sunfish VS typer

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)

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/

interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?