Operations | Monitoring | ITSM | DevOps | Cloud

From Alert to Fix in 10 minutes: How a Slow Query Took Down Placid.app

This is a guest post from Armin Ulrich, a fullstack developer, and founder of placid.app. He also created the MadeWith* network where he shares his projects and allows other developers to share theirs. There are many things I would rather do at 9pm than tracking down a mission-critical bug, but sometimes you don’t have a choice. Let me tell you the story about a slow query that led to a cascading failure–and how it could have been worse.

Get Better Visibility Into App Hangs On Apple Devices

App hangs are the worst kind of bug: they don’t crash, they don’t log, and unless you're actively profiling, good luck catching them in the debugger. Maybe the main thread is blocked because it’s decoding a massive image with UIImage(data:). Maybe a background task is holding a lock or waiting on a DispatchGroup that never finishes. Maybe an async flow is stuck waiting on a continuation that never resumes.

Logs in Sentry: Now in Open Beta

You’re looking at an error in Sentry—a failed payment in your Flask backend or an unexpected null in your Node API. You’ve got the stack trace. The request details. Even the full trace. What you don’t have: the logs your app emitted right before everything went sideways. With Sentry Logs (now in open beta), you can send application logs straight to Sentry and see them automatically connected to the errors and traces you already use.

Want AI to be better at debugging? It's all about context

More code is being shipped today than ever before, accelerated by AI powered code gen tools. We’re in a golden age for builders. But here’s the thing: software still breaks in production. From a recent study by Microsoft, AI models struggle to debug software. It’s because most of these code gen tools lack the one thing every good developer relies on: context. To debug anything, you need context. Having AI tools doesn't change that.

Monitoring your MCP Server in Production (with Sentry)

So you're building an MCP server for your project or service, to allow AI chatbots and agents to interact with it? Great! You've decided to build it using Cloudflare Workers, have written the code, shipped it, and the first users are getting onboard: you're officially running it in production. That's when problems start. I'm not here to dissuade you from shooting your shot, but let's make sure you've got your bases covered in production when something inevitably goes wrong.

Debugging Microservices

Debugging microservices is tough, especially when you're juggling multiple services and relying only on logs. This video cuts through the complexity by showing you how to implement distributed tracing using Sentry. You'll see a practical demonstration in a food ordering app (built with React and Go) of how tracing can give you a clear view of your entire request flow, from the initial button click to the final operation across all your services.

Tracing Just Got a Whole Lot More Useful: Search, Visualize, and Alert with Sentry's new Query Engine

For a while, tracing in Sentry was... fine. You could open up a slow transaction, poke around, find the N+1, and feel like a hero. But if you wanted to answer more complex questions - like why your payment API was getting slower in Europe, or which CDN was silently tanking your image loads - things got harder. We didn't really build it to help with answering broad questions.