android-compose-mvvm-foodies
Android sample app following best practices: Kotlin, Compose, Coroutines and Flow, Hilt, JetPack Navigation, ViewModel, MVVM, Retrofit, Coilandroid-screen-tracker
Screen tracker overlays on top of the target application the currently visible fragment and its activity host. The library provides insight on what UI components are currently on top of the stacks.android-mvvm-rxjava2-dagger2
This repository contains a detailed sample application that uses MVVM as its presentation layer pattern. Essential dependencies are Dagger2 with Dagger-android, RxJava2 with RxAndroid, Room, Retrofit and Espresso.android-kotlin-compass
This repository contains a detailed kotlin sample app that uses MVVM as its presentation layer pattern. Essential dependencies are Dagger2 with Dagger-android, RxJava2, RxKotlin with RxAndroid, and Espresso.android-mvp-dagger2
The repository contains a detailed sample application that uses MVP as its presentation layer pattern. Essential dependencies are Dagger2 with Dagger-android, RxJava with RxAndroid, Room, Retrofit and Espresso.Compose-Profile-Card-Layout
jetpack-compose-course-simple-app
better-android-refactor-code-class
Repository acts as resource reference to the Clean Android code class. In this class we take on together a dirty existing project - an app about restaurants and we will learn how to identify which code can be refactored. Finally we clean the entire project and refactor most of the codebase.mealz-compose-mvvm-flow-coroutines
Reference repository for Jetpack Compose Udemy Coursecompose-mvvm-trending-repos
android-mvvm-dagger2
This repository acts as reference to a Medium Article.Core-UI-Compose
voice-text-nlp-stanford
The repository showcases an android application that transforms a voice command to text and identifies correspondent nouns.android-mvvm-hilt
Sample repository showcasing the use of HILT dependency injection framework.ARCore-NLP-persistent-augmentation
This is a sample project that allows users to match voice commands to 3D models and load them dynamically in the augmented world. The voice command is processed using StanfordNLP while the models are obtained and loaded dynamically from Google Poly API. The AR interaction is implemented through ARCore with Sceneform.Dynamic-Content-Compose
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