Weekly Comment
“Why are these annoying articles or short videos always being recommended? How can I block them?” This is a question my father has been asking frequently lately. Even though I’ve taught him some technical methods (like choosing to reduce similar recommendations or blocking specific accounts), the reality is that once a user’s “profile” is formed, it often takes a considerable amount of time to see any changes in the recommended content. This highlights a brutal truth: in the world of algorithmic recommendations, the weight of user choices is far less than imagined; the platform’s pursuit of traffic is the fundamental driving force. Under this logic, even traffic generated by negative emotions is still traffic, and “haters” are also an important part of big data algorithms.
I first encountered the concept of “data mining” nearly thirty years ago. At that time, a friend of mine was working in IT management at Mars Foods, and he showed me how data mining was applied in enterprise settings. Every day, hundreds of employees manually recorded sales data of products in major supermarkets nationwide—including types, quantities, display positions, and other information. These valuable data were aggregated via fax and VPN, processed overnight, and ultimately formed decision-making reports. Back then, data analysis had clear and pure objectives.
Entering the internet era, data collection has become unprecedentedly convenient and unscrupulous. Every footprint a user leaves online is collected by algorithms and used for profiling. Internet companies cleverly package data mining as the concept of “big data,” combining it with dazzling terms like AI and algorithms to paint a blueprint of “personalized” services. However, is this promise of personalized service truly centered on user needs?
While we criticize traditional media for their conservatism and subjectivity, shouldn’t we also recognize their valuable aspects? Under the constraints of editorial systems and limited pages or channels, content selection at least maintains a basic respect for the moral sensibilities of the times. Now, with the advent of the short video era, algorithms have replaced editors, and the recommendation standards have shifted from being above average morality to merely avoiding legal punishment. Platforms no longer focus on the value orientation of content but display a naked pursuit of traffic.
The so-called “Recommended for You” is nothing more than a carefully designed commercial performance. Its core logic is to maximize platform profits through precise traffic algorithms, consciously or unconsciously constructing an information cocoon. ”Recommendations for Traffic” is the underlying logic of “Recommended for You.”
Originals
Using Transactions Instead of Save in SwiftData and Core Data
Ensuring data consistency and integrity is crucial in data persistence operations. The SwiftData framework introduces the transaction method in ModelContext, providing developers with a more elegant way to organize and manage data operations. This article explores how to use the concept of transactions in SwiftData and Core Data to build more reliable and efficient persistence operations.
Recent Selections
Deep Dive into Environment in SwiftUI
SwiftUI offers a powerful and efficient way to manage global state using @Environment and related tools. In this article, Mohammad Azam provides an in-depth analysis of how to inject and access global state within view hierarchies, optimize state propagation to reduce performance overhead, and simplify complex view structures using these features. The article covers the transition from traditional ObservableObject to the Observation framework and provides practical modular design tips to help developers avoid common pitfalls and build clearer, more testable SwiftUI app architectures.
Staged Spring & Static Friction
Spring animations, with their natural continuity and speed, create intuitive and lifelike interactions for users. However, bringing digital interactions closer to real-world physics requires careful fine-tuning of basic animations. In this article, Claudius Chuxuan Ma showcases several video clips and corresponding code to demonstrate the significant changes brought by these adjustments. From staged spring animations to simulating static friction, the author explores how tweaking animation parameters and physical properties can make interactions feel more authentic and natural.
Addressing Unexpected Terminations when launching from unlocked Camera Control
Weichao Deng (JuniperPhoton) discovered a system issue related to Camera Control and Capture Extension while using iOS 18 on iPhone 16 series devices. The termination mechanism intended for app extensions unexpectedly affected the main app, causing the app to be randomly terminated by the system. In this article, he provides a detailed explanation of the issue’s reproduction steps, log analysis, and a temporary workaround. The author considers this a design flaw and urges other developers to report this issue through feedback channels to draw Apple’s attention.
Time-Based View Updates in SwiftUI
SwiftUI’s TimelineView is a powerful tool for building views that update based on time, making it ideal for real-time clocks, countdown timers, and periodic data visualizations. Aryaman Sharda demonstrates how to use various scheduling modes, such as .periodic, .explicit, and .animation, to achieve precise time-based updates. While TimelineView meets many time-driven needs, Sharda highlights its limitations in event-driven scenarios and suggests considering Observation or Combine for real-time data streams and user interaction use cases.
Exploring Interactive Bottom Sheets in SwiftUI
Starting with iOS 16, SwiftUI introduced the presentationDetents modifier, empowering developers to implement Bottom Sheets with ease. These sheets, widely used in system apps like Maps, Find My, and Stocks, provide flexible interaction experiences. In this article, Pasquale Vittoriosi delves into the core usage of presentationDetents and its associated APIs, enabling developers to define custom sheet heights, drag behaviors, and interaction styles, creating functional yet context-aware custom sheet interfaces.
SwiftUI: Step 0 to Layout Protocol
The SwiftUI Layout Protocol provides a powerful interface that allows developers to create custom layout containers and precisely control the arrangement of subviews. By implementing the sizeThatFits and placeSubviews methods, developers can define the dimensions of the container and the positioning of its subviews. In this article, Itsuki demonstrates the fundamental usage and key features of the Layout Protocol by building a custom layout container that mimics the behavior of VStack.