Monitoring

eBPF (extended Berkeley Packet Filter) lets you attach tiny sandboxed programs to kernel events: syscalls, network packets, scheduler decisions, and filesystem calls. You collect detailed performance data in real time. No kernel source changes, no custom modules, no service restarts. With bpftrace one-liners and the BCC toolkit, you can measure per-process disk latency, trace TCP connections, profile CPU hotspots, and find memory leaks on production Linux. Overhead is usually under 2%.

Yes, you can monitor every houseplant in your home for under $10 per plant. A single ESP32 board running ESPHome (currently at version 2026.3.0) reads capacitive soil moisture sensors, a BH1750 light sensor, and an AHT20 temperature/humidity sensor, then feeds everything straight into Home Assistant . From there, automations send you a notification when a plant needs water, dashboards show moisture trends over weeks, and you stop guessing whether that fern in the corner is actually happy. This guide covers sensor selection, wiring a 4-plant monitoring hub, the complete ESPHome YAML configuration, Home Assistant dashboards, and tips for long-term reliability.

Deploy Prometheus to scrape metrics from node_exporter on each Linux server. Then chart it all in Grafana with CPU, memory, disk, network, and systemd service health. The full stack (Prometheus 3.x, node_exporter 1.10, Grafana 11.6) can watch a 10-server homelab on one Raspberry Pi 4 or a small VM with 1GB RAM. The community Node Exporter Full dashboard (Grafana ID 1860) gives you production-grade views in under 30 minutes.