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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=AI+Security-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>QCon London 2026</category>
      <category>Privacy</category>
      <category>Web Browser</category>
      <category>Edge Computing</category>
      <category>GPU</category>
      <category>AI Security</category>
      <category>Cloud Computing</category>
      <category>Web Development</category>
      <category>Transcripts</category>
      <category>Local Inference</category>
      <category>Machine Learning</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
      <guid>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=AI+Security-presentations</guid>
      <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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    <item>
      <title>Presentation: Architecting the Data Layer for AI Agents: from Transactional Systems to MCP and Semantic Models</title>
      <link>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Security-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/enterprise-data-architecture-ai-agents/en/mediumimage/fabiane-nardon-medium-1787218382028.jpeg"/&gt;&lt;p&gt;Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-deterministic LLMs across precision, security, and cost. Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems.&lt;/p&gt; &lt;i&gt;By Fabiane Nardon&lt;/i&gt;</description>
      <category>Data Mesh</category>
      <category>AI Cost Optimisation</category>
      <category>Semantic Web</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>QCon AI Boston 2026</category>
      <category>AI Architecture</category>
      <category>Model Context Protocol (MCP)</category>
      <category>AI Security</category>
      <category>Agentic AI Architecture</category>
      <category>Transcripts</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Sat, 29 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Security-presentations</guid>
      <dc:creator>Fabiane Nardon</dc:creator>
      <dc:date>2026-08-29T11:00:00Z</dc:date>
      <dc:identifier>/presentations/enterprise-data-architecture-ai-agents/en</dc:identifier>
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