AI-Powered Homelab Monitoring: Why Local LLMs Excel Where Thresholds Fail

Traditional log monitoring relies on inflexible threshold rules and pattern matching, generating alert fatigue in complex environments. This project demonstrates how local LLMs like Ollama can understand context—distinguishing between expected errors and real failures—transforming homelab monitoring from noise into actionable intelligence. For self-hosted infrastructure operators, it offers a privacy-respecting, latency-friendly alternative to cloud-based monitoring services.