The Raspberry Pi Pico SDK is a comprehensive C/C++ software development kit for the RP2040 and RP2350 microcontrollers, providing a hardware abstraction layer, peripheral drivers, synchronization primitives, and build-system integration for embedded firmware. It is not an application itself but a library and toolchain ecosystem consumed by firmware projects, targeting ARM Cortex-M0+/M33 and RISC-V Hazard3 cores. The SDK is architected around modular libraries (pico_*, hardware_*) that expose register-level access via auto-generated structs, runtime initialization with ordered callbacks, an async context abstraction for WiFi/USB/timers, and a lightweight resource claiming system for peripherals. It supports both CMake and Bazel builds, includes a PIO assembler (pioasm), and provides a host-simulation layer for desktop testing. The codebase exhibits strong engineering discipline with extensive use of weak function overrides, config macros, and static assertions, though it carries the inherent complexity of supporting two chip generations, two architectures, and multiple third-party middleware stacks (TinyUSB, lwIP, BTstack, mbedTLS).
ESPectre is a production-grade Wi-Fi motion sensing system that runs on ESP32 microcontrollers. It captures Channel State Information (CSI) from Wi-Fi packets and runs on-device detection algorithms (Lightweight turbulence-based and High Accuracy neural network) to detect human motion without cameras or microphones. The system is designed for local-first operation with no cloud dependency, integrating with Home Assistant via ESPHome, MQTT, or Matter. The architecture is layered: a core sensing layer handles CSI capture, filtering, and feature extraction; a runtime layer manages lifecycle, configuration, and diagnostics; and multiple frontend adapters (native MQTT/HTTP, ESPHome, Matter, MicroPython) expose motion events to home automation systems. A Python CLI provides device discovery, flashing, monitoring, and dataset analysis tools. A web interface at espectre.dev offers browser-based device management. The repository also includes a significant data/research component: a published dataset of CSI recordings, ML training infrastructure, dataset quality validation pipelines, and performance benchmarking tools. These support the development and validation of the detection algorithms and ensure cross-language parity between C++ firmware and Python reference implementations. The codebase demonstrates strong engineering practices: interface-based design for testability, extensive unit and integration tests with coverage gates, cross-language parity enforcement, and a comprehensive CI/CD pipeline. Multiple frontends share a common core SDK, reducing duplication while allowing platform-specific optimizations.