Category: Android

  • GraphQL and Flutter step by step guide

    GraphQL and Flutter step by step guide

    GraphQL: get exactly what you need and nothing more Let’s look at two scenarios: getUser API returns a bunch of irrelevant data where you just need an email address You must retrieve 4–5 APIs to build a screen This is where GraphQL can be useful, in this article, I will demo how to create a GraphQL […]

  • Organize Gradle files with multi-module project

    I have found a better way to organize Gradle files, it is useful for multi-module projects, where dependencies are duplicated. So instead of using a long list of implementations, we can group them into an array and import it as functions, here are the steps: Step 1: Create a dependency.gradle file in your project level folder, […]

  • Google’s ML Kit: Text recognition with sample app of receipts reading

    Google has published a few AI kit and one of them is text recogition, which I decided to try it out using a sample Android app to read receipts. The target is to be able to recognise: total, VAT and type. Full code can be found here. Step 1: Groundworks First of all, we need […]

  • Google’s ML kit: Image recognition in Kotlin

    Google’s ML kit is the new firebase SDK that provides machine learning power to Android and iOS apps. The tool set includes: label image, text recognition, face detection, landmark recognition, barcode scanning and smart reply (coming soon at the time of writing). In this tutorial, I will show you how to set up the image […]

  • Build Android project with multi flavour on Jitpack

    Lately my team has built an Android project that needs to run on multiple channels and platform: Google, Amazon, mobile, tablet and TV. It took us sometime to plan to enable us working in parallel within tight Sprint schedule. At the end we designed a structure that includes a core module which is shared on […]

  • TensorFlow Lite Image recognition: Android with Kotlin

    TensorFlow is a wonderful tool for machine learning, where its main purpose is designed for neural network models. When it comes to mobile, Google has provided us with two libraries: TensorFlow mobile and TensorFlow Lite. They both works on Android and iOS. While TensorFlow mobile is recommended to use for developers who have a pre-trained model (a trained model is like […]