<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - Performance - Presentations</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Performance Presentations feed</description>
    <item>
      <title>Presentation: The AI Gateway: Scaling Centralized Inference across Decentralized Teams</title>
      <link>https://www.infoq.com/presentations/ai-gateway-scalability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-gateway-scalability/en/mediumimage/medium-1778663382364.jpg"/&gt;&lt;p&gt;Meryem Arik discusses why modern engineering teams face "inference chaos" and how AI model gateways provide a critical control layer. She explains the balance between empowering decentralized teams to choose the best models and maintaining centralized oversight for security, RBAC, and cost control.  Explore open-source solutions like LiteLLM and Doubleword to streamline your AI infra.&lt;/p&gt; &lt;i&gt;By Meryem Arik&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Scalability</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 20 May 2026 12:40:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-gateway-scalability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance-presentations</guid>
      <dc:creator>Meryem Arik</dc:creator>
      <dc:date>2026-05-20T12:40:00Z</dc:date>
      <dc:identifier>/presentations/ai-gateway-scalability/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Using AI as a Thinking Partner for Large-Scale Engineering Systems</title>
      <link>https://www.infoq.com/presentations/ai-large-scale-engineering-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-large-scale-engineering-systems/en/mediumimage/medium-1778069080461.jpeg"/&gt;&lt;p&gt;Julie Qiu explains how AI serves as a "thinking partner" for engineering leaders. She discusses five distinct roles - Archaeologist, Experimenter, Critic, Author, and Reviewer - to manage the cognitive load of 400+ repositories. She shares how AI provides the "RAM" needed to synthesize legacy context, pressure-test designs, and accelerate high-level architectural decisions.&lt;/p&gt; &lt;i&gt;By Julie Qiu&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Scalability</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>Culture &amp; Methods</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 15 May 2026 13:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-large-scale-engineering-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance-presentations</guid>
      <dc:creator>Julie Qiu</dc:creator>
      <dc:date>2026-05-15T13:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-large-scale-engineering-systems/en</dc:identifier>
    </item>
  </channel>
</rss>
