android

android tips and tutorials

Android/Kotlin: A FragmentStatePagerAdapter (in TabLayout) example alvin December 1, 2018 - 2:48pm

As a note to self, here’s an example of implementing a FragmentStatePagerAdapter Android class in Kotlin:

Android/Kotlin: A FloatingActionButton setOnClickListener Snackbar example alvin November 30, 2018 - 4:28pm

As a quick note to self, this is an example of how to implement a setOnClickListener on a FloatingActionButton with Android and Kotlin:

fab.setOnClickListener { view ->
    Snackbar.make(
        view,
        "An exciting message!",
        Snackbar.LENGTH_LONG
    ).setAction("Action", null).show()
}

Hopefully that’s enough to get “future me” pointed in the right direction when I need this again. :)

(There are more lambda details at this link.)

Amazon Fire 10 HD: 33% off on Black Friday, 2018 alvin November 23, 2018 - 10:44am

I need a new tablet, and I just might bite the bullet on the Amazon Fire 10 HD today (Black Friday, 2018). For my Android development I don’t like that it’s based on Android 5, but $100 (33% off its regular price) for a tablet with decent performance is hard to pass up for my current needs.

How to make a phone call from your Android app

I came across this Android phone dialer tip yesterday. If you want to make a phone call from an Android application, all you have to do is create a new Intent, either an Intent.ACTION_DIAL (to start the call) or Intent.ACTION_CALL (to place the call).

Here are the three lines of source code you need to get started:

Intent dialIntent = new Intent();
dialIntent.setAction(Intent.ACTION_DIAL);
dialIntent.setData(Uri.parse("tel:8675309"));

Google is introducing a Neural Networks API in Android 8.1 developer preview

Several media outlets are reporting that Google is introducing their Neural Networks API in developer preview of Android 8.1. TechCrunch has a well-written article that includes this:

“The big highlight here is the new Neural Networks API, which brings hardware-accelerated inference to the phone for quickly executing previously trained machine learning models. Bringing these calculations to the edge can bring a lot of utility to the end user by reducing latency and load on the network, while also keeping more sensitive data on-device.”