Weekly Comment
Large language models have become indispensable assistants in my day-to-day work and personal life. Currently, my primary means of interacting with them is conversational. While this style of communication has grown increasingly smooth and intuitive with the improvement of model capabilities, when various needs are interwoven into a single conversation, the resulting efficiency often falls short of what I’d like. To address this, I’ve recently begun exploring how to tailor prompts for dedicated sessions focused on specific tasks.
In doing so, I have not opted for the popular AI agent-building platforms, but rather relied on the MyGPT functionality offered by ChatGPT. Although I possess some programming experience—for instance, breaking complex tasks into simple modules (like functions)—I still face many challenges when transferring this approach to AI tasks. Figuring out how to effectively decompose tasks and ensure each module produces stable, accurate results isn’t as straightforward as writing code. One might say the abstract reasoning required by large language models differs substantially from the logic of traditional programming. Without a deeper grasp of prompt engineering and the inner workings of these models, even designing a small AI tool that yields consistent, accurate outputs can prove quite daunting. In my view, creating a set of efficient, reliable AI mini-tools for work and personal life remains an ongoing endeavor that calls for further exploration.
Fortunately, as M4 devices become more widespread and other efficient local AI inference solutions emerge, the cost of fine-tuning and customizing smaller models is rapidly declining. If specialized models can be combined with carefully optimized prompts, personal AI tool development will likely witness a significant breakthrough, with notable improvements in both performance and reliability.
I look forward to the day, in the not-so-distant future, when these stable and reliable AI mini-tools can seamlessly integrate into my daily routines—much like the small programs I write for myself—serving as even more capable helpers that enhance both efficiency and user experience.
Recent Selections
How SwiftUI Preview Works Under the Hood
Starting with Xcode 16, the working mechanism of SwiftUI Preview has undergone significant changes, with the most notable improvement being the shared build artifacts between Build/Run and Preview, which enhances Preview’s build efficiency. In this article, Onee delves into the inner workings of Preview in Xcode 16, providing a detailed analysis of how Preview utilizes a three-tiered rebuild strategy to handle different levels of code modifications efficiently. The article concludes with a comparison between SwiftUI Preview and Flutter’s Hot Reload, highlighting the current limitations of SwiftUI Preview and potential areas for improvement.
Adopting Swift 6 Across the App Codebase
Swift 6 mode introduces powerful data race detection capabilities but also sets higher standards for code quality. In this article, Majid Jabrayilov shares his practical tips and insights, including how to efficiently use Sendable value types, when to isolate reference types with @MainActor, and how to gracefully manage stateful services using actor. The article provides real-world code examples, offering actionable guidance for concurrent programming with Swift 6.
Codable Conformance for Swift Enums
Since Swift 5.5, enums can automatically conform to Codable even without raw values, greatly improving development efficiency. However, in many cases, developers still need to customize the encoding and decoding process to meet specific requirements. In this article, Natalia Panferova shares various customization techniques for Codable enums, including adjusting case names, associated value keys, and implementing fully custom logic. These techniques offer flexibility for API interactions and data persistence.
Swift 6: Typed Throws
Among the many new features in Swift 6, Typed Throws stands out as a highlight. It allows developers to explicitly specify the error types that functions can throw, significantly improving readability and precision in error handling. While this feature may add some upfront effort, its advantages become evident when calling APIs built with Typed Throws. In this article, Vera Dias provides clear examples and demonstrates how this feature simplifies error handling logic and reduces dependency on generic error catches.
Creating a Debounced Search Context for Performant SwiftUI Searches
In SwiftUI, the searchable modifier makes it easy to implement search functionality, but frequent input can lead to unnecessary search requests and performance issues. Daniel Saidi shares his solution: a reusable search context class combined with debounce techniques, delaying search operations until typing pauses. This approach enhances app responsiveness while significantly reducing redundant operations.
SwiftData CRUD Operations with ModelActor
SwiftData’s @ModelActor provides a safer concurrency experience, but it also poses new challenges for implementing CRUD operations. Leo Dion shares a well-designed and robust API in this article. By introducing the Queryable protocol and type-safe Selector, he simplifies CRUD operation logic and enhances reliability. Additionally, the author highlights the issue of temporary PersistentIdentifiers, reminding developers not to use temporary IDs across contexts.
Implementing iCloud Sync with SwiftData and CKSyncEngine
CKSyncEngine, introduced at WWDC 2023, significantly reduces the complexity of CloudKit data synchronization. While Core Data and SwiftData offer built-in CloudKit sync support, their high level of automation makes it difficult to finely control the sync process. In this article, Yingjie Zhao details how to combine CKSyncEngine with SwiftData to build a more flexible iCloud sync solution. By customizing the sync manager, implementing incremental sync, and handling server changes and errors, the author demonstrates a balanced approach between automation and manual control, providing valuable guidance for creating efficient and reliable sync systems.
While the default sync features of Core Data and SwiftData are more comprehensive and generalized (e.g., addressing CloudKit’s limitations on relationship count and record size), CKSyncEngine’s customizability allows for greater flexibility and a more tailored user experience in specific scenarios.
The Swift Runtime: Your Silent Partner
The Swift Runtime (libswiftCore) is a C++ library that runs alongside all Swift programs, supporting core language features such as memory management, error handling, dynamic dispatch, and the handling of types and generics. Jacob Bartlett starts with a simple piece of Swift code and traces its journey from compilation to execution, including Swift Intermediate Language (SIL), LLVM Intermediate Representation (IR), and runtime ABI function calls. He provides an in-depth analysis of how the Swift Runtime operates and collaborates with the compiler to implement essential language features.
Event
Swift Student Challenge Opens for Applications on February 3
The Swift Student Challenge offers student developers an opportunity to showcase their creativity and programming skills. By creating and submitting projects using Swift Playgrounds, students can develop practical skills for their future careers while becoming part of a vibrant developer community.
Submissions open on February 3 and will remain open for three weeks. Don’t miss this chance to brainstorm innovative ideas and bring your vision to life. Winners will receive special gifts and an opportunity to visit Apple’s Cupertino campus next summer.