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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Apple Intelligence Foundation Language Models: Tech Report 2025 · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2507.13575/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2507.13575/feed.xml" rel="self" type="application/rss+xml" /><item><title>Apple Intelligence Foundation Language Models: Tech Report 2025 — mentioned by avaer</title><link>https://news.ycombinator.com/item?id=49749801</link><guid isPermaLink="false">urn:hackerpapers:mention:2507.13575:49749801</guid><pubDate>Fri, 18 Sep 2026 03:10:40 GMT</pubDate><description>&lt;p&gt;LLM quants seem to eerily converge to modern/not so modern graphics techniques. You wouldn&amp;#x27;t think it would apply but it&amp;#x27;s obvious in hindsight. In fact mining graphics ideas is probably a good inspiration for efficient LLM architecture. For example, the Hadamard activation transform used here feels a lot like multiplying Fourier basis ala DFT; strong parallels to how image codecs work to make the residuals more compressible (especially discrete block codecs like are used in GPU compressed textures). I thought I was being clever suggesting that you could even abuse texture decode units to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2507.13575/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
