Cloud computing is great, but for IoT, it has three killers: Latency, Bandwidth, and Privacy.
What if your microcontroller could "think" for itself? That's TinyML. Today I want to show you how to run actual neural networks on a standard ESP32.
Why do this?
Speed: Inference takes <20ms.
Cost: No cloud function bills.
Privacy: Audio/Video never leaves the device.
The Workflow
Collect Data: Record sensor data (accelerometer, audio, etc.)
Train: Use TensorFlow or Edge Impulse to create a model.
Quantize: Squeeze 32-bit floats into 8-bit integers (crucial for RAM).
Deploy: Convert to a C++ library and flash to the ESP32.
I've written a comprehensive guide that walks you through building a Gesture Recognition Wand from scratch. It covers the hardware setup, training pipeline, and the C++ inference code.
Read the full guide here on my personal blog
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