<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: 0x7d</title><link>https://news.ycombinator.com/user?id=0x7d</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Thu, 23 Apr 2026 12:16:05 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=0x7d" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by 0x7d in "Bypass DeepSeek censorship by speaking in hex"]]></title><description><![CDATA[
<p>Hi HN! This is my article!<p>It was great to put together a writeup of a fun evening or two of work. It looks like this goes much deeper.<p>I'm learning a lot from some of the linked articles, one of the base hypothesise of my work was that the filtering was distinct from the model, due to the cost of training with pre-filtered or censored data at scale: <a href="https://arxiv.org/abs/2307.10719" rel="nofollow">https://arxiv.org/abs/2307.10719</a>, let alone- making it generate a consistent response.<p>However, it looks like this goes further, a separate comment linked this article: <a href="https://news.ycombinator.com/item?id=42858552">https://news.ycombinator.com/item?id=42858552</a> on Chain-Of-Thought abandonment when certain topics are discussed.<p>I'll have to look at served vs trained censorship, in different context.</p>
]]></description><pubDate>Fri, 31 Jan 2025 21:22:03 +0000</pubDate><link>https://news.ycombinator.com/item?id=42892214</link><dc:creator>0x7d</dc:creator><comments>https://news.ycombinator.com/item?id=42892214</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=42892214</guid></item></channel></rss>