<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: niksmather</title><link>https://news.ycombinator.com/user?id=niksmather</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Thu, 08 Oct 2026 04:13:06 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=niksmather" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by niksmather in "Release of Polars 2.0"]]></title><description><![CDATA[
<p>You can convert to numpy using .to_numpy().<p>It's also got much better support for more complex array shapes (e.g. each row storing an array). At least it did last time I used pandas!</p>
]]></description><pubDate>Tue, 06 Oct 2026 17:05:04 +0000</pubDate><link>https://news.ycombinator.com/item?id=49981239</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=49981239</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49981239</guid></item><item><title><![CDATA[New comment by niksmather in "Linear algebra done right"]]></title><description><![CDATA[
<p>I would also say Axler is much better prep for higher level applied math, as well as pure. If you are interested in how the big ideas of linear algebra extend to things like Fourier analysis it's very helpful to see the more abstract explanation of vector spaces.</p>
]]></description><pubDate>Mon, 17 Aug 2026 08:23:55 +0000</pubDate><link>https://news.ycombinator.com/item?id=49327853</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=49327853</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49327853</guid></item><item><title><![CDATA[Show HN: Lefts – a domain specific language for building creative ML models]]></title><description><![CDATA[
<p>Lefts is a small domain specific language for applied machine learning modelling. It is aimed at anyone that builds predictive models for a living and wants to focus on reasoning about model behaviour and building creative architectures, and not on building train/test pipelines or worrying about data leakage.<p>It is simple but quite powerful - I have been using it in my own work to explore new ways of modelling (check out the tutorial on geometric models!), and to breeze past the least interesting parts of being a machine learning engineer. It also has some cool functional programming going on under the hood (check out the design philosophy!).</p>
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<p>Comments URL: <a href="https://news.ycombinator.com/item?id=49209498">https://news.ycombinator.com/item?id=49209498</a></p>
<p>Points: 6</p>
<p># Comments: 0</p>
]]></description><pubDate>Fri, 07 Aug 2026 12:38:54 +0000</pubDate><link>https://nsmat.github.io/lefts/</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=49209498</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49209498</guid></item><item><title><![CDATA[New comment by niksmather in "Patterncollider: Generate and explore quasiperiodic tiling patterns"]]></title><description><![CDATA[
<p>So cool! Really looking forward to playing around with this.<p>It was also a great little explainer of how these tilings get made. One question: how do you guarantee/prove that the tiling you get back from the grid is quasiperiodic?</p>
]]></description><pubDate>Fri, 10 Jul 2026 06:26:55 +0000</pubDate><link>https://news.ycombinator.com/item?id=48856441</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48856441</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48856441</guid></item><item><title><![CDATA[New comment by niksmather in "Spider venom kills varroa mites without harming honeybees"]]></title><description><![CDATA[
<p>Pesticides are bad for bees, but Varroa is too. Until Varroa arrived in Australia the bees there didn't suffer from colony collapse, despite high pesticide use.</p>
]]></description><pubDate>Thu, 09 Jul 2026 06:17:14 +0000</pubDate><link>https://news.ycombinator.com/item?id=48841665</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48841665</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48841665</guid></item><item><title><![CDATA[New comment by niksmather in "30papers.com – Ilya's 30 essential ML papers, in a beginner friendly format"]]></title><description><![CDATA[
<p>That's true of all statistical models, it's not some magic property of neural networks.</p>
]]></description><pubDate>Tue, 07 Jul 2026 21:36:19 +0000</pubDate><link>https://news.ycombinator.com/item?id=48824208</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48824208</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48824208</guid></item><item><title><![CDATA[New comment by niksmather in "I Accidentally Started a Small Business Three Weeks Ago"]]></title><description><![CDATA[
<p>Great stuff - I really hope you manage to scale this up!</p>
]]></description><pubDate>Sun, 05 Jul 2026 07:16:29 +0000</pubDate><link>https://news.ycombinator.com/item?id=48791968</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48791968</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48791968</guid></item><item><title><![CDATA[New comment by niksmather in "Trees to Flows and Back: Unifying Decision Trees and Diffusion Models"]]></title><description><![CDATA[
<p>I can see the mathematical results are interesting, I was more wondering if there was a practical utility to this TreeFlow thing they built.</p>
]]></description><pubDate>Sun, 07 Jun 2026 06:38:48 +0000</pubDate><link>https://news.ycombinator.com/item?id=48432452</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48432452</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48432452</guid></item><item><title><![CDATA[New comment by niksmather in "Trees to Flows and Back: Unifying Decision Trees and Diffusion Models"]]></title><description><![CDATA[
<p>Apologies if I didn't understand the paper, but why do you want to apply diffusion models to tabular datasets in the first place?<p>Do we think they'll be better than decision trees? Is there some tabular problem that can be handled by diffusion but not trees?</p>
]]></description><pubDate>Sat, 06 Jun 2026 19:15:57 +0000</pubDate><link>https://news.ycombinator.com/item?id=48428008</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=48428008</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48428008</guid></item><item><title><![CDATA[New comment by niksmather in "There Will Be a Scientific Theory of Deep Learning"]]></title><description><![CDATA[
<p>Do neural networks work better than other models? They can definitely model a wider class of problems than traditional ML models (images being the canonical example). However, I thought where a like for like comparison was possible they tend to worse than gradient boosting.</p>
]]></description><pubDate>Sat, 25 Apr 2026 06:22:57 +0000</pubDate><link>https://news.ycombinator.com/item?id=47899179</link><dc:creator>niksmather</dc:creator><comments>https://news.ycombinator.com/item?id=47899179</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47899179</guid></item></channel></rss>