Managing multiple AI coding sessions gets messy fast once you use more than one tool. Sessions end up scattered across local transcript files, JSONL logs, and SQLite databases. Finding an old session, checking which model was used, or jumping back into the right project usually means opening each tool separately and remembering where it stores state. ai-dash is a local terminal UI for multi-session management across Claude Code, Codex, and OpenCode.

Go applications expose a useful set of runtime metrics, but raw /metrics output does not make it easy to spot GC pressure, scheduler latency, memory growth, or file descriptor exhaustion. This post covers a go-mixin for Prometheus and Grafana that adds a dashboard and alerts for the Go runtime.

The mixin is available on GitHub. The dashboard is also published in the Grafana dashboard library. It currently ships with one Grafana dashboard and three alert rules:

• Go / Overview - A dashboard for runtime CPU usage, scheduler latency, garbage collection, heap churn, mutex contention, cgo activity, and file descriptor pressure.
• GoHighGcCpu - Alerts when a Go process spends too much CPU time in garbage collection.
• GoHighSchedulerLatency - Alerts when runnable goroutines wait too long to be scheduled.
• GoHighFdUsage - Alerts when a Go process is close to its file descriptor limit.

The repo also includes generated dashboard JSON and Prometheus rule files, so you can either vendor the mixin into your Jsonnet setup or import the generated files directly.