<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Python on Bits-Entangled</title><link>https://stondo.github.io/tags/python/</link><description>Recent content in Python on Bits-Entangled</description><generator>Hugo -- 0.162.1</generator><language>en-us</language><lastBuildDate>Thu, 24 Sep 2026 09:00:00 +0000</lastBuildDate><atom:link href="https://stondo.github.io/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>Top 12% at the AI Chessathon: One CPU Core, Four Dead Neural Nets, and the Residual That Finally Earned Its Elo</title><link>https://stondo.github.io/posts/aichessathon-numba-nnue-engine-58-of-465/</link><pubDate>Thu, 24 Sep 2026 09:00:00 +0000</pubDate><guid>https://stondo.github.io/posts/aichessathon-numba-nnue-engine-58-of-465/</guid><description>I entered an online tournament where AIs play chess: 465 bots, one CPU core, no GPU, no network, a 50 MB zip, and 120 seconds per game. The engine that finished 58th is a numba-jitted alpha-beta in pure Python with a 128-wide NNUE residual on top of a classical evaluation. Four neural networks died to teach me that lower training loss does not play chess.</description></item></channel></rss>