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vscode.dev VS Jupyter

Compare vscode.dev VS Jupyter and see what are their differences

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vscode.dev logo vscode.dev

Now when you go to https://vscode.dev, you'll be presented with a lightweight version of VS Code running fully in the browser.

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • vscode.dev Landing page
    Landing page //
    2023-05-03
  • Jupyter Landing page
    Landing page //
    2023-06-22

vscode.dev features and specs

  • Accessibility
    You can access VSCode.dev from any device with a web browser, making it highly convenient for on-the-go editing.
  • No Installation Required
    Users can start coding immediately without any need to install software, simplifying the setup process.
  • Cross-Platform Compatibility
    VSCode.dev works across different operating systems (Windows, macOS, Linux), offering flexibility.
  • Regular Updates
    The web version receives updates in sync with the desktop version, ensuring you have access to the latest features and improvements.
  • Extension Support
    Many extensions available in the desktop version are also accessible in VSCode.dev, enhancing functionality.

Possible disadvantages of vscode.dev

  • Limited Offline Support
    Unlike the desktop app, VSCode.dev requires an internet connection, which could be a drawback in areas with poor connectivity.
  • Performance Constraints
    Running in a browser may result in decreased performance compared to the desktop version, especially for resource-intensive tasks.
  • Lower Customizability
    The web version may have some limitations in customization options compared to the full-featured desktop app.
  • Security Concerns
    Storing code and editing in a browser might raise security and privacy concerns for some users, particularly when dealing with sensitive information.
  • Dependency on Browser
    The experience can vary depending on the browser used, and it might not be fully optimized for all browsers.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

vscode.dev videos

+ Add

VSCode.Dev (VS Code in the Browser) - A Few Reasons You Might Care

More videos:

  • Review - VSCode In The BROWSER!? | vscode.dev | VS Code Online
  • Review - vscode.dev - VS Code In The Browser!!

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to vscode.dev and Jupyter)

vscode.dev

Jupyter

Text Editors
100 100%
Text Editors
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Open Source
100 100%
Open Source
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare vscode.dev and Jupyter

vscode.dev Reviews

We have no reviews of vscode.dev yet.
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Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

Social recommendations and mentions

vscode.dev might be a bit more popular than Jupyter. We know about 271 links to it since March 2021 and only 216 links to Jupyter. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

vscode.dev mentions (271)

  • Who Needs Software for Development Anyway?
    The GitHub code editor (immediately accessible by changing the ".com" to ".dev" in your browser URL, in case you didn't know) is miles, leagues ahead of what AWS has to offer. It has a full, working version of vscode.dev, which is pretty much the same as github.dev those days, I hear. It will allow you to install supported extensions, do some code completion, run your tests - and even has a shell! You can't... - Source: dev.to / 4 months ago
  • Show HN: I'm tired of sharing code using PasteBin and Slack, so I made this
    Vscode itself can run in the browser (https://vscode.dev/), and presumably so could the extension - so a full live-demo ought to be possible. - Source: Hacker News / 4 months ago
  • Dear AWS, how do I build & develop purely on AWS right now?
    As an alternative for Cloud9, you can use vscode.dev, which runs VS Code in the browser or other alternatives that are more integrated and personalized like gitpod.io or Github Codespaces. - Source: dev.to / 8 months ago
  • Coding on iPad using VSCode, Caddy, and code-server
    Is it ergonomic to code on a tablet without bci? https://vscode.dev can connect to a remote vscode instance in a container e.g. Over Remote Tunnels ; but browsers trap so many keyboard shortcuts. Which container with code-server to run to connect to from vscode client? You can specify a development container that contains code-server with devcontainer.json. vscode, Codespaces and these tools support... - Source: Hacker News / 8 months ago
  • Show HN: Defrag the Game
    Microsoft has an elaborate VS Code demo app that uses this API. Click "Open Folder" to see the API in action): https://vscode.dev. - Source: Hacker News / 8 months ago
View more

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
View more

What are some alternatives?

When comparing vscode.dev and Jupyter, you can also consider the following products

replit - Code, create, andlearn together. Use our free, collaborative, in-browser IDE to code in 50+ languages — without spending a second on setup.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

GitHub Codespaces - GItHub Codespaces is a hosted remote coding environment by GitHub based on Visual Studio Codespaces integrated directly for GitHub.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

StackBlitz - Online VS Code Editor for Angular and React

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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