Why Embedded Systems Deserve Their Own Machine Learning Library

By @hejhdiss The embedded world has always been about doing more with less. Less RAM, less flash, less clock speed — and yet the demand for intelligence at the edge is growing faster than ever. We squeeze RTOS kernels into 64KB, hand-tune ISRs for microsecond response times, and we've gotten very good at writing C that doesn't waste a single cycle. So why are embedded developers still expected to port Python-first ML frameworks — designed for server racks — just to run a simple regression on a microcontroller? They shouldn't be. And that's exactly the argument for a dedicated ML library built for embedded systems, from scratch, on our terms. The Problem with "TinyML" as It Stands Tools like TensorFlow Lite for Microcontrollers and Edge Impulse have done useful work. But they're fundamentally top-down: design in Python, train on a server, quantize, convert, deploy a frozen model blob to the device. The microcontroller is just a runtime. It has no agency. It cannot learn. That's acceptab