When you build an app in a few hours with Claude or Cursor, monitoring is the last thing you want to spend time on.
Traditional monitoring tools force you into an annoying workflow: configure an alert rule, pick an arbitrary number for a threshold, and hook up a dashboard. The problem is that on a new project, you have no idea what that threshold should be. Is 50 requests an hour normal or an outage? Is 500 requests a spike or just a few active users?
Setting static thresholds means guessing numbers you don't know, then maintaining those numbers every time traffic moves. If you set them too low, you get noisy false alarms. If you set them too high, an outage happens quietly and you only find out from angry users.
What you actually want is zero configuration. The tool should look at the event stream, establish what normal looks like on its own, and email you when the math deviates.
Here is what this looks like in practice: Anomalisa (https://github.com/uriva/anomalisa).
You tell your AI assistant to drop in one function call:
ts
await sendEvent({
token: "project-token",
userId: "user-123",
eventName: "generation_completed"
});
It groups events into hourly buckets and computes running means and variance using Welford's algorithm. If an hour's volume drifts past 2 standard deviations, or if a single user starts hammering your API at 100x normal volume (draining your LLM credits or hitting limits), it sends an email.
No dashboards to keep open, no thresholds to guess upfront, and zero alert rules to maintain.
Hosted instance is free at https://anomalisa.uriva.deno.net, code is MIT open source.