<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Web Components on MacWorks</title><link>https://macworks.dev/tags/web-components/</link><description>Recent content in Web Components on MacWorks</description><generator>Hugo</generator><language>en</language><atom:link href="https://macworks.dev/tags/web-components/index.xml" rel="self" type="application/rss+xml"/><item><title>Simon Willison</title><link>https://macworks.dev/docs/today/simonwillison-2026-06-17/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/today/simonwillison-2026-06-17/</guid><description>&lt;h1 id="simon-willison--2026-06-17"&gt;Simon Willison — 2026-06-17&lt;a class="anchor" href="#simon-willison--2026-06-17"&gt;#&lt;/a&gt;&lt;/h1&gt;
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&lt;p&gt;The deep dive into Z.ai&amp;rsquo;s GLM-5.2 model is today&amp;rsquo;s most significant read, offering a hands-on look at a new 753B parameter open-weights giant that is currently topping intelligence and coding benchmarks. It captures the rapid evolution of massive models and provides practical prompt testing on their UI-generation capabilities.&lt;/p&gt;
&lt;h2 id="posts"&gt;Posts&lt;a class="anchor" href="#posts"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;GLM-5.2 is probably the most powerful text-only open weights LLM&lt;/strong&gt; · &lt;a href="https://simonwillison.net/2026/Jun/17/glm-52/#atom-everything"&gt;Source&lt;/a&gt;
Chinese AI lab Z.ai has released GLM-5.2, a massive 753B parameter open-weights model with a 1 million token context window. Simon notes it is currently leading the Artificial Analysis Intelligence Index and ranking second on the Code Arena WebDev leaderboard, which is deeply impressive for a text-only model lacking image inputs. He tested it via OpenRouter with his standard SVG generation prompts, finding it produced a flawless, self-contained animated pelican on a bicycle. However, it disappointingly failed to animate an opossum on an e-scooter, marking a regression from its predecessor, GLM-5.1.&lt;/p&gt;</description></item></channel></rss>