The Sentry Error Monitor

“What was the moment this product clicked?” — share yours

A backend, frontend, or full-stack developer at a product company for whom Sentry is the first place they look when something goes wrong in production. They didn't set Sentry up — it was already there when they joined — but they've learned to read its output. They've been paged because of a Sentry alert. They've traced a production incident back to a specific line using Sentry's stack traces. They've also spent 40 minutes investigating a Sentry error that turned out to be a bot making malformed requests. They've learned to filter.

• →Know immediately when something breaks in production, before a user reports it
• →Trace an error to the specific code, request, and user context that caused it
• →Reduce error volume over time by fixing the issues that matter, not just the noisiest ones

• —Alert fatigue from errors they've already triaged, resolved, and seen re-open
• —Stack traces that lose context at the framework boundary and don't point to their actual code
• —The gap between what Sentry shows and what they need to reproduce the error locally
• —Errors that are grouped together when they're actually different issues with similar traces

• An error that users hit before you do is an error you've already failed to catch
• Volume is a distraction — the worst bugs are often the quietest ones
• The best error report includes what the user was doing, not just what the code did

Uses Sentry at work — rarely sets it up, always uses it. Has Sentry integrated for at least one production service. Reviews Sentry alerts in email, Slack, or PagerDuty. Has source maps configured (or has been frustrated that they're not). Uses Sentry's issue assignment to route errors to the right engineer. Has set up alert rules for error rate thresholds. Checks Sentry's performance tab occasionally. Wishes the performance tab were more useful. Has muted at least 5 recurring errors they've decided not to fix yet and feel mild guilt about.

• →Smarter issue grouping that separates genuinely distinct errors instead of
• →bucketing them by similar stack trace removes the "these are actually three different bugs" problem
• →Source map management that works reliably across deployments removes the
• →"the trace shows minified code" debugability gap
• →Regression detection that flags when a previously resolved issue re-opens
• →with the specific deploy that caused the regression removes the archaeology step
• →User impact scoring that surfaces which errors affect the most users — not just
• →which throw the most exceptions — focuses triage on what actually matters

Pairs with `pagerduty-primary-user` for the error detection-to-incident response workflow. Contrast with `datadog-primary-user` for the error monitoring vs. full observability platform comparison. Use with `vercel-primary-user` for frontend developers deploying on Vercel with Sentry monitoring.