<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: sardukardboard</title><link>https://news.ycombinator.com/user?id=sardukardboard</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Fri, 19 Jun 2026 12:17:45 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=sardukardboard" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by sardukardboard in "Reading for pleasure is sharply down among schoolkids, report shows"]]></title><description><![CDATA[
<p>I recall enjoying books so much i would skip homework and read, regardless of feeling overloaded or not<p>Kids would even read in class and get their novels confiscated for the hour</p>
]]></description><pubDate>Fri, 12 Jun 2026 21:04:19 +0000</pubDate><link>https://news.ycombinator.com/item?id=48509416</link><dc:creator>sardukardboard</dc:creator><comments>https://news.ycombinator.com/item?id=48509416</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48509416</guid></item><item><title><![CDATA[New comment by sardukardboard in "Meta's embrace of AI is making its employees miserable"]]></title><description><![CDATA[
<p>A funny Goodhart’s Law parallel showed up in during GPT-5.1 training, where the model was rewarded for using the web search tool, so it learned the behavior of superficially using web search to calculate “1 + 1” and not utilize the result.<p><a href="https://alignment.openai.com/prod-evals/" rel="nofollow">https://alignment.openai.com/prod-evals/</a></p>
]]></description><pubDate>Sat, 09 May 2026 21:16:47 +0000</pubDate><link>https://news.ycombinator.com/item?id=48078352</link><dc:creator>sardukardboard</dc:creator><comments>https://news.ycombinator.com/item?id=48078352</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48078352</guid></item><item><title><![CDATA[New comment by sardukardboard in "Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models"]]></title><description><![CDATA[
<p>I worked thru David Silver’s RL course a while back, it’s got great explanations as he builds up the equations. It’s light on implementation, but the intuitive side really complements more code-heavy examples that lack the “why” behind the equations.<p><a href="https://davidstarsilver.wordpress.com/teaching/" rel="nofollow">https://davidstarsilver.wordpress.com/teaching/</a></p>
]]></description><pubDate>Mon, 30 Mar 2026 16:32:56 +0000</pubDate><link>https://news.ycombinator.com/item?id=47576445</link><dc:creator>sardukardboard</dc:creator><comments>https://news.ycombinator.com/item?id=47576445</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47576445</guid></item><item><title><![CDATA[New comment by sardukardboard in "The happiest I've ever been"]]></title><description><![CDATA[
<p>It’s not exactly nominative determinism, but maybe this could all be explained by every Ben Wallace being destined for basketball greatness (middle school or nba or otherwise)</p>
]]></description><pubDate>Sun, 01 Mar 2026 04:08:44 +0000</pubDate><link>https://news.ycombinator.com/item?id=47203601</link><dc:creator>sardukardboard</dc:creator><comments>https://news.ycombinator.com/item?id=47203601</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47203601</guid></item></channel></rss>