<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - Model Inference - News</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Model Inference News feed</description>
    <item>
      <title>Pinterest Replaces Journey Clustering with 4B Qwen3 LLM, Reports Improved Engagement</title>
      <link>https://www.infoq.com/news/2026/11/pinterest-journey/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Model+Inference-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/pinterest-journey/en/headerimage/generatedHeaderImage-1793998579474.jpg"/&gt;&lt;p&gt;Pinterest has moved its user-journey inference system from a multi-stage clustering pipeline to a fine-tuned 4-billion-parameter language model, reporting a 1.1% improvement in email click-through and 1.3% higher push opens.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>Model Inference</category>
      <category>Generative AI</category>
      <category>Machine Learning</category>
      <category>Large language models</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 07:25:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/pinterest-journey/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Model+Inference-news</guid>
      <dc:creator>Matt Foster</dc:creator>
      <dc:date>2026-11-09T07:25:00Z</dc:date>
      <dc:identifier>/news/2026/11/pinterest-journey/en</dc:identifier>
    </item>
    <item>
      <title>DoorDash Cuts Grocery Reorder Latency from 16 to 2 Seconds with AI Agents</title>
      <link>https://www.infoq.com/news/2026/10/doordash-ai-grocery-reorder/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Model+Inference-news</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;DoorDash reduced grocery reorder latency from 16 to 2 seconds by precomputing recurring purchase needs using batch LLM inference and deterministic validation. Live catalog resolution keeps product selections current, while an editable shopping interface avoids unnecessary model calls. The company reports that shopping list views doubled and the flow’s order rate increased by approximately 34%&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Low Latency</category>
      <category>Model Inference</category>
      <category>API Gateway</category>
      <category>Orchestration</category>
      <category>Agentic AI Architecture</category>
      <category>Data Catalog</category>
      <category>Model Context Protocol (MCP)</category>
      <category>AI Architecture</category>
      <category>Agents</category>
      <category>Large language models</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 30 Oct 2026 13:31:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/doordash-ai-grocery-reorder/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Model+Inference-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-10-30T13:31:00Z</dc:date>
      <dc:identifier>/news/2026/10/doordash-ai-grocery-reorder/en</dc:identifier>
    </item>
  </channel>
</rss>
