<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Anvil on cornball.ai</title><link>https://cornball.ai/tags/anvil/</link><description>Recent content in Anvil on cornball.ai</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>2024</copyright><lastBuildDate>Wed, 15 Apr 2026 20:13:02 -0500</lastBuildDate><atom:link href="https://cornball.ai/tags/anvil/index.xml" rel="self" type="application/rss+xml"/><item><title>tinytorch, torchlang, ariel | Deep Learning in R</title><link>https://cornball.ai/posts/torchandfriends/</link><pubDate>Wed, 15 Apr 2026 00:00:00 +0000</pubDate><guid>https://cornball.ai/posts/torchandfriends/</guid><description>I&amp;rsquo;m announcing three packages today: tinytorch, torchlang, and ariel. They address different parts of the &amp;rsquo;torch in R&amp;rsquo; story that I want to tell before explaining the packages.
libtorchTen years ago you could tell who was cosplaying as a data scientist when they would post silly things like, &amp;ldquo;You can&amp;rsquo;t use R in production! It&amp;rsquo;s slow! Python is fast!&amp;rdquo; The reality is that most fast code called in Python is just a wrapper around C/C++.</description></item></channel></rss>