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Jupyter VS Augment Code

Compare Jupyter VS Augment Code and see what are their differences

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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.

Augment Code logo Augment Code

Enhances developer collaboration by providing codebase-aware chat, intuitive code suggestions, and advanced AI-driven explanations; accelerates coding tasks, assists in understanding unseen code structures, improving communication vastly within team…
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • Augment Code Landing page
    Landing page //
    2024-10-27

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.

Augment Code features and specs

  • Efficiency
    Augment Code can significantly increase development efficiency by providing AI-assisted coding suggestions, which reduces coding time and errors.
  • Improved Code Quality
    The tool helps in maintaining high code quality by suggesting best practices and optimizing code snippets, leading to more robust applications.
  • Learning Enhancement
    Developers can learn from the AI's suggestions, as it often recommends more efficient or modern coding techniques and libraries.
  • Integration
    Augment Code integrates well with various IDEs and development environments, making it a seamless addition to existing workflows.

Possible disadvantages of Augment Code

  • Dependency
    Over-reliance on AI suggestions can lead to developers not fully understanding the code they are writing or implementing.
  • Cost
    The service may come with subscription fees or charges that could be a barrier for individual developers or smaller teams.
  • Privacy Concerns
    Using a cloud-based AI tool can raise privacy issues, especially if proprietary code is involved and data is sent to external servers.
  • Context Limitations
    The AI might not fully understand the specific context of the project, leading to suggestions that are not perfectly aligned with project goals.

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

Augment Code videos

+ Add

AI Coding Assistant Showdown: Augment Code vs Cursor AI (Which is Better?)

More videos:

  • Review - Augment Code: Developer AI for Real World Work

Category Popularity

0-100% (relative to Jupyter and Augment Code)

Jupyter

Augment Code

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
AI
0 0%
AI
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
AI Agents
0 0%
AI Agents
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 Jupyter and Augment Code

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

Augment Code Reviews

We have no reviews of Augment Code yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Augment Code. While we know about 216 links to Jupyter, we've tracked only 3 mentions of Augment Code. 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.

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 / 7 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 / 8 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 / 9 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 / about 1 year 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 / over 1 year ago
View more

Augment Code mentions (3)

  • I've tried all (46 😵‍💫) AI Coding Agents & IDEs
    Augment Code Works in VS Code and JetBrains. Built for coders. Can execute code, run terminal, find issues, and analyze the code. Find performance optimization ideas in production. - Source: dev.to / 4 months ago
  • Claude 3.7 Sonnet and Claude Code
    At Augment (https://augmentcode.com) we were one of the partner who tested 3.7 pre-launch. And it has been a pretty significant increase in quality and code understanding. Happy to answer some questions FYI, We use Claude 3.7 has part of the new features we are shipping around Code Agent & more. - Source: Hacker News / 7 months ago
  • Chat is a bad UI pattern for development tools
    IMHO, I would agree with you. I think chat is a nice intermediary evolution between the CLI (that we use every day) and whatever comes next. I work at Augment (https://augmentcode.com), which, surprise surprise, is an AI coding assistant. We think about the new modality required to interact with code and AI on a daily basis. Beside increase productivity (and happiness, as you don't have to do mundane tasks like... - Source: Hacker News / 8 months ago

What are some alternatives?

When comparing Jupyter and Augment Code, you can also consider the following products

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.

Runcell - Runcell is an AI agent for Jupyter notebooks that automates writing Python code, executing cells, debugging, and explaining data analysis results in real time. The AI Agent for data science works, automate python coding works in data engineer, etc

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

Cursor - The AI-first Code Editor. Build software faster in an editor designed for pair-programming with AI.

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

Claude Code - Transform hours of debugging into seconds with a single command. Experience coding at thought-speed with Claude's AI that understands your entire codebase—no more context switching, just breakthrough results.

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