
AI
Microcontrollers vs cloud: why AI is moving to the edge
Why newer MCUs, rising cloud costs and real-time requirements are pushing more IoT intelligence onto the device.
ENGINEERING ARCHIVE
Projects, engineering notes and architectural decisions. From the lab to real systems.

Why newer MCUs, rising cloud costs and real-time requirements are pushing more IoT intelligence onto the device.

What changes when a TinyML demo becomes a product: data quality, quantization, memory, latency, OTA, monitoring and lifecycle.

How neural processing units inside embedded SoCs change edge AI design, latency, privacy, power consumption and product architecture.
THE NEXT STEP