AI coding assistants were built for web and application work. Point one at firmware — register maps, interrupt handlers, hard real-time constraints — and the easy wins get complicated: it will happily produce plausible code that quietly misses a timing requirement. Used with judgement, though, the same tools take the tedious work off an engineer’s plate — driver scaffolding, test generation, boilerplate — and leave the hard, safety-relevant decisions where they belong.
The courses cover AI for firmware and driver development, designing architectures that are ready for AI workloads, tool-specific practice such as Claude Code for embedded engineering, and an applied workshop that assembles the pieces into a method you can actually use. They come from the same engineers behind the Embedded AI podcast.