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interviewing.io VS Jupyter

Compare interviewing.io VS Jupyter and see what are their differences

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interviewing.io logo interviewing.io

Free, anonymous technical interview practice

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.
  • interviewing.io Landing page
    Landing page //
    2022-11-02
  • Jupyter Landing page
    Landing page //
    2023-06-22

interviewing.io features and specs

  • Anonymity
    Interviewing.io allows candidates to remain anonymous during the interview process, which can help reduce bias and make candidates more comfortable.
  • High-quality practice
    The platform provides opportunities to practice with real engineers from top tech companies, offering high-quality feedback and experience.
  • Cost-effective
    Many features on Interviewing.io are free, including the ability to conduct practice interviews and access to recordings and feedback.
  • Feedback and metrics
    Candidates receive detailed feedback and performance metrics after each interview, helping them identify areas of improvement.
  • Networking
    The platform can provide valuable networking opportunities by connecting candidates with engineers and potential employers from top tech companies.

Possible disadvantages of interviewing.io

  • Limited industry focus
    Interviewing.io primarily focuses on tech interviews, so it may not be useful for candidates looking for practice in other industries.
  • Variable interviewer quality
    The quality of interviewers can vary, which might affect the consistency of the practice and feedback received.
  • Scheduling challenges
    Finding convenient times for interviews can sometimes be challenging, especially if both the candidate and interviewer have busy schedules.
  • Stress and performance pressure
    Despite being a practice platform, candidates might still experience stress and performance pressure, similar to real interview scenarios.
  • Limited personalization
    The feedback and practice sessions are somewhat standardized, which may not always cater to the specific needs or unique backgrounds of individual candidates.

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.

Analysis of interviewing.io

Overall verdict

  • Interviewing.io is considered a good resource for individuals looking to improve their technical interviewing skills. It is particularly beneficial due to its anonymous nature, which encourages honest feedback and reduces anxiety, and the quality of interviewers involved, who often come from well-known tech companies.

Why this product is good

  • Interviewing.io is a platform designed to help candidates practice technical interviewing through mock interviews, which can be especially useful for those aiming to enter fields such as software engineering. It offers anonymous practice sessions with engineers from top tech companies, providing real-world experience and feedback. The platform also offers flexible scheduling, expert insights, and resources to improve interview performance.

Recommended for

  • Aspiring software engineers
  • Recent computer science graduates
  • Professionals transitioning into tech roles
  • Individuals preparing for technical interviews at major tech companies

interviewing.io videos

+ Add

Technical Interviewing Anonymous: Aline Lerner, CEO @ Interviewing.io

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 interviewing.io and Jupyter)

interviewing.io

Jupyter

Online Learning
100 100%
Online Learning
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Education
100 100%
Online Education
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using interviewing.io and Jupyter. For example, how are they different and which one is better?
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Reviews

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

interviewing.io Reviews

The Best Code Interview Prep Platforms in 2020
Interviewing.io takes a very unique approach to coding interview prep. Rather than providing content and practice coding challenges, Interviewing.io has a library of actual video interviews that you can watch, and you can pay to anonymously take a mock interview with an engineering hiring manager.
Source: www.freecodecamp.org

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

Based on our record, Jupyter should be more popular than interviewing.io. It has been mentiond 216 times since March 2021. 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.

interviewing.io mentions (99)

  • How to Become a Backend Developer in 2025 ?
    Interviewing.io – Anonymous mock interview platform with real engineers from top tech companies. - Source: dev.to / 5 months ago
  • My Journey of Mastering Data Structures and Algorithms in 6 Months: Dos and Don'ts👩🏻‍💻
    Conduct Mock Interviews: Simulate interview scenarios using platforms like Pramp or Interviewing.io. This helps you manage time, pressure, and articulating your thought process. - Source: dev.to / 10 months ago
  • Rebooting (something like) early Triplebyte
    How is this different than https://interviewing.io/ ? - Source: Hacker News / 11 months ago
  • Ask HN: Any previous experience with interviewing.io dedicated coaches?
    Interviewing.io[1] lets users to practice mock interviews (coding interviews) with peers or professional interviewers. These interviews are anonymous. They also offer mentorship sessions with “dedicated coaches” from FAANG or other backgrounds. They claim 99% satisfaction rate and 82% of success (landing a job in the desired company). It sounds really vague and difficult to verify due to the anonymous aspect. Does... - Source: Hacker News / over 1 year ago
  • Guidance for cracking coding interviews
    There is also https://interviewing.io/, but that platform is a rip off. Either you need to pay an arm and a leg, or you need to trade two interviews that you do for others in exchange for a single interview that you receive. Pramp is much better in that respect. With Pramp, you interview the other job-hunter for 30 minutes and they interview you for 30 minutes. It's a much fairer exchange. Source: over 1 year 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 / 3 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 / 4 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 / 5 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 / 9 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 / about 1 year ago
View more

What are some alternatives?

When comparing interviewing.io and Jupyter, you can also consider the following products

AlgoExpert.io - A better way to prep for tech interviews

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.

LeetCode - Practice and level up your development skills and prepare for technical interviews.

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

Interview Cake - Free practice programming interview questions. Interview Cake helps you prep for interviews to land offers at companies like Google and Facebook.

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

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