StatsForecast is a production-grade Python library for large-scale univariate time series forecasting. It provides a unified API (fit/predict/forecast/cross_validation) over 40+ statistical models including AutoARIMA, AutoETS, AutoCES, AutoTheta, MSTL, TBATS, GARCH, and intermittent-demand methods (Croston, ADIDA, IMAPA). The core algorithms are implemented in C++ via pybind11 for performance, with Python wrappers handling model selection, prediction intervals, and conformal prediction. The library supports distributed execution on Spark, Dask, and Ray through a Fugue-based backend, and integrates with pandas, Polars, and MLFlow. While primarily an application library, the repository also contains extensive benchmarking experiments against R packages, Prophet, and NeuralProphet, as well as tutorials and competition notebooks, giving it a hybrid character. The codebase is well-structured with a clear separation of concerns (core API, model implementations, distributed backends, utilities) and includes a comprehensive test suite validated against R reference values.
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.