<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: goosers</title><link>https://news.ycombinator.com/user?id=goosers</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Sat, 30 May 2026 21:32:21 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=goosers" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by goosers in "LLM from scratch, part 28 – training a base model from scratch on an RTX 3090"]]></title><description><![CDATA[
<p>I’m experimenting with this, but using the CPU not the GPU. I’m finishing up writing the series now, but focused more on understanding the architecture than trying to build a useful model. Mine requires talking in the language of Shakespeare, and getting replies in the same, a proof of concept more than a useful tool. <a href="https://www.tag1.com/white-paper/part1-tokenization-building-an-llm-from-scratch-in-rust/" rel="nofollow">https://www.tag1.com/white-paper/part1-tokenization-building...</a><p>I was interested in focusing on repeatability and using text sources anyone can legally obtain. It’s been fascinating, but after much experimentation it’s clear that working with more text and more diverse text would be extremely helpful.</p>
]]></description><pubDate>Wed, 10 Dec 2025 08:36:23 +0000</pubDate><link>https://news.ycombinator.com/item?id=46215450</link><dc:creator>goosers</dc:creator><comments>https://news.ycombinator.com/item?id=46215450</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46215450</guid></item><item><title><![CDATA[Beyond the Magic: How LLMs Work]]></title><description><![CDATA[
<p>Article URL: <a href="https://www.tag1.com/white-paper/how-llms-actually-work/">https://www.tag1.com/white-paper/how-llms-actually-work/</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=45731075">https://news.ycombinator.com/item?id=45731075</a></p>
<p>Points: 1</p>
<p># Comments: 0</p>
]]></description><pubDate>Tue, 28 Oct 2025 10:21:19 +0000</pubDate><link>https://www.tag1.com/white-paper/how-llms-actually-work/</link><dc:creator>goosers</dc:creator><comments>https://news.ycombinator.com/item?id=45731075</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=45731075</guid></item></channel></rss>