Operations | Monitoring | ITSM | DevOps | Cloud

Instead of giving you yet another chatbot, we built AI straight into the parts of Sentry where teams lose time, turning your existing data into instant context — and it’s now available to all Sentry users.

When I was working at Shopify, Black Friday and Cyber Monday were our Superbowl. We initiated code-freeze weeks before to make sure merchants wouldn't have any unexpected issues during one of the most important times of the year. Sometimes, though, you need to ship updates last minute. Picture this: It's Black Friday Eve, 11:47 PM. You've just deployed a new /sale page with 50+ products at discounted prices. Marketing is about to email 500,000 subscribers. Everything tested fine with your sample data.

In e-commerce, we all know that speed = money. I know it, you know it, Amazon knows it, eBay knows it, Shopify knows it, everyone knows it. In this article we’ll see how we can improve the perceived performance of our site’s critical pages, like the Product Details page, the Cart page, the Checkout page. We’re going to use the Speculation Rules API (SRA) to prerender/prefetch them, and also explain how certain frameworks like Next.js offer their own prefetching mechanisms.

We just launched our Cursor Cloud Agent integration. Now when Seer finds a bug, it can hand it off to Cursor—replete with all the context Sentry has about the issue—to write the fix and create a PR for you.

With the first stable releases out of the gate, we’re happy to announce that Sentry’s Godot SDK is now ready for general use, supporting Windows, Linux, macOS, iOS and Android. We started full-time development a year ago with just a few prototypes, and now it's finally here - built on top of the mature Sentry platform SDKs, it comes as a GDExtension add-on that you can easily add to your Godot projects.

Two years ago, Sentry built a metrics product that worked great on paper. But when we dogfooded it, we realized it was not what our customers really needed. Two weeks before launch, we killed the whole thing. Here’s what we learned, why classical time-series metrics break down for debugging modern applications, and how we rebuilt the system from scratch.

Developers don’t need another “run this tool, stare at a number, and feel bad about it” website. So we built something different. WebVitals helps you analyze, optimize, and ship faster websites, all in one place. Built by the same folks who obsess over stack traces and slow queries, it connects the dots between performance metrics and what’s actually slowing your users down. In one place, you can.

As you may have noticed, Sentry just got a major glow-up. For too long our product looked like boring enterprise software, while our brand screamed bold and irreverent. No more. From this moment forward our product now matches the vibe you’ve come to expect from us. The result is something that’s more vibrant, more tactile, and more Sentry. Welcome to the S.C.R.A.P.S.

Previously, I wrote about how debugging, whether done by humans or AI powered tools, depends on context. Without it, even the most capable systems can only tell you what code is broken, but not why it broke. Now that AI can access the same depth of context developers rely on (stack traces, traces, logs, commits, and code), the way we build and operate software is changing. We’re moving from an era of monitoring to one of reasoning.

Some of the team at Sentry spent this past quarter fixing bugs, more than 800 to be exact. Among them was a complex issue causing transaction spikes in our own Sentry project. Using Sentry Logs, we were able to investigate, trace the root cause, and resolve the problem.