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    <title>InfoQ - Generative AI</title>
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      <title>Mini book: Architecting Autonomy: Decentralising Architecture Inside an Organization</title>
      <link>https://www.infoq.com/minibooks/architecting-autonomy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Generative+AI</link>
      <description>&lt;img src="https://res.infoq.com/minibooks/architecting-autonomy/en/smallimage/emag-124-Architecting-Autonomy-thumb-image-1778565056506.jpg"/&gt;&lt;p&gt;As AI accelerates delivery cycles, traditional centralized architecture becomes a bottleneck. This eMag brings together practitioner insights on decentralizing decision-making and moving from approval chains to guardrails. Discover frameworks for rethinking the architect’s role, creating enabling platforms, and balancing edge autonomy with the strategic coherence needed to scale effectively.&lt;/p&gt; &lt;i&gt;By InfoQ&lt;/i&gt;</description>
      <category>Generative AI</category>
      <category>InfoQ Certification Program</category>
      <category>Architecture ICSAET</category>
      <category>Governance</category>
      <category>Emergent Architecture</category>
      <category>Platforms</category>
      <category>AI Architecture</category>
      <category>Leadership</category>
      <category>Scalability</category>
      <category>Artificial Intelligence</category>
      <category>Sociotechnical Architecture</category>
      <category>Architecture Decision Records</category>
      <category>Architecture &amp; Design</category>
      <category>minibook</category>
      <pubDate>Fri, 15 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/minibooks/architecting-autonomy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Generative+AI</guid>
      <dc:creator>InfoQ</dc:creator>
      <dc:date>2026-05-15T11:00:00Z</dc:date>
      <dc:identifier>/minibooks/architecting-autonomy/en</dc:identifier>
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    <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=Generative+AI</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>Generative AI</category>
      <category>Anthropic</category>
      <category>AI Architecture</category>
      <category>Large language models</category>
      <category>Code Quality</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=Generative+AI</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>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=Generative+AI</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>Model Inference</category>
      <category>Generative AI</category>
      <category>Azure</category>
      <category>Observability</category>
      <category>Microsoft Azure</category>
      <category>Cost Optimization</category>
      <category>Artificial Intelligence</category>
      <category>GPT-4</category>
      <category>Cloud</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=Generative+AI</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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