6 related articles

A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.

A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.

Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.

Harvard's open-source textbook cs249r (Machine Learning Systems) has 25,600+ GitHub stars. It covers ML systems engineering, TinyML, and MLOps — free for everyone.
TutorialsA complete hands-on checklist of 110 embedded Linux projects covering audio/video, Rockchip platforms, smart home, and driver development — with clear learning paths from beginner to high-paying roles.
TutorialsA complete solution for XiaoZhi AI voice-controlling smart home devices via MCP protocol and STM32 dual-MCU communication, covering stepper motor control, peripheral switching, and MCP's embedded applications.