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    <title>InfoQ - Development - Presentations</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Development Presentations feed</description>
    <item>
      <title>Presentation: From AI Agent Demo to Production: Automated Testing and Evaluation</title>
      <link>https://www.infoq.com/presentations/ai-agent-testing-evaluation/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-agent-testing-evaluation/en/mediumimage/zhou-yu-medium-1787813732120.jpeg"/&gt;&lt;p&gt;Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases before deployment, and scale self-learning workflows in production.&lt;/p&gt; &lt;i&gt;By Zhou Yu&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Reliability</category>
      <category>Testing</category>
      <category>QCon AI Boston 2026</category>
      <category>Quality</category>
      <category>Large language models</category>
      <category>Performance Evaluation</category>
      <category>Agents</category>
      <category>Simulation</category>
      <category>Automated testing</category>
      <category>Data</category>
      <category>Performance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 07 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-agent-testing-evaluation/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Zhou Yu</dc:creator>
      <dc:date>2026-09-07T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-agent-testing-evaluation/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: A Few Predicted Talks From QConAI 2030</title>
      <link>https://www.infoq.com/presentations/ai-predictions-2030/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-predictions-2030/en/mediumimage/meryem-arik-medium-1785845650090.jpg"/&gt;&lt;p&gt;Meryem Arik discusses her predictions for software engineering in 2030. She explains how token spend management, parallel agent infrastructure, and non-technical builders will reshape IT. She shares insights on agent-driven vendor decisions, upcoming regulatory hurdles, and why software engineers must pivot from pure coding skills toward product leadership and multi-agent coordination.&lt;/p&gt; &lt;i&gt;By Meryem Arik&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Infrastructure</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>Technology Trends</category>
      <category>QCon AI Boston 2026</category>
      <category>Regulation</category>
      <category>Agents</category>
      <category>Governance</category>
      <category>Patterns</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Sat, 05 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-predictions-2030/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Meryem Arik</dc:creator>
      <dc:date>2026-09-05T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-predictions-2030/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From S3 to GPU in One Copy: Rethinking Data Loading for ML Training</title>
      <link>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/en/mediumimage/onur-satici-medium-1787813397611.jpeg"/&gt;&lt;p&gt;Onur Satici explains how Vortex, an open-source columnar file format under the Linux Foundation, revolutionizes high-throughput data loading. He details how cascading lightweight encodings, layout-based segment pruning, and zero-copy memory pipelines eliminate CPU/NVMe bottlenecks to stream S3 data straight to GPUs at speeds up to 60 Gbps without requiring upfront data reprocessing.&lt;/p&gt; &lt;i&gt;By Onur Satici&lt;/i&gt;</description>
      <category>Architecture</category>
      <category>Columnar Databases</category>
      <category>Data Lake</category>
      <category>GPU</category>
      <category>Machine Learning</category>
      <category>S3</category>
      <category>Streaming</category>
      <category>Transcripts</category>
      <category>QCon London 2026</category>
      <category>Data Pipelines</category>
      <category>CUDA</category>
      <category>Rust</category>
      <category>Performance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 04 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Onur Satici</dc:creator>
      <dc:date>2026-09-04T11:00:00Z</dc:date>
      <dc:identifier>/presentations/vortex-columnar-file-format-gpu-streaming/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Instrumentation at Scale: Having Your Performance Cake and Eating It Too</title>
      <link>https://www.infoq.com/presentations/instrumenting-scale/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/instrumenting-scale/en/mediumimage/brian-martin-medium-1787812453109.jpg"/&gt;&lt;p&gt;Brian Martin discusses the real-world performance costs of metrics libraries and shares strategies for low-overhead, "fearless" instrumentation. Drawing from his work at IOP Systems, he explores atomic primitives, per-CPU sharding, lock-free histograms, and eBPF integration to help software architects and engineering leaders maintain full system visibility without sacrificing performance.&lt;/p&gt; &lt;i&gt;By Brian Martin&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>QCon San Francisco 2025</category>
      <category>Performance &amp; Scalability</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 03 Sep 2026 09:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/instrumenting-scale/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Brian Martin</dc:creator>
      <dc:date>2026-09-03T09:28:00Z</dc:date>
      <dc:identifier>/presentations/instrumenting-scale/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Beyond Prompting: Context Engineering for Production-Grade AI</title>
      <link>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/context-engineering-redis-llm-architecture/en/mediumimage/ricardo-ferreira-medium-1787820318843.jpg"/&gt;&lt;p&gt;Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints.&lt;/p&gt; &lt;i&gt;By Ricardo Ferreira&lt;/i&gt;</description>
      <category>Retrieval-Augmented Generation</category>
      <category>Transcripts</category>
      <category>Architecture</category>
      <category>Redis</category>
      <category>Caching</category>
      <category>Generative AI</category>
      <category>QCon AI Boston 2026</category>
      <category>LangChain4j</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Wed, 02 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Ricardo Ferreira</dc:creator>
      <dc:date>2026-09-02T11:00:00Z</dc:date>
      <dc:identifier>/presentations/context-engineering-redis-llm-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Beyond Line Charts: Why Some Diversity in Telemetry Visualization Is Long Overdue</title>
      <link>https://www.infoq.com/presentations/telemetry-data/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/telemetry-data/en/mediumimage/yao-yue-medium-1787812332305.jpeg"/&gt;&lt;p&gt;Yao Yue discusses the fundamental limitations of standard line charts for system observability. Drawing from 15 years of operating large-scale systems, she shares how engineering leaders and software architects can transform telemetry data - moving beyond simple time-series defaults - to build visualizations that directly answer critical capacity, latency, and fleet-sizing questions.&lt;/p&gt; &lt;i&gt;By Yao Yue&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>QCon San Francisco 2025</category>
      <category>Telemetry</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Tue, 01 Sep 2026 12:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/telemetry-data/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Yao Yue</dc:creator>
      <dc:date>2026-09-01T12:35:00Z</dc:date>
      <dc:identifier>/presentations/telemetry-data/en</dc:identifier>
    </item>
    <item>
      <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=Development-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>Transcripts</category>
      <category>Edge Computing</category>
      <category>QCon London 2026</category>
      <category>AI Security</category>
      <category>Cloud Computing</category>
      <category>GPU</category>
      <category>Web Browser</category>
      <category>Machine Learning</category>
      <category>Local Inference</category>
      <category>Web Development</category>
      <category>Privacy</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</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=Development-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>
    </item>
    <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=Development-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>AI Cost Optimisation</category>
      <category>Transcripts</category>
      <category>AI Security</category>
      <category>AI Architecture</category>
      <category>Semantic Web</category>
      <category>QCon AI Boston 2026</category>
      <category>Agentic AI Architecture</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Data Mesh</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</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=Development-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>
    </item>
    <item>
      <title>Presentation: From DVDs to Global Streaming: How Netflix’s Commerce Architecture Actually Evolved</title>
      <link>https://www.infoq.com/presentations/netflix-commerce-architecture-evolution/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/netflix-commerce-architecture-evolution/en/mediumimage/kasia-trapszo-medium-1787217920531.jpg"/&gt;&lt;p&gt;Kasia Trapszo discusses how Netflix evolved its commerce platform from a U.S. DVD service into global infrastructure. She explains navigating international payment realities, adapting to strict regulatory mandates, decomposing monolithic architectures along domain boundaries, and re-architecting systems for massive live-event demand - proving great systems survive by continually evolving.&lt;/p&gt; &lt;i&gt;By Kasia Trapszo&lt;/i&gt;</description>
      <category>payment</category>
      <category>Microservices</category>
      <category>Transcripts</category>
      <category>Distributed Systems</category>
      <category>QCon London 2026</category>
      <category>Leadership</category>
      <category>Technical Debt</category>
      <category>Netflix</category>
      <category>Performance &amp; Scalability</category>
      <category>Design Systems</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 28 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/netflix-commerce-architecture-evolution/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Development-presentations</guid>
      <dc:creator>Kasia Trapszo</dc:creator>
      <dc:date>2026-08-28T11:00:00Z</dc:date>
      <dc:identifier>/presentations/netflix-commerce-architecture-evolution/en</dc:identifier>
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