<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Warpspace Blog</title><link>http://blog.caveduck.io/</link><description>Recent content on Warpspace Blog</description><generator>Hugo -- 0.154.5</generator><language>en-US</language><lastBuildDate>Tue, 07 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://blog.caveduck.io/index.xml" rel="self" type="application/rss+xml"/><item><title>That Free Korean NPU the Government Hands Out — Can You Actually Use It? Notes From Porting an Unsupported Model</title><link>http://blog.caveduck.io/posts/modernbert-on-rebellions-npu/</link><pubDate>Tue, 07 Jul 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/modernbert-on-rebellions-npu/</guid><description>Moving a persona-deviation detector (an mmBERT-based classifier) that we served on GPU in production onto a Korean NPU (Rebellions ATOM+) — the journey from recognizing the problem to writing the adapter, compiling on real hardware, and benchmarking.</description></item><item><title>Neural Network Compilation, Made Simple — How a PyTorch Model Becomes Hardware Language</title><link>http://blog.caveduck.io/posts/neural-net-compilation/</link><pubDate>Mon, 06 Jul 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/neural-net-compilation/</guid><description>What exactly does it mean to &amp;lsquo;compile a model&amp;rsquo;? From the difference with eager execution to graph capture, operator fusion, kernel lowering, static shapes, and memory planning — we walk through it step by step with diagrams and an interactive demo.</description></item><item><title>Efficient ML #3 — Neural Net Pruning</title><link>http://blog.caveduck.io/posts/neural-net-pruning/</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/neural-net-pruning/</guid><description>Starting from LeCun&amp;rsquo;s 1989 Optimal Brain Damage, this is the story of shrinking a model by cutting weights away (pruning). What to cut, how, and how much; the surprise of compression that survives even a 95% cut thanks to retraining; and the point that you only get a real payoff when the hardware (NVIDIA 2:4) backs you up — all with plots we measured ourselves on an MNIST MLP.</description></item><item><title>Efficient ML #2 — Neural Net Quantization</title><link>http://blog.caveduck.io/posts/neural-net-quantization/</link><pubDate>Mon, 20 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/neural-net-quantization/</guid><description>We take the trained 32-bit weights of a neural net and shrink them down to 2–8 bits using two approaches: K-Means (non-uniform) and Linear (uniform, integer arithmetic). From the affine mapping r=S(q−Z) all the way to the compression-ratio vs. accuracy trade-off, with plots measured by actually running the code on an MNIST MLP.</description></item><item><title>Efficient ML #1 — Deep Learning Data Types</title><link>http://blog.caveduck.io/posts/deep-learning-data-types/</link><pubDate>Mon, 13 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/deep-learning-data-types/</guid><description>INT8, FP16, BF16, FP8 (E4M3/E5M2), FP4 — what do all these data types flooding deep learning model cards actually mean, and how do bits get interpreted as numbers? We take them apart one by one, with a widget where clicking bits updates the formula and value in real time.</description></item><item><title>Welcome to Warpspace Blog</title><link>http://blog.caveduck.io/posts/welcome/</link><pubDate>Mon, 06 Apr 2026 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/posts/welcome/</guid><description>Introducing the official Warpspace Blog - where we share our journey building Caveduck.io</description></item><item><title>About</title><link>http://blog.caveduck.io/page/about/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>http://blog.caveduck.io/page/about/</guid><description>About Warpspace Inc. and Caveduck.io</description></item></channel></rss>