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    <title>InfoQ - Artificial Intelligence</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Artificial Intelligence feed</description>
    <item>
      <title>Rigorous Yet Sustainable Human Reviews in the AI Era</title>
      <link>https://www.infoq.com/news/2026/09/human-reviews-AI-era/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/human-reviews-AI-era/en/headerimage/human-reviews-ai-era-header-1788179792608.jpg"/&gt;&lt;p&gt;Mandatory AI checks paired with manual spikes for complex changes keep developers sharp. Teams can boost velocity by skipping peer reviews on low-risk PRs and using AI approvals, provided most developers are code owners and teams are small.&lt;/p&gt; &lt;i&gt;By Ben Linders&lt;/i&gt;</description>
      <category>Code Reviews</category>
      <category>Code Quality</category>
      <category>Craft Conference</category>
      <category>Feedback</category>
      <category>Code Generation</category>
      <category>Artificial Intelligence</category>
      <category>Culture &amp; Methods</category>
      <category>news</category>
      <pubDate>Thu, 03 Sep 2026 11:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/human-reviews-AI-era/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Ben Linders</dc:creator>
      <dc:date>2026-09-03T11:57:00Z</dc:date>
      <dc:identifier>/news/2026/09/human-reviews-AI-era/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Beyond Prompting: Context Engineering for Production-Grade AI</title>
      <link>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/presentations/context-engineering-redis-llm-architecture/en/mediumimage/ricardo-ferreira-medium-1787820318843.jpg"/&gt;&lt;p&gt;Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints.&lt;/p&gt; &lt;i&gt;By Ricardo Ferreira&lt;/i&gt;</description>
      <category>Agents</category>
      <category>QCon AI Boston 2026</category>
      <category>LangChain4j</category>
      <category>Redis</category>
      <category>Large language models</category>
      <category>Generative AI</category>
      <category>Transcripts</category>
      <category>Caching</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Architecture</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 02 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Ricardo Ferreira</dc:creator>
      <dc:date>2026-09-02T11:00:00Z</dc:date>
      <dc:identifier>/presentations/context-engineering-redis-llm-architecture/en</dc:identifier>
    </item>
    <item>
      <title>OpenClaw 2.0 Releases with Simplified Setup and Collaborative Agents</title>
      <link>https://www.infoq.com/news/2026/09/openclaw-2-release/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/openclaw-2-release/en/headerimage/generatedHeaderImage-1788278004063.jpg"/&gt;&lt;p&gt;OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Large language models</category>
      <category>Claude</category>
      <category>ChatGPT</category>
      <category>OpenAI</category>
      <category>Anthropic</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 18:47:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/openclaw-2-release/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-09-01T18:47:00Z</dc:date>
      <dc:identifier>/news/2026/09/openclaw-2-release/en</dc:identifier>
    </item>
    <item>
      <title>HCP Terraform Positions Itself as the Control Plane for AI-Driven Infrastructure</title>
      <link>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/en/headerimage/generatedHeaderImage-1787561761498.jpg"/&gt;&lt;p&gt;HashiCorp is positioning HCP Terraform as the governance and control plane for a new generation of AI-driven infrastructure, arguing that the rapid adoption of coding agents is shifting the biggest infrastructure challenge from writing configuration to verifying and safely executing it.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Terraform</category>
      <category>AI Development</category>
      <category>Artificial Intelligence</category>
      <category>AI Architecture</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-09-01T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/hcp-terraform-ai-driven-control/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Scott Jenson on Evolving Desktop OS, Local-First, &amp; Agentic UX</title>
      <link>https://www.infoq.com/podcasts/evolving-desktop-agentic-ux/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/evolving-desktop-agentic-ux/en/smallimage/infoq-podcast-500-1787734073812.jpg"/&gt;&lt;p&gt;In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models.&lt;/p&gt; &lt;i&gt;By Scott Jenson&lt;/i&gt;</description>
      <category>Operating Systems</category>
      <category>Local First</category>
      <category>Large language models</category>
      <category>UX</category>
      <category>Desktop</category>
      <category>Privacy</category>
      <category>Design</category>
      <category>The InfoQ Podcast</category>
      <category>Artificial Intelligence</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>podcast</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/evolving-desktop-agentic-ux/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Scott Jenson</dc:creator>
      <dc:date>2026-08-31T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/evolving-desktop-agentic-ux/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Running AI at the Edge: Running Real Workloads Directly in the Browser</title>
      <link>https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/presentations/local-ai-browser-inference-privacy/en/mediumimage/james-hall-medium-1787813225372.jpeg"/&gt;&lt;p&gt;James Hall discusses the strategic and technical imperative of moving AI workloads from cloud providers to local edge devices. He shares practical approaches using WebGPU, Transformers.js, and DuckDB to achieve near-native performance in JavaScript. Through real-world case studies, he explains how to minimize data privacy risks, optimize browser inference, and build rigorous evaluation suites.&lt;/p&gt; &lt;i&gt;By James Hall&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>AI Security</category>
      <category>Web Development</category>
      <category>Edge Computing</category>
      <category>QCon London 2026</category>
      <category>Privacy</category>
      <category>Transcripts</category>
      <category>GPU</category>
      <category>Web Browser</category>
      <category>Local Inference</category>
      <category>Cloud Computing</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>James Hall</dc:creator>
      <dc:date>2026-08-31T11:00:00Z</dc:date>
      <dc:identifier>/presentations/local-ai-browser-inference-privacy/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Architecting the Data Layer for AI Agents: from Transactional Systems to MCP and Semantic Models</title>
      <link>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/presentations/enterprise-data-architecture-ai-agents/en/mediumimage/fabiane-nardon-medium-1787218382028.jpeg"/&gt;&lt;p&gt;Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-deterministic LLMs across precision, security, and cost. Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems.&lt;/p&gt; &lt;i&gt;By Fabiane Nardon&lt;/i&gt;</description>
      <category>Model Context Protocol (MCP)</category>
      <category>Agents</category>
      <category>QCon AI Boston 2026</category>
      <category>AI Security</category>
      <category>Large language models</category>
      <category>AI Cost Optimisation</category>
      <category>Data Mesh</category>
      <category>Transcripts</category>
      <category>Semantic Web</category>
      <category>Agentic AI Architecture</category>
      <category>AI Architecture</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Sat, 29 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Fabiane Nardon</dc:creator>
      <dc:date>2026-08-29T11:00:00Z</dc:date>
      <dc:identifier>/presentations/enterprise-data-architecture-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>Google Cloud Launches AI-powered Agents to Simplify Database Lifecycle Management</title>
      <link>https://www.infoq.com/news/2026/08/google-database-operation-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/google-database-operation-agents/en/headerimage/google-database-operations-agents-1787842474177.jpeg"/&gt;&lt;p&gt;Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Database</category>
      <category>Gemini</category>
      <category>Agents</category>
      <category>Cloud</category>
      <category>Google Cloud</category>
      <category>Artificial Intelligence</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Thu, 27 Aug 2026 15:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/google-database-operation-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-08-27T15:00:00Z</dc:date>
      <dc:identifier>/news/2026/08/google-database-operation-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Can Claude Fix Itself? Using LLMs for Incident Response</title>
      <link>https://www.infoq.com/presentations/claude-sre-incidents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/presentations/claude-sre-incidents/en/mediumimage/alex-palcuie-medium-1786539336984.jpg"/&gt;&lt;p&gt;Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise.&lt;/p&gt; &lt;i&gt;By Alex Palcuie&lt;/i&gt;</description>
      <category>Site Reliability Engineering</category>
      <category>Incident Response</category>
      <category>Large language models</category>
      <category>Claude</category>
      <category>On-call</category>
      <category>QCon London 2026</category>
      <category>Automation</category>
      <category>Observability</category>
      <category>Transcripts</category>
      <category>Artificial Intelligence</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 26 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/claude-sre-incidents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Alex Palcuie</dc:creator>
      <dc:date>2026-08-26T11:00:00Z</dc:date>
      <dc:identifier>/presentations/claude-sre-incidents/en</dc:identifier>
    </item>
    <item>
      <title>BMC Vulnerabilities Put Thousands of Servers at Risk of Hardware-Level Compromise</title>
      <link>https://www.infoq.com/news/2026/08/bmc-vulnerabilities/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/bmc-vulnerabilities/en/headerimage/generatedHeaderImage-1787152274946.jpg"/&gt;&lt;p&gt;Security researchers are warning that thousands of enterprise servers could be exposed to compromise through vulnerabilities in their Baseboard Management Controllers (BMCs) - specialized processors embedded in server motherboards that provide administrators with remote, out-of-band control.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Hardware</category>
      <category>Security Vulnerabilities</category>
      <category>Artificial Intelligence</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 25 Aug 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/bmc-vulnerabilities/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-08-25T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/08/bmc-vulnerabilities/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: The Human Edge: Why Brownfield Codebases Need Mob Programming, Not Just AI Vibes</title>
      <link>https://www.infoq.com/podcasts/brownfield-codebases-mob-programming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/brownfield-codebases-mob-programming/en/smallimage/infoq-podcast-500-1787057874183.jpg"/&gt;&lt;p&gt;Asgaut Mjølne Söderbom and Ola Hast discuss the evolution of their software engineering practices past continuous deployment and pair engineering. The conversation continues where it left off in the previous episode and focuses on the experiments in adopting Claude Code and the reasons why they consider it good for everything else, but not coding.&lt;/p&gt; &lt;i&gt;By Asgaut Mjølne Söderbom, Ola Hast&lt;/i&gt;</description>
      <category>Team Performance</category>
      <category>Performance</category>
      <category>Continuous Improvement</category>
      <category>TDD</category>
      <category>Continuous Deployment</category>
      <category>The InfoQ Podcast</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>podcast</category>
      <pubDate>Mon, 24 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/brownfield-codebases-mob-programming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Asgaut Mjølne Söderbom, Ola Hast</dc:creator>
      <dc:date>2026-08-24T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/brownfield-codebases-mob-programming/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Prompt to Prod: Engineering an Autonomous SDLC at Scale</title>
      <link>https://www.infoq.com/presentations/autonomous-ai-software-development-roblox/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</link>
      <description>&lt;img src="https://res.infoq.com/presentations/autonomous-ai-software-development-roblox/en/mediumimage/andrew-swerdlow-medium-1787218240356.jpg"/&gt;&lt;p&gt;Andrew Swerdlow shares how Roblox scales autonomous software development from prompt to production. He discusses building robust security sandboxes, extracting institutional knowledge via code review exemplars, updating engineering infrastructure, and redefining productivity metrics around feature velocity and long-running AI turns to achieve trusted, automated deployment at scale.&lt;/p&gt; &lt;i&gt;By Andrew Swerdlow&lt;/i&gt;</description>
      <category>Code Reviews</category>
      <category>Metrics</category>
      <category>Developer Experience</category>
      <category>QCon AI Boston 2026</category>
      <category>autonomous</category>
      <category>Prompt Engineering</category>
      <category>Orchestration</category>
      <category>Transcripts</category>
      <category>Platform Engineering</category>
      <category>Architecture</category>
      <category>Productivity</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Mon, 24 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/autonomous-ai-software-development-roblox/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Artificial+Intelligence</guid>
      <dc:creator>Andrew Swerdlow</dc:creator>
      <dc:date>2026-08-24T11:00:00Z</dc:date>
      <dc:identifier>/presentations/autonomous-ai-software-development-roblox/en</dc:identifier>
    </item>
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