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    <title>InfoQ - AI, ML &amp; Data Engineering - News</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ AI, ML &amp; Data Engineering News feed</description>
    <item>
      <title>EmbeddingGemma 2 Expands Beyond Text to Support Code, Images, Video, and Audio</title>
      <link>https://www.infoq.com/news/2026/10/embeddinggemma-2-multimodal/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/embeddinggemma-2-multimodal/en/headerimage/cactus-on-device-inference-1792401134408.jpeg"/&gt;&lt;p&gt;Based on Gemma 4 and released under the Apache 2.0 license, Google's EmbeddingGemma 2 is a compact, open-weight, multimodal embedding model designed to make semantic search and retrieval-augmented generation (RAG) more efficient while optimizing performance for on-device deployment.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>retrieval</category>
      <category>Large language models</category>
      <category>Mobile</category>
      <category>Agents</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Google</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 19 Oct 2026 10:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/embeddinggemma-2-multimodal/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-10-19T10:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/embeddinggemma-2-multimodal/en</dc:identifier>
    </item>
    <item>
      <title>Reverse Engineer Anything Project Brings AI Agents to Binary Analysis</title>
      <link>https://www.infoq.com/news/2026/10/reverse-engineer-anything/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/reverse-engineer-anything/en/headerimage/generatedHeaderImage-1791736473714.jpg"/&gt;&lt;p&gt;Reverse Engineer Anything (REA) is an open-source project with over 200K GitHub stars that connects coding agents such as Claude Code and Codex to reverse-engineering tools like Ghidra, Hopper, and IDA. The project lets developers inspect compiled binaries, trace function calls, and investigate how applications implement specific features.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Reverse Engineering</category>
      <category>Static Analysis</category>
      <category>Agents</category>
      <category>Licensing</category>
      <category>Claude</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sat, 17 Oct 2026 06:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/reverse-engineer-anything/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-10-17T06:28:00Z</dc:date>
      <dc:identifier>/news/2026/10/reverse-engineer-anything/en</dc:identifier>
    </item>
    <item>
      <title>Amazon Acquires DuckLabs and Integrates DuckDB in Amazon Aurora PostgreSQL</title>
      <link>https://www.infoq.com/news/2026/10/amazon-acquires-ducklabs/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/amazon-acquires-ducklabs/en/headerimage/generatedHeaderImage-1792158812926.jpg"/&gt;&lt;p&gt;Amazon has acquired DuckLabs, the company behind the analytical database DuckDB. Co-founders Hannes Mühleisen and Mark Raasveldt will continue to lead the team under AWS. The acquisition aims to enhance the development and reach of DuckDB while maintaining its open-source status. Amazon Aurora PostgreSQL gets its first DuckDB-powered feature following the acquisition.&lt;/p&gt; &lt;i&gt;By Gianmarco Nalin&lt;/i&gt;</description>
      <category>Data Analytics</category>
      <category>Cloud</category>
      <category>S3</category>
      <category>Apache Parquet</category>
      <category>duckdb</category>
      <category>Data Lake</category>
      <category>Amazon RDS</category>
      <category>Open Source</category>
      <category>Amazon</category>
      <category>Amazon Web Services</category>
      <category>AWS</category>
      <category>Acquisitions</category>
      <category>Apache Iceberg</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sat, 17 Oct 2026 06:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/amazon-acquires-ducklabs/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Gianmarco Nalin</dc:creator>
      <dc:date>2026-10-17T06:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/amazon-acquires-ducklabs/en</dc:identifier>
    </item>
    <item>
      <title>Google EnvHarness Enables Self-Evolving Agents to Learn in Dynamics Environments</title>
      <link>https://www.infoq.com/news/2026/10/envharness-self-evolving-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/envharness-self-evolving-agents/en/headerimage/cloudflare-writeguard-1792176346559.jpeg"/&gt;&lt;p&gt;Google researchers open sourced EnvHarness, a framework that dynamically adapts agent-training environments to an agent's weaknesses without modifying the agent, environment, or evaluation system. According to the researchers, this approach can improve both learning effectiveness and efficiency while helping agents overcome their weaknesses.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Google</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 16 Oct 2026 19:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/envharness-self-evolving-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-10-16T19:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/envharness-self-evolving-agents/en</dc:identifier>
    </item>
    <item>
      <title>AI in Production: Panellists Highlight Silent Failures, Harness Engineering, and Agent Governance</title>
      <link>https://www.infoq.com/news/2026/10/ai-systems-production/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/ai-systems-production/en/headerimage/generatedHeaderImage-1792118181680.jpg"/&gt;&lt;p&gt;A recent InfoQ webinar examined operational challenges in deploying agentic systems. Key points included issues like semantic and embedding drift, which can lead to significant errors. Experts discussed the need for automated code reviews, continuous evaluation of models, and enhanced privacy measures. The session emphasised a strategic approach to deployment and security management.&lt;/p&gt; &lt;i&gt;By Olimpiu Pop&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Continuous Integration</category>
      <category>Data Privacy</category>
      <category>Data Pipelines</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 16 Oct 2026 14:14:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/ai-systems-production/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Olimpiu Pop</dc:creator>
      <dc:date>2026-10-16T14:14:00Z</dc:date>
      <dc:identifier>/news/2026/10/ai-systems-production/en</dc:identifier>
    </item>
    <item>
      <title>Grafana Applies SLOs and Error Budgets to AI Agent Behaviour</title>
      <link>https://www.infoq.com/news/2026/10/grafana-slo-ai-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/grafana-slo-ai-agent/en/headerimage/generatedHeaderImage-1791475262295.jpg"/&gt;&lt;p&gt;Grafana Labs is proposing a familiar reliability engineering approach for one of the less familiar problems in modern software: how to measure whether an AI agent is actually behaving well.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>AIOps</category>
      <category>Agentic AI Architecture</category>
      <category>Observability</category>
      <category>Grafana</category>
      <category>AI Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 16 Oct 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/grafana-slo-ai-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-10-16T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/grafana-slo-ai-agent/en</dc:identifier>
    </item>
    <item>
      <title>AWS Introduces Strands Decider 2B and Strands Box for AI Agent Development</title>
      <link>https://www.infoq.com/news/2026/10/aws-strands-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/aws-strands-agents/en/headerimage/generatedHeaderImage-1792079603910.jpg"/&gt;&lt;p&gt;AWS has introduced two open-source projects for AI agent development: Strands Decider 2B, a small model designed to make decisions within agent workflows, and Strands Box, a sandbox for controlling agent execution. Both projects address different aspects of building agents that use tools, execute code, and interact with external systems.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Typesafe</category>
      <category>AWS</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 16 Oct 2026 10:47:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/aws-strands-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-10-16T10:47:00Z</dc:date>
      <dc:identifier>/news/2026/10/aws-strands-agents/en</dc:identifier>
    </item>
    <item>
      <title>Microsoft Commits Maia to Multi-Gigawatt Inference, and its Efficiency Metric Draws Questions</title>
      <link>https://www.infoq.com/news/2026/10/azure-maia-300/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/azure-maia-300/en/headerimage/generatedHeaderImage-1791943943820.jpg"/&gt;&lt;p&gt;Microsoft will scale its Maia accelerator family to multi-gigawatt deployments from 2027, making it the primary inference system for its own products. Maia 300 collapses scale-up and scale-out onto one Ethernet fabric and moves KV state between prefill and decode workers without leaving it. Practitioners have questioned the tokens-per-watt claim that leads the announcement.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Hardware</category>
      <category>Cloud</category>
      <category>Performance</category>
      <category>Architecture</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 16 Oct 2026 06:02:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/azure-maia-300/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-10-16T06:02:00Z</dc:date>
      <dc:identifier>/news/2026/10/azure-maia-300/en</dc:identifier>
    </item>
    <item>
      <title>Meta Extends Confidential Computing to AI Glasses with Private Processing</title>
      <link>https://www.infoq.com/news/2026/10/meta-private-process-ai-glasses/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/meta-private-process-ai-glasses/en/headerimage/generatedHeaderImage-1790823797609.jpg"/&gt;&lt;p&gt;Meta has extended Private Processing to AI glasses, using confidential VMs, trusted execution environments, remote attestation, anonymous credentials, OHTTP routing, and TEE-based storage to process AI workloads and persistent user context in the cloud.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Storage</category>
      <category>Privacy</category>
      <category>Data Privacy</category>
      <category>Wearable Tech</category>
      <category>VM</category>
      <category>Facebook</category>
      <category>Trust</category>
      <category>AI Security</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 14 Oct 2026 13:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/meta-private-process-ai-glasses/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-10-14T13:57:00Z</dc:date>
      <dc:identifier>/news/2026/10/meta-private-process-ai-glasses/en</dc:identifier>
    </item>
    <item>
      <title>Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy</title>
      <link>https://www.infoq.com/news/2026/10/antigravity-sdk-local-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/antigravity-sdk-local-models/en/headerimage/agent-friendly-android-cli-1791967537412.jpeg"/&gt;&lt;p&gt;Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Data Privacy</category>
      <category>Google</category>
      <category>Gemma</category>
      <category>AI coding agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 14 Oct 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/antigravity-sdk-local-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-10-14T09:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/antigravity-sdk-local-models/en</dc:identifier>
    </item>
    <item>
      <title>Production Patterns for Agents, Evals, and LLM Costs: QCon AI New York 2026</title>
      <link>https://www.infoq.com/news/2026/10/qcon-ai-newyork-2026-sessionsv2/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/qcon-ai-newyork-2026-sessionsv2/en/headerimage/qcon-ai-new-york-2026-1791875885339.jpg"/&gt;&lt;p&gt;Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15-16) has confirmed sessions from engineers sharing how they handle these constraints.&lt;/p&gt; &lt;i&gt;By Artenisa Chatziou&lt;/i&gt;</description>
      <category>QCon AI New York 2026</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI Architecture</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/qcon-ai-newyork-2026-sessionsv2/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Artenisa Chatziou</dc:creator>
      <dc:date>2026-10-13T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/qcon-ai-newyork-2026-sessionsv2/en</dc:identifier>
    </item>
    <item>
      <title>Spotify More Than Doubled Monthly Code Changes as AI Accelerated Development</title>
      <link>https://www.infoq.com/news/2026/10/spotify-ai-quality-controls/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Spotify reports that AI-assisted development more than doubled monthly merged changes, prompting a review of quality and reliability controls across its engineering organization. The company describes changes to testing, observability, rollback capacity, workload prioritization, and automated maintenance as it adapts to higher software change volume.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Incident Response</category>
      <category>Observability</category>
      <category>Automation</category>
      <category>AI Assisted Coding</category>
      <category>Code Quality</category>
      <category>Productivity</category>
      <category>Continuous Deployment</category>
      <category>Developer Experience</category>
      <category>Continuous Integration</category>
      <category>Automated testing</category>
      <category>AI Coding</category>
      <category>Claude</category>
      <category>Software Development</category>
      <category>AI Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 12 Oct 2026 13:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/spotify-ai-quality-controls/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-10-12T13:57:00Z</dc:date>
      <dc:identifier>/news/2026/10/spotify-ai-quality-controls/en</dc:identifier>
    </item>
    <item>
      <title>Context Language Models: Self-Managing Context to Improve Performance and Reduce Compute Costs</title>
      <link>https://www.infoq.com/news/2026/10/context-language-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/context-language-models/en/headerimage/gemini-agentic-vision-1791724239724.jpg"/&gt;&lt;p&gt;Researchers from Meta, MIT, and the University of Washington introduce Context Language Models (CLMs), a new approach that enables language models to manage and edit their own context rather than relying on predefined mechanisms for summarization, compression, and information retrieval, reporting substantial gains in both performance and computational efficiency.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Context Engineering</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sun, 11 Oct 2026 14:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/context-language-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-10-11T14:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/context-language-models/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Traces Turns the Proxy Layer into OpenTelemetry Spans, with New Volume-Based Pricing</title>
      <link>https://www.infoq.com/news/2026/10/cloudflare-traces-open-beta/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/cloudflare-traces-open-beta/en/headerimage/generatedHeaderImage-1791443919168.jpg"/&gt;&lt;p&gt;Cloudflare has put Traces into open beta, extending automatic tracing from Workers to security rules, transformations, cache, routing and origin handling as OpenTelemetry spans. Traces accept and forward W3C traceparent headers, and Trace Rules allow targeted sampling. Pricing moves to ingestion and retention volume from December 1.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Cloudflare</category>
      <category>Observability</category>
      <category>Architecture</category>
      <category>OpenTelemetry</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Sat, 10 Oct 2026 07:12:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/cloudflare-traces-open-beta/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-10-10T07:12:00Z</dc:date>
      <dc:identifier>/news/2026/10/cloudflare-traces-open-beta/en</dc:identifier>
    </item>
    <item>
      <title>Android Bench 2 Adds Support for Long-Horizon Tasks, Agentic Evaluation, and Continuous Scoring</title>
      <link>https://www.infoq.com/news/2026/10/android-bench-2/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/android-bench-2/en/headerimage/cloudflare-computer-agents-1791561634896.jpeg"/&gt;&lt;p&gt;Google has released Android Bench 2.0, a major update to its benchmark framework for evaluating AI models and agents on Android development tasks. The update introduces long-horizon tasks (LHTs), agent-based evaluation, and continuous scoring to better assess performance on complex, multi-step development tasks.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Mobile</category>
      <category>Google</category>
      <category>Benchmark</category>
      <category>AI coding agents</category>
      <category>Android</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 09 Oct 2026 17:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/android-bench-2/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-10-09T17:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/android-bench-2/en</dc:identifier>
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