your data live? And what happens when AI stops being a destination and starts becoming an ambient layer across everything you do?

AI is making developers dramatically more productive...so why is everyone so exhausted? In this episode, Scott talks with Steve Yegge, legendary blogger and creator of Gas Town, a multi-agent orchestrator he describes as "Kubernetes for coding agents." Steve shares his theory of the "AI Vampire," that working alongside AI drains human energy Colin Robinson-style (What We Do In The Shadows), even as output skyrockets. They dig into what happens when you're managing ten or twenty Claude Code instances at once, who actually captures the value of a 10x productivity boost, and why the most important thing developers can do right now might be to close the laptop and go for a walk.

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/

Sandboxing is having a moment. As agents move from chat windows into terminals, repos, and production-adjacent workflows, the question is no longer “What can AI generate?” but “Where can it safely run?” In this episode, Scott talks with Mark Cavage, President of Docker, about the resurgence of sandboxes as critical infrastructure for the agent era and the thinking behind Docker’s newly released sandbox feature.
They explore why isolation, reproducibility, and least-privilege execution are becoming table stakes for AI-assisted development. From protecting local machines to enabling trustworthy automation loops, Scott and Mark dig into how modern sandboxes differ from traditional containers, what developers should expect from secure agent runtimes, and why the future of “AI that does things” will depend as much on boundaries as it does on model capability.
Learn more about Docker Sandboxes here: https://dockr.ly/4avCKTW