<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - QCon AI Boston 2026 - Presentations</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ QCon AI Boston 2026 Presentations feed</description>
    <item>
      <title>Presentation: Beyond the Harness: Canvas for AI Agents</title>
      <link>https://www.infoq.com/presentations/canvas-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=QCon+AI+Boston+2026-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/canvas-ai-agents/en/mediumimage/nimisha-asthagiri-medium-1792655073821.jpg"/&gt;&lt;p&gt;Nimisha Asthagiri shares how applying proven product engineering and architecture principles creates a bounded canvas for AI agents. She explains how this model provides agents with bounded autonomy, continuous drift detection, and decision records to scale software development resiliently.&lt;/p&gt; &lt;i&gt;By Nimisha Asthagiri&lt;/i&gt;</description>
      <category>AI Harness</category>
      <category>Evolutionary Architecture</category>
      <category>Metrics</category>
      <category>Agents</category>
      <category>Context Engineering</category>
      <category>Architecture</category>
      <category>Transcripts</category>
      <category>Leadership</category>
      <category>QCon AI Boston 2026</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Tue, 03 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/canvas-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=QCon+AI+Boston+2026-presentations</guid>
      <dc:creator>Nimisha Asthagiri</dc:creator>
      <dc:date>2026-11-03T11:00:00Z</dc:date>
      <dc:identifier>/presentations/canvas-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Serving LLMs at Scale: the Hidden KV Cache Advantage</title>
      <link>https://www.infoq.com/presentations/llm-inference-kv-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=QCon+AI+Boston+2026-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/llm-inference-kv-cache/en/mediumimage/medium-1792066105733.jpeg"/&gt;&lt;p&gt;Khawaja Shams shares a distributed systems perspective on scaling LLM inference stacks efficiently. He discusses practical strategies for optimizing GPU utilization, managing KV cache memory cliffs, leveraging tiered storage, reducing tail latency, and implementing disaggregated prefill and decode architectures to improve overall fleet performance, cost economics, and availability.&lt;/p&gt; &lt;i&gt;By Khawaja Shams&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Optimization</category>
      <category>Transcripts</category>
      <category>Valkey</category>
      <category>Distributed Systems</category>
      <category>Redis</category>
      <category>Caching</category>
      <category>GPU</category>
      <category>QCon AI Boston 2026</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Tue, 27 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/llm-inference-kv-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=QCon+AI+Boston+2026-presentations</guid>
      <dc:creator>Khawaja Shams</dc:creator>
      <dc:date>2026-10-27T11:00:00Z</dc:date>
      <dc:identifier>/presentations/llm-inference-kv-cache/en</dc:identifier>
    </item>
  </channel>
</rss>
