What is Replit and what are its top alternatives?
Top Alternatives to Replit
GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...
It is a social development environment for front-end designers and developers.. It functions as an online code editor and open-source learning environment, where developers can create code snippets, creatively named "pens", and test them. ...
Combining automated deployment, instant hosting and collaborative editing, Gomix gets you straight to coding. The apps you create are instantly live, hosted by us, and always up to date with your latest changes. Build products, prototype ideas, and hack solutions to problems. ...
A development platform that enables you to not only edit your files from underlying services like FTP, GitHub, Dropbox and the like, but on top of that gives you the ability to collaborate, embed and share through Codeanywhere on any device. ...
CodeSandbox allows developers to simply go to a URL in their browser to start building. This not only makes it easier to get started, it also makes it easier to share. You can just share your created work by sharing the URL, others can then (without downloading) further develop on these sandboxes. ...
It's somewhat unique. A small PaaS that supports web apps (Python only) as well as scheduled jobs with shell access. It is an expensive way to tinker and run several small apps. ...
The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...
Replit alternatives & related posts
- Can't login with third-party app account1
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- Open source friendly1.8K
- Easy source control1.5K
- Nice UI1.3K
- Great for team collaboration1.1K
- Easy setup864
- Issue tracker503
- Great community484
- Remote team collaboration480
- Great way to share450
- Pull request and features planning441
- Just works145
- Integrated in many tools131
- Free Public Repos119
- Github Gists114
- Github pages110
- Easy to find repos82
- Open source61
- Easy to find projects59
- It's free59
- Network effect56
- Extensive API48
- Developer Profiles33
- Git Powered Wikis32
- Great for collaboration29
- It's fun23
- Community SDK involvement22
- Clean interface and good integrations22
- Learn from others source code19
- Because: Git15
- It integrates directly with Azure14
- Standard in Open Source collab9
- It integrates directly with Hipchat8
- Beautiful user experience7
- Cloud SCM6
- Easy to discover new code libraries6
- Smooth integration5
- It's awesome5
- Nice API5
- Quick Onboarding4
- Remarkable uptime4
- Hands down best online Git service available4
- CI Integration4
- Loved by developers3
- Free HTML hosting3
- Security options3
- Simple but powerful3
- Uses GIT3
- Unlimited Public Repos at no cost3
- Version Control3
- Easy to use and collaborate with others3
- Nice to use2
- Easy and efficient maintainance of the projects1
- Good tools support1
- Free HTML hostings1
- Self Hosted1
- All in one development service1
- Easy to use1
- Easy source control and everything is backed up1
- Leads the copycats1
- Never dethroned1
- IAM integration1
- Issues tracker1
- Very Easy to Use1
- Easy deployment via SSH1
- Free private repos1
- Owned by micrcosoft52
- Expensive for lone developers that want private repos37
- Relatively slow product/feature release cadence15
- API scoping could be better10
- Only 3 collaborators for private repos8
- Limited featureset for issue management3
- GitHub Packages does not support SNAPSHOT versions2
- Does not have a graph for showing history like git lens2
- Have to use a token for the package registry1
- No multilingual interface1
- Takes a long time to commit1
related GitHub posts
I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.
I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!
I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.
Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.
Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.
With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.
If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
- No support for any other git-server than github3
related CodePen posts
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- Instant APPification ;)9
- Auto commits7
- No no. limitation on free projects4
- Easy to use3
- Tons of usable code2
- Awesome support2
- Very fast API creation. Especially for small apps2
- Github Integration1
- UI could be better / cleaner5
- Limited Support/Diffficult to use Non-JS Languages2
- Not good for big projects1
- Cannot delete project, only the source code is1
related Glitch posts
- Sleek interface17
- 3rd party integration16
- Easy to use13
- Web IDE11
- FTP support9
- Fast loading9
- SSH Connections for free5
- Anywhere coding5
- Full root access5
- GitHub integration4
- Preconfigured development stacks4
- SFTP support4
- Private use for free4
- Easy setup3
- Amazon S3 Integration2
- Easy Setup, Containers2
- Code directly by FTP1
related Codeanywhere posts
- Awesome way to fun kickstart your ReactJS apps7
- Online vs-code editor look and feel to start react5
- Is open-source4
- Easiest way to showcase3
- 250 module limit3
- Hard to use the console1
related CodeSandbox posts
- Web apps14
- Easy Setup11
- Shell access8
- Great support8
- Free plan8
- Super-easy to use7
- Many things like Python are pre-installed2
- No root access1
- Really small community1
related PythonAnywhere posts
I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.
- In-line code execution using blocks18
- In-line graphing support10
- Can be themed7
- Multiple kernel support6
- Best web-browser IDE for Python3
- Export to python code3
- LaTex Support2
- HTML export capability1
- Multi-user with Kubernetes1
related Jupyter posts
From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.
I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.
Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead