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    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <item>
      <title>Cloudflare Introduces Temporary Accounts for Autonomous Worker Deployment</title>
      <link>https://www.infoq.com/news/2026/07/cloudflare-temp-accounts/?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/cloudflare-temp-accounts/en/headerimage/generatedHeaderImage-1782506205282.jpg"/&gt;&lt;p&gt;Cloudflare has recently introduced temporary accounts that let AI agents deploy Cloudflare Workers immediately, without first creating or authenticating with a permanent account. If left unclaimed, the accounts and their deployments expire automatically after 60 minutes.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Cloud Computing</category>
      <category>Authentication</category>
      <category>AIOps</category>
      <category>Cloudflare</category>
      <category>Cloud</category>
      <category>Serverless</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 15:16:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/cloudflare-temp-accounts/?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>2026-07-10T15:16:00Z</dc:date>
      <dc:identifier>/news/2026/07/cloudflare-temp-accounts/en</dc:identifier>
    </item>
    <item>
      <title>Slack Introduces Agent Driven End-to-End Testing to Improve Resilience in UI Test Automation</title>
      <link>https://www.infoq.com/news/2026/07/slack-agentic-e2e-testing-ui/?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;Agentic testing is an AI-driven approach to end-to-end test automation introduced by Slack engineering. It uses AI agents that execute workflows based on intent rather than fixed scripts, adapting to UI and system changes at runtime. The approach aims to reduce brittle tests in distributed systems while complementing deterministic unit, integration, and E2E testing strategies.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Software Engineering</category>
      <category>Test Automation</category>
      <category>Test Driven</category>
      <category>Automated testing</category>
      <category>Slack</category>
      <category>Integration Testing</category>
      <category>Test Design</category>
      <category>UI Testing</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 13:48:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/slack-agentic-e2e-testing-ui/?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>2026-07-10T13:48:00Z</dc:date>
      <dc:identifier>/news/2026/07/slack-agentic-e2e-testing-ui/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Chaos Engineering GPU Clusters</title>
      <link>https://www.infoq.com/presentations/chaos-engineering-gpu/?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/chaos-engineering-gpu/en/mediumimage/bryan-oliver-medium-1782819161258.jpeg"/&gt;&lt;p&gt;Bryan Oliver discusses the frontier of AI infrastructure: chaos engineering for large-scale GPU clusters. He shares how engineering leaders can handle complex topologies, network protocols like RDMA, and NUMA misalignments. Discover seven practical fault-injection strategies to maximize multi-million dollar hardware efficiency and build robust observability loops.&lt;/p&gt; &lt;i&gt;By Bryan Oliver&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Chaos Engineering</category>
      <category>Transcripts</category>
      <category>Infrastructure</category>
      <category>GPU</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 10 Jul 2026 13:42:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/chaos-engineering-gpu/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Bryan Oliver</dc:creator>
      <dc:date>2026-07-10T13:42:00Z</dc:date>
      <dc:identifier>/presentations/chaos-engineering-gpu/en</dc:identifier>
    </item>
    <item>
      <title>Linux Foundation Launches Akrites to Protect Critical Open Source Software from AI-Powered  Threats</title>
      <link>https://www.infoq.com/news/2026/07/akrites-open-source-ai-threats/?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/akrites-open-source-ai-threats/en/headerimage/generatedHeaderImage-1783328525989.jpg"/&gt;&lt;p&gt;The Linux Foundation has launched Akrites, a new industry-wide initiative aimed at defending the world's most critical open source software against a rapidly evolving generation of AI-enabled cyber threats.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Threats</category>
      <category>Threat detection</category>
      <category>Linux</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/akrites-open-source-ai-threats/?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>2026-07-10T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/akrites-open-source-ai-threats/en</dc:identifier>
    </item>
    <item>
      <title>GitHub Copilot CLI Gets Tabs and No-Config-File Tool Setup in Redesigned Terminal UI</title>
      <link>https://www.infoq.com/news/2026/07/copilot-cli-terminal-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/2026/07/copilot-cli-terminal-ga/en/headerimage/header-1783593957858.jpeg"/&gt;&lt;p&gt;GitHub has made the redesigned GitHub Copilot CLI terminal interface generally available. It adds a tabbed layout for sessions, gists, issues, and pull requests; an in-session, form-driven setup for MCP servers, skills, and plugins that avoids hand-editing config files; and a cleaner, theme-aware, more accessible UI with screen reader support.&lt;/p&gt; &lt;i&gt;By Mark Silvester&lt;/i&gt;</description>
      <category>Model Context Protocol (MCP)</category>
      <category>Software Engineering</category>
      <category>copilot</category>
      <category>AI Assisted Coding</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 10:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/copilot-cli-terminal-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>2026-07-10T10:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/copilot-cli-terminal-ga/en</dc:identifier>
    </item>
    <item>
      <title>Article: Trade-Offs in Multi-Region Architectures: Latency vs. Cost</title>
      <link>https://www.infoq.com/articles/multi-region-latency-cost-tradeoffs/?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/multi-region-latency-cost-tradeoffs/en/headerimage/multi-region-latency-cost-tradeoffs-header-1783416229477.jpg"/&gt;&lt;p&gt;Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms.&lt;/p&gt; &lt;i&gt;By Uttara Asthana&lt;/i&gt;</description>
      <category>Availability</category>
      <category>Performance</category>
      <category>Architecture</category>
      <category>Cloud</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Fri, 10 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/multi-region-latency-cost-tradeoffs/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Uttara Asthana</dc:creator>
      <dc:date>2026-07-10T09:00:00Z</dc:date>
      <dc:identifier>/articles/multi-region-latency-cost-tradeoffs/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Formal Methods for Every Engineer in an AI-Powered Future</title>
      <link>https://www.infoq.com/podcasts/formal-methods-ai-powered-future/?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/formal-methods-ai-powered-future/en/smallimage/the-infoq-podcast-logo-thumbnail-1782478546952.jpg"/&gt;&lt;p&gt;In this podcast Shane Hastie, Lead Editor for Culture &amp; Methods spoke to Gabriela Moreira about making formal methods accessible through the Quint specification language, how AI is dramatically lowering the barrier to entry for formal specification and model-based testing, and why defining correct system behaviour remains essential human work in an AI-driven world.&lt;/p&gt; &lt;i&gt;By Gabriela Moreira&lt;/i&gt;</description>
      <category>Testing</category>
      <category>Engineering Culture Podcast</category>
      <category>Requirements</category>
      <category>AI Assisted Coding</category>
      <category>Culture &amp; Methods</category>
      <category>podcast</category>
      <pubDate>Fri, 10 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/formal-methods-ai-powered-future/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Gabriela Moreira</dc:creator>
      <dc:date>2026-07-10T09:00:00Z</dc:date>
      <dc:identifier>/podcasts/formal-methods-ai-powered-future/en</dc:identifier>
    </item>
    <item>
      <title>How Datadog Used Claude and Cursor for Test-Driven Production Migration</title>
      <link>https://www.infoq.com/news/2026/07/datadog-ai-production-migration/?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/datadog-ai-production-migration/en/headerimage/datadog-ai-refactoring-1783667933483.jpeg"/&gt;&lt;p&gt;In a recent article, Datadog engineer Arnold Wakim shared what worked, what didn't, and the lessons they learned while evolving a critical production system using AI to overcome hard limits in its storage backend and significantly improve performance.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Performance &amp; Scalability</category>
      <category>Refactoring</category>
      <category>Claude</category>
      <category>Postgres</category>
      <category>FoundationDB</category>
      <category>Large language models</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/datadog-ai-production-migration/?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>2026-07-10T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/datadog-ai-production-migration/en</dc:identifier>
    </item>
    <item>
      <title>WordPress 7.0 Ships with AI Foundations in Core, a Modernized Admin, and New Design Tools</title>
      <link>https://www.infoq.com/news/2026/07/wordpress-7-ai/?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/wordpress-7-ai/en/headerimage/generatedHeaderImage-1782916391166.jpg"/&gt;&lt;p&gt;WordPress 7.0, released on May 20, 2026, includes new AI infrastructure, a redesigned admin interface, and updated design tools. Key features comprise an AI Client, Abilities API, and Command Palette, alongside increased PHP requirements. Community feedback is mixed, particularly regarding AI integration. Developers are advised to consult the official documentation for upgrade guidance.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>WordPress</category>
      <category>Web Development</category>
      <category>Portal/CMS</category>
      <category>Enterprise Content Management</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 10 Jul 2026 06:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/wordpress-7-ai/?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>2026-07-10T06:30:00Z</dc:date>
      <dc:identifier>/news/2026/07/wordpress-7-ai/en</dc:identifier>
    </item>
    <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>Transcripts</category>
      <category>Offline-First</category>
      <category>Case Study</category>
      <category>Data</category>
      <category>QCon San Francisco 2025</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>Banking</category>
      <category>Kubernetes</category>
      <category>Social Skills</category>
      <category>Standardization</category>
      <category>Platforms</category>
      <category>Platform Engineering</category>
      <category>Open Source</category>
      <category>Collaboration</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>Site Reliability Engineering</category>
      <category>OpenAI</category>
      <category>Bugs and Hotfixes</category>
      <category>Open Source Project Releases</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>Mobile</category>
      <category>iOS</category>
      <category>React Native</category>
      <category>C++</category>
      <category>Real Time</category>
      <category>Android</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>Google Cloud Platform</category>
      <category>Cloud</category>
      <category>Database</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</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>Infrastructure as Code</category>
      <category>Platform Engineering</category>
      <category>AWS</category>
      <category>Cloud</category>
      <category>Scaling</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>
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