lila
Pandas
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lila | Pandas | |
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794 | 393 | |
14,578 | 41,923 | |
1.4% | 1.4% | |
10.0 | 10.0 | |
about 23 hours ago | 5 days ago | |
Scala | Python | |
GNU Affero General Public License v3.0 | BSD 3-clause "New" or "Revised" License |
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lila
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Lessons from Open-Source Game Projects
Lichess - Online Chess Server. Scala, TypeScript
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Avoid blundering: 80% of a winning strategy
> the player who committed more blunders lost 86% of the time
In some sense this is almost tautological. While finding an exact definition for a chess blunder isn't straightforward, here is one example from the Lichess UI:
https://github.com/lichess-org/lila/blob/b527746b179cdde6438...
Basically, if you make a move which decreases your winning probability more than 14% over the best move, that's a blunder. But winning probability is a nonlinear function of stockfish centipawns. A drop in 100 centipawns when you're up 15 points isn't a blunder. When the game was equal, it is.
Point is, by the time you know it's a blunder you already know something about the outcome of that move, that it swung the winning probability by more than 14%. So the analysis is kind of just measuring some function of winning probability and saying that it is highly correlated with winning probability.
- How I hacked chess.com with a rookie exploit
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So bad at chess that it’s genuinely upsetting at this point, I need some hope
If you want to improve make it your goal to play the best chess you can, not increase an arbitrary number. Watch YouTube series like John Bartholomew's "Climb the Rating Ladder" for some general insight into what you might be doing wrong. Read Irving Chernev's "Logical Chess: Move By Move" to see the thinking process of high level players. Do lots of puzzles (I like lichess.org for puzzles). And always analyze your games. When you analyze make it your goal to find at least two things you could have improved.
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Humans vs. Stockfish’s eval function
The easiest way to play against Stockfish is perhaps on https://lichess.org/, but it's not the only chess engine that evaluates positions with a neural network.
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Venruki’s take on the current issues with PvP
Lichess.com
- Death wants to take you, but you can challenge it to a game (virtual or not) to stay. what do you play?
- Ask HN: What fuel for my data furnace?
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The DGPT season opener will be sponsored by chess.com!
if you actually like chess, try lichess.org, the free and open-source, no ads ever, premium alternative
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I got a Chessnut Evo to review, here are my thoughts
The Chessnut Evo works almost flawlessly (I did not experience this issue but people have reported having ChessnutVision stop working on occasion which requires turning on/off to fix) with popular chess sites (officially supported are chess.com, lichess.org, Chess Kid and Chessable). I experienced no major lag when playing games on Lichess through the board There is the unavoidable delay of physically moving pieces, so it may not be ideal for blitz But for rapid or longer time controls. the ability to have your OTB games instantly logged and the ability to effortlessly analyze games after is game-changing for me. The one occasional hiccup I encountered was when quickly sliding pieces, it would register an incorrect move. But that’s an easy fix of adjusting the Limbo move delay (I don't like this option as it makes the board feel less responsive I prefer to just be aware and lift pieces instead of sliding).
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
What are some alternatives?
listudy - Listudy - chess training server
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Mindustry - The automation tower defense RTS
tensorflow - An Open Source Machine Learning Framework for Everyone
Anki-Chess-2.0 - An interactive chess template for anki.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
katrain - Improve your Baduk skills by training with KataGo!
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
monkeytype - The most customizable typing website with a minimalistic design and a ton of features. Test yourself in various modes, track your progress and improve your speed.
Keras - Deep Learning for humans
maia-chess - Maia is a human-like neural network chess engine trained on millions of human games.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration