StatsForecast is a production-grade Python library for large-scale statistical time series forecasting. It provides a scikit-learn-like API (fit/predict) with over 30 models including AutoARIMA, AutoETS, AutoCES, AutoTheta, TBATS, MSTL, GARCH, and intermittent demand models. The library achieves high performance through a hybrid architecture: core algorithms are implemented in C++ using Eigen and pybind11, while Python handles orchestration, model selection, and validation. It supports distributed computing via Fugue (Spark, Dask, Ray) and multiprocessing, enabling forecasting on millions of time series. The library also includes conformal prediction intervals, cross-validation, simulation, and feature engineering utilities. While primarily an application library, the repository has a strong research component with extensive benchmarking experiments against R's forecast package, pmdarima, Prophet, NeuralProphet, and Amazon Forecast. These experiments are well-documented with reproducible environments (Docker, conda, Makefiles) and use public datasets (M3, M4, M5, Tourism, ERCOT). The codebase is actively maintained with CI/CD across multiple OS/Python versions, automated wheel building, and documentation pipelines using Quarto and Mintlify. The architecture is clean and modular: models inherit from a common base class (_TS), distributed backends implement a ParallelBackend interface, and C++ code is separated into submodules. Notable reusable components include a generic Nelder-Mead optimizer, a unified distribution framework, and a stepwise ARIMA search algorithm ported from R. The library is well-tested with comprehensive unit and integration tests.
PaoPao-ce is a full-stack micro-community social media platform written in Go (backend) and Vue 3 (frontend). It provides a feature-rich experience similar to Twitter/Weibo, including posts (tweets) with multimedia attachments, comments, likes, collections, private messaging, user profiles, topic/tag support, and a wallet system with Alipay integration. The architecture is modular and configurable, supporting multiple databases (MySQL, PostgreSQL, SQLite), search engines (Meilisearch, Zinc), object storage backends (LocalOSS, MinIO, AliOSS, COS, Huawei OBS, S3), caching layers (Redis, BigCache), and observability tools (OpenTelemetry, Sentry, Pyroscope, pprof). The backend uses Gin for HTTP, GORM for ORM, go-mir for API code generation, and protobuf for gRPC services. The frontend is built with Vue 3, Naive UI, Vite, and Pinia. Key features include feature toggles for different deployment suites, a runtime settings override via admin UI, and background job processing for search indexing and metrics. The platform emphasizes self-hosting and customization, with comprehensive SQL migration scripts and support for desktop clients via Tauri. The codebase demonstrates solid engineering practices with interface-based dependency injection, layered architecture, and code generation, though it carries some technical debt from deprecated backends and WIP features.