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androiddevchallenge-1

A simple application created with Jetpack Compose as a submission for #AndroidDevChallenge

androiddevchallenge-2

argallery-android

Implementation of art gallery application for Android with integration of augmented reality build on Kenticocloud

argallery-android-unity

Implementation of Unity section of art gallery application for Android with the integration of augmented reality build on Kenticocloud

argallery-ios

Implementation of art gallery application for iOs with integration of augmented reality build on Kenticocloud

compose-multiplatform-playground

Playground for Compose Multiplatform

compose-samples

Official Jetpack Compose samples.

coreactor

Coreactor is an MVI framework for Android applications written for Kotlin Coroutines with a focus on readability and simplicity.

currencycoverter

Currency convert which use yahoo.finance.xchange web service.

giftv

gitlab-ci-android

GitLab CI image for building Android apps (fork of jangrewe/gitlab-ci-android with java 17)

koreactor

Android library for building reactive applications based on MVI architecture.

ktlint-gradle

A ktlint gradle plugin

meteorites

List of all meteorites since 2011

ml.school

Machine Learning School

okta-oidc-android

OIDC SDK for Android

privado

Privado CLI scans & monitors your repositories to build up privacy-transparency reports & finds privacy issues.

pv056-anomaly-detection-from-camera-data

The task of this project is to find suitable and accurate way of detecting abnormal camera view on given image data using several methods of machine learning. Data are collected from several camera sources from different places, and for simplicity chosen only from daytime. Given image data displays a surveillance pictures of a road and its surroundings, with vehicles passing by. The datasets consist of normal images, and images with some type of abnormal camera view. There are different types of anomalies displayed on the pictures, whether the anomalies are caused by camera malfunctioning or transmission error, or by external causes (vehicle blocking camera view, objects on road, people on road etc.). The goal of the project is to find a successful method for detecting various abnormal camera views on given dataset using following methods of machine learning: 1) Autoencoders and neural networks 2) SVM and bag of visual words Part of the solution should be comparison of the results of the named methods of machine learning and also comparison of their advantages and disadvantages on given data.

rxdebug

RxDebug is a kotlin extension that provides a very simple way of debugging RxJava2 streams

rxjava

RxJava – Reactive Extensions for the JVM – a library for composing asynchronous and event-based programs using observable sequences for the Java VM.

rxlifecycle

Lifecycle handling APIs for Android apps using RxJava

rxlifecyclepresenter

Example project for "Bring RxLifecycle to presenter world" article https://blog.thefuntasty.com/bring-rxlifecycle-to-presenter-world-703d0da5d6d1

template

KMM Template and KMM app generator script

universal-apk-builder

This action converts given *.aab file to universal *.apk file