gws is a production-grade Rust CLI for driving Google Workspace APIs (Gmail, Drive, Calendar, Sheets, Docs, Chat, Tasks, Classroom, Admin, and more). Its defining architectural bet is dynamic command generation: instead of shipping generated API client crates, it fetches Google's Discovery Service JSON at runtime, builds a clap command tree from the schema, and executes generic HTTP calls against resolved endpoints. A two-phase argument parse extracts the service name first, then re-parses with the dynamically constructed command set, keeping the entire Google API surface reachable without codegen. The repo is a Cargo workspace split into a reusable library crate (google-workspace: discovery models, validation, HTTP client) and the CLI binary crate, and is distributed via crates.io, npm (binary downloader), GitHub Releases, and Nix. Beyond raw API calls, a helper-command system (`+send`, `+upload`, `+agenda`, `+triage`) is implemented through a `Helper` trait that injects subcommands for multi-step workflows — MIME construction for Gmail, streaming multipart uploads for Drive, calendar agenda assembly, and cross-service orchestrations. Authentication supports interactive OAuth2 login, service accounts, pre-obtained tokens, and Application Default Credentials, with credentials encrypted at rest (AES-256-GCM) in an OS keyring or file fallback. A ratatui-based interactive setup wizard bootstraps a GCP project, enables APIs, and configures OAuth consent via gcloud. Output is structured (JSON/table/YAML/CSV/NDJSON) with stable, documented exit codes, making it suitable for both shell scripting and LLM tool calling. The repository also functions as an AI-agent integration layer: it ships 100+ SKILL.md files (per-service references, multi-step recipes, role-based personas) plus TOML registries defining personas and recipes, with a generator that produces skill documentation from CLI metadata. An experimental Model Armor integration sanitizes prompts and responses for prompt-injection attacks. Engineering quality is high throughout: extensive unit tests (several hundred cases, including security-focused edge cases), CI covering 7 build targets with real-credential API smoke tests, cargo-deny dependency auditing, changeset-based automated releases, and a Nix flake for reproducible builds. Notable risks: the entire command surface depends on Google's Discovery Documents staying available and compatible; the Gmail helper module is ~3000 lines and approaching a maintainability ceiling; two divergent OAuth implementations (yup-oauth2 fast path vs proxy-aware reqwest fallback) add maintenance burden; 15+ environment variables increase configuration complexity; and hardcoded API lists in setup tooling must be manually updated as Google adds services. These are real but moderate concerns in an otherwise well-architected, actively maintained codebase.
WhatsApp Contact Sync is a web application that synchronizes profile pictures from WhatsApp to Google Contacts. The system uses a client-server architecture with a Vue.js 3 frontend and Node.js/Express.js backend. Users authenticate with WhatsApp via QR code scanning and with Google via OAuth, then the backend matches contacts by phone number (handling international format variations) and transfers profile pictures while respecting Google's API rate limits. The application features real-time progress updates via WebSocket, configurable sync options (manual/automatic), and a contribution-based access model to support server costs.
Gmail Cleaner is a self-hosted, privacy-focused web application for bulk Gmail management. It allows users to unsubscribe from mailing lists, delete emails by sender, mark emails as read, archive, and manage labels—all via a FastAPI backend with a vanilla JavaScript frontend. The architecture follows a modular service layer: each Gmail operation (scan, delete, mark-read, unsubscribe, etc.) lives in its own file under `app/services/gmail/`, coordinated by an in-memory `AppState` singleton. Background tasks handle long-running operations, and the frontend polls status endpoints to show progress. Authentication uses Google OAuth 2.0 with two modes (local desktop web server and manual URL copy for Docker). The tool is designed to run 100% locally, with no external data collection, and uses batch Gmail API requests (100 per call) for efficiency. The codebase includes comprehensive unit tests, integration test plans, and CI/CD via GitHub Actions.
GeminiProChat is a minimal web UI for interacting with Google's Gemini Pro API. It is built as a server-side rendered application using Astro with SolidJS for interactive components. The application provides a chat interface where users can send messages and receive streaming responses from the Gemini model. It supports features like system role settings, temperature control, message history persistence, password protection, and PWA capabilities. The architecture consists of Astro pages (index, password), API endpoints (auth, generate), and SolidJS components for the chat generator, message rendering with markdown and code highlighting, and a theme toggle with view transitions. External dependencies include the Google Generative AI SDK (patched to use v1beta API), markdown-it for rendering, and Zag.js for slider components. The application can be deployed on Vercel, Netlify, Railway, Zeabur, or via Docker, with environment variables for API key, base URL, password, and model name.