After 1.6 million downloads, the Usage app team shares how they use Sentry's crash monitoring, error tracking, size analysis, and user feedback to stay ahead of Apple platform changes.
Picking a model for your agent is a tradeoff between cost, quality, and speed. Here's a free-tier model that made things up, what an eval would and wouldn't have caught, and why reading the agent traces is how you actually decide.
More code is shipping faster than ever, and most of it is no longer written by people. The speed is the story everyone tells. What matters more is what happens after the code ships, where software is starting to notice it is broken, understand why, and write the fix itself.
Next.js already creates traces for your requests. With @vercel/otel and a couple environment variables, you can export them to any OTLP-compatible backend.
We trained and deployed a new issue grouping model that prevents 20% more duplicate issues, halves incorrect merges, and makes future model upgrades trivial.
Sentry Snapshots is in beta. Diff screenshots on every commit, block the PR if there are visual changes, and use snapshots as context for AI agents.
Sentry's SDK team uses a Claude skill to automatically set up reproduction environments for bug reports, cutting hours of boilerplate setup to minutes.
Errors, traces, logs, and metrics overlap more than you'd think. An opinionated, practical guide to which signal to reach for, and why the choice decides what you (and your coding agents) can debug later.
Sentry replaced its legacy headless CMS with Astro, Markdown, and AI-driven automation — cutting build times from 14 minutes to under 4 and eliminating broken builds.
Teams with existing OpenTelemetry instrumentation do not need to choose between OTel and Sentry. Keep OTel on the backend, use the Sentry SDK on the frontend, and export OTLP events to Sentry.
The bottleneck for self-healing software isn't agent intelligence. It's that agents have no idea what actually broke in production. Sentry MCP and the CLI bridge that gap.
You already have everything you need. Your Sentry traces, logs, and metrics can answer most product analytics questions without a separate tool.
Sentry dashboards just got a major upgrade: create dashboards with AI agents, manage them via the CLI, and clone pre-built templates for common monitoring use cases.
Supabase gives you query insights and logs, but it can't trace across your full stack. Here's how to connect Next.js, Supabase Edge Functions, and Postgres with distributed tracing, log drains, and AI-assisted debugging in Sentry.
