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      <title>Presentation: Exploding GPUs</title>
      <link>https://www.infoq.com/presentations/ai-security-kubernetes-gpu-threats/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Infrastructure</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-security-kubernetes-gpu-threats/en/mediumimage/andrew-martin-medium-1790846331295.jpeg"/&gt;&lt;p&gt;Andrew Martin discusses why AI security is fundamentally tied to Kubernetes security. He shares insights into the evolving AI threat landscape, covering non-deterministic agent behavior, GPU supply chain vulnerabilities, shared compute risks, and practical defense strategies - from prompt firewalling to agentic identity - to help secure cloud-native AI workloads in production environments.&lt;/p&gt; &lt;i&gt;By Andrew Martin&lt;/i&gt;</description>
      <category>Agents</category>
      <category>AI Security</category>
      <category>Kubernetes</category>
      <category>Containers</category>
      <category>Cloud-Native</category>
      <category>QCon London 2026</category>
      <category>Transcripts</category>
      <category>GPU</category>
      <category>Infrastructure</category>
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      <category>presentation</category>
      <pubDate>Fri, 16 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-security-kubernetes-gpu-threats/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Infrastructure</guid>
      <dc:creator>Andrew Martin</dc:creator>
      <dc:date>2026-10-16T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-security-kubernetes-gpu-threats/en</dc:identifier>
    </item>
    <item>
      <title>Article: When Should You Use Liquid Clustering? Benchmarking Lakehouse Layout Strategies</title>
      <link>https://www.infoq.com/articles/liquid-clustering-lakehouse-layout/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Infrastructure</link>
      <description>&lt;img src="https://res.infoq.com/articles/liquid-clustering-lakehouse-layout/en/headerimage/liquid-clustering-lakehouse-layout-header-1791465984987.jpg"/&gt;&lt;p&gt;Physical data layout is one of the most consequential decisions in lakehouse engineering. The author compares Liquid Clustering, ZOrdering, and partitioning across four workload types on Delta Lake, uncovering two undocumented failure modes that likely affect production systems today and offering a decision framework for choosing the right layout strategy.&lt;/p&gt; &lt;i&gt;By Kushal Kumar Vishwakarma&lt;/i&gt;</description>
      <category>Query</category>
      <category>Clustering &amp; Caching</category>
      <category>Benchmark</category>
      <category>Data Lake</category>
      <category>Data Storage</category>
      <category>Performance Evaluation</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Mon, 12 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/liquid-clustering-lakehouse-layout/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Infrastructure</guid>
      <dc:creator>Kushal Kumar Vishwakarma</dc:creator>
      <dc:date>2026-10-12T11:00:00Z</dc:date>
      <dc:identifier>/articles/liquid-clustering-lakehouse-layout/en</dc:identifier>
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