Search Results for "readme"

This is a responsive admin/dashboard UI template built in Flutter. It is designed to work across platforms (web, desktop, mobile) and offers prebuilt UI elements (charts, tables, cards, etc.) so developers can use it as a backend UI scaffold.

...These can be found in the corresponding directories within the same name. Most developers are likely here as they are looking to use the flutter_local_notifications plugin. There is a readme file within each directory with more information.

Goose Swift is a local-first iOS companion project for WHOOP 5.0 devices. It is currently an alpha proof of concept intended for developers who can build the Swift app and Rust core themselves. The app connects to WHOOP 5.0 bands through Bluetooth and routes device data through a local Rust processing layer. It turns available signals into health, recovery, sleep, strain, stress, cardio, energy, coach, and debug views. The project includes a SwiftUI app, a Rust bridge, HealthKit support, a...

This repository collects a broad, categorized list of interview questions (with expected answers) that are commonly asked in iOS or Swift engineering interviews. Topics range from language fundamentals (memory management, ARC, value vs. reference types), to UIKit/SwiftUI, concurrency, Grand Central Dispatch, background execution, networking, design patterns, system frameworks, and architecture decisions. Each question is usually backed by sample explanations or code snippets, helping...

Design-Patterns-In-Swift is a repository that translates classic software engineering design patterns (from sources like the Gang of Four) into Swift code examples, so you can see how those patterns look in a modern, strongly typed, object-/protocol-oriented language. It covers creational, structural, and behavioral patterns: singletons, factories, decorators, observers, strategy, command, mediator, and more. For each pattern, you’ll typically see one or more Swift implementations,...

...As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. The README and releases emphasized this was a macOS-optimized distribution, not an upstream fork of TensorFlow, and it cataloged common installation notes and issues encountered by early Apple Silicon users. In practice, the repo served as a bridge during the transition to native Mac acceleration, making modern TensorFlow viable on Apple hardware while upstream support matured.