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    <title>InfoQ</title>
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    <description>InfoQ feed</description>
    <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>Technical Debt</category>
      <category>GOTO Conference</category>
      <category>Sociotechnical Architecture</category>
      <category>Artificial Intelligence</category>
      <category>Software Engineering</category>
      <category>Collaboration</category>
      <category>Agentic Development Lifecycle</category>
      <category>learning</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>Transcripts</category>
      <category>AI Interpretability</category>
      <category>Responsibility</category>
      <category>Agents</category>
      <category>Governance</category>
      <category>QCon AI Boston 2026</category>
      <category>AI Architecture</category>
      <category>Large language models</category>
      <category>Explainable AI</category>
      <category>Development</category>
      <category>Architecture &amp; Design</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>Azure</category>
      <category>Agents</category>
      <category>CLI</category>
      <category>Visual Studio Code</category>
      <category>Low Code</category>
      <category>Cloud</category>
      <category>LogicApps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</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>Python</category>
      <category>Artificial Intelligence</category>
      <category>Google Cloud</category>
      <category>Hardware</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>Agents</category>
      <category>github</category>
      <category>AI coding agents</category>
      <category>GitHub Actions</category>
      <category>Development</category>
      <category>Architecture &amp; Design</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>Architecture</category>
      <category>Model Context Protocol (MCP)</category>
      <category>AI coding agents</category>
      <category>Software Engineering</category>
      <category>Search</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Benchmark</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>Security</category>
      <category>QCon AI New York 2026</category>
      <category>Artificial Intelligence</category>
      <category>AI coding agents</category>
      <category>Software Engineering</category>
      <category>QCon Software Development Conference</category>
      <category>Retrieval-Augmented Generation</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>
    <item>
      <title>Presentation: Uncorking Queueing Bottlenecks with OpenTelemetry</title>
      <link>https://www.infoq.com/presentations/opentelemetry-queue-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/presentations/opentelemetry-queue-observability/en/mediumimage/julian-oli-medium-1792652839179.jpg"/&gt;&lt;p&gt;Oli Lane and Julian Wreford share how Gearset replaced guesswork in distributed asynchronous queues with OpenTelemetry tracing. They explain why traditional metrics fall short, how to implement user-centric SLOs based on latency distributions, and share practical debugging tips to help senior developers, software architects, and engineering leaders improve system reliability and team culture.&lt;/p&gt; &lt;i&gt;By Julian Wreford, Oli Lane&lt;/i&gt;</description>
      <category>Observability</category>
      <category>Distributed Tracing</category>
      <category>Transcripts</category>
      <category>Asynchronous Architecture</category>
      <category>Queue</category>
      <category>Reliability</category>
      <category>Service Level Objectives</category>
      <category>QCon London 2026</category>
      <category>OpenTelemetry</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>presentation</category>
      <pubDate>Wed, 11 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/opentelemetry-queue-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Julian Wreford, Oli Lane</dc:creator>
      <dc:date>2026-11-11T11:00:00Z</dc:date>
      <dc:identifier>/presentations/opentelemetry-queue-observability/en</dc:identifier>
    </item>
    <item>
      <title>Article: The iOS Crash We Could Not Reproduce</title>
      <link>https://www.infoq.com/articles/ios-crash-sigbus-debugging-experiment/?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/articles/ios-crash-sigbus-debugging-experiment/en/headerimage/ios-crash-sigbus-debugging-experiment-header-1793282827397.jpg"/&gt;&lt;p&gt;This article describes the investigation to find the root cause of a production crash in a large-scale iOS consumer app that had no clean stack trace. The crash occurred intermittently and resisted every local reproduction attempt. The final code change was small, but identifying the root cause required turning a vague SIGBUS report into a controlled set of experiments.&lt;/p&gt; &lt;i&gt;By Abhishek Gang Deb&lt;/i&gt;</description>
      <category>Mobile</category>
      <category>Debugging</category>
      <category>Concurrency</category>
      <category>iOS</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Wed, 11 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/ios-crash-sigbus-debugging-experiment/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Abhishek Gang Deb</dc:creator>
      <dc:date>2026-11-11T09:00:00Z</dc:date>
      <dc:identifier>/articles/ios-crash-sigbus-debugging-experiment/en</dc:identifier>
    </item>
    <item>
      <title>Angular's TypeScript 7-Powered Compiler Enters Beta with Rust-Based Preprocessor</title>
      <link>https://www.infoq.com/news/2026/11/angular-compiler-rust-typescript/?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/angular-compiler-rust-typescript/en/headerimage/generatedHeaderImage-1794323310151.jpg"/&gt;&lt;p&gt;The Angular team has introduced ngp, a compiler preprocessor that rewrites the analysis stage in Rust using Oxc. It replaces ngc's dependence on the Transformer API, which TypeScript 7.0 removed. An early benchmark showed 2x to 3x faster cold builds. Adoption is expected in an Angular 22 minor release; production builds should stay on ngc until then.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>AngularJS</category>
      <category>Angular</category>
      <category>Web Development</category>
      <category>Rust</category>
      <category>TypeScript</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 11 Nov 2026 07:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/angular-compiler-rust-typescript/?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>2026-11-11T07:06:00Z</dc:date>
      <dc:identifier>/news/2026/11/angular-compiler-rust-typescript/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Makes Basin Serverless Analytics Generally Available</title>
      <link>https://www.infoq.com/news/2026/11/cloudflare-basin-analytics/?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/cloudflare-basin-analytics/en/headerimage/generatedHeaderImage-1792696674786.jpg"/&gt;&lt;p&gt;Cloudflare has announced the general availability of Basin, a serverless analytics platform built on Apache Iceberg and R2 Object Storage. The managed service supports open data formats and integrates with tools such as DuckDB, Snowflake, and Apache Spark, with no data egress fees.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Data Analytics</category>
      <category>Data Portability</category>
      <category>SQL</category>
      <category>Apache Iceberg</category>
      <category>Cloudflare</category>
      <category>Cloud</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 19:14:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/cloudflare-basin-analytics/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-11-10T19:14:00Z</dc:date>
      <dc:identifier>/news/2026/11/cloudflare-basin-analytics/en</dc:identifier>
    </item>
    <item>
      <title>Google Finds That Technical Debt Can Derail Coding Agents through "Agent Distraction"</title>
      <link>https://www.infoq.com/news/2026/11/google-agent-distraction/?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-agent-distraction/en/headerimage/google-agetn-distraction-1794330388187.jpeg"/&gt;&lt;p&gt;In a recent article, Google senior staff software engineer Sam Saccone and senior staff data scientist Madhuri Garikapati highlighted the cost of agent distraction, where AI coding agents get derailed by stale technical debt, such as old TODO comments, obsolete bug references, and outdated specs, leading them to embark on unrequested "side-quests".&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Technical Debt</category>
      <category>Google</category>
      <category>AI coding agents</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 18:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-agent-distraction/?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>2026-11-10T18:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-agent-distraction/en</dc:identifier>
    </item>
    <item>
      <title>Pinterest and Intel Take Open-Source Profiling to Internet Scale</title>
      <link>https://www.infoq.com/news/2026/11/pinterest-intel-profiling/?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/pinterest-intel-profiling/en/headerimage/generatedHeaderImage-1793692423906.jpg"/&gt;&lt;p&gt;Pinterest has partnered with Intel to extend gProfiler, an open-source continuous profiler that provides production performance visibility across an internet-scale fleet of infrastructure.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Code Profiler</category>
      <category>Profilers</category>
      <category>Open Source</category>
      <category>Code Generation</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/pinterest-intel-profiling/?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>2026-11-10T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/pinterest-intel-profiling/en</dc:identifier>
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