Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft. It covers the full pipeline for quantitative research and trading: data ingestion and feature engineering (Alpha158, Alpha360, high-frequency), model training (traditional ML like LightGBM/XGBoost, deep learning in PyTorch including LSTM, GRU, Transformer, GATs, and reinforcement learning), backtesting with realistic costs and slippage, portfolio optimization, and online serving for production-like deployment. The platform is highly modular and config-driven, with a core library (qlib) and a separate contrib layer for community extensions. It includes a CLI (qrun) for running full workflows from YAML configs, extensive experiment tracking via MLflow, and a rich set of evaluation metrics. While primarily a library/application, it also functions as a research benchmark suite with many example models and data collectors for global markets. The codebase is active and well-maintained, though some components (e.g., TFT model, older RL code) show signs of technical drift due to dependency version locks and deprecated patterns.
This repository is a curated 'awesome list' for Federated Learning (FL) research, maintained as a Jekyll static site. It aggregates hundreds of linked GitHub repositories and DBLP search strategies. The core data asset is a machine-generated go.mod file that pseudo-pins the linked repositories as Go dependencies, produced by the add_dependent.py script which extracts GitHub URLs from README.md, queries the GitHub API for commit hashes, and writes a go.mod. However, this is not a functional Go module; it is a novelty lockfile. The dblp-search-keywords.md document provides a meticulous analysis of DBLP search syntax to discover FL papers without the 'federate' keyword. The repository has significant hybrid character: while it is primarily a content aggregation site (application-adjacent), its technical substance lies in the data exploration and search strategy documentation (research-oriented). The actual code is minimal and tightly coupled to the repository's specific structure, limiting reusability. The add_dependent.py script is a one-time scraper with hardcoded rate limiting and token placeholder, not a general-purpose tool.
This is a web application for viewing and analyzing Minecraft server performance timings reports. It consists of a PHP backend that handles report upload, caching, and data transformation, and a React-based frontend for interactive visualization. The backend parses a proprietary compressed timing format from Minecraft servers (Paper, Sponge, etc.) into a structured JSON object, caches it, and serves it via REST endpoints. The frontend fetches this data and renders charts, tables, and drill-down views of timing handlers, regions, and plugins. The application is a single-page application built with React 15, Webpack 2, and Babel 6, with a heavy reliance on jQuery-like PHP utilities and the Stiphle rate-limiting library. The architecture is a classic LAMP stack with PHP serving as both the API and the HTML shell. Key entry points are index.php (main page), data.php (API), post.php (upload endpoint), and backend_daemon.php (CLI daemon). The system uses a file-based cache with gzip compression and serialization. The frontend manages state with a global data object and uses Chart.js for TPS and entity graphs. The codebase demonstrates a custom JSON deserialization trait (FromJson) that maps JSON arrays to typed PHP objects using PHPDoc annotations, which is a notable pattern for handling evolving data formats. However, the project shows significant technical debt with outdated dependencies and a monolithic global state approach in the frontend.