Devops

GitHub Copilot setup guide with AI skills and memory

Getting Started with GitHub Copilot: What Actually Works

TL;DR A $20/month GitHub Copilot subscription gives you Claude Sonnet 4.6, GPT-4o, and Gemini inside VS Code. Out of the box it’s useful. With a proper instruction setup — a copilot-instructions.md file, path-scoped rules, and skill documents — it becomes something you actually rely on. Most of the posts on this blog were built with this toolchain, mostly in the context of my k3s cluster, but the patterns apply anywhere. This is how I have it set up. ...

Building an AI Memory System: From Blank Slate to 482 Lines of Hard-Won Knowledge

TL;DR The .github/copilot-instructions.md file started as 10 lines of project description and grew into a 99-line “operating system” for AI assistants. Then it split: failure patterns moved into docs/ai-lessons.md (now 482 lines across 20+ categories), and file-type-specific rules moved into .github/instructions/ with applyTo glob patterns. The same template structure was standardized across 5 repositories. This post traces the three generations of AI instruction architecture and shows how every production incident permanently improves AI reliability. ...

When Your AI Memory System Eats Its Own Context Window

TL;DR The AI memory system I built three weeks ago started causing the problem it was designed to solve: context window exhaustion. Five generic Claude skills — duplicated identically across all 5 repositories in my workspace — consumed 401KB (~100K tokens) of potential context. The gh-cli skill alone was 40KB per copy, accounting for 42% of all skill content. I ran a full audit, deleted 25 duplicate files, and documented the anti-pattern to prevent recurrence. ...

AI-Assisted Infrastructure: Claude, Copilot, and the Memory Protocol

TL;DR Two weeks of building a production Kubernetes cluster with AI pair programming. Claude Opus 4.6 handles complex multi-step infrastructure work via the CLI. GitHub Copilot provides inline code completion in VS Code. AWS Bedrock (Nova Micro, Claude Sonnet 4.5) powers runtime AI services inside the cluster. The key discovery: AI tools without persistent memory are dangerous. Every session starts from zero. The same bugs get recreated, the same anti-patterns get suggested, the same cluster-specific constraints get forgotten. The solution is the “Memory Protocol” – a set of documentation files the AI reads before every session and updates after every discovery. ...