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SnapML provides a way to utilize machine learning to build more immersive Snapchat AR Lenses. Style transfer is a computer vision technology that allows creators and developers to transfer any design from one source image to any targeted image.

Once our style transfer model is ready, we’ll upload and configure the converted ONNX/PB model file in Lens Studio, and from there, upload the resulting to our personal Snapchat app. And, then we can make it public so that others can use our lens.

Continue reading “Create a Style Transfer Snapchat Lens with SnapML’s ML Component”

Fritz AI allows developers to use mobile-optimized machine learning (ML) algorithms to create custom ML models (without code) for use in their apps.

In this article, we’ll walk you through the process of creating an image labeling ML model that can identify different car logos, and then integrating the model into an iOS app.

Continue reading “Implementing a Fritz AI Machine Learning Model in an iOS app”

In order to work with Fritz AI in Flutter, you’ll need some knowledge on how to write native Java/Kotlin code in Flutter. At the moment, there is no native plugin for working with Fritz AI in Flutter, however, since Flutter allows one to write native code, Fritz AI can still be integrated into Flutter. Let’s look at how the image labeling* API from Fritz can be integrated.

Continue reading “Fritz AI in Flutter Applications”

Every single one of us will intermittently try to conjure the right word for a given moment, pausing mid-sentence to try and remember it. For example, forgetting the name of a place caused this hesitation:

We can all relate to this, but as cognition declines, these pauses become more common and more pronounced.

Continue reading “Using Furhat and Rasa to Assist when You Forget a Word Mid-Sentence: A Student Group Project”

Today we’re excited to introduce the Fritz AI Dataset Generator, a new component of Fritz AI Studio, designed to break down a major barrier between developers and the creation of incredible machine learning-powered mobile applications.

That barrier is data acquisition. Despite advances in neural network architectures and their optimizations for mobile deployment, there often isn’t enough available high-quality data on which to train those models.

Continue reading “Introducing the Fritz AI Dataset Generator for Mobile Machine Learning”

In this article, we’re going to see inout in action, work with value types, see how to use mutation tracking to our advantage, and explore the law of exclusivity.

Continue reading “Exclusivity and Mutation Tracking in Swift: Value vs Reference types”

Mobile development provides opportunities for ideas and solutions to find their ways into the pockets of millions of users. As phones become more powerful, the possibilities expand exponentially.

That’s why I’m so excited about SwiftUI. Apple’s adoption of declarative and reactive UI design makes it easier for anyone to create great looking and powerful iOS apps.

Continue reading “SwiftUI: A New Starting Point for iOS Development”