Why Tori Westerhoff says we should talk to strangers

Greg Hinkle, co-founder of Nimbalyst and former VP of Software Engineering at Salesforce, joins Scott to discuss the future of AI-assisted development. They explore the challenges of managing multiple AI coding agents, finding flow state in an agentic world, and why visual workspaces matter. Greg shares Nimbalyst's opinionated approach to integrating tools like Excalidraw, task management, and session organization directly into the developer workflow. https://nimbalyst.com

Kelly Shortridge, author of "Security Chaos Engineering: Sustaining Resilience in Software and Systems" and CPO at Fastly, joins Scott for an ACM ByteCast joint episode about why security should be designed for failure rather than prevention. From airplane coffee makers causing critical failures to squirrels being the real "advanced persistent threat" to power grids, Kelly makes the case that no system is perfectly secure — and the teams that feel most in control are often the least prepared. The conversation covers metrics theater, the cost-resilience tradeoff, why software has unique advantages for simulation that we're not leveraging, and where LLMs fit (and don't fit) in security workflows.

Tori Westerhoff joins Scott to explore the intersection of AI, human psychology, and personal growth. As people increasingly use LLMs for introspection and decision-making, Tori argues that we're missing the diversity of thought that comes from community, even particularly random encounters with strangers. She reveals her own practice: a daily noon reminder to talk to strangers. "If you sycophant yourself, you're never going to grow," she explains. The conversation delves into how LLMs can create echo chambers of thought, and why the randomness of human connection, even just someone on the same bus, helps us update our mental frames and break out of programmed decision-making paradigms.

In this episode, in association with the ACM ByteCast, Scott talks with Eric Allman, one of the foundational figures of the early internet. Best known for creating Sendmail, the mail transfer agent that powered a large portion of global email infrastructure through the formative years of the network, Allman helped shape how messages move across the internet. Their conversation explores the origins of internet email, the messy realities of building software that must operate at planetary scale, and what lessons today’s engineers can learn from the systems and design decisions that quietly underpin modern computing.

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. 

In this episode, Scott talks with Don Syme about the emerging world of agentic developer workflows and what it means when coding tools move from autocomplete helpers to collaborators. They explore how modern tools like GitHub Copilot and GitHub Agentic Workflows are evolving into systems that can plan, execute, and iterate on tasks across a codebase, and what that means for software design, type systems, and developer responsibility.  https://github.github.com/gh-aw/

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.

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.