Weekly Comment
Two years ago, a friend asked me for a programming language recommendation for his six-year-old who was showing interest in coding. Considering he already had an iPad and MacBook, I didn’t hesitate to recommend Swift Playgrounds. A few days ago, another friend asked me a similar question, and while I still recommended Playgrounds, I wasn’t as certain as I was two years ago. I’m just not so sure about whether Swift is still suitable for beginners these days.
This year has perhaps been a bit turbulent for many Swift developers. With the arrival of Swift 6, we’ve had to face an increasing number of compilation warnings. While the intention of preventing data races at the compilation level is good, Swift 6’s “concurrency-first” principle has made coding in some simple scenarios feel restrictive and a bit inconvenient.
Fortunately, the Swift community seems to have taken note of the developers’ feedback. A few days ago, the Swift Language Steering Group (LSG) released their main focus areas for 2025. Among these, the top priority is “making Swift Concurrency easier to approach and adopt.” This is undoubtedly encouraging news.
In a vision document published in November, the development team proposed an idea: allowing developers to choose to compile with default MainActor isolation on a per-module basis. This would create a “single-threaded mode”, thereby resolving some of the false positives caused by Swift 6’s concurrency safety checks. This move will undoubtedly greatly improve the development experience, reducing unnecessary hassles in non-concurrent scenarios.
The Swift language is gradually becoming more complex, which is almost an inevitable trend for every ambitious language that has developed over time. But this complexity should only increase the ceiling of a programming language’s capabilities. Once such complexity impacts the language’s learning curve, it will shake the very foundation of the language.
I sincerely hope that while Swift continues to raise its upper limits, it can also maintain its beginner-friendliness and even further reduce its entry barrier. After all, a good programming language should not only solve complex problems but also make it easy for beginners to get started.
Originals
Model Inheritance in Core Data
One of Core Data’s outstanding features is its ability to allow developers to declare data models in a manner closer to object-oriented programming without worrying about the underlying storage implementation details. Within this framework, model inheritance is a particularly important mechanism. This article delves into the core concepts of model inheritance, including Parent Entity, Sub Entity, and Abstract Entity. We will analyze their advantages and disadvantages and explore how to achieve similar effects without directly using these features.
The inspiration for writing this article came from my recent research into the use of Core Data in Apple’s first-party apps, particularly their practices in data model construction. To my surprise, the frequency and depth of model inheritance usage in these apps exceeded my expectations, which motivated me to further explore the application scenarios and advantages of this mechanism.
Recent Selections
Apple’s Use of Swift and SwiftUI in iOS 18
As always, Alexandre Colucci has delivered a comprehensive analysis of the programming languages and UI frameworks used in iOS 18’s built-in apps at the end of 2024. The article presents clear data showcasing the ongoing growth of Swift and SwiftUI within Apple’s ecosystem.
While SwiftUI’s adoption continues to expand steadily, its growth rate has slowed in recent system versions. To play a larger role in the future, further enhancements in functionality and stability are needed.
SF Symbol: How to for Swift & SwiftUI
SF Symbols, Apple’s high-quality, unified icon library, greatly simplifies icon design challenges, especially for indie developers and small teams. Antoine van der Lee provides a comprehensive guide on using SF Symbols, covering tips, animation techniques, customization options, and licensing restrictions, offering developers an excellent resource to master SF Symbols.
Why is Flow Control Important?
Combine offers several operators related to flow control (backpressure), but Swift’s new concurrency model currently lacks sufficient backpressure APIs for AsyncSequence. In this post, Quinn explores effective data flow management for AsyncSequence, addressing potential memory issues caused by excessive data production speed. He also highlights a proposed Swift community solution: MultiProducerSingleConsumerChannel, a promising tool to enhance flow control capabilities.
Mocking a Network Connection in Your Swift Tests
Mocking network connections helps avoid external dependencies (e.g., network fluctuations or server downtime), ensuring tests are repeatable, independent, and support parallel execution. Donny Wals discusses two common mocking methods: Mocking the network layer, which isolates dependencies for testing business logic, and URLProtocol, which provides fine-grained control over network implementations for complex interaction validation. These complementary methods equip developers with effective tools for building reliable tests.
Bringing Image Playground to Your App
As part of Apple Intelligence, Image Playground empowers users to create unique and engaging images directly on their devices. Antonella Giugliano provides a detailed guide on seamlessly integrating this feature into SwiftUI and UIKit apps, enabling developers to leverage Apple Intelligence models for quick and creative image generation.
Swift Macro Series
The power of Swift Macros lies in their reliance on the SwiftSyntax library, offering precise control over source code. Kingnight delves deeply into macro basics and practical operations in this series, guiding readers through manipulating abstract syntax trees (AST) to achieve powerful code generation. This series provides a systematic and in-depth analysis of macro implementation and techniques, making it an invaluable reference for mastering Swift Macros.
Exploring MLX Swift: Adding On-Device Inference to Your App
MLX is a machine learning framework optimized for Apple Silicon, enabling large language models (LLMs) to run on-device, enhancing privacy and supporting offline usage scenarios. Rudrank Riyam provides a detailed walkthrough of integrating LLM inference into iOS apps using the MLX Swift framework. Through a SwiftUI example, the article demonstrates the entire process—from model loading to text generation—offering a clear guide for developers to get started quickly.