mode date min topic name
This final article connects the embedded AI device to the real world. It describes how inference results leave the MCU, travel through MQTT and HTTP, land in a Rust backend.
The TinyML lifecycle on a microcontroller, from dataset preparation and model training to TensorFlow Lite Micro on bare metal, with the model bound into Rust firmware through a custom FFI wrapper.
This article covers the hardware I use in my AI device project, why I chose it, and how I structured the firmware in Rust.
This series covers the building of an ESP32-S3 device with ML built right into the firmware, along with the decisions, compromises, and challenges that came up along the way.
The whole directory is one click away. Every post, filed by
subject.