This project is dedicated to collecting high-quality macOS software and organizing them systematically by different categories for easy search and use.
- macos
- software
- mac
- mac-osx
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This project is dedicated to collecting high-quality macOS software and organizing them systematically by different categories for easy search and use.
A curated list of awesome iOS ecosystem, including Objective-C and Swift Projects
📖 Design Patterns implemented in Swift 5.0
Gathering all info published, both by Apple and by others, about new framework SwiftUI.
SwiftUI sample app using Clean Architecture. Examples of working with SwiftData persistence, networking, dependency injection, unit testing, and more.
SwiftUI & Combine app using MovieDB API. With a custom Flux (Redux) implementation.
Commonly used sequence and collection algorithms for Swift
A List of Awesome Swift Playgrounds
Example of Modern Domain Driven modularisation of iOS apps
A curated list of awesome Swift Macros
Examples of commonly used data structures and algorithms in Swift.
Sample Code of the App Architecture Book
💎 The Principles of OOD (SOLID) based on Uncle Bob articles.
A summary of Apple's Swift language written on Playgrounds
Neural Network built in Apple Playground using Swift
A curated list of Open Source example iOS apps developed in Swift. An amazing list for people who are beginners and learning ios development and for ios developers who need any example app or feature.
Code examples for Depth APIs in iOS
An iBeacons example app for iOS 10, with Apple Watch (watchOS 3.0) support, written in Swift 3.
Code examples for new features of watchOS 2.
List of awesome iOS & Swift stuff!!
Learn Apple's Swift programming language interactively through these playgrounds.
Example of using SwiftUI to create a beautiful Liquid Swipe control
This repository contains a detailed sample app that implements VIPER architecture in iOS using libraries and frameworks like Alamofire, AlamofireImage, PKHUD, CoreData etc.
The example project of inferencing Pose Estimation using Core ML