Coding agents work best in a predictable environment, but they can also help build it. When agents help create and maintain the configurations they use for their own jobs, both humans and agents benefit from better tools in a virtuous feedback loop. This article shares an open-source dotfiles setup that pairs Unix tooling with AGENTS.md and Agent Skills to optimize terminal and Python AI development.

What happens after you’ve built your first MCP server and actually have to make it work in the real world? In this episode, James dives into the more advanced side of MCP: observability, evals, tool design, code mode, authentication, and the challenges that appear once agents start using your server at scale. We also get into how AWS thinks about MCP across roughly 16,000 APIs, why inefficient tool design often gets blamed on MCP itself, and whether the future could involve more constrained, human-reviewable alternatives to full code mode.

Voice is becoming one of the most natural ways to interact with AI. As AI agents move beyond text and into real-time conversations, voice is opening up an entirely new layer of interaction between people, software, and intelligent systems. From customer support and personal assistants to enterprise workflows, healthcare, and entirely new applications, voice agents are rapidly becoming a major interface for AI.