Alternatives to Haxe logo

Alternatives to Haxe

TypeScript, Rust, Nim, Godot, and Lua are the most popular alternatives and competitors to Haxe.
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What is Haxe and what are its top alternatives?

It is an open source toolkit based on a modern, high level, strictly typed programming language, a cross-compiler, a complete cross-platform standard library and ways to access each platform's native capabilities.
Haxe is a tool in the Templating Languages & Extensions category of a tech stack.
Haxe is an open source tool with 6.6K GitHub stars and 688 GitHub forks. Here’s a link to Haxe's open source repository on GitHub

Top Alternatives to Haxe

  • TypeScript
    TypeScript

    TypeScript is a language for application-scale JavaScript development. It's a typed superset of JavaScript that compiles to plain JavaScript. ...

  • Rust
    Rust

    Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...

  • Nim
    Nim

    It is an efficient, expressive and elegant language which compiles to C/C++/JS and more. It combines successful concepts from mature languages like Python, Ada and Modula. ...

  • Godot
    Godot

    It is an advanced, feature-packed, multi-platform 2D and 3D open source game engine. It is developed by hundreds of contributors from all around the world. ...

  • Lua
    Lua

    Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode for a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. ...

  • MonoGame
    MonoGame

    It is a free C# framework used by game developers to make games for multiple platforms and other systems. It is also used to make Windows and Windows Phone games run on other systems. ...

  • Electron
    Electron

    With Electron, creating a desktop application for your company or idea is easy. Initially developed for GitHub's Atom editor, Electron has since been used to create applications by companies like Microsoft, Facebook, Slack, and Docker. The Electron framework lets you write cross-platform desktop applications using JavaScript, HTML and CSS. It is based on io.js and Chromium and is used in the Atom editor. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Haxe alternatives & related posts

TypeScript logo

TypeScript

96.5K
500
A superset of JavaScript that compiles to clean JavaScript output
96.5K
500
PROS OF TYPESCRIPT
  • 173
    More intuitive and type safe javascript
  • 105
    Type safe
  • 80
    JavaScript superset
  • 48
    The best AltJS ever
  • 27
    Best AltJS for BackEnd
  • 15
    Powerful type system, including generics & JS features
  • 11
    Compile time errors
  • 11
    Nice and seamless hybrid of static and dynamic typing
  • 10
    Aligned with ES development for compatibility
  • 7
    Angular
  • 7
    Structural, rather than nominal, subtyping
  • 5
    Starts and ends with JavaScript
  • 1
    Garbage collection
CONS OF TYPESCRIPT
  • 5
    Code may look heavy and confusing
  • 4
    Hype

related TypeScript posts

Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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Adebayo Akinlaja
Engineering Manager at Andela · | 30 upvotes · 3.6M views

I picked up an idea to develop and it was no brainer I had to go with React for the frontend. I was faced with challenges when it came to what component framework to use. I had worked extensively with Material-UI but I needed something different that would offer me wider range of well customized components (I became pretty slow at styling). I brought in Evergreen after several sampling and reads online but again, after several prototype development against Evergreen—since I was using TypeScript and I had to import custom Type, it felt exhaustive. After I validated Evergreen with the designs of the idea I was developing, I also noticed I might have to do a lot of styling. I later stumbled on Material Kit, the one specifically made for React . It was promising with beautifully crafted components, most of which fits into the designs pages I had on ground.

A major problem of Material Kit for me is it isn't written in TypeScript and there isn't any plans to support its TypeScript version. I rolled up my sleeve and started converting their components to TypeScript and if you'll ask me, I am still on it.

In summary, I used the Create React App with TypeScript support and I am spending some time converting Material Kit to TypeScript before I start developing against it. All of these components are going to be hosted on Bit.

If you feel I am crazy or I have gotten something wrong, I'll be willing to listen to your opinion. Also, if you want to have a share of whatever TypeScript version of Material Kit I end up coming up with, let me know.

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Rust logo

Rust

5.9K
1.2K
A safe, concurrent, practical language
5.9K
1.2K
PROS OF RUST
  • 146
    Guaranteed memory safety
  • 133
    Fast
  • 89
    Open source
  • 75
    Minimal runtime
  • 73
    Pattern matching
  • 64
    Type inference
  • 57
    Algebraic data types
  • 57
    Concurrent
  • 47
    Efficient C bindings
  • 43
    Practical
  • 37
    Best advances in languages in 20 years
  • 32
    Safe, fast, easy + friendly community
  • 30
    Fix for C/C++
  • 25
    Stablity
  • 24
    Zero-cost abstractions
  • 23
    Closures
  • 20
    Extensive compiler checks
  • 20
    Great community
  • 18
    Async/await
  • 18
    No NULL type
  • 15
    Completely cross platform: Windows, Linux, Android
  • 15
    No Garbage Collection
  • 14
    Great documentations
  • 14
    High-performance
  • 12
    Generics
  • 12
    Super fast
  • 12
    High performance
  • 11
    Safety no runtime crashes
  • 11
    Fearless concurrency
  • 11
    Compiler can generate Webassembly
  • 11
    Macros
  • 11
    Guaranteed thread data race safety
  • 10
    Helpful compiler
  • 9
    RLS provides great IDE support
  • 9
    Prevents data races
  • 9
    Easy Deployment
  • 8
    Real multithreading
  • 8
    Painless dependency management
  • 7
    Good package management
  • 5
    Support on Other Languages
  • 1
    Type System
CONS OF RUST
  • 28
    Hard to learn
  • 24
    Ownership learning curve
  • 12
    Unfriendly, verbose syntax
  • 4
    High size of builded executable
  • 4
    Many type operations make it difficult to follow
  • 4
    No jobs
  • 4
    Variable shadowing
  • 1
    Use it only for timeoass not in production

related Rust posts

Caue Carvalho
Shared insights
on
RustRustGolangGolangPythonPythonRubyRubyC#C#

Hello!

I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.

Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.

Thanks in advance.

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James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 328.9K views
Shared insights
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PythonPythonRustRust
at

Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.

For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).

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Nim logo

Nim

210
61
A statically typed compiled systems programming language
210
61
PROS OF NIM
  • 15
    Expressive like Python
  • 15
    Extremely fast
  • 11
    Very fast compilation
  • 7
    Macros
  • 5
    Cross platform
  • 4
    Optional garbage collection
  • 3
    Easy C interoperability
  • 1
    Readable operators
CONS OF NIM
  • 4
    Small Community
  • 0
    [object Object]

related Nim posts

Godot logo

Godot

222
47
Free and open source 2D and 3D game engine
222
47
PROS OF GODOT
  • 14
    Open source
  • 7
    Supports both C++, C# and GDScript
  • 7
    Cross-Platform
  • 7
    Easy to port
  • 5
    Simple
  • 4
    Avaible on Steam For Free
  • 3
    GDScript is Based On Python
CONS OF GODOT
  • 1
    Harder to learn
  • 1
    Performance in 3D
  • 1
    Need opengl 2.1 / 3.3
  • 1
    Somewhat poor 3D performance and lacks automatic LODs

related Godot posts

Lua logo

Lua

2.4K
180
Powerful, fast, lightweight, embeddable scripting language
2.4K
180
PROS OF LUA
  • 41
    Fast learning curve
  • 26
    Very easy to embed in C programs
  • 26
    Efficient memory usage
  • 20
    Open source
  • 19
    Good for game scripting
  • 9
    Pretty simple to learn
  • 8
    Quick to code
  • 8
    Simple Language
  • 7
    Syntax is amazing
  • 7
    Supported in most game engines
  • 6
    D
  • 2
    Coroutines
  • 1
    Can be used for a wide variety of development
CONS OF LUA
  • 4
    Nooby
  • 2
    Not widespread
  • 1
    D
  • 0
    Python

related Lua posts

Shared insights
on
LuaLuaPythonPythonJavaScriptJavaScriptC++C++

I want to learn a coding language so that I can get a job right out of high school I'm currently 15 and a half. What should I learn and where, and where should I look for jobs with little to no experience in coding jobs? From what I've seen my top 4 coding languages to learn are C++, JavaScript, Python, and Lua.

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Rogério R. Alcântara

For a Visual Studio Code/Atom developer that works mostly with Node.js/TypeScript/Ruby/Go and wants to get rid of graphic-text-editors-IDE-like at once, which one is worthy of investing time to pick up?

I'm a total n00b on the subject, but I've read good things about Neovim's Lua support, and I wonder what would be the VIM response/approach for it?

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MonoGame logo

MonoGame

33
1
A free C# framework used by game developers
33
1
PROS OF MONOGAME
  • 1
    Cross-platform
CONS OF MONOGAME
  • 1
    Can't working in vs mac 2019
  • 1
    No GUI

related MonoGame posts

Electron logo

Electron

11.5K
148
Build cross platform desktop apps with JavaScript, HTML, and CSS
11.5K
148
PROS OF ELECTRON
  • 69
    Easy to make rich cross platform desktop applications
  • 53
    Open source
  • 14
    Great looking apps such as Slack and Visual Studio Code
  • 8
    Because it's cross platform
  • 4
    Use Node.js in the Main Process
CONS OF ELECTRON
  • 19
    Uses a lot of memory
  • 8
    User experience never as good as a native app
  • 4
    No proper documentation
  • 4
    Does not native
  • 1
    Each app needs to install a new chromium + nodejs
  • 1
    Wrong reference for dom inspection

related Electron posts

Paul Whittemore
Developer and Owner at Appurist Software · | 15 upvotes · 1.1M views

I'm building most projects using: Server: either Fastify (all projects going forward) or ExpressJS on Node.js (existing, previously) on the server side, and Client app: either Vuetify (currently) or Quasar Framework (going forward) on Vue.js with vuex on Electron for the UI to deliver both web-based and desktop applications for multiple platforms.

The direct support for Android and iOS in Quasar Framework will make it my go-to client UI platform for any new client-side or web work. On the server, I'll probably use Fastly for all my server work, unless I get into Go more in the future.

Update: The mobile support in Quasar is not a sufficiently compelling reason to move me from Vuetify. I have decided to stick with Vuetify for a UI for Vue, as it is richer in components and enables a really great-looking professional result. For mobile platforms, I will just use Cordova to wrap the Vue+Vuetify app for mobile, and Electron to wrap it for desktop platforms.

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Vue.js vuex Vue Router Quasar Framework Electron Node.js npm Yarn Git GitHub Netlify My tech stack that helps me develop quickly and efficiently. Wouldn't want it any other way.

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Python logo

Python

250.8K
6.9K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
250.8K
6.9K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 965
    Readable code
  • 848
    Beautiful code
  • 789
    Rapid development
  • 692
    Large community
  • 439
    Open source
  • 394
    Elegant
  • 283
    Great community
  • 274
    Object oriented
  • 222
    Dynamic typing
  • 78
    Great standard library
  • 62
    Very fast
  • 56
    Functional programming
  • 52
    Easy to learn
  • 47
    Scientific computing
  • 36
    Great documentation
  • 30
    Productivity
  • 29
    Matlab alternative
  • 29
    Easy to read
  • 25
    Simple is better than complex
  • 21
    It's the way I think
  • 20
    Imperative
  • 19
    Very programmer and non-programmer friendly
  • 19
    Free
  • 17
    Powerfull language
  • 17
    Machine learning support
  • 16
    Fast and simple
  • 14
    Scripting
  • 12
    Explicit is better than implicit
  • 11
    Ease of development
  • 10
    Clear and easy and powerfull
  • 9
    Unlimited power
  • 8
    It's lean and fun to code
  • 8
    Import antigravity
  • 7
    Print "life is short, use python"
  • 7
    Python has great libraries for data processing
  • 6
    Although practicality beats purity
  • 6
    Fast coding and good for competitions
  • 6
    There should be one-- and preferably only one --obvious
  • 6
    High Documented language
  • 6
    Readability counts
  • 6
    Rapid Prototyping
  • 6
    I love snakes
  • 6
    Now is better than never
  • 6
    Flat is better than nested
  • 6
    Great for tooling
  • 5
    Great for analytics
  • 5
    Web scraping
  • 5
    Lists, tuples, dictionaries
  • 4
    Complex is better than complicated
  • 4
    Socially engaged community
  • 4
    Plotting
  • 4
    Beautiful is better than ugly
  • 4
    Easy to learn and use
  • 4
    Easy to setup and run smooth
  • 4
    Simple and easy to learn
  • 4
    Multiple Inheritence
  • 4
    CG industry needs
  • 3
    List comprehensions
  • 3
    Powerful language for AI
  • 3
    Flexible and easy
  • 3
    It is Very easy , simple and will you be love programmi
  • 3
    Many types of collections
  • 3
    If the implementation is easy to explain, it may be a g
  • 3
    If the implementation is hard to explain, it's a bad id
  • 3
    Special cases aren't special enough to break the rules
  • 3
    Pip install everything
  • 3
    No cruft
  • 3
    Generators
  • 3
    Import this
  • 2
    Can understand easily who are new to programming
  • 2
    Securit
  • 2
    Should START with this but not STICK with This
  • 2
    A-to-Z
  • 2
    Because of Netflix
  • 2
    Only one way to do it
  • 2
    Better outcome
  • 2
    Good for hacking
  • 2
    Batteries included
  • 2
    Procedural programming
  • 1
    Sexy af
  • 1
    Automation friendly
  • 1
    Slow
  • 1
    Best friend for NLP
  • 0
    Powerful
  • 0
    Keep it simple
  • 0
    Ni
CONS OF PYTHON
  • 53
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 22
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 12
    Very slow
  • 8
    Indentations matter a lot
  • 8
    Not everything is expression
  • 7
    Incredibly slow
  • 7
    Explicit self parameter in methods
  • 6
    Requires C functions for dynamic modules
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 5
    Fake object-oriented programming
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Official documentation is unclear.
  • 5
    Hard to obfuscate
  • 5
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 13.3M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Shared insights
on
TensorFlowTensorFlowDjangoDjangoPythonPython

Hi, I have an LMS application, currently developed in Python-Django.

It works all very well, students can view their classes and submit exams, but I have noticed that some students are sharing exam answers with other students and let's say they already have a model of the exams.

I want with the help of artificial intelligence, the exams to have different questions and in a different order for each student, what technology should I learn to develop something like this? I am a Python-Django developer but my focus is on web development, I have never touched anything from A.I.

What do you think about TensorFlow?

Please, I would appreciate all your ideas and opinions, thank you very much in advance.

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