What is Haxe and what are its top alternatives?
Top Alternatives to Haxe
- TypeScript
TypeScript is a language for application-scale JavaScript development. It's a typed superset of JavaScript that compiles to plain JavaScript. ...
- 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
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
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 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
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
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 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
- More intuitive and type safe javascript174
- Type safe106
- JavaScript superset80
- The best AltJS ever48
- Best AltJS for BackEnd27
- Powerful type system, including generics & JS features15
- Compile time errors11
- Nice and seamless hybrid of static and dynamic typing11
- Aligned with ES development for compatibility10
- Angular7
- Structural, rather than nominal, subtyping7
- Starts and ends with JavaScript5
- Garbage collection1
- Code may look heavy and confusing5
- Hype4
related TypeScript posts
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...
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.
- Guaranteed memory safety145
- Fast132
- Open source88
- Minimal runtime75
- Pattern matching72
- Type inference63
- Algebraic data types57
- Concurrent57
- Efficient C bindings47
- Practical43
- Best advances in languages in 20 years37
- Safe, fast, easy + friendly community32
- Fix for C/C++30
- Stablity25
- Zero-cost abstractions24
- Closures23
- Extensive compiler checks20
- Great community20
- Async/await18
- No NULL type18
- Completely cross platform: Windows, Linux, Android15
- No Garbage Collection15
- Great documentations14
- High-performance14
- Generics12
- Super fast12
- High performance12
- Safety no runtime crashes11
- Fearless concurrency11
- Compiler can generate Webassembly11
- Macros11
- Guaranteed thread data race safety11
- Helpful compiler10
- RLS provides great IDE support9
- Prevents data races9
- Easy Deployment9
- Real multithreading8
- Painless dependency management8
- Good package management7
- Support on Other Languages5
- Type System1
- Hard to learn28
- Ownership learning curve24
- Unfriendly, verbose syntax12
- High size of builded executable4
- Many type operations make it difficult to follow4
- No jobs4
- Variable shadowing4
- Use it only for timeoass not in production1
related Rust posts
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.
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).
- Expressive like Python15
- Extremely fast15
- Very fast compilation11
- Macros6
- Cross platform5
- Optional garbage collection4
- Easy C interoperability3
- Readable operators1
- Small Community4
- [object Object]0
related Nim posts
- Open source13
- Easy to port7
- Supports both C++, C# and GDScript6
- Cross-Platform6
- Simple5
- Avaible on Steam For Free4
- GDScript is Based On Python3
- Harder to learn1
- Performance in 3D1
- Need opengl 2.1 / 3.31
- Somewhat poor 3D performance and lacks automatic LODs1
related Godot posts
- Fast learning curve41
- Very easy to embed in C programs26
- Efficient memory usage26
- Open source20
- Good for game scripting19
- Pretty simple to learn9
- Quick to code8
- Simple Language8
- Syntax is amazing7
- Supported in most game engines7
- D6
- Coroutines2
- Can be used for a wide variety of development1
- Nooby4
- Not widespread2
- D1
- Python0
related Lua posts
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.
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?
- Cross-platform1
- Can't working in vs mac 20191
- No GUI1
related MonoGame posts
- Easy to make rich cross platform desktop applications69
- Open source53
- Great looking apps such as Slack and Visual Studio Code14
- Because it's cross platform8
- Use Node.js in the Main Process4
- Uses a lot of memory19
- User experience never as good as a native app8
- No proper documentation4
- Does not native4
- Each app needs to install a new chromium + nodejs1
- Wrong reference for dom inspection1
related Electron posts
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.
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.
Python
- Great libraries1.2K
- Readable code962
- Beautiful code847
- Rapid development788
- Large community690
- Open source438
- Elegant393
- Great community282
- Object oriented272
- Dynamic typing220
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn49
- Scientific computing45
- Great documentation35
- Productivity29
- Easy to read28
- Matlab alternative28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Free18
- Very programmer and non-programmer friendly18
- Powerfull language17
- Machine learning support17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- It's lean and fun to code8
- Import antigravity8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- Although practicality beats purity6
- Now is better than never6
- Great for tooling6
- Readability counts6
- Rapid Prototyping6
- I love snakes6
- Flat is better than nested6
- Fast coding and good for competitions6
- There should be one-- and preferably only one --obvious6
- High Documented language6
- Great for analytics5
- Lists, tuples, dictionaries5
- Easy to learn and use4
- Simple and easy to learn4
- Easy to setup and run smooth4
- Web scraping4
- CG industry needs4
- Socially engaged community4
- Complex is better than complicated4
- Multiple Inheritence4
- Beautiful is better than ugly4
- Plotting4
- Many types of collections3
- Flexible and easy3
- It is Very easy , simple and will you be love programmi3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- List comprehensions3
- No cruft3
- Generators3
- Import this3
- If the implementation is easy to explain, it may be a g3
- Can understand easily who are new to programming2
- Batteries included2
- Securit2
- Good for hacking2
- Better outcome2
- Only one way to do it2
- Because of Netflix2
- A-to-Z2
- Should START with this but not STICK with This2
- Powerful language for AI2
- Automation friendly1
- Sexy af1
- Slow1
- Procedural programming1
- Ni0
- Powerful0
- Keep it simple0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
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
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages