<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: schlich</title><link>https://news.ycombinator.com/user?id=schlich</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Fri, 18 Sep 2026 14:55:29 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=schlich" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by schlich in "Bend – A language that blocks AI mistakes via proof, on CPU and GPU"]]></title><description><![CDATA[
<p>you might be interested in property-based testing, which somewhat enumerates tests based on invariance  and induction</p>
]]></description><pubDate>Fri, 18 Sep 2026 11:30:11 +0000</pubDate><link>https://news.ycombinator.com/item?id=49752836</link><dc:creator>schlich</dc:creator><comments>https://news.ycombinator.com/item?id=49752836</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49752836</guid></item><item><title><![CDATA[New comment by schlich in "Neural Boids"]]></title><description><![CDATA[
<p>Cool stuff! It's an interesting approach to the starling phenomenon.  I'm familiar with the phenomenon through the lens of phase transitions and the critical point, which you allude to in the article briefly. Any further thoughts on how your neural-network based approach maps conceptually to the critical point and related models of emergent behavior?</p>
]]></description><pubDate>Sun, 08 Mar 2026 21:41:49 +0000</pubDate><link>https://news.ycombinator.com/item?id=47301825</link><dc:creator>schlich</dc:creator><comments>https://news.ycombinator.com/item?id=47301825</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47301825</guid></item></channel></rss>