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    <title>InfoQ - Presentations</title>
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    <description>InfoQ Presentations feed</description>
    <item>
      <title>Presentation: Beyond Observability: Evolving Production Operations in the Age of AI</title>
      <link>https://www.infoq.com/presentations/ai-production-operations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-production-operations/en/mediumimage/infoq-live-medium-1790856304338.jpg"/&gt;&lt;p&gt;The panelists discuss how production operations are evolving with AI, turning operational data into actionable insights for incident response. They explain how automation and new architectural practices help engineering teams build more understandable systems, while exploring how AI reshapes software delivery and changes the historically deterministic nature of production applications.&lt;/p&gt; &lt;i&gt;By Michael Hausenblas, Sujana Sooreddy, Noam Levi, Renato Losio&lt;/i&gt;</description>
      <category>AIOps</category>
      <category>Incident Response</category>
      <category>Transcripts</category>
      <category>Automation</category>
      <category>Architecture</category>
      <category>InfoQ Live</category>
      <category>InfoQ Live - September 2026</category>
      <category>Observability</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 01 Oct 2026 12:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-production-operations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Michael Hausenblas, Sujana Sooreddy, Noam Levi, Renato Losio</dc:creator>
      <dc:date>2026-10-01T12:30:00Z</dc:date>
      <dc:identifier>/presentations/ai-production-operations/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Keeping the Mainline Green Across Diverse Language Monorepos</title>
      <link>https://www.infoq.com/presentations/mergequeue/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/mergequeue/en/mediumimage/dhruva-juloori-medium-1790247259869.jpeg"/&gt;&lt;p&gt;Dhruva Juloori discusses how Uber maintains green mainlines across massive monorepos handling 65,000+ monthly changes. He explains how SubmitQueue uses binary speculation trees, conflict analysis, and machine learning models to predict build success and execution times. Dhruva shares how bypassing large diffs slashed CI resource usage by 53% while accelerating PR landing times by 37%.&lt;/p&gt; &lt;i&gt;By Dhruva Juloori&lt;/i&gt;</description>
      <category>QCon San Francisco 2025</category>
      <category>Transcripts</category>
      <category>Uber</category>
      <category>Continuous Integration</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 01 Oct 2026 09:33:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/mergequeue/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Dhruva Juloori</dc:creator>
      <dc:date>2026-10-01T09:33:00Z</dc:date>
      <dc:identifier>/presentations/mergequeue/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Context Is the New Code</title>
      <link>https://www.infoq.com/presentations/context-as-code-devops-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/context-as-code-devops-agents/en/mediumimage/patrick-medium-1789632704652.jpeg"/&gt;&lt;p&gt;Patrick Debois discusses how to manage, evaluate, distribute, and observe context using proven software engineering practices. He shares how treating context like code - complete with testing, CI/CD, package managers, and security scanning - enables engineering leaders to reliably scale AI coding agents, maintain control over non-deterministic outputs, and build long-term organizational knowledge.&lt;/p&gt; &lt;i&gt;By Patrick Debois&lt;/i&gt;</description>
      <category>Performance Evaluation</category>
      <category>Monitoring</category>
      <category>Artificial Intelligence</category>
      <category>Testing</category>
      <category>Code Quality</category>
      <category>Transcripts</category>
      <category>Software Development Lifecycle</category>
      <category>QCon London 2026</category>
      <category>Agents</category>
      <category>Culture &amp; Methods</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>presentation</category>
      <pubDate>Wed, 30 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/context-as-code-devops-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Patrick Debois</dc:creator>
      <dc:date>2026-09-30T11:00:00Z</dc:date>
      <dc:identifier>/presentations/context-as-code-devops-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From Staff Platform Engineer to a16z Founder: What I Wish I'd Known</title>
      <link>https://www.infoq.com/presentations/staff-founder-lessons/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/staff-founder-lessons/en/mediumimage/GonzaloGloMaldonado-medium-1790247060435.jpg"/&gt;&lt;p&gt;Gonzalo Maldonado explains how technical leaders can translate engineering skills into startup success using their "VC Abstraction Layer Knowledge" (VALK). They share hard-won lessons on finding product-market fit, structuring pitches like technical docs, avoiding co-founder drama, and evaluating internal platforms as commercial ventures in a post-ZIRP environment.&lt;/p&gt; &lt;i&gt;By Gonzalo Maldonado&lt;/i&gt;</description>
      <category>Best Practices</category>
      <category>QCon San Francisco 2025</category>
      <category>Transcripts</category>
      <category>Staff Plus</category>
      <category>Culture &amp; Methods</category>
      <category>presentation</category>
      <pubDate>Tue, 29 Sep 2026 09:14:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/staff-founder-lessons/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Gonzalo Maldonado</dc:creator>
      <dc:date>2026-09-29T09:14:00Z</dc:date>
      <dc:identifier>/presentations/staff-founder-lessons/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From Consumers to Builders: Turning 200 of our Team into Agent Creators in 2 Weeks</title>
      <link>https://www.infoq.com/presentations/building-internal-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/building-internal-ai-agents/en/mediumimage/medium-1790246283432.jpeg"/&gt;&lt;p&gt;Ben Maraney shares how Forter demystified AI agent creation for technical and non-technical staff. He discusses leveraging custom MCP servers, combining no-code and code-based platforms, sidestepping complex RAG setups, and aligning security and legal teams to accelerate internal agent adoption across R&amp;D.&lt;/p&gt; &lt;i&gt;By Ben Maraney&lt;/i&gt;</description>
      <category>Model Context Protocol (MCP)</category>
      <category>Productivity</category>
      <category>QCon AI Boston 2026</category>
      <category>Large language models</category>
      <category>Compliance</category>
      <category>AI Security</category>
      <category>Transcripts</category>
      <category>Agents</category>
      <category>Governance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 28 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/building-internal-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Ben Maraney</dc:creator>
      <dc:date>2026-09-28T11:00:00Z</dc:date>
      <dc:identifier>/presentations/building-internal-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Adaptive Recommenders in the Real World: Inference, Evals, and System Design</title>
      <link>https://www.infoq.com/presentations/adaptive-recommendation-systems-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/adaptive-recommendation-systems-architecture/en/mediumimage/mallika-medium-1789632229433.jpeg"/&gt;&lt;p&gt;Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in production under real-world operational constraints like latency, cost, and observability.&lt;/p&gt; &lt;i&gt;By Mallika Rao&lt;/i&gt;</description>
      <category>Scaling</category>
      <category>Machine Learning</category>
      <category>QCon AI Boston 2026</category>
      <category>Transcripts</category>
      <category>Data</category>
      <category>Observability</category>
      <category>Performance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Sat, 26 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/adaptive-recommendation-systems-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Mallika Rao</dc:creator>
      <dc:date>2026-09-26T11:00:00Z</dc:date>
      <dc:identifier>/presentations/adaptive-recommendation-systems-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Spritely: Infrastructure for the Future of the Internet</title>
      <link>https://www.infoq.com/presentations/spritely-decentralized-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/spritely-decentralized-architecture/en/mediumimage/cd-medium-1789633126216.jpg"/&gt;&lt;p&gt;Christine Lemmer-Webber and David Thompson explain how Spritely's architecture solves core decentralization challenges. They share insights on capability-based access control with Goblins, actor-model communication via OCapN, and petname systems. Discover how Spritely leverages Scheme, WebAssembly (Hoot), and local-first CRDTs to build secure, peer-to-peer applications without central servers.&lt;/p&gt; &lt;i&gt;By Christine Lemmer-Webber, David Thompson&lt;/i&gt;</description>
      <category>WebAssembly</category>
      <category>Local First</category>
      <category>Actor Model</category>
      <category>Security</category>
      <category>Transcripts</category>
      <category>QCon London 2026</category>
      <category>CRDT</category>
      <category>P2P</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 25 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/spritely-decentralized-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Christine Lemmer-Webber, David Thompson</dc:creator>
      <dc:date>2026-09-25T11:00:00Z</dc:date>
      <dc:identifier>/presentations/spritely-decentralized-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Designing Fast, Delightful UX with LLMs for Mobile Frontends</title>
      <link>https://www.infoq.com/presentations/llm-mobile-frontend/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/llm-mobile-frontend/en/mediumimage/bala-medium-1789631604448.jpeg"/&gt;&lt;p&gt;Balakrishnan Ramdoss discusses how to architect production-grade, AI-powered conversational apps at scale. He explains how to overcome model latency, leverage server-driven UI and Backend-for-Frontend patterns to dynamically render multi-modal interfaces, optimize prompts for UI selection, and integrate low-latency, privacy-first on-device AI for mobile applications.&lt;/p&gt; &lt;i&gt;By Balakrishnan Ramdoss&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Large language models</category>
      <category>QCon San Francisco 2025</category>
      <category>Transcripts</category>
      <category>Mobile</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 24 Sep 2026 09:24:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/llm-mobile-frontend/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Balakrishnan Ramdoss</dc:creator>
      <dc:date>2026-09-24T09:24:00Z</dc:date>
      <dc:identifier>/presentations/llm-mobile-frontend/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: APIs for Agents: Rethinking API Programs in the MCP Era</title>
      <link>https://www.infoq.com/presentations/mcp-calm-api-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/mcp-calm-api-architecture/en/mediumimage/jim-gough-andreea-niculcea-medium-1789021410351.jpg"/&gt;&lt;p&gt;Jim Gough and Andreea Niculcea explain how Morgan Stanley uses Architecture as Code with CALM to modernize its API program. They demonstrate integrating Model Context Protocol (MCP) and Agent-to-Agent (A2A) communications, enforcing automated governance through deployment gates, and executing zero-downtime platform upgrades to safely scale enterprise AI and agentic workflows.&lt;/p&gt; &lt;i&gt;By Jim Gough, Andreea Niculcea&lt;/i&gt;</description>
      <category>Enterprise Architecture</category>
      <category>Deployment</category>
      <category>Agent2Agent</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Compliance</category>
      <category>Transcripts</category>
      <category>API</category>
      <category>QCon London 2026</category>
      <category>Platform Engineering</category>
      <category>Governance</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Wed, 23 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/mcp-calm-api-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Jim Gough, Andreea Niculcea</dc:creator>
      <dc:date>2026-09-23T11:00:00Z</dc:date>
      <dc:identifier>/presentations/mcp-calm-api-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Maximizing Success with Limited Time, Resources, and Energy: Lessons from Startup Engineering</title>
      <link>https://www.infoq.com/presentations/lessons-startup-engineering/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/lessons-startup-engineering/en/mediumimage/david-medium-1789631290867.jpeg"/&gt;&lt;p&gt;David Gudeman discusses battle-tested architectural patterns for resource-constrained engineering teams. Drawing from over a decade of startup experience, he explains how pairing GCP, Firebase, and Cloud Run accelerates product-market fit. Learn how to eliminate redundant frontend state, structure event-driven backends, and maintain lean DevOps while building for long-term scalability.&lt;/p&gt; &lt;i&gt;By David Gudeman&lt;/i&gt;</description>
      <category>Best Practices</category>
      <category>QCon San Francisco 2025</category>
      <category>Transcripts</category>
      <category>Startup</category>
      <category>Culture &amp; Methods</category>
      <category>presentation</category>
      <pubDate>Tue, 22 Sep 2026 09:04:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/lessons-startup-engineering/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>David Gudeman</dc:creator>
      <dc:date>2026-09-22T09:04:00Z</dc:date>
      <dc:identifier>/presentations/lessons-startup-engineering/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents</title>
      <link>https://www.infoq.com/presentations/ai-agent-harness/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-agent-harness/en/mediumimage/vino-medium-1789631870270.jpeg"/&gt;&lt;p&gt;OpenAI’s Vinoth Govindarajan discusses why production AI agents fail beyond model hallucination. Using real-world case studies like OpenClaw, he explains the key principles of reliable agent harnesses: establishing explicit state ownership, serializing concurrent state mutations, scoping execution authority, and validating actions at the user-visible edge.&lt;/p&gt; &lt;i&gt;By Vinoth Govindarajan&lt;/i&gt;</description>
      <category>AI Harness</category>
      <category>QCon AI Boston 2026</category>
      <category>Large language models</category>
      <category>Transcripts</category>
      <category>OpenAI</category>
      <category>Architecture</category>
      <category>Distributed Systems</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 21 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-agent-harness/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=presentations</guid>
      <dc:creator>Vinoth Govindarajan</dc:creator>
      <dc:date>2026-09-21T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-agent-harness/en</dc:identifier>
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