<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <item>
      <title>Mistral Adds Remote Agents and Work Mode to Le Chat</title>
      <link>https://www.infoq.com/news/2026/05/mistral-agents-lechat/?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/2026/05/mistral-agents-lechat/en/headerimage/generatedHeaderImage-1777663082615.jpg"/&gt;&lt;p&gt;Mistral has released Mistral Medium 3.5, a 128-billion parameter model designed to handle instruction following, reasoning, and coding within a single system, and introduced new cloud-based agent capabilities in its Vibe and Le Chat products.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Agents</category>
      <category>Large language models</category>
      <category>Mistral AI</category>
      <category>OpenAI</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 05 May 2026 10:08:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/mistral-agents-lechat/?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>2026-05-05T10:08:00Z</dc:date>
      <dc:identifier>/news/2026/05/mistral-agents-lechat/en</dc:identifier>
    </item>
    <item>
      <title>Article: Three Pillars of Platform Engineering: A Virtuous Cycle</title>
      <link>https://www.infoq.com/articles/platform-reliability-cycle/?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/platform-reliability-cycle/en/headerimage/platform-reliability-cycle-header-1777467114542.jpg"/&gt;&lt;p&gt;Platform engineering succeeds when reliability and ergonomics reinforce each other rather than compete. This article explores three foundational pillars: automated reliability, developer ergonomics, and operator ergonomics. Together, they establish a virtuous cycle that strengthens system stability, reduces operational burden, and empowers teams to scale infrastructure with confidence.&lt;/p&gt; &lt;i&gt;By Pratik Agarwal&lt;/i&gt;</description>
      <category>Developer Experience</category>
      <category>Site Reliability Engineering</category>
      <category>Observability</category>
      <category>Distributed Systems</category>
      <category>Platform Engineering</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Tue, 05 May 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/platform-reliability-cycle/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Pratik Agarwal</dc:creator>
      <dc:date>2026-05-05T09:00:00Z</dc:date>
      <dc:identifier>/articles/platform-reliability-cycle/en</dc:identifier>
    </item>
    <item>
      <title>Figma Builds In-House Redis Proxy to Hit Six Nines Uptime</title>
      <link>https://www.infoq.com/news/2026/05/figma-redis-figcache/?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/2026/05/figma-redis-figcache/en/headerimage/generatedHeaderImage-1777405837575.jpg"/&gt;&lt;p&gt;Figma has published a detailed account of how it built an in-house Redis proxy service called FigCache, replacing a fragmented caching stack that had become a liability for site availability. The system, described in a post by Kevin Lin, has been in production since the second half of 2025 and has delivered what the company describes as six nines of uptime across its caching layer.&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Redis</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Tue, 05 May 2026 07:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/figma-redis-figcache/?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>2026-05-05T07:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/figma-redis-figcache/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Introduces Flagship: an Edge-Native Feature Flag Service Built on OpenFeature</title>
      <link>https://www.infoq.com/news/2026/05/cloudflare-flagship-openfeature/?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/2026/05/cloudflare-flagship-openfeature/en/headerimage/generatedHeaderImage-1776925782675.jpg"/&gt;&lt;p&gt;Cloudflare recently announced the closed beta of Flagship, a new feature flag service built directly into its global edge platform. The service lets teams control feature rollouts and experiment with changes without redeploying code, while evaluating flags locally in Cloudflare Workers rather than calling external flag services.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Continuous Delivery</category>
      <category>Cloudflare</category>
      <category>Low Latency</category>
      <category>Edge Computing</category>
      <category>Cloud Native Computing Foundation</category>
      <category>Feature Toggle</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Tue, 05 May 2026 06:24:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/cloudflare-flagship-openfeature/?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>2026-05-05T06:24:00Z</dc:date>
      <dc:identifier>/news/2026/05/cloudflare-flagship-openfeature/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Processes 10M+ Daily Insights with New Security Overview Dashboard</title>
      <link>https://www.infoq.com/news/2026/05/cloudflare-security-dashboard/?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/2026/05/cloudflare-security-dashboard/en/headerimage/cloudflaredashboard-1776822044625.jpeg"/&gt;&lt;p&gt;Cloudflare has launched a Security Overview dashboard that consolidates security signals into prioritized action items. It surfaces millions of daily insights, helping teams identify and remediate critical risks faster. Built on distributed checkers and real-time event processing, it integrates analytics workflows to reduce investigation overhead and improve response efficiency.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Security Vulnerabilities</category>
      <category>Cloudflare</category>
      <category>Real Time</category>
      <category>Observability</category>
      <category>Threat detection</category>
      <category>Security Assessment</category>
      <category>Threats</category>
      <category>Security</category>
      <category>Incident Response</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 04 May 2026 14:33:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/cloudflare-security-dashboard/?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>2026-05-04T14:33:00Z</dc:date>
      <dc:identifier>/news/2026/05/cloudflare-security-dashboard/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The Human Scalability Problem: Why Your Teams Don’t Scale Like Your Code</title>
      <link>https://www.infoq.com/presentations/human-scalability/?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/human-scalability/en/mediumimage/CharlottedeJongSchouwenburg-medium-1776859417660.jpeg"/&gt;&lt;p&gt;Charlotte de Jong Schouwenburg discusses the "human bottlenecks" of hyper-growth. While systems scale, human cooperation often breaks down due to communication overload and lost context. She shares proven tools for behavioral scalability - including communication architecture and "engineering trust" - to help leaders maintain high-performing, autonomous teams without sacrificing speed or culture.&lt;/p&gt; &lt;i&gt;By Charlotte de Jong Schouwenburg&lt;/i&gt;</description>
      <category>Scalability</category>
      <category>Teamwork</category>
      <category>InfoQ Dev Summit Munich 2025</category>
      <category>Transcripts</category>
      <category>Culture &amp; Methods</category>
      <category>presentation</category>
      <pubDate>Mon, 04 May 2026 12:40:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/human-scalability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Charlotte de Jong Schouwenburg</dc:creator>
      <dc:date>2026-05-04T12:40:00Z</dc:date>
      <dc:identifier>/presentations/human-scalability/en</dc:identifier>
    </item>
    <item>
      <title>Article: From Batch to Micro-Batch Streaming: Lessons Learned the Hard Way in a Delta Index Pipeline</title>
      <link>https://www.infoq.com/articles/micro-batch-streaming-lessons-learned/?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/micro-batch-streaming-lessons-learned/en/headerimage/micro-batch-streaming-lessons-learned-header-1777381781538.jpg"/&gt;&lt;p&gt;This article describes how a production delta-index pipeline migrated from scheduled batch to micro-batch Spark Structured Streaming. It covers why record-level streaming was rejected, how partition-based watermarks replaced fragile S3 completion markers,  overlap-window correctness, and restart-as-design strategies for better predictability in object-store–based ingestion systems.&lt;/p&gt; &lt;i&gt;By Parveen Saini&lt;/i&gt;</description>
      <category>Apache Spark</category>
      <category>Spark Streaming</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Mon, 04 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/micro-batch-streaming-lessons-learned/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Parveen Saini</dc:creator>
      <dc:date>2026-05-04T11:00:00Z</dc:date>
      <dc:identifier>/articles/micro-batch-streaming-lessons-learned/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Roq: Leveraging Quarkus to Build Static Sites at the Speed of Go</title>
      <link>https://www.infoq.com/podcasts/leveraging-quarkus-build-static-sites/?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/leveraging-quarkus-build-static-sites/en/smallimage/the-infoq-podcast-logo-thumbnail-1776931514871.jpg"/&gt;&lt;p&gt;Andy Damevin, a developer who worked on Quarkus for almost a decade, talks about Roq. A project that started as an experiment to try to see if it’s possible to build a static web site generator on top of quarkus. He touches on the rationale for choosing Java and Quarkus, how to migrate to Roq, and the platform's future.&lt;/p&gt; &lt;i&gt;By Andy Damevin&lt;/i&gt;</description>
      <category>The InfoQ Podcast</category>
      <category>Quarkus</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>podcast</category>
      <pubDate>Mon, 04 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/leveraging-quarkus-build-static-sites/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Andy Damevin</dc:creator>
      <dc:date>2026-05-04T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/leveraging-quarkus-build-static-sites/en</dc:identifier>
    </item>
    <item>
      <title>DoorDash Used Copilot to Convert Its XCTest-Based iOS Test Suite to Swift Testing</title>
      <link>https://www.infoq.com/news/2026/05/doordash-copilot-swift-testing/?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/2026/05/doordash-copilot-swift-testing/en/headerimage/doordash-copilot-swift-testing-1777887802258.jpeg"/&gt;&lt;p&gt;Using Copilot along with strong reliability safeguards, DoorDash migrated their iOS XCTest-based test suite to Swift Testing, thus modernizing a large test suite quickly, safely, and with measurable performance gains, says DoorDash engineer Matheus Gois.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Xcode</category>
      <category>Continuous Integration</category>
      <category>Mobile</category>
      <category>iOS</category>
      <category>Unit Testing</category>
      <category>copilot</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 04 May 2026 10:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/doordash-copilot-swift-testing/?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>2026-05-04T10:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/doordash-copilot-swift-testing/en</dc:identifier>
    </item>
    <item>
      <title>Java News Roundup: OpenJDK JEPs, GlassFish, Spring AI, JReleaser, A2A Java SDK, Google ADK, Gradle</title>
      <link>https://www.infoq.com/news/2026/05/java-news-roundup-apr27-2026/?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/2026/05/java-news-roundup-apr27-2026/en/headerimage/java-news-roundup-image-1777885944693.jpg"/&gt;&lt;p&gt;This week's Java roundup for April 27th, 2026, features news highlighting: OpenJDK JEPs for JDK 27; the fifth milestone release of Spring AI 2.0; the second milestone release of GlassFish 9.0; point releases of Quarkus, JReleaser, Gradle, LangChain4j and Google ADK for Java; the second beta release of Hardwood; and the first beta release of A2A Java SDK 1.0.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>Agent2Agent</category>
      <category>LangChain</category>
      <category>Hardwood</category>
      <category>Gradle</category>
      <category>Quarkus</category>
      <category>Java</category>
      <category>Open JDK</category>
      <category>Google ADK for Java</category>
      <category>JDK 27</category>
      <category>Micronaut</category>
      <category>Glassfish</category>
      <category>JReleaser</category>
      <category>Spring AI</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 04 May 2026 09:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/java-news-roundup-apr27-2026/?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>2026-05-04T09:15:00Z</dc:date>
      <dc:identifier>/news/2026/05/java-news-roundup-apr27-2026/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Builds High-Performance Infrastructure for Running LLMs</title>
      <link>https://www.infoq.com/news/2026/05/cloudflare-llm-infrastructure/?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/2026/05/cloudflare-llm-infrastructure/en/headerimage/generatedHeaderImage-1776661318905.jpg"/&gt;&lt;p&gt;Cloudflare has recently announced new infrastructure designed to run large AI language models across its global network. As these models rely on costly hardware and must handle large volumes of incoming and outgoing text, Cloudflare separates the model's input processing and output generation onto different optimized systems.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>AI Architecture</category>
      <category>Cloudflare</category>
      <category>GPU</category>
      <category>Large language models</category>
      <category>Big Data Infrastructure</category>
      <category>Optimization</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sun, 03 May 2026 10:58:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/cloudflare-llm-infrastructure/?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>2026-05-03T10:58:00Z</dc:date>
      <dc:identifier>/news/2026/05/cloudflare-llm-infrastructure/en</dc:identifier>
    </item>
    <item>
      <title>DuckLake 1.0: Data Lake Format with SQL Catalog Metadata</title>
      <link>https://www.infoq.com/news/2026/05/ducklake-sql-catalog/?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/2026/05/ducklake-sql-catalog/en/headerimage/generatedHeaderImage-1776423164012.jpg"/&gt;&lt;p&gt;DuckDB Labs recently released DuckLake 1.0, a data lake format that stores table metadata in a SQL database rather than across many files in object storage. The first implementation is available as a DuckDB extension and includes catalog-stored small updates, improved sorting and partitioning options, and compatibility with Iceberg-style data features.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Apache Iceberg</category>
      <category>Data Partitioning</category>
      <category>Data Lake</category>
      <category>duckdb</category>
      <category>SQL</category>
      <category>Data Storage</category>
      <category>Data Catalog</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Sat, 02 May 2026 06:48:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/ducklake-sql-catalog/?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>2026-05-02T06:48:00Z</dc:date>
      <dc:identifier>/news/2026/05/ducklake-sql-catalog/en</dc:identifier>
    </item>
    <item>
      <title>JobRunr Introduces ClawRunr, an Open-Source Java AI Agent</title>
      <link>https://www.infoq.com/news/2026/05/clawrunr/?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/2026/05/clawrunr/en/headerimage/generatedHeaderImage-1777644148544.jpg"/&gt;&lt;p&gt;JobRunr has introduced ClawRunr, an open-source Java AI agent for scheduled, recurring, and one-off background tasks. Formerly JavaClaw, it runs on users' hardware and combines conversational interaction with persistent task execution, MCP tools, browser automation, and web, Telegram, and Discord channels, while using JobRunr for scheduling, retries, and monitoring.&lt;/p&gt; &lt;i&gt;By Diogo Carleto&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 01 May 2026 15:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/clawrunr/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Diogo Carleto</dc:creator>
      <dc:date>2026-05-01T15:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/clawrunr/en</dc:identifier>
    </item>
    <item>
      <title>Confluent Moves Schema IDs to Kafka Headers to Simplify Schema Governance</title>
      <link>https://www.infoq.com/news/2026/05/confluent-kafka-header-schema-id/?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/2026/05/confluent-kafka-header-schema-id/en/headerimage/generatedHeaderImage-1776736992912.jpg"/&gt;&lt;p&gt;Confluent introduces a new approach in Apache Kafka that moves schema IDs from message payloads to record headers, aiming to simplify schema governance and evolution. The update integrates with Schema Registry, improves compatibility across serialization formats, and reduces coupling between data and metadata in event-driven architectures.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Protocol Buffers</category>
      <category>Event Stream Processing</category>
      <category>Schema</category>
      <category>Avro</category>
      <category>Data Pipelines</category>
      <category>Data Analytics</category>
      <category>Apache Flink</category>
      <category>Streaming</category>
      <category>JSON</category>
      <category>Apache Kafka</category>
      <category>Machine Learning</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Fri, 01 May 2026 14:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/confluent-kafka-header-schema-id/?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>2026-05-01T14:06:00Z</dc:date>
      <dc:identifier>/news/2026/05/confluent-kafka-header-schema-id/en</dc:identifier>
    </item>
    <item>
      <title>Meta Deploys Unified AI Agents to Automate Performance Optimization at Hyperscale</title>
      <link>https://www.infoq.com/news/2026/05/meta-ai-agents-hyperscale/?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/2026/05/meta-ai-agents-hyperscale/en/headerimage/generatedHeaderImage-1777275523688.jpg"/&gt;&lt;p&gt;Meta has unveiled a new AI-driven capacity efficiency platform that uses unified AI agents to automatically detect and resolve performance issues across its global infrastructure, marking a significant step toward self-optimizing systems at hyperscale.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Scaling</category>
      <category>Agents</category>
      <category>Performance &amp; Scalability</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 01 May 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/meta-ai-agents-hyperscale/?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>2026-05-01T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/meta-ai-agents-hyperscale/en</dc:identifier>
    </item>
  </channel>
</rss>
