In this article, we will learn how memory usage patterns can affect the real-time performance of an embedded application, drawing from a recent experience tracing an audio DSP application running on an embedded Linux platform. First, I will introduce the product in question and the real-time audio software I developed for it. Then, I’ll describe the issues I encountered with audio callbacks and the strategy I followed to determine the cause of the issues, ending with my solution and lessons learned.

The advancements in GenAI technology are creating a significant impact across domains/sectors, and the Kubernetes ecosystem is no exception. Numerous interesting GenAI projects and products have emerged aimed at enhancing the efficiency of Kubernetes cluster creation and management. From simplifying application containerization for engineers to addressing complex Kubernetes-related queries or troubleshooting issues within a cluster, GenAI demonstrates immense potential.

The JTAG interface is an important tool for debugging and testing embedded systems, providing low-level access to the internal workings of microcontrollers and other integrated circuits. However, this powerful interface also presents significant security threats. In the sixth and final part of this Diving into JTAG article series, we will focus on security issues related to JTAG and the Debug Port.

Grafana Alloy 1.3 is here! First introduced earlier this year, Alloy is our open source distribution of the OpenTelemetry Collector. It has native pipelines for OpenTelemetry and Prometheus telemetry formats, and it uses the same components, code, and concepts that were previously introduced in Grafana Agent Flow. This new release introduces live debugging, enhancing debugging capabilities across key components, which are the building blocks of Alloy.

In this article, we will explore how to use GitHub Actions to automate building STM32CubeIDE projects. Eclipse-based IDEs like STM32CubeIDE are often used for developing embedded systems but can be a little tricky to build in a headless environment.

The software world is changing rapidly due to advancements in GenAI. These technologies are disrupting traditional processes and driving automation across every part of the SDLC. The market for AI code tools is estimated to reach $30 billion by 2032. It started with code generation, then moved to testing, QA, automatic pull requests, and beyond.

Monitoring tools are great at letting you know when something is broken and the overall impact. We should know, we make an error monitoring tool. Observability tools are good for well, observing. But here’s the thing, you (we) don’t observe code. We (you) push code. So what the collective “we” need is a tool that makes it easy to ship, improve, and maintain reliable and performant code.