FreakWAN is a LoRa-based wide-area network (WAN) messaging system designed for ESP32 and T-ECHO devices. It implements a fully distributed, open chat network independent of internet or cellular infrastructure. The system supports plaintext and encrypted messaging, group chats, media sharing (small images), sensor data collection, and includes features like duty cycle tracking, automatic retransmission with ACK suppression, and neighborhood discovery via HELLO messages. The codebase is written in MicroPython (with a C port for T-ECHO) and is entirely self-contained, relying only on standard libraries. Architecturally, the application runs as a single asyncio event loop with multiple tasks for periodic message sending, HELLO advertising, serial input handling, and optional IRC/Telegram bot integration. The radio layer is abstracted behind SX1276 and SX1262 drivers, and the display uses a terminal-like Scroller view with splash screen and status icons. The protocol layer defines message types (Data, Ack, Hello, Ping, Pong) and supports encryption via AES-CBC with HMAC integrity. The repository is well-structured, with clear separation of concerns and reusable components that can be extracted for other projects.
micropython-ulab is a C library that provides a subset of NumPy and SciPy functionality for MicroPython and CircuitPython on microcontrollers. It implements a multi-dimensional ndarray type with broadcasting, universal functions, linear algebra (Cholesky, eig, inv, QR), FFT, signal processing (sosfilt), numerical integration, optimization, random number generation, and file I/O. The library is designed for performance and memory frugality, using compile-time feature flags (ulab.h) to trim flash usage. Key entry points include ulab.numpy (array creation, math, stats, linalg, fft, io), ulab.scipy (integrate, linalg, optimize, signal, special), and utils (buffer conversion, spectrogram). The architecture is modular with separate C modules for each functional area, a reusable user module for custom extensions, and extensive test coverage for 1D–4D arrays and complex numbers. The library is actively maintained with frequent releases, but some SciPy functions remain incomplete and the codebase has repetitive dtype-dispatch macros that increase maintenance burden.
This repository provides a custom MicroPython firmware that integrates TensorFlow Lite for Microcontrollers (TFLM), enabling TinyML inference on microcontrollers such as ESP32, RP2040, and STM32. It exposes a ‘microlite’ Python module with classes for loading TFLite models (interpreter, tensor) and an audio frontend for spectrogram extraction. The repo includes three pre-trained example applications: hello-world (sine regression), micro-speech (keyword spotting), and person detection (camera-based). The architecture is monolithic per board, built via CMake (ESP-IDF, RP2040 SDK) or Make (STM32). The build system and board definitions are deeply coupled to specific MicroPython and TFLM versions, and the code demonstrates careful memory management (custom GC heap allocators, arena buffer patterns) to run ML models on resource-constrained devices. While primarily a firmware/application project, it also contains pre-processed audio data and model files, giving it a slight hybrid nature. The codebase is functional and structured, but has several rough edges: duplicate model files, hardcoded source lists, and incomplete board support (STM32 noted as broken). It is actively maintained with CI workflows and recent commits, but technical drift is medium due to reliance on upstream main branches.