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As developers, we know that debugging can be a time-consuming process. Hunting down elusive bugs or trying to reproduce an issue based on vague user reports can turn a simple fix into an hours-long journey. While leveraging logs, metrics, and tracing to reproduce locally or try to understand what happened can help us identify a root case, we’re often missing a critical component to truly being able to understand the impact on our users.

Learn how to use the Grok Debugger effectively for log parsing, with practical tips, debugging techniques, and pattern optimization.

While using a full-blown filesystem for storing your data in non-volatile memory is common practice, those filesystems are often too big, not to mention annoying to use, for the things I want to do. My solution? I’ve been hard at work creating the sequential-storage crate. In this blog post I’d like to go over what it is, why I created it and what it does.

Caching is one of the big draws for people using the Next.js framework. Its on-by-default, “just works” nature sets you up for high performance applications right out of the gate. However, improving web performance comes at the cost of a complex caching system. This complexity is a source silent errors in the form of stale and incorrect data. Next.js does its best to choose the right caching behavior for each page.

At Sentry, we don't just build debugging tools for developers—we use them ourselves. This story demonstrates how we leveraged our own platform to solve a mysterious performance issue that was causing significant latency spikes in our critical infrastructure which is used in nearly every backend request.

Debugging Django applications can be challenging, but it’s key to keeping your app running smoothly in production. From unexpected bugs to performance slowdowns, finding and fixing issues efficiently keeps users happy and reduces downtime. For example, when an error occurs on a critical page, like a checkout page, identifying the issue quickly is crucial to avoid disrupting user transactions.

Every pod has its purpose. During an application deployment, all the workloads in the cluster work cohesively and ensure that the deployment launches—without any hiccups. When a pod is created, it starts its lifetime in a Pending state. A pod is in the Pending state when it is still in the process of being scheduled for deployment. When it is scheduled for deployment and the containers have started, it converts to the Running phase. It only takes a few seconds for the phase transition.

In this very Memfault-centric post, I’ll be talking about how we shipped our SDK as an ESP-IDF component. This is a continuation of our efforts to make it easier to integrate Memfault into your projects, specifically targeting ESP32-based projects.