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Android Debug Database is an Android library that provides a web-based interface for debugging databases and shared preferences in Android applications. When included in an app's debug build, it starts a lightweight HTTP server that serves a web UI allowing developers to inspect databases, run SQL queries, edit data directly, and manage shared preferences. The architecture consists of a core library (debug-db-base) that handles server logic and database interactions, with optional modules for encrypted database support (debug-db-encrypt) and sample applications demonstrating usage. The system uses a client-server model where the server runs inside the Android app and the client is a standard web browser connecting via HTTP to localhost. Key features include support for SQLite databases, Room databases (including inMemory), SharedPreferences, and SQLCipher-encrypted databases, with a responsive web interface built using Bootstrap, jQuery, and DataTables.
This is an Android application that serves as a learning resource for RxJava 2. It contains over 30 example activities demonstrating various RxJava operators (map, flatMap, zip, filter, take, skip, buffer, concat, merge, etc.), Subjects (Publish, Behavior, Replay, Async), and practical patterns like caching with multiple data sources, pagination with PublishProcessor, search with debounce/switchMap, an event bus (RxBus), and networking with AndroidNetworking. The code is written in Java and targets Android API 28 (Android 9). It uses old dependencies (Gradle 3.2.0, RxJava 2.2.2, AndroidX alpha versions) and the build system relies on the deprecated jcenter() repository. The architecture is straightforward: each example is a self-contained Activity that creates an Observable and logs results to a TextView. While the code is functional and educational, it is not production-grade and exhibits significant technical drift due to outdated libraries and practices.
This repository is a comprehensive collection of educational tutorials that implement machine learning algorithms and applications entirely from scratch using only NumPy. It covers a vast range of topics including core ML algorithms (linear regression, SVM, K-means, PCA, etc.), neural networks (CNN, RNN, LSTM, GAN, transformer), computer vision (object detection, face recognition, OCR), natural language processing (sentiment analysis, chatbots, text summarization, BPE tokenizer), time series forecasting (ARIMA, LSTM, Prophet), recommendation systems, anomaly detection, and reinforcement learning. Each tutorial is a self-contained Python script that generates synthetic data with planted structure, trains a hand-coded model, and validates against naive baselines. The code is heavily commented and designed for teaching, not production. There are no real datasets, no external dependencies beyond NumPy, and no shared library—each file is independent, leading to significant code duplication across tutorials. The repository serves as an excellent learning resource for understanding the inner workings of ML algorithms, but it is not suitable for direct reuse in production systems without substantial refactoring.
This is an Android application designed as a learning resource for Kotlin Coroutines. It provides a series of hands-on examples demonstrating common coroutine patterns in Android development, including single/parallel/series network calls with Retrofit, Room database operations, long-running tasks, timeout handling, and error handling strategies (try-catch, CoroutineExceptionHandler, supervisorScope). The architecture follows a simple MVVM pattern with ViewModel, LiveData, and a sealed UiState class. The code is straightforward and intended for educational purposes, with a clear separation of layers (API, database, UI). While it does not exhibit advanced or production-grade architecture, it serves as a practical reference for developers learning coroutines.