ProxyPool is a production-grade proxy pool application that automatically collects free HTTP/HTTPS proxies from over a dozen public sources, validates their availability, and stores them in Redis or SSDB. It provides a Flask RESTful API for consuming proxies (get, pop, all, count, delete) and uses APScheduler for periodic fetching and validation. The architecture is a monolith with two main processes: a scheduler that runs fetch/check cycles and an API server (Flask with Gunicorn on Linux). The codebase demonstrates strong software engineering practices: a plugin-based fetcher system with dynamic module loading and caching, a clean Proxy data model with JSON serialization, a decorator-based validator registration pattern, and a layered test suite (unit, API, integration) with fakeredis mocking. External dependencies include Redis/SSDB, APScheduler, Flask, requests, and lxml. The project is actively maintained with recent commits and a comprehensive test suite.
This repository is a Python library and CLI application for parsing, compiling, and calculating Excel formulas and workbooks entirely outside Microsoft Excel. It loads .xlsx, .ods, and JSON model files, builds a directed acyclic computation graph with the schedula library, and evaluates formulas cell-by-cell with support for array formulas, circular references, custom functions, LAMBDA/LET, unit conversions, and sub-model extraction. Primary APIs include ExcelModel, Parser, Ranges, and get_functions(), with additional entry points through a Click CLI (calc, build, test, serve) and a Flask-based web application. Architecturally, the project is a library with CLI and web wrappers. Formula text is tokenized and parsed into an AST, then compiled by AstBuilder into a schedula dispatch pipeline. Workbook loading, reference resolution, and cyclic reference handling are coordinated in the excel package, while hundreds of Excel function implementations are organized in categories such as math, statistics, financial, text, lookup, engineering, and date/time. The function layer relies heavily on numpy, scipy, pandas, statsmodels, and numpy-financial, and uses standardized wrappers to convert Python functions into Excel-compatible array functions with proper error propagation. Strengths include a broad data-driven test suite, an elegant range algebra class, Tarjan-based cycle detection, and a mature parser design. Weaknesses include very large function modules that mix many edge cases, incomplete Excel function categories (Automation, Cube, Database, Web), legacy Python-version conditionals, and heavy external dependencies that may lag behind current Excel behavior. Although the repo contains curated data files and spreadsheet fixtures, its dominant character is a software application rather than a data pipeline or research repository.
ListenBrainz Server is the backend and frontend for ListenBrainz, an open-source music listening history platform operated by the MetaBrainz Foundation. In production it ingests listening events from multiple sources (Spotify, Last.fm, Libre.fm, Maloja, file uploads, and direct API submissions), stores them in TimescaleDB, enriches them with MusicBrainz identifiers via an MBID mapping pipeline, and exposes them through a public REST API, a Last.fm-compatible API, WebSocket streams, and a React single-page application. The system also computes per-user and site-wide statistics, artist/recording/user similarity, collaborative-filtering recommendations, and annual 'Year in Music' summaries using Apache Spark batch jobs orchestrated through RabbitMQ and HDFS. The architecture is a hybrid of a traditional Flask web application and a set of background data-processing services. The main web server handles user authentication, API endpoints, playlist management, social features, and page rendering, while separate workers consume RabbitMQ queues for timescale writes, Spark job requests, metadata cache crawling (Spotify, Apple Music, SoundCloud, Internet Archive), and background imports/exports. The frontend is a large TypeScript/React application with React Router, TanStack Query, Jotai state, and a custom multi-source audio player called BrainzPlayer. Although the repository is primarily an application, it contains substantial data-engineering machinery—Spark incremental stats engines, dump generation/import, MBID canonicalization, and similarity algorithms—making it a genuinely hybrid codebase with the application character dominant. The codebase is well-structured for its size: it has clear module boundaries, abstract base classes for importers and OAuth services, a reusable incremental stats query-provider pattern, and extensive unit and integration tests. However, it also carries significant complexity and technical debt: multiple database backends, duplicated SQL across Spark entity stats, legacy compatibility endpoints, hardcoded external URLs, and some skipped or heavily mocked tests. The project is actively maintained, with a steady stream of schema migrations and feature additions through 2026.
This repository implements a full-stack AI-powered food recommendation application called '今天吃什么' (What to eat today). The system combines a Next.js frontend with a Python Flask backend that uses a GraphRAG (Retrieval-Augmented Generation) architecture leveraging Neo4j for graph-based recipe relationships and Milvus for vector similarity search. Users interact via a chat interface to receive personalized recipe recommendations with step-by-step cooking instructions. The application includes a comprehensive recipe knowledge base in markdown format, Docker-based deployment orchestration, and a modular RAG pipeline with intelligent query routing and session management.
A monolithic Python application for crawling public data from the Chinese social network Weibo (Sina Weibo). It retrieves user profiles, weibo posts (including text, images, videos, live photos), comments, and reposts via the Weibo mobile API. Data can be exported to CSV, JSON, SQLite, MySQL, MongoDB, Markdown, or forwarded to a custom HTTP endpoint. It provides a CLI entry point for scheduled crawling (using the `schedule` library) and a Flask-based REST API for remote trigger and data retrieval. The codebase is heavily centralized—the core `Weibo` class (~3000 lines) handles fetching, parsing, downloading media, writing to multiple outputs, and anti‑ban throttling. A lightweight LLM analysis module can optionally enrich each weibo with sentiment, summary, and anomaly detection. The repository is community‑maintained with 4.6k stars, 402 open issues, and frequent commits as of mid‑2026. It is primarily an application but has a strong data‑collection character; the data outputs are suitable for subsequent research or archiving.