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    <item>
      <title>Presentation: Fine Tuning the Enterprise: Reinforcement Learning in Practice</title>
      <link>https://www.infoq.com/presentations/rft-openai-model/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/rft-openai-model/en/mediumimage/WenjieZiWillHang-medium-1782220624463.jpg"/&gt;&lt;p&gt;The speakers discuss Agent RFT, OpenAI’s platform for fine-tuning reasoning models via real-time tool interactions and custom reward signals. They explain how reinforcement learning solves complex credit assignment challenges within the context window. They share enterprise success stories, showing how Agent RFT eliminates long-tail token loops and drives extreme efficiency.&lt;/p&gt; &lt;i&gt;By Wenjie Zi, Will Hang&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 03 Jul 2026 09:22:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/rft-openai-model/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Wenjie Zi, Will Hang</dc:creator>
      <dc:date>2026-07-03T09:22:00Z</dc:date>
      <dc:identifier>/presentations/rft-openai-model/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Enhancing Reliability Using Service-Level Prioritized Load Shedding at Netflix</title>
      <link>https://www.infoq.com/presentations/service-level-prioritized-load-shedding/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/service-level-prioritized-load-shedding/en/mediumimage/medium-1782221254342.jpg"/&gt;&lt;p&gt;The speakers discuss Netflix’s architecture for surviving extreme traffic spikes. They explain the mechanics of prioritized load shedding embedded in their Envoy sidecar proxy, allowing user-initiated requests to steal capacity from non-critical traffic. They share automated platform strategies for continuous chaos load testing, config generation, and retry storm mitigation.&lt;/p&gt; &lt;i&gt;By Anirudh Mendiratta, Benjamin Fedorka&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Platform Engineering</category>
      <category>Resilience</category>
      <category>QCon San Francisco 2025</category>
      <category>Architecture &amp; Design</category>
      <category>presentation</category>
      <pubDate>Thu, 02 Jul 2026 09:20:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/service-level-prioritized-load-shedding/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Anirudh Mendiratta, Benjamin Fedorka</dc:creator>
      <dc:date>2026-07-02T09:20:00Z</dc:date>
      <dc:identifier>/presentations/service-level-prioritized-load-shedding/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Graph RAG: Building Smarter Retrieval Workflows with Knowledge Graphs</title>
      <link>https://www.infoq.com/presentations/graph-rag-llm/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/graph-rag-llm/en/mediumimage/CassieShum-medium-1782291352027.jpeg"/&gt;&lt;p&gt;Cassie Shum discusses the architectural evolution of GraphRAG and why data foundations are critical for advanced AI workflows. She explains how traditional vector RAG falls short when addressing global context, multi-hop reasoning, and provenance. She shares enterprise strategies for building semantically structured knowledge graphs that shift raw orchestrating logic down to the data layer.&lt;/p&gt; &lt;i&gt;By Cassie Shum&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 01 Jul 2026 14:01:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/graph-rag-llm/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Cassie Shum</dc:creator>
      <dc:date>2026-07-01T14:01:00Z</dc:date>
      <dc:identifier>/presentations/graph-rag-llm/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Trustworthy Productivity: Securing AI-Accelerated Development</title>
      <link>https://www.infoq.com/presentations/ai-development/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-development/en/mediumimage/SriramMadapusiVasudevan-medium-1782220895596.jpg"/&gt;&lt;p&gt;Sriram Madapusi Vasudevan discusses industry-converging patterns for securing autonomous AI agents in production. He explains the critical vulnerabilities hidden inside the ReAct loop across context, reasoning, and tool execution. He shares how to mitigate risks like memory poisoning and rogue tool execution using defense-in-depth strategies, LLM-as-a-judge critics, and MAESTRO threat modeling.&lt;/p&gt; &lt;i&gt;By Sriram Madapusi Vasudevan&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Developer Experience</category>
      <category>Transcripts</category>
      <category>QCon San Francisco 2025</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 30 Jun 2026 14:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-development/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Sriram Madapusi Vasudevan</dc:creator>
      <dc:date>2026-06-30T14:35:00Z</dc:date>
      <dc:identifier>/presentations/ai-development/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Million PDFs: Building a Modern Document Infrastructure with Rust and Typst</title>
      <link>https://www.infoq.com/presentations/document-infrastructure-rust-typst/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/document-infrastructure-rust-typst/en/mediumimage/ErikSteiger-medium-1782220478687.jpg"/&gt;&lt;p&gt;Erik Steiger discusses the operational pain of legacy PDF generation in regulated banking and manufacturing. He explains how transitioning from resource-heavy engines like Puppeteer and LaTeX to a serverless Rust architecture powered by Typst can drop render latencies below 2ms. He shares how applying Git and Docker concepts to template registries ensures ironclad compliance and rapid debugging.&lt;/p&gt; &lt;i&gt;By Erik Steiger&lt;/i&gt;</description>
      <category>Performance &amp; Scalability</category>
      <category>Transcripts</category>
      <category>Rust</category>
      <category>InfoQ Dev Summit Munich 2025</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 29 Jun 2026 12:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/document-infrastructure-rust-typst/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Erik Steiger</dc:creator>
      <dc:date>2026-06-29T12:35:00Z</dc:date>
      <dc:identifier>/presentations/document-infrastructure-rust-typst/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Architectural Patterns: Moving Beyond Cloud-Native to Local-First - Insights from Adam Wiggins</title>
      <link>https://www.infoq.com/podcasts/natural-evolution-cloud-native/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/natural-evolution-cloud-native/en/smallimage/the-infoq-podcast-logo-thumbnail-1782207787976.jpg"/&gt;&lt;p&gt;In this episode, Heroku co-founder and Ink &amp; Switch founder Adam Wiggins argues for a 'local-first' architecture that reconciles cloud-based collaboration with the performance and data ownership of local software. He explores the role of CRDTs and version control primitives in non-code domains, and examines how a hybrid AI future might leverage local models for core productivity tasks.&lt;/p&gt; &lt;i&gt;By Adam Wiggins&lt;/i&gt;</description>
      <category>The InfoQ Podcast</category>
      <category>CRDT</category>
      <category>Cloud-Native</category>
      <category>Architecture</category>
      <category>Cloud</category>
      <category>Architecture &amp; Design</category>
      <category>podcast</category>
      <pubDate>Mon, 29 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/natural-evolution-cloud-native/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Adam Wiggins</dc:creator>
      <dc:date>2026-06-29T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/natural-evolution-cloud-native/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: AI Works, Pull Requests Don’t: How AI is Breaking the SDLC and What to Do about it</title>
      <link>https://www.infoq.com/presentations/ai-sdlc-pull-request/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-sdlc-pull-request/en/mediumimage/michael-webster-medium-1781688909041.jpeg"/&gt;&lt;p&gt;Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability.&lt;/p&gt; &lt;i&gt;By Michael Webster&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Software Development Lifecycle</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 26 Jun 2026 14:17:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-sdlc-pull-request/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Michael Webster</dc:creator>
      <dc:date>2026-06-26T14:17:00Z</dc:date>
      <dc:identifier>/presentations/ai-sdlc-pull-request/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Rust at the Core - Accelerating Polyglot SDK Development</title>
      <link>https://www.infoq.com/presentations/rust-polyglot-sdk/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</link>
      <description>&lt;img src="https://res.infoq.com/presentations/rust-polyglot-sdk/en/mediumimage/spencer-judge-medium-1781688097548.jpeg"/&gt;&lt;p&gt;Spencer Judge discusses the architectural pattern of building a shared core in Rust with language-specific layers on top. Drawing from his work on Temporal's SDKs, he shares lessons on navigating FFI boundaries, bridging async concepts, and managing memory safely. He explains the limitations of native extensions and how emerging tech like WebAssembly can streamline cross-language architecture.&lt;/p&gt; &lt;i&gt;By Spencer Judge&lt;/i&gt;</description>
      <category>SDK</category>
      <category>Transcripts</category>
      <category>Rust</category>
      <category>QCon San Francisco 2025</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 25 Jun 2026 10:23:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/rust-polyglot-sdk/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=InfoQ</guid>
      <dc:creator>Spencer Judge</dc:creator>
      <dc:date>2026-06-25T10:23:00Z</dc:date>
      <dc:identifier>/presentations/rust-polyglot-sdk/en</dc:identifier>
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