<?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>Instacart Scales Personalized Marketing via Configuration-Driven Multi-Tenant Platform</title>
      <link>https://www.infoq.com/news/2026/07/instacart-multi-tenant-marketing/?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;Instacart redesigned its personalized marketing system using a configuration-driven multi-tenant architecture on Storefront Pro. The system replaces retailer-specific implementations with a shared execution engine, enabling scalable personalization, faster configuration propagation in under a minute, and 99.9% delivery success across hundreds of retail banners through a unified campaign platform.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>SMS</category>
      <category>Marketing</category>
      <category>Distributed Systems</category>
      <category>Multi-Tenant Data</category>
      <category>Observability</category>
      <category>Configuration Management Tools</category>
      <category>Event Driven Architecture</category>
      <category>Multi-tenancy</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 01 Jul 2026 14:05:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/instacart-multi-tenant-marketing/?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>2026-07-01T14:05:00Z</dc:date>
      <dc:identifier>/news/2026/07/instacart-multi-tenant-marketing/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Graph RAG: Building Smarter Retrieval Workflows with Knowledge Graphs</title>
      <link>https://www.infoq.com/presentations/graph-rag-llm/?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/graph-rag-llm/en/mediumimage/CassieShum-medium-1782291352027.jpeg"/&gt;&lt;p&gt;Cassie Shum discusses the architectural evolution of GraphRAG and why data foundations are critical for advanced AI workflows. She explains how traditional vector RAG falls short when addressing global context, multi-hop reasoning, and provenance. She shares enterprise strategies for building semantically structured knowledge graphs that shift raw orchestrating logic down to the data layer.&lt;/p&gt; &lt;i&gt;By Cassie Shum&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Large language models</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 01 Jul 2026 14:01:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/graph-rag-llm/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Cassie Shum</dc:creator>
      <dc:date>2026-07-01T14:01:00Z</dc:date>
      <dc:identifier>/presentations/graph-rag-llm/en</dc:identifier>
    </item>
    <item>
      <title>HeroUI v3 Lands as a Ground-Up Rewrite for React and React Native, Built on Tailwind CSS v4</title>
      <link>https://www.infoq.com/news/2026/07/heroui-v3-rewrite/?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/heroui-v3-rewrite/en/headerimage/generatedHeaderImage-1782890990794.jpg"/&gt;&lt;p&gt;HeroUI v3 is a redesigned React component library, previously NextUI, offering over 75 components, including 21 new ones, and a new React Native library with 37 components. Built on React Aria and Tailwind CSS v4, it emphasizes accessibility and customization. The library has experienced many updates since its release, and migration from the previous version is necessary.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>React Native</category>
      <category>CSS</category>
      <category>Web Development</category>
      <category>React</category>
      <category>Web Components</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 01 Jul 2026 12:16:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/heroui-v3-rewrite/?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>2026-07-01T12:16:00Z</dc:date>
      <dc:identifier>/news/2026/07/heroui-v3-rewrite/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The Infrastructure Challenge Behind Production AI</title>
      <link>https://www.infoq.com/presentations/ai-infrastructure-scaling-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/ai-infrastructure-scaling-architecture/en/mediumimage/infoq-live-medium-1782888285223.jpg"/&gt;&lt;p&gt;The panelists explain the realities of running AI systems reliably at scale. While building models is solved, maintaining production databases under constant pressure is not. They discuss the emerging architectural decisions separating teams that scale gracefully from those facing catastrophic outages, and what engineering leaders must rethink today.&lt;/p&gt; &lt;i&gt;By Simerus Mahesh, Alex Infanzon, Meryem Arik, Luca Bianchi, Renato Losio&lt;/i&gt;</description>
      <category>Virtual Events</category>
      <category>InfoQ Live</category>
      <category>Infrastructure</category>
      <category>Virtual Panel</category>
      <category>Transcripts</category>
      <category>MLOps</category>
      <category>InfoQ Live - June 2026</category>
      <category>Scalability</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 01 Jul 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-infrastructure-scaling-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Simerus Mahesh, Alex Infanzon, Meryem Arik, Luca Bianchi, Renato Losio</dc:creator>
      <dc:date>2026-07-01T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-infrastructure-scaling-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Trustworthy Productivity: Securing AI-Accelerated Development</title>
      <link>https://www.infoq.com/presentations/ai-development/?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-development/en/mediumimage/SriramMadapusiVasudevan-medium-1782220895596.jpg"/&gt;&lt;p&gt;Sriram Madapusi Vasudevan discusses industry-converging patterns for securing autonomous AI agents in production. He explains the critical vulnerabilities hidden inside the ReAct loop across context, reasoning, and tool execution. He shares how to mitigate risks like memory poisoning and rogue tool execution using defense-in-depth strategies, LLM-as-a-judge critics, and MAESTRO threat modeling.&lt;/p&gt; &lt;i&gt;By Sriram Madapusi Vasudevan&lt;/i&gt;</description>
      <category>QCon San Francisco 2025</category>
      <category>Developer Experience</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 30 Jun 2026 14:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-development/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Sriram Madapusi Vasudevan</dc:creator>
      <dc:date>2026-06-30T14:35:00Z</dc:date>
      <dc:identifier>/presentations/ai-development/en</dc:identifier>
    </item>
    <item>
      <title>Elastic Open-Sources Atlas Agent Memory Based on Cognitive Science</title>
      <link>https://www.infoq.com/news/2026/06/elastic-atlas-agent-memory/?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/elastic-atlas-agent-memory/en/headerimage/generatedHeaderImage-1782569563186.jpg"/&gt;&lt;p&gt;Elastic open-sourced Atlas, a system built on Elasticsearch that maintains three categories of memory for agents. Atlas integrates with agents via MCP and maintains per-user isolation of memories. When evaluated on question-answering capability, it scored 0.89 Recall@10.&lt;/p&gt; &lt;i&gt;By Anthony Alford&lt;/i&gt;</description>
      <category>ElasticSearch</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 30 Jun 2026 13:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/elastic-atlas-agent-memory/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Anthony Alford</dc:creator>
      <dc:date>2026-06-30T13:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/elastic-atlas-agent-memory/en</dc:identifier>
    </item>
    <item>
      <title>Microsoft Brings AI-Powered Vulnerability Remediation to Azure DevOps with Copilot Autofix</title>
      <link>https://www.infoq.com/news/2026/06/azuredevops-copilot-autofix/?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/azuredevops-copilot-autofix/en/headerimage/generatedHeaderImage-1782552787591.jpg"/&gt;&lt;p&gt;Microsoft has announced the limited public preview of Copilot Autofix for GitHub Advanced Security for Azure DevOps, extending AI-powered vulnerability remediation to teams using Azure Repos.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>github</category>
      <category>Microsoft Azure</category>
      <category>copilot</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 30 Jun 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/azuredevops-copilot-autofix/?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>2026-06-30T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/azuredevops-copilot-autofix/en</dc:identifier>
    </item>
    <item>
      <title>AWS Launches Lambda MicroVMs for Isolated Agent and User Code Execution</title>
      <link>https://www.infoq.com/news/2026/06/aws-lambda-microvms/?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/aws-lambda-microvms/en/headerimage/generatedHeaderImage-1782380968528.jpg"/&gt;&lt;p&gt;AWS launched Lambda MicroVMs, a new serverless compute primitive that runs each user session or AI agent in its own Firecracker virtual machine with hardware-level isolation, snapshot-based rapid launch, and state preservation for up to eight hours. Reddit community analysis found the minimum setup costs $3.03/day, roughly 9x Fargate spot pricing.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>AWS Lambda</category>
      <category>Cloud Architecture</category>
      <category>AWS</category>
      <category>Containers</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 30 Jun 2026 09:09:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/aws-lambda-microvms/?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>2026-06-30T09:09:00Z</dc:date>
      <dc:identifier>/news/2026/06/aws-lambda-microvms/en</dc:identifier>
    </item>
    <item>
      <title>Article: Scaling Java-Based Real-Time Systems: The Hidden Tradeoffs of Event-Driven Design</title>
      <link>https://www.infoq.com/articles/tradeoffs-event-driven-design/?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/tradeoffs-event-driven-design/en/headerimage/tradeoffs-event-driven-design-header-1782458803116.jpg"/&gt;&lt;p&gt;Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, this article details where the design breaks down—state management, partition limits, deduplication, JVM tuning, cascading consumer failures—and the Redis-backed patterns that fixed each.&lt;/p&gt; &lt;i&gt;By Sagar Deepak Joshi&lt;/i&gt;</description>
      <category>Apache Kafka</category>
      <category>Redis</category>
      <category>Spring Boot</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Tue, 30 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/tradeoffs-event-driven-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Sagar Deepak Joshi</dc:creator>
      <dc:date>2026-06-30T09:00:00Z</dc:date>
      <dc:identifier>/articles/tradeoffs-event-driven-design/en</dc:identifier>
    </item>
    <item>
      <title>Java News Roundup: Hardwood 1.0, Endive 1.0, Azul Payara, Quarkus, WildFly, LangChain4j, OSSI</title>
      <link>https://www.infoq.com/news/2026/06/java-news-roundup-jun22-2026/?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/2026/06/java-news-roundup-jun22-2026/en/headerimage/java-news-roundup-image-1782763644221.jpg"/&gt;&lt;p&gt;This week's Java roundup for June 22nd, 2026, features news highlighting: the GA releases of Hardwood 1.0 and Endive 1.0; the June 2026 edition of Azul Payara; point releases of Quarkus, LangChain4j; the first beta release of WildFly 41; and introducing Eliya JDK and the Open Source Sustainability Initiative (OSSI), the latter of which was founded by HeroDevs and Commonhaus Foundation.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>LangChain</category>
      <category>JDK 28</category>
      <category>Endive</category>
      <category>Hardwood</category>
      <category>Azul Payara</category>
      <category>Quarkus</category>
      <category>JDK 27</category>
      <category>Eliya JDK</category>
      <category>JBoss WildFly</category>
      <category>Java</category>
      <category>Open Source Sustainability Initiative</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 29 Jun 2026 23:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/java-news-roundup-jun22-2026/?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>2026-06-29T23:30:00Z</dc:date>
      <dc:identifier>/news/2026/06/java-news-roundup-jun22-2026/en</dc:identifier>
    </item>
    <item>
      <title>Eliya 25 Brings a JVM-Level Diagnostic Profile to OpenJDK 25 LTS</title>
      <link>https://www.infoq.com/news/2026/06/eliya-jvm-diagnostic-profile/?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/eliya-jvm-diagnostic-profile/en/headerimage/java-istock-image-01-1762720446658-1782700328980.jpg"/&gt;&lt;p&gt;Asymm Systems has released Eliya 25.0.3, an OpenJDK 25 LTS distribution aimed at improving production diagnostics in Java environments. It consolidates several HotSpot features into an opt-in Production profile. Eliya is designed for teams needing reliable diagnostic data, especially in regulated settings. Future enhancements are planned for Phase 2.&lt;/p&gt; &lt;i&gt;By A N M Bazlur Rahman&lt;/i&gt;</description>
      <category>Monitoring</category>
      <category>Open JDK</category>
      <category>Observability</category>
      <category>JDK</category>
      <category>JDK 25</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 29 Jun 2026 14:50:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/eliya-jvm-diagnostic-profile/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>A N M Bazlur Rahman</dc:creator>
      <dc:date>2026-06-29T14:50:00Z</dc:date>
      <dc:identifier>/news/2026/06/eliya-jvm-diagnostic-profile/en</dc:identifier>
    </item>
    <item>
      <title>Inside Target’s LLM-Based System for Semantic Matching in Marketing Forecast Pipelines</title>
      <link>https://www.infoq.com/news/2026/06/target-ai-campaign-forecasting/?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/target-ai-campaign-forecasting/en/headerimage/generatedHeaderImage-1780529558601.jpg"/&gt;&lt;p&gt;Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows. Evaluation shows 75% top-1 and 100% top-3 coverage. The system reduces manual effort, improves consistency, and uses feedback loops to refine retrieval using campaign outcomes.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Systems Thinking</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Large Concept Models</category>
      <category>Data Analytics</category>
      <category>vector databases</category>
      <category>Observability</category>
      <category>Evolutionary Architecture</category>
      <category>MLOps</category>
      <category>Machine Learning</category>
      <category>Marketing</category>
      <category>Generative AI</category>
      <category>Model Fine Tuning</category>
      <category>Business Analytics</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 29 Jun 2026 14:26:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/target-ai-campaign-forecasting/?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>2026-06-29T14:26:00Z</dc:date>
      <dc:identifier>/news/2026/06/target-ai-campaign-forecasting/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Million PDFs: Building a Modern Document Infrastructure with Rust and Typst</title>
      <link>https://www.infoq.com/presentations/document-infrastructure-rust-typst/?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/document-infrastructure-rust-typst/en/mediumimage/ErikSteiger-medium-1782220478687.jpg"/&gt;&lt;p&gt;Erik Steiger discusses the operational pain of legacy PDF generation in regulated banking and manufacturing. He explains how transitioning from resource-heavy engines like Puppeteer and LaTeX to a serverless Rust architecture powered by Typst can drop render latencies below 2ms. He shares how applying Git and Docker concepts to template registries ensures ironclad compliance and rapid debugging.&lt;/p&gt; &lt;i&gt;By Erik Steiger&lt;/i&gt;</description>
      <category>InfoQ Dev Summit Munich 2025</category>
      <category>Rust</category>
      <category>Performance &amp; Scalability</category>
      <category>Transcripts</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 29 Jun 2026 12:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/document-infrastructure-rust-typst/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Erik Steiger</dc:creator>
      <dc:date>2026-06-29T12:35:00Z</dc:date>
      <dc:identifier>/presentations/document-infrastructure-rust-typst/en</dc:identifier>
    </item>
    <item>
      <title>Article: Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution</title>
      <link>https://www.infoq.com/articles/security-ai-threat-evolution/?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/security-ai-threat-evolution/en/headerimage/security-ai-threat-evolution-header-1782202845102.jpg"/&gt;&lt;p&gt;This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows.&lt;/p&gt; &lt;i&gt;By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic&lt;/i&gt;</description>
      <category>AI Security</category>
      <category>Governance</category>
      <category>Virtual Panel</category>
      <category>Adversarial Machine Learning</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Mon, 29 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/security-ai-threat-evolution/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic</dc:creator>
      <dc:date>2026-06-29T11:00:00Z</dc:date>
      <dc:identifier>/articles/security-ai-threat-evolution/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Architectural Patterns: Moving Beyond Cloud-Native to Local-First - Insights from Adam Wiggins</title>
      <link>https://www.infoq.com/podcasts/natural-evolution-cloud-native/?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/natural-evolution-cloud-native/en/smallimage/the-infoq-podcast-logo-thumbnail-1782207787976.jpg"/&gt;&lt;p&gt;In this episode, Heroku co-founder and Ink &amp; Switch founder Adam Wiggins argues for a 'local-first' architecture that reconciles cloud-based collaboration with the performance and data ownership of local software. He explores the role of CRDTs and version control primitives in non-code domains, and examines how a hybrid AI future might leverage local models for core productivity tasks.&lt;/p&gt; &lt;i&gt;By Adam Wiggins&lt;/i&gt;</description>
      <category>The InfoQ Podcast</category>
      <category>Architecture</category>
      <category>Cloud</category>
      <category>CRDT</category>
      <category>Cloud-Native</category>
      <category>Architecture &amp; Design</category>
      <category>podcast</category>
      <pubDate>Mon, 29 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/natural-evolution-cloud-native/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Adam Wiggins</dc:creator>
      <dc:date>2026-06-29T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/natural-evolution-cloud-native/en</dc:identifier>
    </item>
  </channel>
</rss>
