<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: flerovium114</title><link>https://news.ycombinator.com/user?id=flerovium114</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Fri, 19 Jun 2026 23:56:08 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=flerovium114" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by flerovium114 in "Gribouille 0.3.0: A Grammar of Graphics for Typst"]]></title><description><![CDATA[
<p>In what setting are you mixing LaTeX and C code?</p>
]]></description><pubDate>Fri, 19 Jun 2026 11:08:30 +0000</pubDate><link>https://news.ycombinator.com/item?id=48597245</link><dc:creator>flerovium114</dc:creator><comments>https://news.ycombinator.com/item?id=48597245</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48597245</guid></item><item><title><![CDATA[New comment by flerovium114 in "The Hunt for Dark Breakfast"]]></title><description><![CDATA[
<p>pizza</p>
]]></description><pubDate>Fri, 27 Feb 2026 11:51:11 +0000</pubDate><link>https://news.ycombinator.com/item?id=47179477</link><dc:creator>flerovium114</dc:creator><comments>https://news.ycombinator.com/item?id=47179477</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47179477</guid></item><item><title><![CDATA[New comment by flerovium114 in "Derivatives, Gradients, Jacobians and Hessians"]]></title><description><![CDATA[
<p>Have you tried using Enzyme (<a href="https://enzyme.mit.edu/" rel="nofollow">https://enzyme.mit.edu/</a>)? It operates on the LLVM IR, so it's available in any language that breaks down into LLVM (e.g., Julia, where I've used it for surface gradients) and it produces highly optimized AD code. Pretty cool stuff.</p>
]]></description><pubDate>Sun, 17 Aug 2025 21:44:28 +0000</pubDate><link>https://news.ycombinator.com/item?id=44935188</link><dc:creator>flerovium114</dc:creator><comments>https://news.ycombinator.com/item?id=44935188</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44935188</guid></item><item><title><![CDATA[New comment by flerovium114 in "How randomness improves algorithms (2023)"]]></title><description><![CDATA[
<p>Randomized numerical linear algebra has proven very useful as well. It allows you to use a black-box function implementing matrix-vector multiplication (MVM) to compute standard decompositions like SVD, QR, etc. Very useful when MVM is O(N log N) or better.</p>
]]></description><pubDate>Sat, 16 Aug 2025 12:41:29 +0000</pubDate><link>https://news.ycombinator.com/item?id=44922823</link><dc:creator>flerovium114</dc:creator><comments>https://news.ycombinator.com/item?id=44922823</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44922823</guid></item></channel></rss>