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    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <item>
      <title>Siri AI Developer Preview Lets iOS, iPadOS, and macOS Apps Integrate with Siri</title>
      <link>https://www.infoq.com/news/2026/11/apple-siri-ai-integration/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/apple-siri-ai-integration/en/headerimage/ios-27-virtualization-1794583601204.jpeg"/&gt;&lt;p&gt;Apple released the Siri AI Developer Preview in the 27.3 beta releases of its operating systems, enabling developers and apps to integrate with the assistant and hand off actions to it. During the Preview, access to Siri AI is restricted to developer builds and internal TestFlight testers.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Apple</category>
      <category>iOS</category>
      <category>visionOS</category>
      <category>watchOS</category>
      <category>MacOS</category>
      <category>Mobile</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 13 Nov 2026 16:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/apple-siri-ai-integration/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-11-13T16:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/apple-siri-ai-integration/en</dc:identifier>
    </item>
    <item>
      <title>Google Open Sources Ambient Quality Agent (AQuA) to Diagnose Production AI Agent Failures</title>
      <link>https://www.infoq.com/news/2026/11/google-ambient-agent-prod/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/google-ambient-agent-prod/en/headerimage/generatedHeaderImage-1792894383462.jpg"/&gt;&lt;p&gt;Google has open-sourced AQuA, a reference implementation that analyzes production AI agent sessions, verifies recurring failure patterns, and investigates root causes against deployed source snapshots. In a 32-session replay, developers reduced seat selection bypasses from 15 sessions to two and dietary preference omissions from seven to zero after prompt changes.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Quality</category>
      <category>Monitoring</category>
      <category>AI Architecture</category>
      <category>Google</category>
      <category>Observability</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 13 Nov 2026 13:53:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-ambient-agent-prod/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-11-13T13:53:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-ambient-agent-prod/en</dc:identifier>
    </item>
    <item>
      <title>Netflix Turns Operational Data Into a Knowledge Graph for AI-Driven Observability</title>
      <link>https://www.infoq.com/news/2026/11/netflix-ai-driven-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/netflix-ai-driven-observability/en/headerimage/generatedHeaderImage-1793693699423.jpg"/&gt;&lt;p&gt;Netflix is developing an ontology-driven knowledge graph to accelerate production investigations and provide AI agents with a shared understanding of its operational environment.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>AIOps</category>
      <category>Observability</category>
      <category>Netflix</category>
      <category>AI Coding</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 13 Nov 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/netflix-ai-driven-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-11-13T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/netflix-ai-driven-observability/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The Influence Toolkit</title>
      <link>https://www.infoq.com/presentations/bbc-software-architecture-influence/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/presentations/bbc-software-architecture-influence/en/mediumimage/henry-wilson-medium-1792653480613.jpg"/&gt;&lt;p&gt;Henry Wilson shares actionable tools from the BBC Sounds re-architecture to help senior engineering leaders drive technical strategy. He discusses building buy-in, navigating active resistors, using RFCs and ADRs, and balancing tradeoffs - showing that effective software architecture is fundamentally about communication, relationship-building, and organizational influence.&lt;/p&gt; &lt;i&gt;By Henry Wilson&lt;/i&gt;</description>
      <category>Architecture Decision Records</category>
      <category>Leadership</category>
      <category>Architecture</category>
      <category>Transcripts</category>
      <category>The BBC</category>
      <category>Staff Plus</category>
      <category>Platform Engineering</category>
      <category>Communication</category>
      <category>QCon London 2026</category>
      <category>Culture &amp; Methods</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 13 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/bbc-software-architecture-influence/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Henry Wilson</dc:creator>
      <dc:date>2026-11-13T11:00:00Z</dc:date>
      <dc:identifier>/presentations/bbc-software-architecture-influence/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Doing The Right Thing: Accountability In The Age Of AI Coding Summary</title>
      <link>https://www.infoq.com/podcasts/accountability-age-ai-coding-summary/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/accountability-age-ai-coding-summary/en/smallimage/engineering-culture-500-1794303925695.jpg"/&gt;&lt;p&gt;In this podcast, Shane Hastie, Lead Editor for Culture &amp; Methods, spoke to Hugo Venturini about who is accountable when AI writes the code, how AI is reshaping code review and traditional engineering roles, and how leaders build a culture of transparency and psychological safety.&lt;/p&gt; &lt;i&gt;By Hugo Venturini&lt;/i&gt;</description>
      <category>Quality</category>
      <category>Leadership</category>
      <category>Teamwork</category>
      <category>Safety</category>
      <category>Software Engineering</category>
      <category>Product Management</category>
      <category>Trust</category>
      <category>Culture</category>
      <category>Engineering Culture Podcast</category>
      <category>AI Coding</category>
      <category>Psychological Safety</category>
      <category>Culture &amp; Methods</category>
      <category>podcast</category>
      <pubDate>Fri, 13 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/accountability-age-ai-coding-summary/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Hugo Venturini</dc:creator>
      <dc:date>2026-11-13T09:00:00Z</dc:date>
      <dc:identifier>/podcasts/accountability-age-ai-coding-summary/en</dc:identifier>
    </item>
    <item>
      <title>Dropbox Shares How Its In-House Cookie Auditor Tests Consent Behaviour Across 200+ Web Surfaces</title>
      <link>https://www.infoq.com/news/2026/11/dropbox-cookie-auditor/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/dropbox-cookie-auditor/en/headerimage/generatedHeaderImage-1794331089979.jpg"/&gt;&lt;p&gt;Dropbox engineers described a Playwright-based auditor that checks cookie consent behavior across more than 200 web surfaces. It runs three tests per page: US visitor, EU visitor, and Global Privacy Control signal. Cookie rules are kept outside the code. A companion tool finds live URLs. Weekly reports separate likely violations from false positives.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>JavaScript</category>
      <category>Automated testing</category>
      <category>Web Development</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 13 Nov 2026 06:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/dropbox-cookie-auditor/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Daniel Curtis</dc:creator>
      <dc:date>2026-11-13T06:15:00Z</dc:date>
      <dc:identifier>/news/2026/11/dropbox-cookie-auditor/en</dc:identifier>
    </item>
    <item>
      <title>Technical Debt is Social; it is the Price of Learning by Doing</title>
      <link>https://www.infoq.com/news/2026/11/tech-debt-learning-doing/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/tech-debt-learning-doing/en/headerimage/header-1794404225270.jpg"/&gt;&lt;p&gt;Technical debt is not necessarily a sign of poor engineering. It can be the price of learning through software in use. As AI accelerates code production beyond our capacity to understand code and context, technical debt increasingly becomes a sociotechnical challenge.&lt;/p&gt; &lt;i&gt;By Ben Linders&lt;/i&gt;</description>
      <category>GOTO Conference</category>
      <category>Sociotechnical Architecture</category>
      <category>Technical Debt</category>
      <category>Artificial Intelligence</category>
      <category>learning</category>
      <category>Software Engineering</category>
      <category>Collaboration</category>
      <category>Agentic Development Lifecycle</category>
      <category>Culture &amp; Methods</category>
      <category>news</category>
      <pubDate>Thu, 12 Nov 2026 11:05:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/tech-debt-learning-doing/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Ben Linders</dc:creator>
      <dc:date>2026-11-12T11:05:00Z</dc:date>
      <dc:identifier>/news/2026/11/tech-debt-learning-doing/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Opening the Black Box: Critical Explainability for AI Agent Tool Selection with Kiji Inspector</title>
      <link>https://www.infoq.com/presentations/explainable-ai-llm-agent-decisions/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/presentations/explainable-ai-llm-agent-decisions/en/mediumimage/HannesHapke-medium-1793278895069.jpeg"/&gt;&lt;p&gt;Hannes Hapke shares how Dataiku’s open-source Kiji Inspector applies Sparse Autoencoders (SAEs) directly to decision tokens to expose why agents pick specific tools. Moving past superficial chain-of-thought narratives, he discusses causal ablation, layer activation extraction via patched vLLM engines, and production monitoring workflows for software architects and senior engineering leaders.&lt;/p&gt; &lt;i&gt;By Hannes Hapke&lt;/i&gt;</description>
      <category>AI Interpretability</category>
      <category>Governance</category>
      <category>QCon AI Boston 2026</category>
      <category>Responsibility</category>
      <category>AI Architecture</category>
      <category>Transcripts</category>
      <category>Explainable AI</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Thu, 12 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/explainable-ai-llm-agent-decisions/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Hannes Hapke</dc:creator>
      <dc:date>2026-11-12T11:00:00Z</dc:date>
      <dc:identifier>/presentations/explainable-ai-llm-agent-decisions/en</dc:identifier>
    </item>
    <item>
      <title>Azure Logic Apps Standard Gets a Local CLI in Preview</title>
      <link>https://www.infoq.com/news/2026/11/logic-apps-local-cli/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/logic-apps-local-cli/en/headerimage/generatedHeaderImage-1793791837422.jpg"/&gt;&lt;p&gt;Microsoft has released a public preview of the logicapp extension for the Azure CLI, adding local project creation, workflow authoring, a runtime lifecycle, connector management, run inspection, and test scaffolding for Logic Apps Standard, work that previously required the VS Code extension. The product team pitches the terminal surface as determinism for AI-assisted development.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>CLI</category>
      <category>Visual Studio Code</category>
      <category>Azure</category>
      <category>LogicApps</category>
      <category>Agents</category>
      <category>Low Code</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 12 Nov 2026 10:23:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/logic-apps-local-cli/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-11-12T10:23:00Z</dc:date>
      <dc:identifier>/news/2026/11/logic-apps-local-cli/en</dc:identifier>
    </item>
    <item>
      <title>Google Open-Sources TPU Sync for KV Cache and Weight Transfer on TPUs</title>
      <link>https://www.infoq.com/news/2026/11/google-tpu-sync/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/google-tpu-sync/en/headerimage/generatedHeaderImage-1794402814105.jpg"/&gt;&lt;p&gt;Google Cloud has open-sourced TPU Sync, a native C++ library that moves KV cache blocks and model weights between TPU HBM, host DRAM, shared storage, and the data center network.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Hardware</category>
      <category>Google Cloud</category>
      <category>Artificial Intelligence</category>
      <category>Python</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 12 Nov 2026 10:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-tpu-sync/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-11-12T10:06:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-tpu-sync/en</dc:identifier>
    </item>
    <item>
      <title>Author Q+A: finprim is a Case for One Financial Validation Library in TypeScript</title>
      <link>https://www.infoq.com/news/2026/11/finprim-validation-qanda/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/finprim-validation-qanda/en/headerimage/generatedHeaderImage-1793961374899.jpg"/&gt;&lt;p&gt;finprim is an MIT-licensed TypeScript library that offers IBAN, BIC, card, sort code, VAT, routing number and loan functions through one API with no runtime dependencies. Its author built it after duplicated validators drifted apart across web and mobile. It validates and formats data only, and does not process payments. He is cautious about adding SEPA and ACH file generation.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>TypeScript</category>
      <category>Validation</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 12 Nov 2026 09:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/finprim-validation-qanda/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Daniel Curtis</dc:creator>
      <dc:date>2026-11-12T09:28:00Z</dc:date>
      <dc:identifier>/news/2026/11/finprim-validation-qanda/en</dc:identifier>
    </item>
    <item>
      <title>LinkedIn Replaces Argo CD with a Custom GitOps Engine for Kubernetes Deployments</title>
      <link>https://www.infoq.com/news/2026/11/kubernetes-argocd-linkedin/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/kubernetes-argocd-linkedin/en/headerimage/generatedHeaderImage-1794414453878.jpg"/&gt;&lt;p&gt;In a recent post, LinkedIn revealed it has replaced Argo CD with its own GitOps engine. This new system deploys applications across over 250 Kubernetes clusters. It supports more than 4,000 services and manages over 100,000 deployments each week.&lt;/p&gt; &lt;i&gt;By Claudio Masolo&lt;/i&gt;</description>
      <category>Argo CD</category>
      <category>Continuous Deployment</category>
      <category>Kubernetes</category>
      <category>LinkedIn</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 12 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/kubernetes-argocd-linkedin/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Claudio Masolo</dc:creator>
      <dc:date>2026-11-12T09:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/kubernetes-argocd-linkedin/en</dc:identifier>
    </item>
    <item>
      <title>GitHub Makes Agentic Workflows Generally Available with Isolation and Budget Controls</title>
      <link>https://www.infoq.com/news/2026/11/github-agentic-workflows-ga/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/github-agentic-workflows-ga/en/headerimage/header-1793990975266.jpeg"/&gt;&lt;p&gt;GitHub Agentic Workflows is generally available, running coding agents in GitHub Actions with read-only defaults and separately scoped write jobs. Those protections do not cover direct write permissions or custom jobs, so they depend on how each workflow is configured.&lt;/p&gt; &lt;i&gt;By Mark Silvester&lt;/i&gt;</description>
      <category>AI coding agents</category>
      <category>github</category>
      <category>GitHub Actions</category>
      <category>Agents</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Wed, 11 Nov 2026 17:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/github-agentic-workflows-ga/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Mark Silvester</dc:creator>
      <dc:date>2026-11-11T17:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/github-agentic-workflows-ga/en</dc:identifier>
    </item>
    <item>
      <title>JioHotstar Benchmarks Knowledge Bases for AI Coding Agents</title>
      <link>https://www.infoq.com/news/2026/11/jiohotstar-codingagent-retrieval/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;JioHotstar evaluated 6 approaches to code retrieval for AI coding agents using SWE, CodeScale Bench, and an internal question-answering harness. Structural search performed well on concrete tasks, while semantic retrieval helped with ambiguous requirements. The findings highlight trade-offs in accuracy, token use, and tool design, underscoring the need to match retrieval tools to task types.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>AI coding agents</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Architecture</category>
      <category>Search</category>
      <category>Benchmark</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Software Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 11 Nov 2026 14:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/jiohotstar-codingagent-retrieval/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-11-11T14:28:00Z</dc:date>
      <dc:identifier>/news/2026/11/jiohotstar-codingagent-retrieval/en</dc:identifier>
    </item>
    <item>
      <title>How LinkedIn, Netflix &amp; DoorDash Run Agents, Evals &amp; Inference in Production: QCon AI New York 2026</title>
      <link>https://www.infoq.com/news/2026/11/qcon-ai-newyork-2026-program/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/11/qcon-ai-newyork-2026-program/en/headerimage/qcon-ai-new-york-2026-1794387431153.jpg"/&gt;&lt;p&gt;QCon AI New York has published its full program for December 15–16, 2026. Engineers from LinkedIn, Netflix, DoorDash, Figma, Pinterest, Honeycomb, Harvey, Google DeepMind and OpenAI describe AI systems they run in production. Talks cover agent permissions and guardrails, eval loops, inference cost, AI code review, and team adoption.&lt;/p&gt; &lt;i&gt;By Artenisa Chatziou&lt;/i&gt;</description>
      <category>QCon AI New York 2026</category>
      <category>AI coding agents</category>
      <category>QCon Software Development Conference</category>
      <category>Artificial Intelligence</category>
      <category>Security</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Software Engineering</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 11 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/qcon-ai-newyork-2026-program/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Artenisa Chatziou</dc:creator>
      <dc:date>2026-11-11T11:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/qcon-ai-newyork-2026-program/en</dc:identifier>
    </item>
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