SVMPrefs VS Closures

Compare SVMPrefs vs Closures and see what are their differences.

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SVMPrefs Closures
- -
8 1,741
- -
0.0 0.0
over 3 years ago about 3 years ago
Swift Swift
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

SVMPrefs

Posts with mentions or reviews of SVMPrefs. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning SVMPrefs yet.
Tracking mentions began in Dec 2020.

Closures

Posts with mentions or reviews of Closures. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Closures yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing SVMPrefs and Closures you can also consider the following projects:

WhatsNewKit - Showcase your awesome new app features 📱

BlocksKit

EZSwiftExtensions - :smirk: How Swift standard types and classes were supposed to work.

libextobjc

SwiftGen - The Swift code generator for your assets, storyboards, Localizable.strings, … — Get rid of all String-based APIs!

SwifterSwift - A handy collection of more than 500 native Swift extensions to boost your productivity.

swift-algorithm-club - Algorithms and data structures in Swift, with explanations!

Result - Swift type modelling the success/failure of arbitrary operations.

RandomKit - Random data generation in Swift

Dollar - A functional tool-belt for Swift Language similar to Lo-Dash or Underscore.js in Javascript

SwiftLinkPreview - It makes a preview from an URL, grabbing all the information such as title, relevant texts and images.