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Lichess VS NumPy

Compare Lichess VS NumPy and see what are their differences

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Lichess logo Lichess

The complete chess experience, play and compete in tournaments with friends others around the world.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Lichess Landing page
    Landing page //
    2023-01-04
  • NumPy Landing page
    Landing page //
    2023-05-13

Lichess features and specs

  • Free to Use
    Lichess is completely free to use, with no hidden fees or subscription models required to access its features.
  • Open Source
    The platform is open-source, allowing anyone to contribute to its development or customize it according to their needs.
  • Ad-Free
    Lichess does not run advertisements, providing a cleaner and more enjoyable user experience.
  • Variants
    Offers a wide range of chess variants like Crazyhouse, Chess960, and Atomic, catering to diverse player interests.
  • Community Features
    Features strong community elements such as forums, tournaments, and team play, enhancing social interaction.
  • Analysis Tools
    Provides powerful game analysis tools, including Stockfish integration, to help players improve their skills.
  • Accessibility
    The platform has a clean and intuitive interface that is accessible to both beginners and experienced players.
  • Mobile Apps
    Lichess offers mobile applications for both iOS and Android, allowing users to play and learn chess on the go.

Possible disadvantages of Lichess

  • No Official Recognition
    Lichess is not officially recognized by the major chess organizations, which might be a limitation for professional players.
  • Lesser User Base Compared to Competitors
    Although it has a strong community, its user base is smaller when compared to competitors like Chess.com.
  • Limited Social Features
    Lacks some of the advanced social features found on other platforms, such as comprehensive user profiles and social media integration.
  • Server Issues
    Occasionally faces server reliability issues during peak times, which can disrupt gameplay.
  • Learning Resources
    Although there are learning resources available, they are not as extensive or structured as those found on some other platforms.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of Lichess

Overall verdict

  • Lichess.org is an excellent platform for playing, learning, and improving in chess. It provides a high-quality, ad-free experience that is accessible to everyone, making it a favorite among many chess players worldwide.

Why this product is good

  • Lichess.org is considered good because it offers a wide range of features for chess enthusiasts of all levels, including online play, puzzles, tournaments, studies, and more. It is completely free, open source, and does not contain ads. The platform supports various time controls and chess variants, which makes it versatile for different preferences. It also has a strong community and active development, ensuring continuous improvements and new features.

Recommended for

  • Beginners looking to learn and improve their chess skills
  • Casual players interested in playing games at their convenience
  • Advanced players seeking competitive matches and analysis tools
  • Chess enthusiasts who enjoy exploring different variants of chess
  • Coaches and teachers who want resources for instructing students

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Lichess videos

+ Add

Review of LiChess, Chess.com, ChessClub, ICC, Playchess, chesscube Reviewed!

More videos:

  • Review - Learning from your mistakes - Lichess has best online chess features and it is free!
  • Review - Introduction to Game Analysis on Lichess

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Lichess and NumPy)

Lichess

NumPy

Chess
100 100%
Chess
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Games
100 100%
Games
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Lichess and NumPy

Lichess Reviews

Chess.com vs Lichess.org
Chess.com and Lichess.org - just “Lichess” from here on; pronounced as lee-chess - are the two most popular chess servers on the internet.
Source: siddhesh.substack.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, Lichess should be more popular than NumPy. It has been mentiond 912 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.

Lichess mentions (912)

  • Ask HN: What are you working on? (May 2025)
    I'm a big chess buff, and will say that the hard part is not making an engine (which is hard!), but making an engine that plays poorly, well. What I mean is: engines are very smart and better than the best human. When you make a "dumb" engine, you are telling a chess god to intentionally make mistakes. The mistakes they make, however, are not the same mistakes a beginner chess player would make. Today, beginners... - Source: Hacker News / 3 days ago
  • PlayQuoridor: A Free, Open-Source Real-Time Quoridor Server
    Read about it more here: https://lichess.org/@/Neodimi/blog/playquoridor-a-free-open-source-real-time-quoridor-server/qHDnpxlq. - Source: Hacker News / 3 months ago
  • Implementing zen mode in React
    Zen mode is a popular UX pattern that creates a distraction-free experience for application users. It's a simple approach where part of application interface is hidden on user's demand. I have encountered this mode in applications where the main functions are based on focus and tranquility. One of them is Lichess, the second chess platform in the World. I am a chess enthusiast and I am trying (with poor results)... - Source: dev.to / 4 months ago
  • Mastering the Isolated Queen Pawn (IQP)
    Https://lichess.org is an excellent open source chess server with plenty of learning resources for pure beginners. As you progress learning resources sadly get more and more expensive indeed. Not to mention the cost of tournaments (travel and accomodation expenses add up very quickly). - Source: Hacker News / 5 months ago
  • Analyzing the World Chess Championship 2024: Empirical Synthesized Approach
    You don't have a good grasp of data analysis then. You used the data to tell yourself a story "the experts are biased!, not to gain a real deeper understanding". This story should already be suspect because the experts, when commentating, had access to the data you looked at. The eval bar was always there. But they interpreted it. Your assumption seems to be that by calculating some trivial statistics and not... - Source: Hacker News / 5 months ago
View more

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Lichess and NumPy, you can also consider the following products

Chess.com - Play chess on Chess.com

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Chessvia.ai - Chessvia AI offers a revolutionary chess experience with Chessy, your personal AI chess coach that speaks, listens, and adapts to your style.

OpenCV - OpenCV is the world's biggest computer vision library

Chessmaster - Chessmaster is a chess playing computer game series which is now owned and developed by Ubisoft.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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