scalaonandroid VS TilesFX

Compare scalaonandroid vs TilesFX and see what are their differences.

scalaonandroid

A tutorial and examples of how to write Android apps in Scala 2.13 and Scala 3. (by makingthematrix)

TilesFX

A JavaFX library containing tiles that can be used for dashboards. (by HanSolo)
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scalaonandroid TilesFX
11 2
168 1,383
- -
6.7 0.0
9 months ago 5 months ago
Scala Java
GNU General Public License v3.0 only Apache License 2.0
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.

scalaonandroid

Posts with mentions or reviews of scalaonandroid. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-11.

TilesFX

Posts with mentions or reviews of TilesFX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-24.

What are some alternatives?

When comparing scalaonandroid and TilesFX you can also consider the following projects:

uni-app - A cross-platform framework using Vue.js

charts - A JavaFX library that contains different kind of charts

ScalaFX - ScalaFX simplifies creation of JavaFX-based user interfaces in Scala

LogicNG - The Next Generation Logic Library

jfx - JavaFX mainline development

XChart - XChart is a light-weight Java library for plotting data.

FXGL - Java / JavaFX / Kotlin Game Library (Engine)

medusa - A JavaFX library for Gauges

controlsfx - High quality UI controls to complement the core JavaFX distribution

android - :iphone: Home Assistant Companion for Android

ChartFx - A scientific charting library focused on performance optimised real-time data visualisation at 25 Hz update rates for data sets with a few 10 thousand up to 5 million data points.