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    <title>InfoQ</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ feed</description>
    <item>
      <title>Cloudflare Makes Basin Serverless Analytics Generally Available</title>
      <link>https://www.infoq.com/news/2026/11/cloudflare-basin-analytics/?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/2026/11/cloudflare-basin-analytics/en/headerimage/generatedHeaderImage-1792696674786.jpg"/&gt;&lt;p&gt;Cloudflare has announced the general availability of Basin, a serverless analytics platform built on Apache Iceberg and R2 Object Storage. The managed service supports open data formats and integrates with tools such as DuckDB, Snowflake, and Apache Spark, with no data egress fees.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Cloudflare</category>
      <category>SQL</category>
      <category>Data Analytics</category>
      <category>Apache Iceberg</category>
      <category>Data Portability</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 19:14:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/cloudflare-basin-analytics/?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>2026-11-10T19:14:00Z</dc:date>
      <dc:identifier>/news/2026/11/cloudflare-basin-analytics/en</dc:identifier>
    </item>
    <item>
      <title>Google Finds that Technical Debt Can Derail Coding Agents Through "Agent Distraction"</title>
      <link>https://www.infoq.com/news/2026/11/google-agent-distraction/?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/2026/11/google-agent-distraction/en/headerimage/google-agetn-distraction-1794330388187.jpeg"/&gt;&lt;p&gt;In a recent article, Google senior staff software engineer Sam Saccone and senior staff data scientist Madhuri Garikapati highlighted the cost of agent distraction, where AI coding agents get derailed by stale technical debt, such as old TODO comments, obsolete bug references, and outdated specs, leading them to embark on unrequested "side-quests".&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>AI coding agents</category>
      <category>Technical Debt</category>
      <category>Google</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 18:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-agent-distraction/?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>2026-11-10T18:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-agent-distraction/en</dc:identifier>
    </item>
    <item>
      <title>Pinterest and Intel Take Open-Source Profiling to Internet Scale</title>
      <link>https://www.infoq.com/news/2026/11/pinterest-intel-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/2026/11/pinterest-intel-profiling/en/headerimage/generatedHeaderImage-1793692423906.jpg"/&gt;&lt;p&gt;Pinterest has partnered with Intel to extend gProfiler, an open-source continuous profiler that provides production performance visibility across an internet-scale fleet of infrastructure.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Code Generation</category>
      <category>Open Source</category>
      <category>Profilers</category>
      <category>Code Profiler</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/pinterest-intel-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>2026-11-10T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/pinterest-intel-profiling/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Batch Intelligence at Scale: Cost-Efficient Multi-Agent LLM Workflows with Built-In Resilience</title>
      <link>https://www.infoq.com/presentations/scaling-multi-agent-llm-batch/?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/scaling-multi-agent-llm-batch/en/mediumimage/AdityaMulik-medium-1793278766506.jpeg"/&gt;&lt;p&gt;Aditya Mulik explains how Walmart Global Tech deployed multi-agent LLM workflows across 4,700 stores for nightly batch inventory forecasting. He discusses tackling production hurdles - schema violations, cross-department data mismatches, and network drops - via MCP grounding and loop agents, while slashing cloud inference costs from $100k+ to $10k per month shifting from PTUs to pay-as-you-go.&lt;/p&gt; &lt;i&gt;By Aditya Mulik&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>Resilience</category>
      <category>QCon AI Boston 2026</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Transcripts</category>
      <category>Batch Processing</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI Cost Optimisation</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 10 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/scaling-multi-agent-llm-batch/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Aditya Mulik</dc:creator>
      <dc:date>2026-11-10T11:00:00Z</dc:date>
      <dc:identifier>/presentations/scaling-multi-agent-llm-batch/en</dc:identifier>
    </item>
    <item>
      <title>Article: LLM Reliability Layers Under Test: Accuracy, Yield, and the Limits of Verification</title>
      <link>https://www.infoq.com/articles/llm-reliability-layers-limits-accuracy/?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/llm-reliability-layers-limits-accuracy/en/headerimage/llm-reliability-layers-limits-accuracy-header-1793873839325.jpg"/&gt;&lt;p&gt;Verification layers sort outputs by trust rather than fixing errors. In a benchmark of six reliability layers, voting and self-correction failed to recover accuracy - they only withheld doubtful records. Production systems gain more reliability by replacing generation with deterministic lookups, tracking yield over raw accuracy, and resolving extraction bottlenecks before stacking checks.&lt;/p&gt; &lt;i&gt;By Pushpdeep Mishra, Wejdan Bagais&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>AI Architecture</category>
      <category>Reliability</category>
      <category>InfoQ Certification Program</category>
      <category>Verification</category>
      <category>Software Engineering</category>
      <category>AI Engineering Certification</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Tue, 10 Nov 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/llm-reliability-layers-limits-accuracy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Pushpdeep Mishra, Wejdan Bagais</dc:creator>
      <dc:date>2026-11-10T09:00:00Z</dc:date>
      <dc:identifier>/articles/llm-reliability-layers-limits-accuracy/en</dc:identifier>
    </item>
    <item>
      <title>Google Cloud Spend Caps Reach General Availability with API and Monthly Resets</title>
      <link>https://www.infoq.com/news/2026/11/google-cloud-spend-caps/?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/2026/11/google-cloud-spend-caps/en/headerimage/generatedHeaderImage-1793609514337.jpg"/&gt;&lt;p&gt;Google Cloud has made spend caps on Cloud Billing Budgets generally available, pausing new usage of a service in a project once a monthly budget is hit, without deleting resources. GA adds an API, estimated live costs, and automatic monthly resets, but caps cover only the Gemini API, the Agent Platform, Cloud Run, and Cloud Run functions, and overages during enforcement delays are still billed.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Google Cloud Platform</category>
      <category>Cost Optimization</category>
      <category>Google Cloud</category>
      <category>Cloud Computing</category>
      <category>FinOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 08:39:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-cloud-spend-caps/?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>2026-11-10T08:39:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-cloud-spend-caps/en</dc:identifier>
    </item>
    <item>
      <title>Effect 4.0 Ships: Dependency-Free Rewrite with 5x Smaller Bundles and Long-Term Support</title>
      <link>https://www.infoq.com/news/2026/11/effect-4-0-release/?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/2026/11/effect-4-0-release/en/headerimage/generatedHeaderImage-1794245718049.jpg"/&gt;&lt;p&gt;Effect 4.0, a TypeScript framework for typed errors and structured concurrency, was released on 30 September 2026 with a rewritten fiber runtime. The team reports a bundle size drop from 35.6 kB to 7.1 kB and about 6.4x higher throughput, though these figures are not independently verified. It also adds long-term support through September 2029.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>JavaScript</category>
      <category>Web Development</category>
      <category>TypeScript</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Tue, 10 Nov 2026 06:33:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/effect-4-0-release/?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>2026-11-10T06:33:00Z</dc:date>
      <dc:identifier>/news/2026/11/effect-4-0-release/en</dc:identifier>
    </item>
    <item>
      <title>Java News Roundup: TornadoVM, A2A Java, Open Liberty, Open J Proxy, Gradle, EclipseStore</title>
      <link>https://www.infoq.com/news/2026/11/java-news-roundup-nov02-2026/?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/2026/11/java-news-roundup-nov02-2026/en/headerimage/java-news-roundup-image-1794249482778.jpg"/&gt;&lt;p&gt;This week's Java roundup for November 2nd, 2026, features news highlighting: point releases of TornadoVM, TornadoInsight,  A2A for Java, Gradle and EclipseStore; the November 2026 edition of Open Liberty; the third release candidate of Open J Proxy; the second release candidates of Micrometer Metrics and Micrometer Tracing; and the third beta release of Quarkus 4.0.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>TornadoVM</category>
      <category>JReleaser</category>
      <category>EclipseStore</category>
      <category>Agent2Agent</category>
      <category>Gradle</category>
      <category>Micrometer</category>
      <category>Java</category>
      <category>Quarkus</category>
      <category>Open Liberty</category>
      <category>JDK 28</category>
      <category>Open J Proxy</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 18:40:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/java-news-roundup-nov02-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Michael Redlich</dc:creator>
      <dc:date>2026-11-09T18:40:00Z</dc:date>
      <dc:identifier>/news/2026/11/java-news-roundup-nov02-2026/en</dc:identifier>
    </item>
    <item>
      <title>Google Releases Gemini 4 Argon for Robotics Control, Simulation, and Policy Improvement</title>
      <link>https://www.infoq.com/news/2026/11/google-gemini-argon/?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/2026/11/google-gemini-argon/en/headerimage/generatedHeaderImage-1794224974378.jpg"/&gt;&lt;p&gt;Google has released Gemini 4 Argon framing it as a general-purpose physical-reasoning model for robot control, Real2Sim scene construction, multimodal labeling of robot video, and iterative policy improvement, rather than as a single end-effector policy.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Robotics</category>
      <category>Artificial Intelligence</category>
      <category>Google</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 15:44:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/google-gemini-argon/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-11-09T15:44:00Z</dc:date>
      <dc:identifier>/news/2026/11/google-gemini-argon/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Develops Public Certificate Authority for Post-Quantum Web Security</title>
      <link>https://www.infoq.com/news/2026/11/cloudflare-merkle-certificates/?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;Cloudflare is developing a public certificate authority for Merkle Tree Certificates (MTCs) to reduce the bandwidth overhead of post-quantum cryptography. A Chrome experiment served billions of MTCs to 50% of Chrome Beta 146 for selected domains, reporting 9% faster median performance. Wider deployment depends on browser support, transparency, and production-scale validation.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Network Security</category>
      <category>Web Browser</category>
      <category>Architecture</category>
      <category>Internet</category>
      <category>Security</category>
      <category>Cryptography</category>
      <category>Digital Signature</category>
      <category>Quantum Computing</category>
      <category>Chrome</category>
      <category>Digital Identity</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 14:35:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/cloudflare-merkle-certificates/?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>2026-11-09T14:35:00Z</dc:date>
      <dc:identifier>/news/2026/11/cloudflare-merkle-certificates/en</dc:identifier>
    </item>
    <item>
      <title>AWS Launches TorchNeuron to Run PyTorch Models on AWS Trainium Without Modifications</title>
      <link>https://www.infoq.com/news/2026/11/aws-torchneuron-trainium-pytorch/?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/2026/11/aws-torchneuron-trainium-pytorch/en/headerimage/functiongemma-1794220315671.jpeg"/&gt;&lt;p&gt;At the recent PyTorch Conference North America 2026, Amazon introduced TorchNeuron, a native PyTorch backend that enables developers to run existing PyTorch code on AWS Trainium AI chips with virtually no code modifications.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>PyTorch</category>
      <category>AWS</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/aws-torchneuron-trainium-pytorch/?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>2026-11-09T11:00:00Z</dc:date>
      <dc:identifier>/news/2026/11/aws-torchneuron-trainium-pytorch/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Distributed LLM Inference on Kubernetes with llm-d, Wasm, &amp; TornadoVM</title>
      <link>https://www.infoq.com/podcasts/distributed-llm-inference-kubernetes/?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/podcasts/distributed-llm-inference-kubernetes/en/smallimage/infoq-podcast-500-1793797932006.jpg"/&gt;&lt;p&gt;In this episode, Edoardo Vacchi discusses the evolving landscape of WebAssembly on the backend, running Java workloads on GPUs using TornadoVM, and optimizing distributed AI inference using llm-d and vLLM on Kubernetes. Edoardo explains how llm-d manages cache locality (KV and prefix caches), request routing, and batch inference across Kubernetes clusters to maximize expensive GPU utilization.&lt;/p&gt; &lt;i&gt;By Edoardo Vacchi&lt;/i&gt;</description>
      <category>The InfoQ Podcast</category>
      <category>TornadoVM</category>
      <category>WebAssembly</category>
      <category>Artificial Intelligence</category>
      <category>Kubernetes</category>
      <category>Large language models</category>
      <category>Java</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>podcast</category>
      <pubDate>Mon, 09 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/distributed-llm-inference-kubernetes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Edoardo Vacchi</dc:creator>
      <dc:date>2026-11-09T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/distributed-llm-inference-kubernetes/en</dc:identifier>
    </item>
    <item>
      <title>Article: Agentic AI Architecture: Current Challenges and Future Opportunities</title>
      <link>https://www.infoq.com/articles/agentic-ai-architecture-challenges-opportunities/?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/agentic-ai-architecture-challenges-opportunities/en/headerimage/agentic-ai-architecture-challenges-opportunities-header-1793709540690.jpg"/&gt;&lt;p&gt;Agentic AI architecture extends microservices and cloud-native architectures with AI agents that work with context, memory, and protocols such as MCP and A2A. The article covers bounded-context integration, task-dependent coordination strategies, new security risks, and observability needs. It expects gradual adoption alongside existing architectures, with platforms absorbing common components.&lt;/p&gt; &lt;i&gt;By Rafał Gancarz&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Context Engineering</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Large language models</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Mon, 09 Nov 2026 10:52:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/agentic-ai-architecture-challenges-opportunities/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Rafał Gancarz</dc:creator>
      <dc:date>2026-11-09T10:52:00Z</dc:date>
      <dc:identifier>/articles/agentic-ai-architecture-challenges-opportunities/en</dc:identifier>
    </item>
    <item>
      <title>Pinterest Replaces Journey Clustering with 4B Qwen3 LLM, Reports Improved Engagement</title>
      <link>https://www.infoq.com/news/2026/11/pinterest-journey/?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/2026/11/pinterest-journey/en/headerimage/generatedHeaderImage-1793998579474.jpg"/&gt;&lt;p&gt;Pinterest has moved its user-journey inference system from a multi-stage clustering pipeline to a fine-tuned 4-billion-parameter language model, reporting a 1.1% improvement in email click-through and 1.3% higher push opens.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>Model Inference</category>
      <category>Generative AI</category>
      <category>Large language models</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 07:25:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/pinterest-journey/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global</guid>
      <dc:creator>Matt Foster</dc:creator>
      <dc:date>2026-11-09T07:25:00Z</dc:date>
      <dc:identifier>/news/2026/11/pinterest-journey/en</dc:identifier>
    </item>
    <item>
      <title>Tokio Releases Topcoat: A Batteries-Included Full-Stack Rust Framework</title>
      <link>https://www.infoq.com/news/2026/11/topcoat-rust-framework/?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/2026/11/topcoat-rust-framework/en/headerimage/generatedHeaderImage-1793983973654.jpg"/&gt;&lt;p&gt;Topcoat is an open-source, server-rendered full-stack web framework for Rust from the Tokio team, now public at version 0.9. It uses Rust expressions translated to JavaScript for reactivity instead of WebAssembly. It targets typical database-driven apps. Releases are unstable, and authentication and background jobs remain on the roadmap.&lt;/p&gt; &lt;i&gt;By Daniel Curtis&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>Rust</category>
      <category>Django</category>
      <category>Frameworks</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 09 Nov 2026 06:42:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/11/topcoat-rust-framework/?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>2026-11-09T06:42:00Z</dc:date>
      <dc:identifier>/news/2026/11/topcoat-rust-framework/en</dc:identifier>
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