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    <title>InfoQ - Large language models</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Large language models feed</description>
    <item>
      <title>OpenAI Open-Sources Symphony, a SPEC.md for Autonomous Coding Agent Orchestration</title>
      <link>https://www.infoq.com/news/2026/05/openai-symphony-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/openai-symphony-agents/en/headerimage/openai-symphony-1779047353917.jpg"/&gt;&lt;p&gt;OpenAI Symphony is an agent orchestrator that uses project-management tools, like issue trackers, as a control plan to coordinate multiple coding agents. Instead of developers managing interactive coding sessions, Symphony manages "tasks" by assigning each one to a dedicated agent that works autonomously to completion. Once a task is finished, a human is in charge to review the resulting output.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>OpenAI</category>
      <category>Large language models</category>
      <category>Open Source</category>
      <category>Agents</category>
      <category>Orchestration</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Sun, 17 May 2026 20:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/openai-symphony-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-05-17T20:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/openai-symphony-agents/en</dc:identifier>
    </item>
    <item>
      <title>Ubuntu Embraces Local AI Instead of Cloud-First OS Integration</title>
      <link>https://www.infoq.com/news/2026/05/ubuntu-on-device-ai/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/ubuntu-on-device-ai/en/headerimage/ubuntu-on-device-ai-1778958515321.jpeg"/&gt;&lt;p&gt;Ubuntu has outlined its AI strategy, describing it as a deliberate departure from industry trends towards cloud-centric, AI-first operating systems. Instead, the company says, Ubuntu will focus future releases on local intelligence, modular design, and strict user control.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Operating Systems</category>
      <category>Linux</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sat, 16 May 2026 20:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/ubuntu-on-device-ai/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-05-16T20:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/ubuntu-on-device-ai/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Introduces Routines for Claude Code Automation</title>
      <link>https://www.infoq.com/news/2026/05/anthropic-routines-claude/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/anthropic-routines-claude/en/headerimage/generatedHeaderImage-1778774115333.jpg"/&gt;&lt;p&gt;Anthropic has introduced a new feature called Routines for Claude Code, allowing developers to configure automated coding workflows that run on schedules, through API calls, or in response to external events.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Claude</category>
      <category>OpenAI</category>
      <category>Large language models</category>
      <category>Software Development</category>
      <category>github</category>
      <category>Artificial Intelligence</category>
      <category>Anthropic</category>
      <category>AI Coding</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 15 May 2026 15:51:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/anthropic-routines-claude/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-05-15T15:51:00Z</dc:date>
      <dc:identifier>/news/2026/05/anthropic-routines-claude/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Accelerating LLM-Driven Developer Productivity at Zoox</title>
      <link>https://www.infoq.com/presentations/ai-software-development/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-software-development/en/mediumimage/medium-1778065503665.jpg"/&gt;&lt;p&gt;Amit Navindgi discusses the systematic shift at Zoox from fragmented documentation to an AI-driven ecosystem. He explains how they built "Cortex," a secure platform integrating RAG, multi-modal LLMs, and contributor-friendly agent APIs.  He shares practical strategies for driving adoption through AI champions and hackathons, emphasizing the move from deterministic workflows to autonomous agents.&lt;/p&gt; &lt;i&gt;By Amit Navindgi&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Transcripts</category>
      <category>Software Development</category>
      <category>Artificial Intelligence</category>
      <category>QCon San Francisco 2025</category>
      <category>Culture &amp; Methods</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Thu, 14 May 2026 13:05:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-software-development/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Amit Navindgi</dc:creator>
      <dc:date>2026-05-14T13:05:00Z</dc:date>
      <dc:identifier>/presentations/ai-software-development/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Traces Six Weeks of Claude Code Quality Complaints to Three Overlapping Product Changes</title>
      <link>https://www.infoq.com/news/2026/05/anthropic-claude-code-postmortem/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/anthropic-claude-code-postmortem/en/headerimage/generatedHeaderImage-1778491231246.jpg"/&gt;&lt;p&gt;Anthropic published a postmortem tracing six weeks of Claude Code quality complaints to three overlapping product-layer changes: a reasoning effort downgrade, a caching bug that progressively erased the model's own thinking, and a system prompt verbosity limit that caused a 3% quality drop. The API and model weights were unaffected. All issues were resolved April 20.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Generative AI</category>
      <category>AI Architecture</category>
      <category>Code Quality</category>
      <category>Anthropic</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 14 May 2026 09:16:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/anthropic-claude-code-postmortem/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-05-14T09:16:00Z</dc:date>
      <dc:identifier>/news/2026/05/anthropic-claude-code-postmortem/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Launches Claude Platform on AWS</title>
      <link>https://www.infoq.com/news/2026/05/anthropic-claude-aws/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/anthropic-claude-aws/en/headerimage/generatedHeaderImage-1778682420283.jpg"/&gt;&lt;p&gt;Anthropic has announced the general availability of Claude Platform on AWS, a new deployment option that gives AWS customers direct access to Anthropic’s native Claude platform using AWS authentication, billing, and monitoring services.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Claude</category>
      <category>AWS</category>
      <category>Large language models</category>
      <category>Software Development</category>
      <category>Cloud Computing</category>
      <category>Artificial Intelligence</category>
      <category>Anthropic</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 13 May 2026 19:20:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/anthropic-claude-aws/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-05-13T19:20:00Z</dc:date>
      <dc:identifier>/news/2026/05/anthropic-claude-aws/en</dc:identifier>
    </item>
    <item>
      <title>Coder Agents Enable Running AI Coding Workflows on Self-Hosted Infrastructure</title>
      <link>https://www.infoq.com/news/2026/05/coder-agents-self-hosted-ai/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/coder-agents-self-hosted-ai/en/headerimage/coder-agents-self-hosted-ai-1778516884639.jpeg"/&gt;&lt;p&gt;Coder Agents is a model-agnostic platform designed to let organizations run AI coding agents on their own infrastructure, rather than relying on cloud-based services. This allows teams to maintain full control over code, data, and execution environments.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI Coding</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 11 May 2026 17:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/coder-agents-self-hosted-ai/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-05-11T17:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/coder-agents-self-hosted-ai/en</dc:identifier>
    </item>
    <item>
      <title>Article: Local-First AI Inference: a Cloud Architecture Pattern for Cost-Effective Document Processing</title>
      <link>https://www.infoq.com/articles/local-first-ai-inference-cloud/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</link>
      <description>&lt;img src="https://res.infoq.com/articles/local-first-ai-inference-cloud/en/headerimage/Local-First-AI-Inference-A-Cloud-Architecture-Pattern-for-Cost-Effective-Document-Processing-header-1778141518292.jpg"/&gt;&lt;p&gt;The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.&lt;/p&gt; &lt;i&gt;By Obinna Iheanachor&lt;/i&gt;</description>
      <category>GPT-4</category>
      <category>Microsoft Azure</category>
      <category>Generative AI</category>
      <category>Model Inference</category>
      <category>Observability</category>
      <category>Azure</category>
      <category>Artificial Intelligence</category>
      <category>Cloud</category>
      <category>Cost Optimization</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Mon, 11 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/local-first-ai-inference-cloud/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models</guid>
      <dc:creator>Obinna Iheanachor</dc:creator>
      <dc:date>2026-05-11T11:00:00Z</dc:date>
      <dc:identifier>/articles/local-first-ai-inference-cloud/en</dc:identifier>
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