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    <title>InfoQ - Architecture &amp; Design</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Architecture &amp; Design feed</description>
    <item>
      <title>Kubernetes 1.37 Released: Stable Metrics API  and Rootless Kubelet in Beta</title>
      <link>https://www.infoq.com/news/2026/09/kubernetes-1-37/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;The Cloud Native Computing Foundation (CNCF) announced the release of Kubernetes 1.37, named "Garhwal", emphasizing its focus on stability, security,   and AI/ML workload optimization.&lt;/p&gt; &lt;i&gt;By Mostafa Radwan&lt;/i&gt;</description>
      <category>Containers</category>
      <category>Kubernetes</category>
      <category>Cloud Computing</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 21 Sep 2026 01:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/kubernetes-1-37/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Mostafa Radwan</dc:creator>
      <dc:date>2026-09-21T01:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/kubernetes-1-37/en</dc:identifier>
    </item>
    <item>
      <title>Alibaba Open Sources OpenCodeReview for AI-Assisted Code Review</title>
      <link>https://www.infoq.com/news/2026/09/alibaba-opencodereview/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/alibaba-opencodereview/en/headerimage/generatedHeaderImage-1789473748016.jpg"/&gt;&lt;p&gt;Alibaba recently open-sourced OpenCodeReview, an AI-powered code review CLI that combines deterministic pipelines for file selection, bundling, and rule matching with an LLM agent for dynamic code analysis. It supports built-in checks for issues such as null-pointer exceptions, thread safety, XSS, and SQL injection.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Static Analysis</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Code Reviews</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Sun, 20 Sep 2026 11:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/alibaba-opencodereview/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-09-20T11:57:00Z</dc:date>
      <dc:identifier>/news/2026/09/alibaba-opencodereview/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Measures Origin TLS Preferences, Cutting Handshake Retries from 52% to 3.7%</title>
      <link>https://www.infoq.com/news/2026/09/cloudflare-automatic-key-exchang/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/cloudflare-automatic-key-exchang/en/headerimage/generatedHeaderImage-1789633148686.jpg"/&gt;&lt;p&gt;Cloudflare has replaced its static X25519 guess for origin TLS handshakes with per-origin measurement. HelloRetryRequests on scanned origins fell from roughly 52% to 3.7%, removing over 150 ms from p90 latency. Post-quantum connections completing in one round trip rose from 0% to 99.2%, though only 12.8% of origins support it.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Cloudflare</category>
      <category>Cryptography</category>
      <category>Performance</category>
      <category>Infrastructure</category>
      <category>Networking</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Sun, 20 Sep 2026 08:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/cloudflare-automatic-key-exchang/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-09-20T08:15:00Z</dc:date>
      <dc:identifier>/news/2026/09/cloudflare-automatic-key-exchang/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP</title>
      <link>https://www.infoq.com/presentations/linkedin-context-engineering/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/presentations/linkedin-context-engineering/en/mediumimage/ajay-prakash-medium-1789021803159.jpg"/&gt;&lt;p&gt;Ajay Prakash discusses how LinkedIn overcomes AI agent limitations in large codebases. He explains Contextual Agent Playbooks and Tools - built on Model Context Protocol (MCP) - which serves procedural memory, code search, and runbooks directly to coding agents. Prakash shares architectural details and operational guardrails that deliver a 20% productivity boost with zero loss in reliability.&lt;/p&gt; &lt;i&gt;By Ajay Prakash&lt;/i&gt;</description>
      <category>LinkedIn</category>
      <category>Agents</category>
      <category>QCon AI Boston 2026</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>presentation</category>
      <pubDate>Sat, 19 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/linkedin-context-engineering/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Ajay Prakash</dc:creator>
      <dc:date>2026-09-19T11:00:00Z</dc:date>
      <dc:identifier>/presentations/linkedin-context-engineering/en</dc:identifier>
    </item>
    <item>
      <title>AWS Lambda Pushes Serverless toward Long-Running Workloads</title>
      <link>https://www.infoq.com/news/2026/09/lambda-90-minute-timeout/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/lambda-90-minute-timeout/en/headerimage/generatedHeaderImage-1789110995851.jpg"/&gt;&lt;p&gt;AWS Lambda now allows functions running on Lambda Managed Instances to run for up to 90 minutes, six times longer than the previous 15-minute limit, further blurring the line between a Lambda invocation and a traditional server. The limit remains unchanged for traditional synchronous requests.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Serverless</category>
      <category>AWS Lambda</category>
      <category>Cloud</category>
      <category>AWS</category>
      <category>Event Driven Architecture</category>
      <category>Cloud Computing</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Sat, 19 Sep 2026 09:11:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/lambda-90-minute-timeout/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-09-19T09:11:00Z</dc:date>
      <dc:identifier>/news/2026/09/lambda-90-minute-timeout/en</dc:identifier>
    </item>
    <item>
      <title>DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags</title>
      <link>https://www.infoq.com/news/2026/09/doordash-feature-flag-cleanup/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/doordash-feature-flag-cleanup/en/headerimage/generatedHeaderImage-1788418529526.jpg"/&gt;&lt;p&gt;DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Feature Injection</category>
      <category>A/B Testing</category>
      <category>Developer Experience</category>
      <category>Experiment Driven Development</category>
      <category>Static Analysis</category>
      <category>AI Assisted Coding</category>
      <category>Automation</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>git</category>
      <category>AI Coding</category>
      <category>Large language models</category>
      <category>github</category>
      <category>Agents</category>
      <category>Feature Toggle</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 18 Sep 2026 13:50:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/doordash-feature-flag-cleanup/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-18T13:50:00Z</dc:date>
      <dc:identifier>/news/2026/09/doordash-feature-flag-cleanup/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Complexity and Creativity in Software Engineering</title>
      <link>https://www.infoq.com/presentations/ai-software-engineering-complexity/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-software-engineering-complexity/en/mediumimage/philip-mortimer-medium-1789021228203.jpeg"/&gt;&lt;p&gt;Phillip Mortimer discusses the shift toward write-only software driven by AI code generation. He explains why traditional pull requests are broken and shares how engineering leaders can manage complexity by decoupling intent from implementation, automating code reviews, and building self-healing architecture to unleash developer creativity across senior engineering and architecture teams.&lt;/p&gt; &lt;i&gt;By Phillip Mortimer&lt;/i&gt;</description>
      <category>QCon London 2026</category>
      <category>Testing</category>
      <category>Management</category>
      <category>AI Assisted Coding</category>
      <category>Code Quality</category>
      <category>Leadership</category>
      <category>Architecture</category>
      <category>Productivity</category>
      <category>Transcripts</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>presentation</category>
      <pubDate>Fri, 18 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-software-engineering-complexity/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Phillip Mortimer</dc:creator>
      <dc:date>2026-09-18T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-software-engineering-complexity/en</dc:identifier>
    </item>
    <item>
      <title>Article: Architecting Secure and Scalable Facial Verification Systems</title>
      <link>https://www.infoq.com/articles/secure-scalable-facial-verification/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/articles/secure-scalable-facial-verification/en/headerimage/secure-scalable-facial-verification-header-1789468796366.jpg"/&gt;&lt;p&gt;When three thousand employees verify at once, synchronous API calls collapse. This article presents a four-layer architecture for high-volume face verification: client-side filtering that cut cloud costs 30%, decoupled detection and verification enabling 10x scaling, risk-based dynamic thresholds, and zero-trust privacy with consent gates and automated data purging for GDPR and HIPAA.&lt;/p&gt; &lt;i&gt;By Praveen Kumar Gopalakrishnan&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Architecture</category>
      <category>Security</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Fri, 18 Sep 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/secure-scalable-facial-verification/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Praveen Kumar Gopalakrishnan</dc:creator>
      <dc:date>2026-09-18T09:00:00Z</dc:date>
      <dc:identifier>/articles/secure-scalable-facial-verification/en</dc:identifier>
    </item>
    <item>
      <title>GPT-6 Astra is the First Model OpenAI Classifies as Critical for Cybersecurity</title>
      <link>https://www.infoq.com/news/2026/09/gpt-6-astra-critical-cyber/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/gpt-6-astra-critical-cyber/en/headerimage/generatedHeaderImage-1789311714190.jpg"/&gt;&lt;p&gt;OpenAI has classified GPT-6 Astra at the Critical cybersecurity threshold under its Preparedness Framework, a first. In expert-led testing the model found previously unknown vulnerabilities in a browser and an OS kernel and built working exploits. The same system card reports a substantial decline in chain-of-thought monitorability.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Generative AI</category>
      <category>Cloud</category>
      <category>Agents</category>
      <category>Architecture</category>
      <category>Azure</category>
      <category>Governance</category>
      <category>Machine Learning</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Thu, 17 Sep 2026 04:59:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/gpt-6-astra-critical-cyber/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-09-17T04:59:00Z</dc:date>
      <dc:identifier>/news/2026/09/gpt-6-astra-critical-cyber/en</dc:identifier>
    </item>
    <item>
      <title>Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads</title>
      <link>https://www.infoq.com/news/2026/09/dropbox-riviera-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Asynchronous Architecture</category>
      <category>Tika</category>
      <category>plugins</category>
      <category>Apache</category>
      <category>Large language models</category>
      <category>Platform Engineering</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Distributed Systems</category>
      <category>Enterprise Content Management</category>
      <category>Architecture</category>
      <category>Data Pipelines</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 16 Sep 2026 14:42:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/dropbox-riviera-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-16T14:42:00Z</dc:date>
      <dc:identifier>/news/2026/09/dropbox-riviera-ai-platform/en</dc:identifier>
    </item>
    <item>
      <title>Lyft Moves Streaming Fleet to Apache Flink Kubernetes Operator</title>
      <link>https://www.infoq.com/news/2026/09/lyft-flink-k8s-operator/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/lyft-flink-k8s-operator/en/headerimage/header-1788786681315.jpeg"/&gt;&lt;p&gt;Lyft has moved hundreds of production Flink jobs from a 2020 in-house Kubernetes operator to the Apache Flink Kubernetes Operator, unlocking last-state upgrades, in-place autoscaling and resource autotuning across the fleet.&lt;/p&gt; &lt;i&gt;By Mark Silvester&lt;/i&gt;</description>
      <category>Apache Flink</category>
      <category>Kubernetes</category>
      <category>Kubernetes Operator</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Wed, 16 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/lyft-flink-k8s-operator/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Mark Silvester</dc:creator>
      <dc:date>2026-09-16T11:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/lyft-flink-k8s-operator/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review</title>
      <link>https://www.infoq.com/presentations/duolingo-ai-literacy-code-review/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/presentations/duolingo-ai-literacy-code-review/en/mediumimage/sarah-deitke-medium-1788338781338.jpeg"/&gt;&lt;p&gt;Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates.&lt;/p&gt; &lt;i&gt;By Sarah Deitke&lt;/i&gt;</description>
      <category>Developer Experience</category>
      <category>QCon London 2026</category>
      <category>Culture</category>
      <category>Adoption</category>
      <category>Automation</category>
      <category>Code Reviews</category>
      <category>Metrics</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>presentation</category>
      <pubDate>Wed, 16 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/duolingo-ai-literacy-code-review/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Sarah Deitke</dc:creator>
      <dc:date>2026-09-16T11:00:00Z</dc:date>
      <dc:identifier>/presentations/duolingo-ai-literacy-code-review/en</dc:identifier>
    </item>
    <item>
      <title>Java 27 Delivers Post-Quantum Cryptography, Future Language Innovation, Helidon 27, JavaFX 27</title>
      <link>https://www.infoq.com/news/2026/09/java27-released/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/java27-released/en/headerimage/java-istock-image-01-1789513423942.jpg"/&gt;&lt;p&gt;Oracle has released version 27 of the Java programming language and virtual machine. As the second non-LTS release since JDK 25, the final feature set includes nine JEPs, five of which are still progressing through the preview and incubator stages. This release focuses on strengthening security, future language innovation, and projects under the auspices of the Java Verified Portfolio.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>Java</category>
      <category>Helidon</category>
      <category>Application Security</category>
      <category>JDK 28</category>
      <category>Quantum Computing</category>
      <category>Project Valhalla</category>
      <category>JavaFX</category>
      <category>JDK 27</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Wed, 16 Sep 2026 10:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/java27-released/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Michael Redlich</dc:creator>
      <dc:date>2026-09-16T10:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/java27-released/en</dc:identifier>
    </item>
    <item>
      <title>From Memory-Hungry HNSW to Quantized SPANN: the Technical Evolution of Pinterest's Manas Platform</title>
      <link>https://www.infoq.com/news/2026/09/pinterest-search/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/pinterest-search/en/headerimage/HG-1789489973604.jpg"/&gt;&lt;p&gt;Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized performance, and is transitioning to multi-vector models for refined relevance matching.&lt;/p&gt; &lt;i&gt;By Olimpiu Pop&lt;/i&gt;</description>
      <category>Search</category>
      <category>Algorithms</category>
      <category>Infrastructure</category>
      <category>Optimization</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 16 Sep 2026 06:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/pinterest-search/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Olimpiu Pop</dc:creator>
      <dc:date>2026-09-16T06:06:00Z</dc:date>
      <dc:identifier>/news/2026/09/pinterest-search/en</dc:identifier>
    </item>
    <item>
      <title>How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes</title>
      <link>https://www.infoq.com/news/2026/09/turnstile-system-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Sun's Solaris influenced modern systems engineering, with key innovations like the Slab Allocator for efficient memory management, OpenZFS for advanced storage, and DTrace for observability. Its turnstile mechanism addressed issues with mutexes, promoting lean locking designs that are reflected in contemporary programming languages and web browsers, enhancing performance and memory efficiency.&lt;/p&gt; &lt;i&gt;By Olimpiu Pop&lt;/i&gt;</description>
      <category>Solaris</category>
      <category>Concurrency</category>
      <category>Architecture</category>
      <category>Operating Systems</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Tue, 15 Sep 2026 06:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/turnstile-system-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture+%26+Design</guid>
      <dc:creator>Olimpiu Pop</dc:creator>
      <dc:date>2026-09-15T06:06:00Z</dc:date>
      <dc:identifier>/news/2026/09/turnstile-system-design/en</dc:identifier>
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