I’m using different MCUS from NXP with VS Code, and for this I’m mostly using the NXP MCUXpresso extension for VS Code.
At the same time, I’m using the cortex-debug extension for other ARM Cortex-M based MCUs, for example the Raspberry Pi Pico series.
Does it makes sense to use cortex-debug for NXP devices, and what would be the benefit? More precise: can I use the NXP MCU-Link (LinkServer based) debug probes with cortex-debug, to debug another NXP MCU?
In my latest research project I’m using the NXP FRDM-MCXN947 board. As part of that project I have to collect and export application trace data for later analysis. In that project I’m using the the Tracealyzer product from Percepio.
In this article I describe different ways to export trace data from the embedded target: using generic GDB commands or with an trace exporter in VS Code.
Continue readingFor an university research project, I need to write some console output and the same time write a file with data from the embedded board to the host. A logical choice for this is using semihosting.
Debugging a C/C++ application inside a Docker Container using VS Code is surprisingly easy.
This article shows the needed steps.
Continue readingIn Tutorial: Getting Started with MCUXpresso SDK – Repositories with VS Code and Tutorial: Getting Started with MCUXpresso SDK – west I showed how to get the MCUXpresso SDK. In this article it is about tweaking and streamlining the project.
The result is a clean, portable and self-contained MCUXpresso SDK project.
The engineering and development landscape is constantly changing. This includes the embedded tooling and SDKs.
Back in the year 2017 I have used the version 2 (see Tutorial: Using Eclipse with NXP MCUXpresso SDK v2 and Processor Expert). Back then, it was Eclipse with make. Now we are in 2026 and things have changed to VS Code, cmake, ninja, python and west.
Luckily, although more and different tools are needed these days, the installation experience has improved significantly. It has shifted from installing multiple different tools to a streamlined installation process.
Continue readingEmbedded System have a lifetime of 10 or 15 years. During that time the software and tools has to be maintained. This can be very challenging. Who knows if that compiler or tool used is still available in 10 years from now? Additionally installing and configuring the tool chain and environment for a new team member is difficult. Even worse: using a different host operating system for the cross development can produce different results or introduce issues.
One solution for all these problems is to use Docker images and containers. I can pack all the necessary tools and software into a virtual environment and container. But developing inside a container comes with many challenges. In this article I’ll show how Visual Studio Code or VS Code makes working with containers very easy. In this article I show how easy it is to use modern development tools and methodologies for embedded development.
Continue readingThe MCU-Link is a small and inexpensive $10 CMSIS-DAP debug probe from NXP. It can work with OpenOCD, but has better target support using the NXP LinkServer which implements a gdb server. This makes it an ideal combination for scripting or automated testing.
With semihosting I can use standard I/O function like printf() and I can read and write data on the host through the debug connection. If used with care, this is a great feature especially for unit testing.
Developing for an embedded target means using a certain version of GNU compiler, debugger and other tools. The challenge gets bigger if working with multiple different tool chains and environments.
Conda is package, dependency and environment management tool. While it is heavily used for Python and Data Science development, it is surprisingly working very well to set up and managing environments for embedded development. Conda is great for managing non-Python dependencies and setups.
Continue reading
MCU on Eclipse 