Nimbus VS Highlightr

Compare Nimbus vs Highlightr and see what are their differences.

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Nimbus Highlightr
1 2
6,448 1,596
- -
2.3 2.8
6 months ago about 1 month ago
Objective-C 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.

Nimbus

Posts with mentions or reviews of Nimbus. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-23.

Highlightr

Posts with mentions or reviews of Highlightr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-09.

What are some alternatives?

When comparing Nimbus and Highlightr you can also consider the following projects:

DTCoreText - Methods to allow using HTML code with CoreText

MarkdownKit - A simple and customizable Markdown Parser for Swift

YYText - Powerful text framework for iOS to display and edit rich text.

ZSSRichTextEditor - A beautiful rich text WYSIWYG editor for iOS with a syntax highlighted source view

Notepad - [iOS] A fully themeable markdown editor with live syntax highlighting.

RichEditorView - RichEditorView is a simple, modular, drop-in UIView subclass for Rich Text Editing.

Smile - :smile: Emoji in Swift

PhoneNumberKit - A Swift framework for parsing, formatting and validating international phone numbers. Inspired by Google's libphonenumber.

Marklight - Markdown syntax highlighter for iOS

Iconic - :art: Auto-generated icon font library for iOS, watchOS and tvOS

Twitter Text Obj - Twitter Text Libraries. This code is used at Twitter to tokenize and parse text to meet the expectations for what can be used on the platform.