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The introduction of iOS 13 brought a major upgrade to Apple’s augmented reality framework. ARKit 3 arrived with a lot of interesting new features — people occlusion, motion tracking, simultaneous front and back camera, and collaborative support. These enhancements to ARKit strongly indicate Apple’s ambition for pushing AR immersion even further.

Up until iOS 12, we had SceneKit, SpriteKit, and Metal as the primary rendering frameworks. Among these, SceneKit, the 3D graphics framework, had been the most logical choice for building ARKit apps.

Continue reading Introduction to RealityKit on iOS— Entities, Gestures, and Ray Casting

If you follow my writing (here or here), then you know I’ve been working on a new Android app called AfterShoot — it’s an AI-powered Android app that helps users take better pictures while also managing their digital waste.

Continue reading Implementing Scoped Storage in Android 10

OpenCV is a powerful tool for doing intensive computer vision operations in real-time, even on resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various image effects. Some of these effects might use just 1 image, while others might use more than 1 image.

Continue reading Image Effects for Android Using OpenCV: Vertical and Horizontal Stitching

Tesla’s Autopilot is certainly the most advanced in the world. If Comma.ai is the Androïd, Tesla is definitely the Apple of self-driving cars. Recently, Tesla has released “Tesla Vision”, their new system equipped only with cameras… making it one of the only companies in the world not to use RADARs!

This is not the only place where Tesla is going against the crowd, their entire strategy is based on fleet and selling products, while most of its competitors sell autonomous delivery and transportation services. You can learn more about it on my article on Tesla vs Waymo here.

Continue reading Computer Vision at Tesla

In this blog post, you’ll be learning about how to implement and leverage location data in Android.

Leveraging location data from user devices, you can find nearby shops, cafes, cinemas, hospitals, your time zone, provide location-based suggestions to users, share locations with other users or 3rd parties, reach your destination, and more!

Today, most major applications like Google Maps, Facebook, Uber, Swiggy, etc. are using this feature to provide some of the best services to the users as well.

Continue reading Handling Location Data in Android

To help sift through some of the incredible projects, research, demos, and more in 2019, here’s a look at 17 of the most popular and talked-about projects in machine learning, curated from the r/MachineLearning subreddit. I hope you find something inspiring, educational, or both in this list.

Continue reading Best of Machine Learning in 2019: Reddit Edition

Hyperparameters have inevitably been used by anyone who has practiced machine learning, either in industry, academia, or even self-study. It can be an intimidating term if you’re just starting your dive into machine learning.

And once you’ve figured out how to pronounce the term, the key to understanding hyperparameters is knowing how they differ from parameters.

Continue reading “Tuning Machine Learning Hyperparameters”

In my time as an Android developer, I’ve run into issues with designing app icons for different Android devices and OS versions. Luckily, however, I found a workable solution: to create adaptive icons with Android Studio that work across devices and versions of Android.

Creating an adaptive Icon for Android using Android Studio is relatively easy—but before we jump into an implementation, let’s first take a closer look at what an adaptive icon actually is.

Continue reading Implementing Adaptive Icons in Android Using Android Studio