This repository is a multi-platform conference deadline tracking ecosystem centered on CCF (China Computer Federation) rankings, also covering CORE and THCPL. The primary production system is a Rust/Leptos web application compiled to WebAssembly and served as a static site (ccfddl.com), providing interactive filtering, countdowns, timezone conversion, favorites, calendar subscriptions, and email reminders. A Cloudflare Worker backend handles GitHub OAuth authentication, D1 SQLite database for user data (stars, message wall, email reminders), and serves a Model Context Protocol (MCP) endpoint for AI agent integration. Additional clients include a Python CLI, a Chrome extension (Manifest V3), and an iCal/RSS feed generator. The data layer consists of curated YAML files for conference metadata and historical acceptance rates, which are validated, merged, and transformed into JSON by a Python build pipeline. The repository also includes a static SEO page generator, a deterministic social media posting skill for X/Twitter, and agent skills for AI-assisted data maintenance. While the core value is the curated conference data, the dominant character is a software application with multiple user-facing interfaces and a serverless backend.
MegaDetector is a production-grade object detection pipeline specifically designed for camera trap images in wildlife conservation. It detects animals, people, and vehicles in images and videos, outputting bounding boxes with confidence scores in a standardized JSON format (MegaDetector output format). The system provides a comprehensive set of command-line tools for batch inference, post-processing (repeat detection elimination, classification smoothing, format conversion), and integration with platforms like Wildlife Insights and LILA. The architecture is a modular Python package with separate modules for detection (supporting PyTorch and TensorFlow backends), classification, data management, post-processing, visualization, and taxonomy mapping. Key entry points include `run_detector_batch.py` for batch inference, `run_detector.py` for single-image inference, `postprocess_batch_results.py` for generating HTML previews, and `separate_detections_into_folders.py` for organizing results. Data flows from raw images through the detector to JSON results, which are then post-processed (e.g., false positive reduction, confidence thresholding) and optionally converted to other formats (COCO, YOLO, Wildlife Insights). External dependencies include PyTorch, TensorFlow, ultralytics, OpenCV, PIL, and various utility libraries. The repository also includes a C# Windows GUI application for JSON subsetting and integration guides for Timelapse and digiKam.
GreenResourcesManager is an Electron + Vue 3 desktop application for local-first management of personal digital media collections—games, images, videos, novels, audio, websites, and software. It provides a config-driven UI with customizable pages, tag-based filtering, batch import, and built-in readers (EPUB, PDF, comic viewer, video player). Notable features include a desktop pet gamification system (affection, hunger, earnings), a disguise mode for privacy, a plugin system, and a local HTTP API for external access. Data is stored in SQLite with legacy JSON file backup. The application is actively developed (last commit April 2026) with a sizable feature set and internationalization (zh-CN, zh-TW, en, ja). However, the codebase shows significant technical drift: near-duplicate main-process code in `electron/` and `public/js/`, incomplete migration from JSON to SQLite, hardcoded Windows-specific paths, and extensive debug logging scattered throughout. The pet subsystem is tightly coupled and the plugin system remains marked as under development. While the config-driven UI pattern and composable architecture offer reuse potential, overall craftsmanship is average—functional but with notable boilerplate and coupling.