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      <title>Presentation: Running AI at the Edge: Running Real Workloads Directly in the Browser</title>
      <link>https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Edge+Computing-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/local-ai-browser-inference-privacy/en/mediumimage/james-hall-medium-1787813225372.jpeg"/&gt;&lt;p&gt;James Hall discusses the strategic and technical imperative of moving AI workloads from cloud providers to local edge devices. He shares practical approaches using WebGPU, Transformers.js, and DuckDB to achieve near-native performance in JavaScript. Through real-world case studies, he explains how to minimize data privacy risks, optimize browser inference, and build rigorous evaluation suites.&lt;/p&gt; &lt;i&gt;By James Hall&lt;/i&gt;</description>
      <category>AI Security</category>
      <category>Cloud Computing</category>
      <category>GPU</category>
      <category>QCon London 2026</category>
      <category>Privacy</category>
      <category>Web Browser</category>
      <category>Edge Computing</category>
      <category>Web Development</category>
      <category>Machine Learning</category>
      <category>Local Inference</category>
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      <category>presentation</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
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      <dc:creator>James Hall</dc:creator>
      <dc:date>2026-08-31T11:00:00Z</dc:date>
      <dc:identifier>/presentations/local-ai-browser-inference-privacy/en</dc:identifier>
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