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    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <item>
      <title>Presentation: Accelerating Netflix Data: A Cross-Team Journey from Offline to Online</title>
      <link>https://www.infoq.com/presentations/netflix-data-offline-online/?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/netflix-data-offline-online/en/mediumimage/rajasekhar-ummadisetty-ken-kurzweil-medium-1782819781543.jpg"/&gt;&lt;p&gt;Raj Ummadisetty and Ken Kurzweil share Netflix's architectural pivot to CloudStream, a repeatable capture, conversion, and deployment framework. They discuss shifting key-value abstractions from stateless to stateful to move terabytes of bulk data safely. Software architects will learn to exploit data access patterns, use "Pathfinder" prototypes, and maintain a 99% faster rollout.&lt;/p&gt; &lt;i&gt;By Rajasekhar Ummadisetty, Ken Kurzweil&lt;/i&gt;</description>
      <category>QCon San Francisco 2025</category>
      <category>Offline-First</category>
      <category>Case Study</category>
      <category>Transcripts</category>
      <category>Data</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Thu, 09 Jul 2026 15:20:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/netflix-data-offline-online/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Rajasekhar Ummadisetty, Ken Kurzweil</dc:creator>
      <dc:date>2026-07-09T15:20:00Z</dc:date>
      <dc:identifier>/presentations/netflix-data-offline-online/en</dc:identifier>
    </item>
    <item>
      <title>How Open Source Enables Collaboration in Creating a Platform</title>
      <link>https://www.infoq.com/news/2026/07/open-source-platform/?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/2026/07/open-source-platform/en/headerimage/header-1783344860328.jpg"/&gt;&lt;p&gt;A platform is a collaboration system: platform teams depend on application teams, and both need shared standards. Engineers trust a platform through its predictable behavior, not its features.  Being an engineer is about problem-solving and being passionate about it. And being an engineer means sharing your passion for problem-solving.&lt;/p&gt; &lt;i&gt;By Ben Linders&lt;/i&gt;</description>
      <category>Kubernetes</category>
      <category>Collaboration</category>
      <category>Platform Engineering</category>
      <category>Banking</category>
      <category>Open Source</category>
      <category>Platforms</category>
      <category>Social Skills</category>
      <category>Standardization</category>
      <category>Culture &amp; Methods</category>
      <category>news</category>
      <pubDate>Thu, 09 Jul 2026 11:53:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/open-source-platform/?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>2026-07-09T11:53:00Z</dc:date>
      <dc:identifier>/news/2026/07/open-source-platform/en</dc:identifier>
    </item>
    <item>
      <title>OpenAI Fixes 18-Year-Old GNU libunwind Bug by Treating Crash Debugging Like Epidemiology</title>
      <link>https://www.infoq.com/news/2026/07/openai-libunwind-core-dumps/?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/2026/07/openai-libunwind-core-dumps/en/headerimage/generatedHeaderImage-1783451097516.jpg"/&gt;&lt;p&gt;OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Bugs and Hotfixes</category>
      <category>Open Source Project Releases</category>
      <category>OpenAI</category>
      <category>Site Reliability Engineering</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 09 Jul 2026 10:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/openai-libunwind-core-dumps/?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>2026-07-09T10:15:00Z</dc:date>
      <dc:identifier>/news/2026/07/openai-libunwind-core-dumps/en</dc:identifier>
    </item>
    <item>
      <title>Article: Beat-Aligned Mobile Audio Streaming with Virtual Chunks and Native Playback</title>
      <link>https://www.infoq.com/articles/android-beat-aligned-mobile-audio-streaming/?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/android-beat-aligned-mobile-audio-streaming/en/headerimage/android-beat-aligned-mobile-audio-streaming-header-1783408153943.jpg"/&gt;&lt;p&gt;In this article, I describe the challenges and the design of a React Native real-time mobile beat-aligned playback system for iOS and Android. The system combines personalization with low-latency, and seamless navigation and was the result of careful analysis and experimentation to address strict mobile and network constraints as well as meet user expectations.&lt;/p&gt; &lt;i&gt;By Vladyslav Melnychenko&lt;/i&gt;</description>
      <category>Real Time</category>
      <category>Android</category>
      <category>React Native</category>
      <category>iOS</category>
      <category>Mobile</category>
      <category>C++</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Thu, 09 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/android-beat-aligned-mobile-audio-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Vladyslav Melnychenko</dc:creator>
      <dc:date>2026-07-09T09:00:00Z</dc:date>
      <dc:identifier>/articles/android-beat-aligned-mobile-audio-streaming/en</dc:identifier>
    </item>
    <item>
      <title>AlloyDB Ships Proxy Models That Replace LLM Calls with Local Inference Inside the Database</title>
      <link>https://www.infoq.com/news/2026/07/alloydb-ai-proxy-models/?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/2026/07/alloydb-ai-proxy-models/en/headerimage/generatedHeaderImage-1783079201479.jpg"/&gt;&lt;p&gt;Google shipped AlloyDB AI functions GA with a proxy model architecture that trains a lightweight local model from LLM outputs, then runs queries at database speed without external calls. Smart batching delivers 2,400x throughput improvement. The proxy model reaches 100,000 rows per second in preview, but benchmark numbers apply only to ai.if in internal testing.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>AI Architecture</category>
      <category>Database</category>
      <category>Cloud</category>
      <category>Google Cloud Platform</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 09 Jul 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/alloydb-ai-proxy-models/?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>2026-07-09T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/alloydb-ai-proxy-models/en</dc:identifier>
    </item>
    <item>
      <title>AWS Details How One Customer Scaled to One Million Lambda Functions</title>
      <link>https://www.infoq.com/news/2026/07/aws-lambda-1m/?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;AWS has outlined how ProGlove, an industrial-wearables manufacturer, was able to scale its SaaS platform to run more than one million AWS Lambda functions spread across thousands of dedicated customer accounts.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>Platform Engineering</category>
      <category>Infrastructure as Code</category>
      <category>Scaling</category>
      <category>Cloud</category>
      <category>AWS</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 09 Jul 2026 07:44:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/aws-lambda-1m/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Matt Foster</dc:creator>
      <dc:date>2026-07-09T07:44:00Z</dc:date>
      <dc:identifier>/news/2026/07/aws-lambda-1m/en</dc:identifier>
    </item>
    <item>
      <title>The Kubernetes Approach to AI-Assisted Maintainership Prioritises Human Accountability</title>
      <link>https://www.infoq.com/news/2026/07/kubernetes-ai-policy/?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;The Kubernetes community has introduced a framework for integrating AI into open-source maintainership, emphasising human accountability in code quality and oversight. AI tools may streamline workflows, but ultimate responsibility lies with human maintainers. The framework requires disclosure of AI usage in contributions and prohibits AI-generated commit messages.&lt;/p&gt; &lt;i&gt;By Olimpiu Pop&lt;/i&gt;</description>
      <category>Kubernetes</category>
      <category>Open Source</category>
      <category>AI Policy</category>
      <category>Large language models</category>
      <category>Code Generation</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 09 Jul 2026 07:07:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/kubernetes-ai-policy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Olimpiu Pop</dc:creator>
      <dc:date>2026-07-09T07:07:00Z</dc:date>
      <dc:identifier>/news/2026/07/kubernetes-ai-policy/en</dc:identifier>
    </item>
    <item>
      <title>Airbnb Shares Architecture Behind Sitar-Agent Dynamic Configuration Sidecar for Kubernetes Services</title>
      <link>https://www.infoq.com/news/2026/07/sitar-agent-sidecar-config/?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;Airbnb engineers detailed Sitar-agent, a Kubernetes sidecar for dynamic configuration delivery across tens of thousands of pods, processing updates several times per minute. The system was redesigned with Java, Amazon S3 snapshot bootstrapping, and a migration from Sparkey to SQLite to improve reliability, startup performance, and configuration availability at scale.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Kubernetes</category>
      <category>S3</category>
      <category>Edge</category>
      <category>Cloud</category>
      <category>Distributed Systems</category>
      <category>Caching</category>
      <category>Configuration Management</category>
      <category>Platforms</category>
      <category>Microservices</category>
      <category>Sidecar</category>
      <category>SQLite</category>
      <category>Feature Toggle</category>
      <category>Ruby</category>
      <category>Reliability</category>
      <category>Java</category>
      <category>Agents</category>
      <category>Bootstrap</category>
      <category>Architecture &amp; Design</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Wed, 08 Jul 2026 14:25:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/sitar-agent-sidecar-config/?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>2026-07-08T14:25:00Z</dc:date>
      <dc:identifier>/news/2026/07/sitar-agent-sidecar-config/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The Multi-Agent Approach: Building Reliable and Controllable Software Development Automation</title>
      <link>https://www.infoq.com/presentations/multi-agent-ai-architecture/?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/multi-agent-ai-architecture/en/mediumimage/itamar-friedman-medium-1782818996066.jpeg"/&gt;&lt;p&gt;Itamar Friedman discusses how architects and engineering leaders can break through the AI productivity ceiling using adaptive multi-agent systems. He shares insights on moving past simple autocomplete to resilient workflows by integrating autonomous testing, intelligent code review, and robust arbitration. Learn how to govern agent communication and build a context-driven SDLC that scales.&lt;/p&gt; &lt;i&gt;By Itamar Friedman&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 08 Jul 2026 14:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/multi-agent-ai-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Itamar Friedman</dc:creator>
      <dc:date>2026-07-08T14:06:00Z</dc:date>
      <dc:identifier>/presentations/multi-agent-ai-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Switching from PostgreSQL to ClickHouse for Improved Performance and Scalability</title>
      <link>https://www.infoq.com/news/2026/07/momentic-postgres-clickhouse/?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/2026/07/momentic-postgres-clickhouse/en/headerimage/momentic-postgres-clickhouse-1783452382371.jpeg"/&gt;&lt;p&gt;Momentic, the company behind an AI-driven software testing platform, recently rearchitected its caching system to handle over 2 million queries per day across 20 billion total entries, while maintaining an average response latency of around 250 ms. This improvement was made possible by transitioning from PostgreSQL to the column-oriented database ClickHouse.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Caching</category>
      <category>Postgres</category>
      <category>ClickHouse</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 07 Jul 2026 20:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/momentic-postgres-clickhouse/?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>2026-07-07T20:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/momentic-postgres-clickhouse/en</dc:identifier>
    </item>
    <item>
      <title>AWS Expands DevOps Agent with AI-Powered Release Management to Validate Code before Production</title>
      <link>https://www.infoq.com/news/2026/07/aws-devops-ai-agent/?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/2026/07/aws-devops-ai-agent/en/headerimage/generatedHeaderImage-1783328078228.jpg"/&gt;&lt;p&gt;Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Artificial Intelligence</category>
      <category>Release Management</category>
      <category>AWS</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 07 Jul 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/aws-devops-ai-agent/?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>2026-07-07T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/aws-devops-ai-agent/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery</title>
      <link>https://www.infoq.com/presentations/reliable-ai-platforms/?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/reliable-ai-platforms/en/mediumimage/aaron-erickson-medium-1782819611516.jpg"/&gt;&lt;p&gt;Aaron Erickson explains how NVIDIA designs and tests purpose-built AI agent hierarchies. For senior developers and architects, he outlines why balancing deterministic tools with agentic discovery is crucial. Discover how to leverage rare context, implement LLM-as-a-judge test pyramids, and avoid the paradox of choice to build highly reliable, production-grade AI systems at scale.&lt;/p&gt; &lt;i&gt;By Aaron Erickson&lt;/i&gt;</description>
      <category>QCon San Francisco 2025</category>
      <category>Model</category>
      <category>Artificial Intelligence</category>
      <category>Reliability</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 07 Jul 2026 08:03:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/reliable-ai-platforms/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Aaron Erickson</dc:creator>
      <dc:date>2026-07-07T08:03:00Z</dc:date>
      <dc:identifier>/presentations/reliable-ai-platforms/en</dc:identifier>
    </item>
    <item>
      <title>How HubSpot Scaled Semantic Search to 20 Billion Vectors</title>
      <link>https://www.infoq.com/news/2026/07/hubspot-semantic-vector-search/?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/2026/07/hubspot-semantic-vector-search/en/headerimage/generatedHeaderImage-1783111896066.jpg"/&gt;&lt;p&gt;SaaS software vendor HubSpot has described how its semantic search platform grew from a proof of concept into an internal service that now manages more than 20 billion vectors across 38-plus teams. The company says the system now supports agents, RAG, and contact deduplication, and that the increase in agent usage has made retrieval quality and latency more important than before.&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Scaling</category>
      <category>vector databases</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 07 Jul 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/hubspot-semantic-vector-search/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Matt Saunders</dc:creator>
      <dc:date>2026-07-07T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/hubspot-semantic-vector-search/en</dc:identifier>
    </item>
    <item>
      <title>Node.js 26: Temporal API Enabled by Default, V8 14.6, and a Round of Deprecations</title>
      <link>https://www.infoq.com/news/2026/07/nodejs-26-temporal/?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/2026/07/nodejs-26-temporal/en/headerimage/generatedHeaderImage-1782903228392.jpg"/&gt;&lt;p&gt;Node.js 26 has been released, featuring the Temporal API enabled by default, an updated V8 engine to version 14.6, and the Undici HTTP client upgraded to 8.0. The release also removes deprecated legacy APIs. Developers should note migration points related to NODE_MODULE_VERSION changes. Node.js 26 is current for six months before entering long-term support.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>Node.js</category>
      <category>JavaScript</category>
      <category>Web Development</category>
      <category>Date&amp;Time</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Tue, 07 Jul 2026 06:51:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/nodejs-26-temporal/?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>2026-07-07T06:51:00Z</dc:date>
      <dc:identifier>/news/2026/07/nodejs-26-temporal/en</dc:identifier>
    </item>
    <item>
      <title>Java News Roundup: Strict Field Initialization, GlassFish, GraalVM, JReleaser, RefactorFirst</title>
      <link>https://www.infoq.com/news/2026/07/java-news-roundup-jun29-2026/?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/2026/07/java-news-roundup-jun29-2026/en/headerimage/java-news-roundup-image-1783370367210.jpg"/&gt;&lt;p&gt;This week's Java roundup for June 29th, 2026, features news highlighting: a new JEP candidate, Strict Field Initialization; point releases of GraalVM, JReleaser, RefactorFirst and Java Operator SDK; maintenance releases of GlassFish and Micronaut; the second milestone release of Grails 8.0; and the beta release of Open Liberty 26.0.0.7.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>Grails</category>
      <category>JDK 28</category>
      <category>RefactorFirst</category>
      <category>JReleaser</category>
      <category>GraalVM</category>
      <category>Glassfish</category>
      <category>Open JDK</category>
      <category>Open Liberty</category>
      <category>JDK 27</category>
      <category>Java Operator SDK</category>
      <category>Micronaut</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 06 Jul 2026 23:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/java-news-roundup-jun29-2026/?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>2026-07-06T23:30:00Z</dc:date>
      <dc:identifier>/news/2026/07/java-news-roundup-jun29-2026/en</dc:identifier>
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