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    <title>InfoQ</title>
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    <description>InfoQ feed</description>
    <item>
      <title>PingCAP Breach Exposes Customer Records and Source Code</title>
      <link>https://www.infoq.com/news/2026/10/pingcap-cloud-data-breach/?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/2026/10/pingcap-cloud-data-breach/en/headerimage/generatedHeaderImage-1792787545089.jpg"/&gt;&lt;p&gt;PingCAP recently confirmed unauthorized access to its internal systems and the theft of source code and customer-related data. The attacker, cyber extortion group FulcrumSec, claimed to have stolen 10.6 TB of data, including TiDB Cloud control-plane records, customer SQL statements, API keys, connection logs, billing information, internal documents, and 413 private source-code repositories.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Cloud Security</category>
      <category>Security Breach</category>
      <category>Distributed_Data</category>
      <category>Cloud Data Management</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sun, 25 Oct 2026 06:27:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/pingcap-cloud-data-breach/?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>2026-10-25T06:27:00Z</dc:date>
      <dc:identifier>/news/2026/10/pingcap-cloud-data-breach/en</dc:identifier>
    </item>
    <item>
      <title>Google Optimizes Agent Development Kit's 200K-line Python Codebase Overnight with Gemini 4 Argon</title>
      <link>https://www.infoq.com/news/2026/10/google-argon-optimizes-adk/?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/2026/10/google-argon-optimizes-adk/en/headerimage/functiongemma-1792855681633.jpeg"/&gt;&lt;p&gt;Google explained how an 11-hour unattended run of Gemini 4 Argon produced two accepted optimizations for Google Agent Development Kit's codebase, reducing in-memory session-preparation overhead by about 40% in long sessions and cold package import time by 21%.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Large language models</category>
      <category>Google</category>
      <category>Python</category>
      <category>AI coding agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sat, 24 Oct 2026 16:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/google-argon-optimizes-adk/?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>2026-10-24T16:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/google-argon-optimizes-adk/en</dc:identifier>
    </item>
    <item>
      <title>Google’s Data Agent Kit Hits GA, Splitting Agent Guardrails Between Skills and IAM</title>
      <link>https://www.infoq.com/news/2026/10/data-agent-kit-ga/?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;Google has made Data Agent Kit generally available, a free set of MCP tools and 37 open-source skills that connect coding agents to more than fifteen Google Data Cloud services. Two kinds of guardrail ship with it, and practitioners need to tell them apart: one is a skill asking the agent to stop, the other an IAM deny policy that refuses the call.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Google Cloud</category>
      <category>Database</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Sat, 24 Oct 2026 08:34:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/data-agent-kit-ga/?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>2026-10-24T08:34:00Z</dc:date>
      <dc:identifier>/news/2026/10/data-agent-kit-ga/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Reclaims 100 TB of RAM by Optimizing Consistent Hashing</title>
      <link>https://www.infoq.com/news/2026/10/cloudflare-hashing-optimization/?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/2026/10/cloudflare-hashing-optimization/en/headerimage/generatedHeaderImage-1790410589563.jpg"/&gt;&lt;p&gt;Cloudflare recently documented how it reduced the memory footprint of its internal load-balancing service by more than 100 TB by optimizing the Rust implementation of consistent hashing in pingora-ketama. The changes compacted hash-ring entries and used statistical analysis of hash distribution and collision rates to reduce the number of hashes per backend by about 90%.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Load Balancing</category>
      <category>Rust</category>
      <category>Performance Tuning</category>
      <category>Cloudflare</category>
      <category>Hashing</category>
      <category>Infrastructure Optimisation</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Sat, 24 Oct 2026 08:10:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/cloudflare-hashing-optimization/?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>2026-10-24T08:10:00Z</dc:date>
      <dc:identifier>/news/2026/10/cloudflare-hashing-optimization/en</dc:identifier>
    </item>
    <item>
      <title>CSIRO Uses Serverless Architecture to Query 100x More Genomic Variants on AWS</title>
      <link>https://www.infoq.com/news/2026/10/csiro-serverless-genomic-variant/?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;CSIRO has developed Serverless Beacon, a serverless architecture for querying genomic variants without copying VCF files into a central database. The system combines Amazon S3, Lambda, Athena and DynamoDB, using metadata indexes and HTTP byte range requests to query compressed genomic regions. Tests reported 1.52 second average latency for 10,000 base pair queries.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Scalability</category>
      <category>AWS Lambda</category>
      <category>Serverless</category>
      <category>Cost Optimization</category>
      <category>S3</category>
      <category>Applied Research</category>
      <category>Distributed Systems</category>
      <category>Architecture</category>
      <category>AWS</category>
      <category>Distributed Query</category>
      <category>Science</category>
      <category>Cloud Architecture</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 23 Oct 2026 14:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/csiro-serverless-genomic-variant/?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>2026-10-23T14:30:00Z</dc:date>
      <dc:identifier>/news/2026/10/csiro-serverless-genomic-variant/en</dc:identifier>
    </item>
    <item>
      <title>AWS Introduces Lambda Web Functions for Node.js Applications</title>
      <link>https://www.infoq.com/news/2026/10/aws-lambda-web-functions/?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/2026/10/aws-lambda-web-functions/en/headerimage/generatedHeaderImage-1792762388734.jpg"/&gt;&lt;p&gt;AWS recently introduced Lambda Web Functions, allowing developers to run Node.js web applications using frameworks such as Express and Hono directly on Lambda, with support for concurrent requests and response streaming.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>AWS Lambda</category>
      <category>Serverless</category>
      <category>Node.js</category>
      <category>Web Applications</category>
      <category>AWS</category>
      <category>Cloud Architecture</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 23 Oct 2026 12:49:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/aws-lambda-web-functions/?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>2026-10-23T12:49:00Z</dc:date>
      <dc:identifier>/news/2026/10/aws-lambda-web-functions/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Brings On-Demand CPU and Memory Profiling to Production Workers</title>
      <link>https://www.infoq.com/news/2026/10/cloudflare-cpu-memory-profiling/?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/2026/10/cloudflare-cpu-memory-profiling/en/headerimage/generatedHeaderImage-1791971960113.jpg"/&gt;&lt;p&gt;Cloudflare has introduced on-demand CPU and memory profiling for Workers and Durable Objects, allowing developers to investigate performance bottlenecks in production using interactive flamegraphs.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Cloudflare</category>
      <category>Observability</category>
      <category>Performance</category>
      <category>Memory</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 23 Oct 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/cloudflare-cpu-memory-profiling/?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>2026-10-23T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/cloudflare-cpu-memory-profiling/en</dc:identifier>
    </item>
    <item>
      <title>Article: Multi-Cloud FinOps: An Engineering Framework for Cross-Cloud Cost Optimization</title>
      <link>https://www.infoq.com/articles/multi-cloud-finops-cost-optimization/?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-cloud-finops-cost-optimization/en/headerimage/multi-cloud-finops-cost-optimization-header-1792499448017.jpg"/&gt;&lt;p&gt;Multi-cloud flexibility comes with hidden egress costs driven by service placement and communication patterns. This article applies Backend FinOps to cross-cloud architecture, covering cost visibility through FOCUS, workload placement, synchronization models, and CI/CD-integrated cost governance. A controlled experiment shows architecture changes saved far more than rate negotiation alone.&lt;/p&gt; &lt;i&gt;By Vivek Arora&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Cost Optimization</category>
      <category>FinOps</category>
      <category>Architecture</category>
      <category>Cloud Computing</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Fri, 23 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/multi-cloud-finops-cost-optimization/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Vivek Arora</dc:creator>
      <dc:date>2026-10-23T11:00:00Z</dc:date>
      <dc:identifier>/articles/multi-cloud-finops-cost-optimization/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Move Fast, Don’t Break Trust: Shipping Constantly with Humans and Beyond</title>
      <link>https://www.infoq.com/presentations/monzo-microservices-devex/?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/monzo-microservices-devex/en/mediumimage/suhail-patel-medium-1791448951499.jpg"/&gt;&lt;p&gt;Suhail Patel discusses how Monzo structures its software engineering organization and technical stack to ship safely hundreds of times a day. He shares insights into opinionated microservice abstractions, tenancy-based staging, static analysis checks, continuous profiling, demonstrating how a strong internal platform enables engineers and LLMs to iterate rapidly without sacrificing reliability.&lt;/p&gt; &lt;i&gt;By Suhail Patel&lt;/i&gt;</description>
      <category>Observability</category>
      <category>Microservices</category>
      <category>Security</category>
      <category>Transcripts</category>
      <category>Platform Engineering</category>
      <category>Go Language</category>
      <category>QCon London 2026</category>
      <category>Code Quality</category>
      <category>Continuous Deployment</category>
      <category>AI Development</category>
      <category>Developer Experience</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>presentation</category>
      <pubDate>Fri, 23 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/monzo-microservices-devex/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Suhail Patel</dc:creator>
      <dc:date>2026-10-23T11:00:00Z</dc:date>
      <dc:identifier>/presentations/monzo-microservices-devex/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Adds Opt-In Security Scanning for Critical Open-Source Projects</title>
      <link>https://www.infoq.com/news/2026/10/anthropic-oss-security-scanner/?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/2026/10/anthropic-oss-security-scanner/en/headerimage/generatedHeaderImage-1792523938215.jpg"/&gt;&lt;p&gt;AI company Anthropic has launched OSS Scanner, a free, opt-in service to find vulnerabilities in open-source projects. Enrolled maintainers receive periodic security reports generated by the company's strongest models, including Claude Mythos. The reports are sent without human review, and project maintainers can choose to validate their findings and decide which proposed fixes to accept.&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Anthropic</category>
      <category>AI Security</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 23 Oct 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/anthropic-oss-security-scanner/?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>2026-10-23T09:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/anthropic-oss-security-scanner/en</dc:identifier>
    </item>
    <item>
      <title>NgRx 22: Resource Extensions and Dynamic Deep Signals for Angular</title>
      <link>https://www.infoq.com/news/2026/10/ngrx-22-angular/?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/2026/10/ngrx-22-angular/en/headerimage/generatedHeaderImage-1792664456149.jpg"/&gt;&lt;p&gt;NgRx 22 adds experimental resource extensions that change Angular's resource behavior, so value() is retained during reloads and no longer throws on errors. It also introduces dynamic deep signals for union types, a SignalStoreFeatureType utility, and ESLint v10 support. Upgrading requires Angular 22 and TypeScript 6.0, and most breaking changes have automated migrations.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>JavaScript</category>
      <category>AngularJS</category>
      <category>TypeScript</category>
      <category>Angular</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 23 Oct 2026 08:08:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/ngrx-22-angular/?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>2026-10-23T08:08:00Z</dc:date>
      <dc:identifier>/news/2026/10/ngrx-22-angular/en</dc:identifier>
    </item>
    <item>
      <title>Study Finds AI-generated Code Fails to Boost Productivity Due to Downstream Bottlenecks</title>
      <link>https://www.infoq.com/news/2026/10/jellyfish-ai-productivity/?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/2026/10/jellyfish-ai-productivity/en/headerimage/claude-built-c-compiler-1792676797236.jpeg"/&gt;&lt;p&gt;A recent Harvard study of 718 firms, using the Jellyfish analytics platform, finds that although AI coding assistants and agents increase code volume, this does not translate into a corresponding increase in completed work, partly because AI-generated code increases the workload in later stages of development, particularly code review.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Claude</category>
      <category>copilot</category>
      <category>Productivity</category>
      <category>AI coding agents</category>
      <category>github</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 22 Oct 2026 14:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/jellyfish-ai-productivity/?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>2026-10-22T14:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/jellyfish-ai-productivity/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Actors for Agents: Patterns for Production AI</title>
      <link>https://www.infoq.com/presentations/actors-for-production-ai-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/presentations/actors-for-production-ai-agents/en/mediumimage/medium-1792065780503.jpeg"/&gt;&lt;p&gt;Manju Rajashekhar explains how the actor model solves the complex runtime challenges of production AI agents. By treating agents as distributed systems, he discusses state ownership, message passing, write-through state, and supervision boundaries to manage failures, continuations, and mutable execution graphs, moving agentic workflows beyond fragile demos to resilient architecture.&lt;/p&gt; &lt;i&gt;By Manju Rajashekhar&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Fault Tolerance</category>
      <category>Transcripts</category>
      <category>Architecture</category>
      <category>Distributed Systems</category>
      <category>Actor Model</category>
      <category>QCon AI Boston 2026</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Thu, 22 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/actors-for-production-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Manju Rajashekhar</dc:creator>
      <dc:date>2026-10-22T11:00:00Z</dc:date>
      <dc:identifier>/presentations/actors-for-production-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>Vercel Labs Open-Sources fx: a Zig Coding Agent Designed to Be Embedded</title>
      <link>https://www.infoq.com/news/2026/10/vercel-zig-fx-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/2026/10/vercel-zig-fx-agent/en/headerimage/generatedHeaderImage-1792649903570.jpg"/&gt;&lt;p&gt;Vercel Labs has released fx, an experimental open-source coding agent written in Zig. It runs as a CLI, a script component, or an embedded library, including a WebAssembly build. Early feedback questioned its model-agnostic claim and a name conflict with an existing tool. Vercel says the project is pre-1.0 and changing quickly.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>Agents</category>
      <category>AI Coding</category>
      <category>OpenAI</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 22 Oct 2026 09:33:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/vercel-zig-fx-agent/?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>2026-10-22T09:33:00Z</dc:date>
      <dc:identifier>/news/2026/10/vercel-zig-fx-agent/en</dc:identifier>
    </item>
    <item>
      <title>Article: AI Changes the Data Interface, Not the Query Engine</title>
      <link>https://www.infoq.com/articles/ai-changes-data-interface/?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/ai-changes-data-interface/en/headerimage/header-1792487239242.jpg"/&gt;&lt;p&gt;This article presents a disaster-recovery analytics workflow in which an AI agent generates constrained SQL or Python, DuckDB computes reproducible facts from Parquet, and the agent interprets the results. It explains the determinism split, replayable analysis, and four signals for deciding when an embedded engine is sufficient or a managed lakehouse is justified.&lt;/p&gt; &lt;i&gt;By Peihao Yuan&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>duckdb</category>
      <category>Data Lake</category>
      <category>Disaster Recovery</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Thu, 22 Oct 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/ai-changes-data-interface/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Peihao Yuan</dc:creator>
      <dc:date>2026-10-22T09:00:00Z</dc:date>
      <dc:identifier>/articles/ai-changes-data-interface/en</dc:identifier>
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