MMM-GooglePhotos is a MagicMirror² module that displays photos from Google Albums on a smart mirror dashboard. It features a clean architecture separating UI logic (MMM-GooglePhotos.js) from backend processing (node_helper.js), with a well-abstracted Google Photos API client (GPhotos.js). The module handles OAuth2 authentication, automatic token refresh, paginated API requests, photo filtering (by date, dimensions, aspect ratio), sorting, and caching to minimize API calls. It supports user interactions via notifications for navigating photos and uploading new images to designated albums.
Ente is a multi-product, end-to-end encrypted cloud platform providing photo storage (Ente Photos), two-factor authentication (Ente Auth), secure document vault (Ente Locker), social photo sharing (Ente Space), AI chat assistant (Ente Ensu), ephemeral pastebin (Ente Paste), and media casting (Ente Cast). The system is built as a monorepo with a Go server backend, Rust core libraries for cryptography, machine learning, and media processing, and client applications across web (React/Next.js), mobile (Flutter, native Android/iOS), desktop (Electron), and CLI (Go and Rust). Architecture follows a zero-knowledge design: all encryption (XChaCha20-Poly1305, Argon2, X25519) happens client-side, with keys never exposed to the server. The server handles user accounts, file storage (S3-compatible), billing (Stripe), ML indexing, and sharing. Key entry points include the Go server at `server/cmd/museum`, Rust WASM bindings for web crypto, and Flutter apps with native plugins. Data flows involve client-side encryption before upload, server-side metadata storage in PostgreSQL, and decryption on download. External dependencies include PostgreSQL, S3-compatible object storage, Stripe, ONNX Runtime for ML, and various cloud infrastructure (Cloudflare Workers, Grafana, Prometheus).
PhotoPrism is a self-hosted photo management server that indexes, organizes, and serves personal photo and video collections. In production, it extracts EXIF/XMP/IPTC metadata, generates thumbnails and previews, transcodes videos, performs AI-powered object/face recognition and NSFW detection, and provides a web UI for browsing, searching, sharing, and editing media. It also supports WebDAV access, OAuth2/OIDC authentication, and multi-instance clustering. The architecture is a monolithic Go application with a REST API (Gin framework) and a Vue.js frontend. It uses a relational database (MariaDB/MySQL or SQLite) via GORM. Background workers handle indexing, face recognition, place detection, and cleanup. It integrates with external tools: FFmpeg for video transcoding, libvips for image processing, TensorFlow/ONNX for AI models, and exiftool for metadata extraction. The frontend communicates via REST and WebSocket. Key entry points include the CLI (cmd/photoprism/photoprism.go) with subcommands for import, index, search, and user management, and the API layer (internal/api) exposing endpoints for photos, albums, labels, faces, places, and vision. The indexing pipeline scans configured directories, extracts metadata, generates thumbnails, runs AI models, and stores results in the database. The system also supports background sync with remote WebDAV services and backup/restore. External dependencies include FFmpeg, exiftool, libvips, TensorFlow, ONNX Runtime, Darktable, RawTherapee, yt-dlp, and various Go libraries. It can optionally integrate with OpenAI/Ollama for vision captioning. The codebase is large and mature, with extensive utility packages and a well-organized module structure.
Photonix is a self-hosted, full-stack photo management application with AI-powered classification and search. The backend is a Django monolith exposing a GraphQL API, with background workers for importing, thumbnail generation, raw processing, and ML classification (color, objects, faces, location, style, events). The frontend is a React 19 single-page app built with TanStack Router, Apollo Client, Zustand state management, and a dark-themed shadcn/ui component library. The system supports multi-user libraries, invitations, map view, albums, and fine-grained search with facets. The architecture relies on a PostgreSQL database, Redis for task coordination and locks, and supervisor to manage numerous background processes. While the project shows solid engineering and a modern tech stack, it carries some technical debt from vendored ML models, TensorFlow 1.x compatibility, and hardcoded download URLs, and is explicitly not yet feature-complete.