sunfish VS spaCy

The NNUE version does but the non-NNUE version doesn't use numpy. That numpy import is for a neural network based evaluation functions. The base engine still plays at a strong human level and doesn't use numpy to do any of the heavy lifting. It does rely on itertools and collections though.

I'm currently working on a chess bot in C++. The literature and common sense tells you that faster is better, which was my main reason for choosing C++. Then I discovered Sunfish on GitHub that in around 400 lines of Python (without any low level libraries like ctypes) and it still beat me somewhat convincingly. It was definitely fast enough.

Just one file: Sunfish.py

sunfish – A Python Chess Engine in 111 lines of code

https://github.com/thomasahle/sunfish sunfish is a pretty simple one that uses piece square tables for evaluating positions. Wouldn’t be too difficult to change or even tune

It's from 1990. My father passed it down to me. It works... Uhmm.. idk. There's a proprietary chess engine built in. It says it's around 1700 elo points strong. I am a big noob when it comes to chess engines but if you're interested in learning you can check out sunfish , but (Python) programming skills are required.

Incomprehensible scheiße code. I looked around and I like this one because it has "meta-level" definition of movements and liitle bit of strategy. You could implement context-free chess games with varying rules for us congenitally lazy and dull-witted. https://github.com/thomasahle/sunfish/blob/master/sunfish.py

sunfish https://github.com/thomasahle/sunfish (sunfish is the most compact and still understandable code in my opinion)

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.