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.
This repository provides a worker client for the AI Horde distributed computing network. It enables users to contribute their GPU/CPU resources to process AI generation jobs (image generation via Stable Diffusion, image interrogation/post-processing, and text generation via LLMs) in exchange for kudos, the platform's currency. The application features a modular architecture with separate components for configuration management, job processing, model management, and user interface. The system operates by registering with the AI Horde API, polling for available jobs, processing them using locally loaded ML models, and submitting results back to earn kudos. It supports three worker types: Dreamer (image generation, though now deprecated in favor of horde-worker-reGen), Alchemist (image interrogation and post-processing), and Scribe (text generation). Key features include automatic model downloading, dynamic model loading based on demand, GPU monitoring, and both terminal and web-based user interfaces for configuration and monitoring.
Reggaeton Be Gone is a single-file Raspberry Pi application that continuously samples ambient audio and uses an Edge Impulse machine learning model to classify whether reggaeton music is playing. When the classification confidence exceeds a configurable threshold (default 0.95), the script triggers a Bluetooth test routine against a configured speaker address, using either repeated rfcomm connection attempts or l2ping packets. The system also displays status messages on a 128×32 OLED screen and logs events to a file. The code is a tightly coupled prototype with hardcoded paths, global state, and no error handling, reflecting its 'dirty and messy' design as acknowledged by the author. There are no reusable components beyond trivial display and logging helpers; the project's value lies in its novel concept and successful integration of audio ML with Bluetooth experimentation on physical hardware.