Swift on The Final Artefact

Using Xcode Pre- and Post-actions to Observe Changes to Defaults

Mon, 24 Nov 2025 16:01:56 +0000

Because UserDefaults persistence is opportunistic, it’s often hard to tell when values truly flush and what changed between runs. In the article I set up an app group prefs watcher with fswatch, convert the binary plist to XML via plutil, and log a timestamped diff for every write—no breakpoints, no in-app logging. If you’ve ever wondered “what did my app really persist?”, this workflow makes it obvious.

Bring your Python ML Model to iOS App in under Three Minutes

Thu, 24 Jul 2025 00:00:00 +0000

Integrating Python-based machine learning models into iOS applications can be challenging, particularly when converting models into a Swift-compatible format. This example will demonstrate a simple image classification task using the Fashion-MNIST dataset and CoreML conversion tools. The goal is to illustrate the effort required to deploy small-to-medium complexity ML models within iOS applications. The demonstration is based on a Convolutional Neural Network (CNN) built with PyTorch, but the concepts apply broadly to other Python-based models as well.

Using Swift for Data Science Workflows

Tue, 04 Mar 2025 00:00:00 +0000

Why Swift?

Data science is dominated by Python and R, with some usage of Julia, Scala, Java, and C++. While Swift may not be the most popular choice, it offers several notable benefits—especially for developers already invested in the Apple ecosystem.

Key Advantages

Performance Considerations
As a compiled language, Swift often runs faster than languages like Python or R. This can be especially beneficial when handling large datasets or complex computations.