Two people, one tandem bike, and a lot of near misses along Amsterdam’s canals. Dexter Horthy, CEO and co-founder of HumanLayer and the person who coined “context engineering,” climbs on the back to talk about a problem every AI-heavy engineering team eventually hits: code that runs today that nobody understands three months later. The ride starts with an old joke from the Claude 3.7 days: ship the slop, it’ll be GPT 7’s problem. That joke is aging badly now that GPT 6 is out and still can’t design clean abstractions. From there it becomes a tour of how agents are actually used right now. Computer use checks in for flights and books government appointments. Two hours of raw footage became a rough but promising product demo through Opus 4.8 and the Palmier MCP while Dex was out for a run. And the best MCP tools turn out to be the ones that mirror what Claude Code and Codex were trained to call.

What happens when the AI revolution collides with the physical limits of our infrastructure? In this episode of Debug Mode, Erica Hughberg is joined by Zachary Smith to explore how AI is transforming the world of cloud computing, data centers, and hardware — and why the biggest challenges ahead go far beyond GPUs and model performance.

AI can write your code, choose its own tools, and book your next trip. But who checks that it actually did the job? In this episode of Debug Mode, we explore what it takes to give AI agents more responsibility—and how to keep track of what they’re doing. We discuss intent-based tool selection, MCP and WebMCP, and why a convincing demo doesn’t always translate into reliable automation. We also get into the personal AI assistant everyone seems to be building: one that manages travel, handles household tasks, and hopefully remembers to order coffee before you run out. Then it’s back to code: QA agents, automated verification, tests that capture business intent, and the growing challenge of reviewing AI-generated contributions.