He uses AI to assemble local network lab topologies and configuration details from high-level requirements and reference examples, including iterative debugging of misconfigurations.
He defines the desired topology, provides references, evaluates whether the environment behaves correctly, and decides when the generated setup is acceptable.
A developer describes the desired local test environment and AI drafts the container, VM, or network topology files needed to stand it up.
ChatGPT / ClaudeAI produces initial router, Kubernetes, or service configuration snippets from requirements and example files so the engineer can assemble a working lab faster.
GitHub Copilot / ChatGPTAn engineer shares broken configs and command output with AI to identify likely mismatches in addresses, ports, routes, or dependencies during local lab setup.
Claude / CursorI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
I had a draft post sitting in my local repo for a while, where I was about to scream about how AI is overestimated. Well, that post aged pretty badly. I never published it, and looking back at the notes I’m glad I didn’t. So what I’m going to write today will only be about my current workflow and how I actually use AI in my daily work — no hype, no predictions, just what I’ve found useful.