Ai

TL;DR I run Wiki.js on k3s as the cluster’s internal knowledge base. It is not a place I write documentation — it is a place the AI writes documentation after completing work. When Claude finishes deploying a service, debugging an incident, or refactoring infrastructure, it commits the results to the wiki with architecture diagrams, decision rationale, and operational notes. I am the primary reader. When I want to understand how something works, or why a specific decision was made three weeks ago, I go to the wiki instead of digging through git history or re-reading code. ...

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. ...

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. ...

TL;DR Jellyseerr already knows what I have. Radarr and Sonarr already know how to find things. The missing piece was a front door that understood intent instead of requiring me to search for specific titles. I wired Jellyseerr’s REST API to Claude and gave it a system prompt that knows my taste profile. Now I can say “download 100GB of family-friendly anime I might like” and get a queue of requests back. A Kubernetes CronJob runs the same prompt on a schedule so the library grows without me thinking about it. ...

TL;DR Today I deployed two significant additions to the cluster: an AI-powered Alert Responder that uses AWS Bedrock (Amazon Nova Micro) to analyze Prometheus alerts and post remediation suggestions to Slack, and a multi-user dev workspace with per-user environments. I also hardened the cluster by constraining all workloads to the correct architecture nodes and fixing arm64 scheduling issues. The Alert Responder Running 13+ applications on a homelab cluster means alerts fire regularly. Most are straightforward — high memory, restart loops, certificate expiry warnings — but analyzing each one, determining root cause, and knowing the right remediation command gets tedious, especially at 2 AM. ...