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    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <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>
    <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>Queue</category>
      <category>Asynchronous Architecture</category>
      <category>Transcripts</category>
      <category>Reliability</category>
      <category>Observability</category>
      <category>Distributed Tracing</category>
      <category>OpenTelemetry</category>
      <category>Service Level Objectives</category>
      <category>QCon London 2026</category>
      <category>Culture &amp; Methods</category>
      <category>Architecture &amp; Design</category>
      <category>Development</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>iOS</category>
      <category>Debugging</category>
      <category>Concurrency</category>
      <category>Mobile</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>Web Development</category>
      <category>Rust</category>
      <category>TypeScript</category>
      <category>AngularJS</category>
      <category>Angular</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>Cloud</category>
      <category>Cloudflare</category>
      <category>SQL</category>
      <category>Data Analytics</category>
      <category>Apache Iceberg</category>
      <category>Data Portability</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>AI coding agents</category>
      <category>Technical Debt</category>
      <category>Google</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 Generation</category>
      <category>Open Source</category>
      <category>Profilers</category>
      <category>Code Profiler</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>
    </item>
    <item>
      <title>Presentation: Batch Intelligence at Scale: Cost-Efficient Multi-Agent LLM Workflows with Built-In Resilience</title>
      <link>https://www.infoq.com/presentations/scaling-multi-agent-llm-batch/?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/scaling-multi-agent-llm-batch/en/mediumimage/AdityaMulik-medium-1793278766506.jpeg"/&gt;&lt;p&gt;Aditya Mulik explains how Walmart Global Tech deployed multi-agent LLM workflows across 4,700 stores for nightly batch inventory forecasting. He discusses tackling production hurdles - schema violations, cross-department data mismatches, and network drops - via MCP grounding and loop agents, while slashing cloud inference costs from $100k+ to $10k per month shifting from PTUs to pay-as-you-go.&lt;/p&gt; &lt;i&gt;By Aditya Mulik&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>Resilience</category>
      <category>QCon AI Boston 2026</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Transcripts</category>
      <category>Batch Processing</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI Cost Optimisation</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 10 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/scaling-multi-agent-llm-batch/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Aditya Mulik</dc:creator>
      <dc:date>2026-11-10T11:00:00Z</dc:date>
      <dc:identifier>/presentations/scaling-multi-agent-llm-batch/en</dc:identifier>
    </item>
    <item>
      <title>Article: LLM Reliability Layers Under Test: Accuracy, Yield, and the Limits of Verification</title>
      <link>https://www.infoq.com/articles/llm-reliability-layers-limits-accuracy/?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/llm-reliability-layers-limits-accuracy/en/headerimage/llm-reliability-layers-limits-accuracy-header-1793873839325.jpg"/&gt;&lt;p&gt;Verification layers sort outputs by trust rather than fixing errors. In a benchmark of six reliability layers, voting and self-correction failed to recover accuracy - they only withheld doubtful records. Production systems gain more reliability by replacing generation with deterministic lookups, tracking yield over raw accuracy, and resolving extraction bottlenecks before stacking checks.&lt;/p&gt; &lt;i&gt;By Pushpdeep Mishra, Wejdan Bagais&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>AI Architecture</category>
      <category>Reliability</category>
      <category>InfoQ Certification Program</category>
      <category>Verification</category>
      <category>Software Engineering</category>
      <category>AI Engineering Certification</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Tue, 10 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/llm-reliability-layers-limits-accuracy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Pushpdeep Mishra, Wejdan Bagais</dc:creator>
      <dc:date>2026-11-10T09:00:00Z</dc:date>
      <dc:identifier>/articles/llm-reliability-layers-limits-accuracy/en</dc:identifier>
    </item>
    <item>
      <title>Google Cloud Spend Caps Reach General Availability with API and Monthly Resets</title>
      <link>https://www.infoq.com/news/2026/11/google-cloud-spend-caps/?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-cloud-spend-caps/en/headerimage/generatedHeaderImage-1793609514337.jpg"/&gt;&lt;p&gt;Google Cloud has made spend caps on Cloud Billing Budgets generally available, pausing new usage of a service in a project once a monthly budget is hit, without deleting resources. GA adds an API, estimated live costs, and automatic monthly resets, but caps cover only the Gemini API, the Agent Platform, Cloud Run, and Cloud Run functions, and overages during enforcement delays are still billed.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Google Cloud Platform</category>
      <category>Cost Optimization</category>
      <category>Google Cloud</category>
      <category>Cloud Computing</category>
      <category>FinOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 08:39:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-cloud-spend-caps/?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>2026-11-10T08:39:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-cloud-spend-caps/en</dc:identifier>
    </item>
    <item>
      <title>Effect 4.0 Ships: Dependency-Free Rewrite with 5x Smaller Bundles and Long-Term Support</title>
      <link>https://www.infoq.com/news/2026/11/effect-4-0-release/?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/effect-4-0-release/en/headerimage/generatedHeaderImage-1794245718049.jpg"/&gt;&lt;p&gt;Effect 4.0, a TypeScript framework for typed errors and structured concurrency, was released on 30 September 2026 with a rewritten fiber runtime. The team reports a bundle size drop from 35.6 kB to 7.1 kB and about 6.4x higher throughput, though these figures are not independently verified. It also adds long-term support through September 2029.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>JavaScript</category>
      <category>Web Development</category>
      <category>TypeScript</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 06:33:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/effect-4-0-release/?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>2026-11-10T06:33:00Z</dc:date>
      <dc:identifier>/news/2026/11/effect-4-0-release/en</dc:identifier>
    </item>
    <item>
      <title>Java News Roundup: TornadoVM, A2A Java, Open Liberty, Open J Proxy, Gradle, EclipseStore</title>
      <link>https://www.infoq.com/news/2026/11/java-news-roundup-nov02-2026/?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/java-news-roundup-nov02-2026/en/headerimage/java-news-roundup-image-1794249482778.jpg"/&gt;&lt;p&gt;This week's Java roundup for November 2nd, 2026, features news highlighting: point releases of TornadoVM, TornadoInsight,  A2A for Java, Gradle and EclipseStore; the November 2026 edition of Open Liberty; the third release candidate of Open J Proxy; the second release candidates of Micrometer Metrics and Micrometer Tracing; and the third beta release of Quarkus 4.0.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>TornadoVM</category>
      <category>JReleaser</category>
      <category>EclipseStore</category>
      <category>Agent2Agent</category>
      <category>Gradle</category>
      <category>Micrometer</category>
      <category>Java</category>
      <category>Quarkus</category>
      <category>Open Liberty</category>
      <category>JDK 28</category>
      <category>Open J Proxy</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 18:40:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/java-news-roundup-nov02-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Michael Redlich</dc:creator>
      <dc:date>2026-11-09T18:40:00Z</dc:date>
      <dc:identifier>/news/2026/11/java-news-roundup-nov02-2026/en</dc:identifier>
    </item>
    <item>
      <title>Google Releases Gemini 4 Argon for Robotics Control, Simulation, and Policy Improvement</title>
      <link>https://www.infoq.com/news/2026/11/google-gemini-argon/?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-gemini-argon/en/headerimage/generatedHeaderImage-1794224974378.jpg"/&gt;&lt;p&gt;Google has released Gemini 4 Argon framing it as a general-purpose physical-reasoning model for robot control, Real2Sim scene construction, multimodal labeling of robot video, and iterative policy improvement, rather than as a single end-effector policy.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Robotics</category>
      <category>Artificial Intelligence</category>
      <category>Google</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 15:44:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-gemini-argon/?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>2026-11-09T15:44:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-gemini-argon/en</dc:identifier>
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