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    <title>InfoQ</title>
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    <item>
      <title>Vercel Introduces Eve, an Open-Source Framework for Building AI Agents</title>
      <link>https://www.infoq.com/news/2026/06/vercel-eve-agents/?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/2026/06/vercel-eve-agents/en/headerimage/generatedHeaderImage-1782478004947.jpg"/&gt;&lt;p&gt;Vercel has released Eve, an open-source framework for building, deploying, and operating AI agents in production. The framework uses a filesystem-based project structure to organize agent instructions, tools, skills, subagents, communication channels, and scheduled tasks, enabling developers to define agent behavior while reducing the amount of supporting infrastructure they need to implement.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Artificial Intelligence</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Cloud Computing</category>
      <category>Open Source</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 26 Jun 2026 16:39:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/vercel-eve-agents/?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>2026-06-26T16:39:00Z</dc:date>
      <dc:identifier>/news/2026/06/vercel-eve-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: AI Works, Pull Requests Don’t: How AI Is Breaking the SDLC and What To Do About It</title>
      <link>https://www.infoq.com/presentations/ai-sdlc-pull-request/?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/ai-sdlc-pull-request/en/mediumimage/michael-webster-medium-1781688909041.jpeg"/&gt;&lt;p&gt;Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability.&lt;/p&gt; &lt;i&gt;By Michael Webster&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Software Development Lifecycle</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 26 Jun 2026 14:17:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-sdlc-pull-request/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Michael Webster</dc:creator>
      <dc:date>2026-06-26T14:17:00Z</dc:date>
      <dc:identifier>/presentations/ai-sdlc-pull-request/en</dc:identifier>
    </item>
    <item>
      <title>Dapr 1.18 Introduces Verifiable Execution, Bringing Cryptographic Trust to AI Agents and Workflows</title>
      <link>https://www.infoq.com/news/2026/06/dapr-1-18-cryptographic-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/2026/06/dapr-1-18-cryptographic-ai/en/headerimage/generatedHeaderImage-1782128447195.jpg"/&gt;&lt;p&gt;Diagrid has announced the release of Dapr 1.18, introducing what it calls Verifiable Execution, a new set of capabilities designed to bring cryptographic trust, provenance, and tamper-evident execution records to distributed applications and AI agents.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Artificial Intelligence</category>
      <category>dapr</category>
      <category>Trust</category>
      <category>Cryptography</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 26 Jun 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/dapr-1-18-cryptographic-ai/?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>2026-06-26T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/dapr-1-18-cryptographic-ai/en</dc:identifier>
    </item>
    <item>
      <title>Argo CD 3.5 Tightens Supply Chain Security with Internal mTLS and Source Integrity</title>
      <link>https://www.infoq.com/news/2026/06/argocd-supply-chain-security/?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/2026/06/argocd-supply-chain-security/en/headerimage/generatedHeaderImage-1782400527464.jpg"/&gt;&lt;p&gt;The Argo CD project released a v3.5 release candidate in June 2026. This version adds mutual TLS enforcement for internal components. It also includes Git commit signature verification for supply chain security and native ApplicationSet management in the UI. The release also graduates two significant features: impersonation and Source Hydrator, from alpha to beta.&lt;/p&gt; &lt;i&gt;By Claudio Masolo&lt;/i&gt;</description>
      <category>GitOps</category>
      <category>Security</category>
      <category>Argo CD</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 26 Jun 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/argocd-supply-chain-security/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Claudio Masolo</dc:creator>
      <dc:date>2026-06-26T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/argocd-supply-chain-security/en</dc:identifier>
    </item>
    <item>
      <title>How Cloudflare Solved a Congestion Bug in quiche</title>
      <link>https://www.infoq.com/news/2026/06/cloudflare-bug-quiche/?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/2026/06/cloudflare-bug-quiche/en/headerimage/generatedHeaderImage-1782410835538.jpg"/&gt;&lt;p&gt;Cloudflare has recently shared how they uncovered an issue in their Rust implementation of CUBIC, a congestion controller algorithm, which prevented it from recovering from a scenario of heavy packet loss at the start of a connection.&lt;/p&gt; &lt;i&gt;By Gianmarco Nalin&lt;/i&gt;</description>
      <category>QUIC Protocol</category>
      <category>Bugs and Hotfixes</category>
      <category>UDP</category>
      <category>Cloud</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 25 Jun 2026 19:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/cloudflare-bug-quiche/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Gianmarco Nalin</dc:creator>
      <dc:date>2026-06-25T19:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/cloudflare-bug-quiche/en</dc:identifier>
    </item>
    <item>
      <title>Building a European Cloud Orchestration Platform within an Enterprise</title>
      <link>https://www.infoq.com/news/2026/06/europe-cloud-enterprise/?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/2026/06/europe-cloud-enterprise/en/headerimage/europe-cloud-enterprise-header-1782131709734.jpg"/&gt;&lt;p&gt;Modern cloud deployments involve many tools with different lifecycles, creating a heavy burden on engineers. The Kubernetes ecosystem offers a unified Control Plane approach. Sharing best practices through tech talks and inner-source collaboration can create an engaged community and drive adoption.&lt;/p&gt; &lt;i&gt;By Ben Linders&lt;/i&gt;</description>
      <category>Kubernetes</category>
      <category>AWS</category>
      <category>Azure</category>
      <category>Cloud Adoption</category>
      <category>Cloud</category>
      <category>Sovereignty</category>
      <category>Collaboration</category>
      <category>Funding</category>
      <category>Cloud Native Architecture</category>
      <category>Failure</category>
      <category>Culture &amp; Methods</category>
      <category>news</category>
      <pubDate>Thu, 25 Jun 2026 11:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/europe-cloud-enterprise/?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>2026-06-25T11:06:00Z</dc:date>
      <dc:identifier>/news/2026/06/europe-cloud-enterprise/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Rust at the Core - Accelerating Polyglot SDK Development</title>
      <link>https://www.infoq.com/presentations/rust-polyglot-sdk/?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/rust-polyglot-sdk/en/mediumimage/spencer-judge-medium-1781688097548.jpeg"/&gt;&lt;p&gt;Spencer Judge discusses the architectural pattern of building a shared core in Rust with language-specific layers on top. Drawing from his work on Temporal's SDKs, he shares lessons on navigating FFI boundaries, bridging async concepts, and managing memory safely. He explains the limitations of native extensions and how emerging tech like WebAssembly can streamline cross-language architecture.&lt;/p&gt; &lt;i&gt;By Spencer Judge&lt;/i&gt;</description>
      <category>SDK</category>
      <category>Transcripts</category>
      <category>Rust</category>
      <category>QCon San Francisco 2025</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 25 Jun 2026 10:23:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/rust-polyglot-sdk/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Spencer Judge</dc:creator>
      <dc:date>2026-06-25T10:23:00Z</dc:date>
      <dc:identifier>/presentations/rust-polyglot-sdk/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Ships Agent Skills for Zero Trust Deployment and Migration</title>
      <link>https://www.infoq.com/news/2026/06/cloudflare-one-stack-agents/?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/2026/06/cloudflare-one-stack-agents/en/headerimage/generatedHeaderImage-1782201861296.jpg"/&gt;&lt;p&gt;Cloudflare released the Cloudflare One stack, an open-source library of agent skills for planning, deploying, and managing Zero Trust environments. The skills include automated migration logic for Zscaler and Palo Alto Networks, the same logic used in Cloudflare's Descaler program that has moved enterprise customers in hours rather than months.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Access Control</category>
      <category>Information Security</category>
      <category>Cloudflare</category>
      <category>Cloud</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 25 Jun 2026 09:27:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/cloudflare-one-stack-agents/?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>2026-06-25T09:27:00Z</dc:date>
      <dc:identifier>/news/2026/06/cloudflare-one-stack-agents/en</dc:identifier>
    </item>
    <item>
      <title>Slack Outlines Four-Phase Journey to a Multi-Cloud AI Serving Platform</title>
      <link>https://www.infoq.com/news/2026/06/slack-multicloud/?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/2026/06/slack-multicloud/en/headerimage/generatedHeaderImage-1782335922071.jpg"/&gt;&lt;p&gt;Slack has outlined how its AI serving infrastructure evolved through four distinct phases, moving from a self-managed Amazon SageMaker deployment to a multi-cloud architecture spanning AWS Bedrock and Google Cloud Vertex AI.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>Performance &amp; Scalability</category>
      <category>Distributed Systems</category>
      <category>Cloud Architecture</category>
      <category>Large language models</category>
      <category>Cloud</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 25 Jun 2026 07:02:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/slack-multicloud/?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>2026-06-25T07:02:00Z</dc:date>
      <dc:identifier>/news/2026/06/slack-multicloud/en</dc:identifier>
    </item>
    <item>
      <title>Grab Builds Secure Agentic AI Workload Platform</title>
      <link>https://www.infoq.com/news/2026/06/grab-ai-platform/?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;Grab's security team built Palana, a Kubernetes-native secure execution platform, to run autonomous AI agents safely. Unlike deterministic software, model-driven agents exhibit unpredictable tool-use, code-writing, and prompt injection risks. Palana contains these threats at the infrastructure level using isolated namespaces, out-of-process control planes, and proxy-mediated, Vault-backed secrets.&lt;/p&gt; &lt;i&gt;By Patrick Farry&lt;/i&gt;</description>
      <category>Agents</category>
      <category>AI Security</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 25 Jun 2026 02:08:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/grab-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Patrick Farry</dc:creator>
      <dc:date>2026-06-25T02:08:00Z</dc:date>
      <dc:identifier>/news/2026/06/grab-ai-platform/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Lead: HTML Increasingly Better Than Markdown at Keeping Humans Engaged in Agentic Loops</title>
      <link>https://www.infoq.com/news/2026/06/anthropic-html-markdown-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/2026/06/anthropic-html-markdown-agent/en/headerimage/generatedHeaderImage-1782335059166.jpg"/&gt;&lt;p&gt;Thariq Shihipar, engineering lead for the Claude Code team, recently published a blog post (Using Claude Code: The Unreasonable Effectiveness of HTML) arguing that HTML, with its richer visualizations, color, and interactivity, improves the productivity of human-agent communication in many settings, especially when compared to default Markdown outputs.&lt;/p&gt; &lt;i&gt;By Bruno Couriol&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>HTML</category>
      <category>AI Coding</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Wed, 24 Jun 2026 23:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/anthropic-html-markdown-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Bruno Couriol</dc:creator>
      <dc:date>2026-06-24T23:06:00Z</dc:date>
      <dc:identifier>/news/2026/06/anthropic-html-markdown-agent/en</dc:identifier>
    </item>
    <item>
      <title>Google OpenRL is an Experimental Self-hosted API for LLM Post-Training Fine-Tuning</title>
      <link>https://www.infoq.com/news/2026/06/google-open-rl-fine-tuning/?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/2026/06/google-open-rl-fine-tuning/en/headerimage/google-open-rl-fine-tuning-1782322457170.jpeg"/&gt;&lt;p&gt;Google's GKE Labs has introduced OpenRL, an open-source project that provides a self-hosted API for post-training and fine-tuning Large Language Models (LLMs) on standard Kubernetes clusters.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Kubernetes</category>
      <category>Google</category>
      <category>Open Source</category>
      <category>Model Fine Tuning</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 24 Jun 2026 18:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/google-open-rl-fine-tuning/?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>2026-06-24T18:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/google-open-rl-fine-tuning/en</dc:identifier>
    </item>
    <item>
      <title>AI Is Moving up the Software Lifecycle: from Code Review to PRD Governance</title>
      <link>https://www.infoq.com/news/2026/06/ai-prd-code-review-governance/?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;Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>AI Development</category>
      <category>Documentation</category>
      <category>Requirements</category>
      <category>Software Development Lifecycle</category>
      <category>AI Coding</category>
      <category>AI Assisted Coding</category>
      <category>Agents</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>Governance</category>
      <category>Code Reviews</category>
      <category>Software Engineering</category>
      <category>Workflow / BPM</category>
      <category>Architecture &amp; Design</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 24 Jun 2026 14:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/ai-prd-code-review-governance/?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>2026-06-24T14:57:00Z</dc:date>
      <dc:identifier>/news/2026/06/ai-prd-code-review-governance/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Rules for Understanding Language Models</title>
      <link>https://www.infoq.com/presentations/5-principles-llm-behavior/?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/5-principles-llm-behavior/en/mediumimage/naomi-saphra-medium-1781688751052.jpg"/&gt;&lt;p&gt;Naomi Saphra discusses 5 rules governing language model behavior, breaking down why LLMs act like populations rather than individuals. She explains how tokenization creates strange semantic blind spots and highlights the mechanics of sycophancy, showing how models leverage subtle data associations to match user biases and demographics - even guessing political views based on favorite sports teams.&lt;/p&gt; &lt;i&gt;By Naomi Saphra&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 24 Jun 2026 11:25:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/5-principles-llm-behavior/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Naomi Saphra</dc:creator>
      <dc:date>2026-06-24T11:25:00Z</dc:date>
      <dc:identifier>/presentations/5-principles-llm-behavior/en</dc:identifier>
    </item>
    <item>
      <title>Article: Beyond CLEAN and MVP: Architecting an Offline-first Reactive Data Layer in Android</title>
      <link>https://www.infoq.com/articles/rdla-offline-first-reactive-android-data-layer/?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/rdla-offline-first-reactive-android-data-layer/en/headerimage/rdla-offline-first-reactive-android-data-layer-header-1781776366032.jpg"/&gt;&lt;p&gt;With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns.&lt;/p&gt; &lt;i&gt;By Mervyn Anthony&lt;/i&gt;</description>
      <category>Mobile</category>
      <category>Asynchronous Architecture</category>
      <category>MVP</category>
      <category>Reactive Programming</category>
      <category>Clean Architecture</category>
      <category>Android</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Wed, 24 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/rdla-offline-first-reactive-android-data-layer/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Mervyn Anthony</dc:creator>
      <dc:date>2026-06-24T09:00:00Z</dc:date>
      <dc:identifier>/articles/rdla-offline-first-reactive-android-data-layer/en</dc:identifier>
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