! networking, automation, and the occasional LLM experiment
A vendor-agnostic, YANG-model-driven MCP service that enables LLMs to safely interact with network infrastructure — with human operators always in control through structured approval flows and full auditability.
AI agents are starting to look like operating systems. The LLM is the CPU, the agent is the OS, and skills and MCPs are the applications — here's why that analogy holds up.
AI can generate Cisco configs and Terraform plans with impressive fluency, but the hard part of network engineering was never the syntax. It's interop, requirements, and the mental models we build to design and troubleshoot.
Worth watching: The 5 Levels of AI Coding. A solid framework for thinking about where you actually sit on the AI-assisted development spectrum — from basic autocomplete all the way to fully autonomous agents. Honest about what each level demands from the engineer and where the real productivity gains (and risks) live.
Most of us are somewhere in the middle and kidding ourselves about it. Good gut check.
Watched the Cisco Live 2026 Amsterdam opening keynote. Cisco is going full AI mode — no surprise there, but interesting to see how they’re positioning it across the portfolio. Will be following the rest of Cisco Live remotely to catch the technical sessions and see what’s actually substance vs. slide deck hype.
Testing Claude Code’s new Agent Teams feature — spinning up multiple specialized agents that work in parallel. The tradeoff is clear: order of magnitude speed, order of magnitude risk of technical debt. But having multiple domain experts collaborating (instead of one generalist context-switching) does help catch things.
Already used it to build out this Astro blog. Next experiment: ChessKids — an AI-powered chess tutorial for my 6-year-old daughter. Teaching chess to kids feels like a good test case for agentic workflows: visual, structured rules, incremental difficulty.