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    <item>
      <title>Podcast: Doing The Right Thing: Accountability In The Age Of AI Coding Summary</title>
      <link>https://www.infoq.com/podcasts/accountability-age-ai-coding-summary/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=podcasts</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/accountability-age-ai-coding-summary/en/smallimage/engineering-culture-500-1794303925695.jpg"/&gt;&lt;p&gt;In this podcast, Shane Hastie, Lead Editor for Culture &amp; Methods, spoke to Hugo Venturini about who is accountable when AI writes the code, how AI is reshaping code review and traditional engineering roles, and how leaders build a culture of transparency and psychological safety.&lt;/p&gt; &lt;i&gt;By Hugo Venturini&lt;/i&gt;</description>
      <category>Trust</category>
      <category>Culture</category>
      <category>AI Coding</category>
      <category>Leadership</category>
      <category>Software Engineering</category>
      <category>Engineering Culture Podcast</category>
      <category>Quality</category>
      <category>Product Management</category>
      <category>Safety</category>
      <category>Psychological Safety</category>
      <category>Teamwork</category>
      <category>Culture &amp; Methods</category>
      <category>podcast</category>
      <pubDate>Fri, 13 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/accountability-age-ai-coding-summary/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=podcasts</guid>
      <dc:creator>Hugo Venturini</dc:creator>
      <dc:date>2026-11-13T09:00:00Z</dc:date>
      <dc:identifier>/podcasts/accountability-age-ai-coding-summary/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Distributed LLM Inference on Kubernetes with llm-d, Wasm, &amp; TornadoVM</title>
      <link>https://www.infoq.com/podcasts/distributed-llm-inference-kubernetes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=podcasts</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/distributed-llm-inference-kubernetes/en/smallimage/infoq-podcast-500-1793797932006.jpg"/&gt;&lt;p&gt;In this episode, Edoardo Vacchi discusses the evolving landscape of WebAssembly on the backend, running Java workloads on GPUs using TornadoVM, and optimizing distributed AI inference using llm-d and vLLM on Kubernetes. Edoardo explains how llm-d manages cache locality (KV and prefix caches), request routing, and batch inference across Kubernetes clusters to maximize expensive GPU utilization.&lt;/p&gt; &lt;i&gt;By Edoardo Vacchi&lt;/i&gt;</description>
      <category>Java</category>
      <category>WebAssembly</category>
      <category>TornadoVM</category>
      <category>The InfoQ Podcast</category>
      <category>Artificial Intelligence</category>
      <category>Kubernetes</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>podcast</category>
      <pubDate>Mon, 09 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/distributed-llm-inference-kubernetes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=podcasts</guid>
      <dc:creator>Edoardo Vacchi</dc:creator>
      <dc:date>2026-11-09T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/distributed-llm-inference-kubernetes/en</dc:identifier>
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