Lens Trend Spotlight: “Cyber Core”

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On the Snapchat Lens Explore page, hundreds of thousands of lenses are listed each day. Different than your carousel, on the Explore tab users can search and discover new lenses based off of keywords, the people they follow, and their recommended interests.

Every few months, some of Snapchat’s Lens Creators — official and not — will shift their designs to meet a new trend. This trend is usually reflected through popular TikTok videos, Instagram’s trending aesthetics, or a pop culture event. Currently, following the rise of sentimentality for the early 2000s, trends like maximalism and cyber-core have resurfaced, tackling the combination of old-school internet and the new wave of abstract-yet-useable Lens concepts to make some pretty interesting combinations.

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How LiDAR Object Detection Works

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LiDAR is a sensor currently changing the world. It is integrated in self-driving vehicles, autonomous drones, robots, satellites, rockets, and many more.

This sensor understands the world in 3D using laser beams and measuring the time it takes for the laser to come back. The output is a point cloud.

It’s a technology harder to understand than the camera because less people have access to it. I had access to it and today will help you understand how LiDAR detection works for self-driving cars.

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Image Effects for Android using OpenCV: Image Blending

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OpenCV is a powerful tool for doing intensive operations in real-time, even for resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various effects over images.

In part 1, horizontal and vertical image stitching is discussed to merge 2 or more images.

In part 2, the cartoon effect is applied to a single image.

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Image Classification for Android Devices Using NumPy and Kivy

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In a previous tutorial, I introduced the Kivy Python framework as a tool to run NumPy (Numerical Python) on Android. As a brief refresher, Kivy is a cross-platform framework for building natural user interfaces (NUIs). It’s very simple to learn and then use iteratively. The same code, unchanged, can run on Linux, Windows, Mac, iOS, and Android.

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Getting Started with Augmented Reality Using Unity’s AR Foundation Framework

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Have you ever wanted to make your own augmented reality app, but don’t know where to start? Or, are you just interested in AR technology more broadly?

In this series, we’ll demonstrate how to use Unity’s game engine to make a small AR app that can demonstrate important principles of the technology. To do so, we’ll dive deep in to Unity’s AR Foundation framework.

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How to build a xylophone app with Audio API, React Native, and Expo

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React Native, when used with Expo as a toolchain, eases the common pain points in managing iOS and Android applications. After saying that, I realized that there’s a delight in using this ever-growing open source mobile application framework.

Expo has gained a lot of credibility as a framework that provides collective solutions for building React Native applications by lowering the time and effort of developers using it. They’re continuing to enhance it from time to time to keep up with the latest changes in React Native community. That said, Expo SDK33 is a blast.

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Creating a side-scroller mobile game with Unreal Engine 4—with no prior experience

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Have you always wanted to make a game but don’t know where to start? Or are you just interested in game technology?

In this article series, we’ll demonstrate how to use Unreal Engine 4 to make a side-scroller game and package it for Android. This game will have various elements like collecting pickups/coins, a health system, and also enemy AI. If you’ve played Mario in past then you’ll love it. We’ll try to build our game along those lines only— even if you don’t have any prior programming experience.

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Hands-on with Feature Selection Techniques: Filter Methods

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Filter methods select features from a dataset independently for any machine learning algorithm. These methods rely only on the characteristics of these variables, so features are filtered out of the data before learning begins.

These methods are powerful and simple and help to quickly remove features— and they are generally the first step in any feature selection pipeline.

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