Appwrite is an open-source backend-as-a-service (BaaS) platform that provides a comprehensive set of APIs for authentication, databases, storage, serverless functions, messaging, realtime, and more. The codebase is a hybrid monolithic-microservice architecture: a central PHP application (using the Swoole coroutine-based HTTP server) handles REST, GraphQL, and WebSocket requests, while background workers (over 20 types) process asynchronous tasks such as email delivery, function execution, database migrations, and usage analytics. The platform supports multiple database backends (MariaDB, MongoDB, PostgreSQL, ClickHouse) and a variety of messaging providers (SMS, email, push). It is designed for multi-tenant deployments with per-project isolation, OAuth2 social login, and extensive API versioning. The repository also contains a large collection of reusable PHP packages (the Utopia framework) that provide caching, queueing, query building, DNS resolution, NATS messaging, HTTP client, PSR-7 implementation, and more. These packages are independently published and can be extracted for use in other projects.
FlutterFire is a large monorepo of Flutter plugins that expose Firebase services to Flutter/Dart applications. It is not an end-user application but a client-side SDK layer: each Firebase product (Auth, Firestore, Storage, Functions, Messaging, Crashlytics, Remote Config, Analytics, App Check, AI, and more) is implemented as a separately published Flutter plugin with a shared Dart API, an abstract platform interface, and native implementations for Android, iOS, macOS, Web, and Windows. The architecture relies on Pigeon-generated message channels for typed Dart-to-native communication, custom codecs for Firebase-specific types, and event channels for real-time streams such as auth state, Firestore snapshots, and storage task progress. The primary entry point is Firebase.initializeApp() via firebase_core, after which each plugin's host API methods are invoked from Dart and dispatched to the native Firebase SDKs. The repository includes extensive example apps, unit tests, and GitHub Actions CI workflows with emulator-based integration tests across platforms.
Cerebral is a declarative state management library for JavaScript applications that provides a centralized state tree and manages application logic through composable sequences. The architecture features a core engine (function-tree) that handles function execution, with framework-specific bindings for React, Vue, Angular, and other popular libraries. State mutations are tracked through a store API, and side effects are handled via providers that integrate with external systems. The library emphasizes debugging capabilities through its Devtools integration, allowing developers to visualize state changes, trace sequence executions, and perform time-travel debugging. Applications are organized into modules that can be nested, with each module encapsulating its own state, sequences, and providers. Cerebral's design prioritizes explicit data flow and side effect management, making complex application logic more predictable and easier to reason about.
This is a large, production-grade collaborative crafting companion for Final Fantasy XIV, consisting of an Angular web application (with SSR) and an Electron desktop client, plus a NestJS API and Firebase backend. The primary functionality includes crafting list management with tree-based ingredient breakdown, a full crafting rotation simulator, gearset planning with stat calculations and materia optimization, real-time gathering alarms based on Eorzea time and weather, marketboard price tracking via Universalis, inventory capture and optimization, fishing data and cooperative fishing train tool, commission board for player-to-player crafting services, free company workshop and airship/submarine management, island sanctuary workshop optimization, and a crowd-sourced data reporting system (Allagan Reports) for fish/hookset/bite times and desynthesis results. The codebase demonstrates advanced Angular/NgRx patterns, extensive use of RxJS for reactive data flows, a sophisticated lazy data loading system for game data extracted from the FFXIV client, and tight desktop integration via Electron IPC and packet capture. While there are some legacy patterns and a large feature surface that introduces maintenance complexity, the architecture is well-separated into libraries, the core data abstraction (LazyDataFacade, FirestoreRelationalStorage, base dialog components) is highly reusable, and the project remains actively developed with a large user and contributor community.
This repository contains the iOS client for the ntfy.sh notification service. It is a SwiftUI-based application that allows users to subscribe to topics for receiving push notifications, manage subscriptions, view notifications with attachments, and configure settings. The architecture follows a pattern similar to MVVM using ObservableObjects for state management, with Core Data for persistence and a Notification Service Extension for processing incoming notifications. Key features include Firebase Cloud Messaging integration, attachment handling (download, preview, share, save), critical alerts support, and background polling for notification synchronization. The app supports both the default ntfy.sh server and self-hosted instances.
FirebaseUI-Android is an open-source library providing pre-built UI components for Firebase Authentication, Realtime Database, Cloud Firestore, and Cloud Storage. It is a multi-module Android library published on Maven Central. The primary module, `firebase-ui-auth`, has been rewritten in Jetpack Compose with Material 3, offering a high-level composable (`FirebaseAuthScreen`) and a Kotlin DSL for configuration. It supports 12+ identity providers (Google, Facebook, Email, Phone, Apple, etc.), multi-factor authentication (SMS/TOTP), credential linking, anonymous upgrade, and email link sign-in. The `firebase-ui-database` and `firebase-ui-firestore` modules provide RecyclerView adapters with paging support (AndroidX Paging 3) for displaying Firebase data. The `firebase-ui-storage` module integrates with Glide for loading images from Firebase Storage. A demo app and end-to-end tests against the Firebase Auth Emulator are included. Architecturally, the library is split into separate Gradle modules: `auth`, `database`, `firestore`, `storage`, `common`, and `lint`. The auth module uses Kotlin coroutines, StateFlow, and a sealed class state machine (`AuthState`) to manage authentication flows. The database/firestore modules are older Java-based implementations using lifecycle-aware adapters and observable arrays. The library maintains a version catalog (libs.versions.toml) with modern dependencies (Kotlin 2.4.10, AGP 9.3.0, Compose BOM 2026.06.01). Key entry points include `FirebaseAuthScreen` (Compose), `FirebaseAuthUI` (singleton for sign-out/delete/auth state), `AuthFlowController` (Activity-based alternative), `FirebaseRecyclerAdapter`/`FirestoreRecyclerAdapter`, and `FirebaseImageLoader` (Glide integration). The primary data flows involve Firebase SDKs for authentication, database listeners, and image loading. The library is designed to be embedded in host Android apps, handling complex auth flows with minimal developer effort. The codebase demonstrates strong craftsmanship in the auth module’s state machine and Compose UI, but shows signs of technical drift due to mixed UI generations (legacy XML layouts alongside new Compose), some deprecated API usage (e.g., `fetchSignInMethodsForEmail`, `startActivityForResult`), and incomplete localizations. The database/firestore modules are mature but use older patterns. Overall, the library is well-structured and actively maintained, with a beta version (10.0.0-beta04) indicating ongoing development.
This repository is a collection of Android quickstart samples demonstrating various Firebase products. Each sample is a standalone Android application showcasing integration with Firebase services such as Authentication, Realtime Database, Firestore, Cloud Storage, Cloud Functions, Analytics, and more. The samples are provided in both Java and Kotlin, often with parallel implementations, and follow modern Android architecture patterns including ViewModels, Navigation Component, and Jetpack Compose in newer samples. The repository includes shared internal modules for common UI components (chooserx) and custom lint rules (lintchecks) to maintain code quality across samples. The primary purpose is to serve as educational resources for developers learning to integrate Firebase into Android applications.
Androidify is a modular Android application that allows users to create personalized Android robot avatars using AI. It accepts a photo (from camera or gallery) or a text prompt, then generates a custom Android mascot image via a fine-tuned Imagen model through Firebase Vertex AI, with on-device Gemini Nano as a fallback for prompt generation and image validation. The app uses ML Kit for pose detection in the camera flow and subject segmentation for background removal. It features adaptive Jetpack Compose UIs across phones, tablets, foldables, and Android XR with shared element transitions. A companion Wear OS app enables one-tap installation of the generated avatar as a watch face, including APK creation and transfer via the Wearable Data Layer. The architecture is multi-module with custom Gradle plugins, Hilt DI, and feature flags via Firebase Remote Config. It is primarily an applied AI sample app, not a data research project, though it heavily relies on configurable AI prompts and model names.
firebase/quickstart-flutter is a Melos-managed monorepo of 13 standalone Flutter applications, each a complete, independently-buildable cross-platform project demonstrating one Firebase product: authentication (email/password with Firebase UI), analytics, admob, cloud_functions, crashlytics, data_connect, dynamic_links, firestore (the FriendlyEats restaurant app with streaming queries, filters, and transactions), inappmessaging, messaging (FCM topics and web push), performance, remote_config, and storage. Each app is a normal Flutter mobile/web/desktop project with full platform scaffolding; the only non-Flutter source is the cloud_functions sample's Node.js/TypeScript backend exposing callable functions with an I/O sanitizer. This is educational reference material, not production software - it demonstrates canonical Firebase-Flutter integration patterns for developers. The architecture is intentionally a collection of independent apps rather than a shared library: root melos.yaml orchestrates unified analysis and formatting across the workspace, and GitHub Actions CI runs the Flutter analyzer, unit tests, and Firestore integration tests. Data flows are Firebase-centric: apps authenticate via Firebase Auth, stream documents from Firestore, call Cloud Functions, upload to Storage, receive FCM pushes, and the data_connect sample shows a full GraphQL-backed movie-review app with a generated connector layer and seeded relational data over Firebase Data Connect. Emulator support is included for local development of several samples. Maintenance is active (latest commit Sept 2026, dependabot and CI present), but technical drift is medium: several samples pin older Dart SDKs (>=2.16/2.18), Gradle versions range from 6.7 to 8.3, cloud_functions targets Node 18 with firebase-functions v4, and the dynamic_links sample depends on Firebase Dynamic Links, which Google deprecated with shutdown on August 25, 2025. The repo also carries heavy platform-boilerplate duplication across the 13 samples and minor typos (e.g., 'Proflie Screen', 'sanatizeInput'). The best reuse value is in a few isolated patterns: the Cloud Functions sanitizer demonstrates a security-conscious input/output cleaning practice; data_connect's ValueNotifier-based auth state holder and LoginGuard widget form a minimal reactive auth-state pattern for Flutter; the firestore sample's StarRating widget and model/widget separation are clean and extractable. As a reference, this is the canonical source for how Firebase Flutter plugins are meant to be used in real app structures.
FitMart is a full-stack fitness e-commerce application built with React 19, Node.js/Express 5, MongoDB (Mongoose 9), Firebase Authentication, and Razorpay payments. It provides a product catalog, shopping cart, checkout flow, order management, user profiles, fitness calculators (BMI, calorie), workout tracking, a rewards system, and an AI chatbot powered by Gemini. The admin dashboard includes KPI grids, customer segmentation, inventory management, sales reports, and bug tracking. The architecture is a monolith with separate client and server directories, using Docker for containerization and GitHub Actions for CI/CD. While the codebase is well-structured with modern patterns (Zod validation, atomic database operations, Redis caching), it suffers from incomplete features, hardcoded values, security gaps (dev admin bypass, hardcoded admin UID), inconsistent error contracts, and limited test coverage. The high number of open issues (276) suggests community engagement but also maintenance challenges.
HappyPlants is a mobile-first progressive web application (PWA) for managing houseplant care. The core functionality allows users to create and organize plant entries, tracking details like watering schedules, sunlight requirements, growth cycles, notes, and photos. It is implemented as a Vue.js 2 SPA with Vuex state management and vue-router, using Firebase (Authentication, Firestore, Storage) for cloud sync with an offline-first fallback via localforage (IndexedDB). The repository is a monorepo managed by Lerna, containing two parallel application versions: an archived v1 (JavaScript) and an evolving v2 alpha (TypeScript with @vue/composition-api), plus a landing page (Nuxt.js), an unused Express server, Firebase Cloud Functions, and shared packages for components and styles. Key entry points include main.js/main.ts bootstrapping, router definitions, and a Vuex store organized by domain modules. The app relies heavily on the Vue ecosystem, Firebase, and utility libraries like anime.js for animations and localforage for local storage. While the core concept is well-executed with tests and responsive design, the codebase exhibits duplication between v1 and v2, some deprecated dependencies, and an incomplete v2 rewrite, limiting overall craftsmanship.
Geph Android is a production VPN client that wraps a Rust-based circumvention engine (geph5-client) and a web UI (gephgui) inside a native Android application. The architecture is a hybrid of a WebView-hosted UI and a native daemon process: the main activity loads a local HTML/JS UI via WebViewAssetLoader, and a JavaScript bridge (init.js) communicates with Kotlin through @JavascriptInterface to invoke RPC calls over a Unix socket to the daemon. The daemon is spawned as a separate process (libgeph.so) and manages the VPN tunnel via Android's VpnService. The app supports multiple build variants (debug, releaseAPK for sideloading with auto-update, releasePlay for Google Play with Firebase Analytics and Play In-App Review) using a factory pattern to inject platform-specific features. Key data flows include user authentication via broker RPC, VPN start/stop lifecycle, and periodic update checks that download and verify APK signatures. External dependencies include the Rust engine binary, a prebuilt web UI, JNA for native access, and Firebase services for the Play variant.
UKIKU is a feature-rich Android application for streaming and downloading anime episodes, built primarily in Kotlin with some legacy Java. It scrapes content from animeav1.com (and related sites) using Jsoup and custom JavaScript extraction, bypassing Cloudflare protection via a WebView-based mechanism. The app offers a comprehensive anime tracking experience: browsing a directory, searching, viewing details, watching episodes via ExoPlayer (with Chromecast and Android TV support), downloading (including HLS), managing favorites and watch history, queuing episodes, earning achievements, and syncing user data to Firebase Firestore or Dropbox. It also includes a gamified economy system (loli-coins) and extensive ad integration (AdMob, AppLovin). The architecture follows MVVM with Room database, WorkManager, and coroutines, but suffers from significant technical debt: duplicate UI layouts (material vs. non-material), deprecated libraries (Anko, RxJava, old MaterialDialogs), fragile scraping logic, weak encryption, and hardcoded API keys. Despite these issues, the app is functional and has a large user base (203 stars, 39 forks). It is not a data research project; it is a consumer Android application.