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<title>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : Tools</title>
<link>https://news.jatlink.uk/rss/category/ai-tools</link>
<description>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : Tools</description>
<dc:language>en</dc:language>
<dc:rights>Copyright 2024 Jat Link Limited &amp; All Rights Reserved.</dc:rights>

<item>
<title>NVIDIA, Microsoft Kick Off a New Beginning for Windows PCs with RTX Spark and AI Agents</title>
<link>https://news.jatlink.uk/23188</link>
<guid>https://news.jatlink.uk/23188</guid>
<description><![CDATA[ At a Microsoft event in San Francisco on Wednesday, Jensen Huang and Satya Nadella outlined how NVIDIA and Microsoft are co-engineering hardware and software for AI agents to run on Windows PCs. NVIDIA was founded because of Windows, Huang said. Now AI agents are coming to Windows. “If you look at the entire journey of […] ]]></description>
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<pubDate>Wed, 07 Oct 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Microsoft, Kick, Off, New, Beginning, for, Windows, PCs, with, RTX, Spark, and, Agents</media:keywords>
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<item>
<title>Introducing Claude Haiku 5.5 on AWS</title>
<link>https://news.jatlink.uk/23186</link>
<guid>https://news.jatlink.uk/23186</guid>
<description><![CDATA[ Claude Haiku 5.5 is now available on Amazon Bedrock and Claude Platform on AWS. According to Anthropic, it is the fastest, most efficient model in the Claude 5.5 family, built for subagents and high-volume, cost-sensitive work, and costs around 75% less than Claude Haiku 4.5 for most tasks. This post covers its improvements and how to get started. ]]></description>
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<pubDate>Wed, 07 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Claude, Haiku, 5.5, AWS</media:keywords>
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<item>
<title>Rethinking access control for RAG with Amazon Quick and Amazon Bedrock</title>
<link>https://news.jatlink.uk/23187</link>
<guid>https://news.jatlink.uk/23187</guid>
<description><![CDATA[ Enterprise RAG unlocks insights from knowledge sources like SharePoint, Google Drive, and Confluence, but those sources carry complex permissions. Learn how Amazon Quick and Amazon Bedrock Knowledge Bases enforce document-level access controls in real time, verifying permissions directly with authoritative sources at query time. ]]></description>
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<pubDate>Wed, 07 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rethinking, access, control, for, RAG, with, Amazon, Quick, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Radisson Hotel Group brings hotel discovery into ChatGPT</title>
<link>https://news.jatlink.uk/23184</link>
<guid>https://news.jatlink.uk/23184</guid>
<description><![CDATA[ Radisson partnered with Accenture to build a ChatGPT plugin using OpenAI technology, helping travelers find, compare, and book hotels while planning their trips. ]]></description>
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<pubDate>Wed, 07 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Radisson, Hotel, Group, brings, hotel, discovery, into, ChatGPT</media:keywords>
</item>

<item>
<title>GPT&amp;6 and Intelligent UI for everyone</title>
<link>https://news.jatlink.uk/23185</link>
<guid>https://news.jatlink.uk/23185</guid>
<description><![CDATA[ GPT‑6 is rolling out globally in ChatGPT with Intelligent UI, delivering faster responses with visuals and interactive experiences you can explore and use directly. ]]></description>
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<pubDate>Wed, 07 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-6, and, Intelligent, for, everyone</media:keywords>
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<item>
<title>Helping teens learn, plan, and shape the future of AI</title>
<link>https://news.jatlink.uk/23183</link>
<guid>https://news.jatlink.uk/23183</guid>
<description><![CDATA[ College Planner is coming to ChatGPT for Teens to help students manage college applications, alongside new flashcards, quizzes, and a teen AI council. ]]></description>
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<pubDate>Wed, 07 Oct 2026 21:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Helping, teens, learn, plan, and, shape, the, future</media:keywords>
</item>

<item>
<title>Building AI builders: Playbook for closing the AI knowledge&amp;capability gap</title>
<link>https://news.jatlink.uk/23167</link>
<guid>https://news.jatlink.uk/23167</guid>
<description><![CDATA[ The biggest barrier to AI adoption isn&#039;t awareness. It&#039;s the gap between talking about AI and building with it. Here&#039;s the playbook we used to turn non-technical, customer-facing professionals into confident AI builders in six weeks, and how your organization can replicate it. ]]></description>
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<pubDate>Wed, 07 Oct 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, builders:, Playbook, for, closing, the, knowledge-capability, gap</media:keywords>
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<item>
<title>How Cornerstone OnDemand cut database diagnosis by 78% with Amazon Bedrock</title>
<link>https://news.jatlink.uk/23168</link>
<guid>https://news.jatlink.uk/23168</guid>
<description><![CDATA[ Cornerstone OnDemand built Orion AI, a multi-agent system on Amazon Bedrock and Strands Agents, to turn database operations from reactive firefighting into proactive automation. A three-person team cut database diagnosis from 45 minutes to 10, a 78% reduction, in six months. See the design decisions other teams can reuse. ]]></description>
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<pubDate>Wed, 07 Oct 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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<item>
<title>Beyond hours saved: Building the business case for agentic automation</title>
<link>https://news.jatlink.uk/23164</link>
<guid>https://news.jatlink.uk/23164</guid>
<description><![CDATA[ The RPA-era ROI model misses most of the value agentic automation creates. This post gives AI center of excellence leaders a framework to size the full value of agents across time savings, exception handling, decision quality, and maintenance economics, and to prioritize which workflows to automate first. ]]></description>
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<pubDate>Wed, 07 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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<item>
<title>How Qlik built grounded, enterprise&amp;scale AI with Amazon Bedrock</title>
<link>https://news.jatlink.uk/23165</link>
<guid>https://news.jatlink.uk/23165</guid>
<description><![CDATA[ Qlik built Qlik Answers on Amazon Bedrock to give its 40,000+ customers grounded, sourced answers across structured and unstructured enterprise data. Learn how a layered, multi-agent architecture with cross-Region inference and Amazon Bedrock Guardrails delivers trusted AI at global scale. ]]></description>
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<pubDate>Wed, 07 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Qlik, built, grounded, enterprise-scale, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Automate remediation post AWS DevOps Agent investigation</title>
<link>https://news.jatlink.uk/23166</link>
<guid>https://news.jatlink.uk/23166</guid>
<description><![CDATA[ AWS DevOps Agent can diagnose production incidents but is kept in observe-and-report mode so it does not change resources directly. This post shows how to use AWS Lambda Durable Functions, Amazon EventBridge, and Amazon Bedrock to turn its investigation summaries into pre-validated fixes an on-call engineer can approve with a single action. ]]></description>
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<pubDate>Wed, 07 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, remediation, post, AWS, DevOps, Agent, investigation</media:keywords>
</item>

<item>
<title>Sharing AI progress in mathematics</title>
<link>https://news.jatlink.uk/23124</link>
<guid>https://news.jatlink.uk/23124</guid>
<description><![CDATA[ OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub. ]]></description>
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<pubDate>Wed, 07 Oct 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sharing, progress, mathematics</media:keywords>
</item>

<item>
<title>How Jump Trading is scaling quant research with ChatGPT</title>
<link>https://news.jatlink.uk/23125</link>
<guid>https://news.jatlink.uk/23125</guid>
<description><![CDATA[ Jump Trading uses OpenAI to expand quantitative research. See how longer-running AI workflows combine multiple data sources with human review. ]]></description>
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<pubDate>Wed, 07 Oct 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Jump, Trading, scaling, quant, research, with, ChatGPT</media:keywords>
</item>

<item>
<title>Building a context&amp;aware AI assistant on AgentCore and OpenClaw</title>
<link>https://news.jatlink.uk/23106</link>
<guid>https://news.jatlink.uk/23106</guid>
<description><![CDATA[ Off-the-shelf AI assistants forget you between conversations. This post shows how to build a personal assistant that accumulates context using OpenClaw on Amazon Bedrock AgentCore runtime, with AgentCore memory turning disposable chats into durable, structured knowledge you can retrieve with metadata filters. ]]></description>
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<pubDate>Tue, 06 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, context-aware, assistant, AgentCore, and, OpenClaw</media:keywords>
</item>

<item>
<title>Atlassian and OpenAI expand partnership to turn enterprise knowledge into action</title>
<link>https://news.jatlink.uk/23104</link>
<guid>https://news.jatlink.uk/23104</guid>
<description><![CDATA[ Atlassian and OpenAI are expanding their partnership to connect frontier models with enterprise knowledge and help teams plan, build, and deliver work. ]]></description>
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<pubDate>Tue, 06 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Atlassian, and, OpenAI, expand, partnership, turn, enterprise, knowledge, into, action</media:keywords>
</item>

<item>
<title>Advancing computer use with Ironclad</title>
<link>https://news.jatlink.uk/23105</link>
<guid>https://news.jatlink.uk/23105</guid>
<description><![CDATA[ Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work. ]]></description>
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<pubDate>Tue, 06 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, computer, use, with, Ironclad</media:keywords>
</item>

<item>
<title>Why Telecom Operators Are Building Their AI Strategy on Open Models</title>
<link>https://news.jatlink.uk/23090</link>
<guid>https://news.jatlink.uk/23090</guid>
<description><![CDATA[ Telecom operators are increasingly building their AI strategies on open models — and the reasons go beyond mere cost.  Open models give telcos the ability to trust, control and customize AI across their most critical workloads — from autonomous networks to customer care.  NVIDIA’s latest State of AI in Telecommunications report reflects this shift, with […] ]]></description>
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<pubDate>Tue, 06 Oct 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Why, Telecom, Operators, Are, Building, Their, Strategy, Open, Models</media:keywords>
</item>

<item>
<title>Responsible AI governance: How AWS positions customers to align with ISO/IEC 42005:2025</title>
<link>https://news.jatlink.uk/23086</link>
<guid>https://news.jatlink.uk/23086</guid>
<description><![CDATA[ AWS invests in tools that help customers align with international standards for responsible AI governance. In this post, we explore the AI system impact assessment: what it is, how it improves enterprise-wide risk management, and how ISO/IEC 42005:2025 codifies best practices for conducting and documenting these assessments. ]]></description>
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<pubDate>Tue, 06 Oct 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Responsible, governance:, How, AWS, positions, customers, align, with, ISOIEC, 42005:2025</media:keywords>
</item>

<item>
<title>Best practices for Amazon SageMaker HyperPod administration and governance</title>
<link>https://news.jatlink.uk/23087</link>
<guid>https://news.jatlink.uk/23087</guid>
<description><![CDATA[ Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers. ]]></description>
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<pubDate>Tue, 06 Oct 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, for, Amazon, SageMaker, HyperPod, administration, and, governance</media:keywords>
</item>

<item>
<title>Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio</title>
<link>https://news.jatlink.uk/23088</link>
<guid>https://news.jatlink.uk/23088</guid>
<description><![CDATA[ Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools. ]]></description>
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<pubDate>Tue, 06 Oct 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, Amazon, SageMaker, HyperPod, Spaces, directly, from, SageMaker, Studio</media:keywords>
</item>

<item>
<title>Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic</title>
<link>https://news.jatlink.uk/23089</link>
<guid>https://news.jatlink.uk/23089</guid>
<description><![CDATA[ Add a voice travel concierge to an airline app with Amazon Bedrock AgentCore, Amazon Nova Sonic for real-time speech, and Amazon Bedrock Knowledge Bases for policy answers. Travelers change seats, check delays, and ask policy questions by voice, while the agent reaches your backend through MCP tools and confirms every change before it writes. ]]></description>
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<pubDate>Tue, 06 Oct 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, voice, travel, concierge, with, Amazon, Bedrock, AgentCore, Managed, Knowledge, Base, and, Nova, Sonic</media:keywords>
</item>

<item>
<title>Introducing GLM 5.3 on Amazon Bedrock</title>
<link>https://news.jatlink.uk/23047</link>
<guid>https://news.jatlink.uk/23047</guid>
<description><![CDATA[ GLM 5.3 from Z.ai is now available on Amazon Bedrock: a 753B-parameter mixture-of-experts model built for coding and long-horizon agentic tasks. Learn how to invoke it with the OpenAI-compatible APIs, cut cost and latency with prompt caching, and run an authorized security test with the open-source Strix agent. ]]></description>
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<pubDate>Tue, 06 Oct 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GLM, 5.3, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Supercharge regulated workloads with Claude Code and Amazon Bedrock</title>
<link>https://news.jatlink.uk/23028</link>
<guid>https://news.jatlink.uk/23028</guid>
<description><![CDATA[ Anthropic Claude Opus 5.5 and Claude Sonnet 5.5 are available on Amazon Bedrock in the AWS GovCloud (US) Regions. Learn how to use them with Claude Code, Anthropic&#039;s agentic coding tool, for compliance-aligned, AI-assisted development on regulated and ITAR workloads. ]]></description>
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<pubDate>Mon, 05 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Supercharge, regulated, workloads, with, Claude, Code, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent</title>
<link>https://news.jatlink.uk/23029</link>
<guid>https://news.jatlink.uk/23029</guid>
<description><![CDATA[ Amazon SageMaker optimized generative AI inference introduces the aws-ai-ml skill through the Agent Toolkit for AWS, giving coding agents like Kiro, Claude Code, and Codex deep expertise in inference optimization and benchmarking. Describe what you want, and your agent generates executable SageMaker Python SDK v3 code to benchmark, recommend, and compare deployments. ]]></description>
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<pubDate>Mon, 05 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, agent, skill:, Amazon, SageMaker, optimized, generative, inference, for, your, coding, agent</media:keywords>
</item>

<item>
<title>From Scan to Treatment Plan, AI Helps Close Breast Cancer’s Deadliest Gaps</title>
<link>https://news.jatlink.uk/23005</link>
<guid>https://news.jatlink.uk/23005</guid>
<description><![CDATA[ Breast cancer is the most commonly diagnosed cancer among American women — yet the gaps in care are wide. A majority of women over age 40 skip the recommended annual screening. Radiologists are reading more mammograms with fewer colleagues. And when a diagnosis arrives, the tests that inform treatment can take weeks to return results.  […] ]]></description>
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<pubDate>Mon, 05 Oct 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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</item>

<item>
<title>Downgrading user roles in Amazon Quick</title>
<link>https://news.jatlink.uk/23003</link>
<guid>https://news.jatlink.uk/23003</guid>
<description><![CDATA[ Amazon Quick doesn&#039;t offer a direct console path to downgrade a user from Admin or Author to Reader. This post walks through two reliable methods: a manual delete-and-recreate approach and an AWS CLI step-down sequence that downgrades roles safely while preserving asset ownership. ]]></description>
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<pubDate>Mon, 05 Oct 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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<item>
<title>Evaluating multi&amp;agent systems for explainability and helpfulness with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/23004</link>
<guid>https://news.jatlink.uk/23004</guid>
<description><![CDATA[ Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent supply chain decisioning system and evaluate it with Amazon Bedrock AgentCore Evaluations using built-in, custom, and explainability evaluators. ]]></description>
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<pubDate>Mon, 05 Oct 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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<item>
<title>Making Amazon Quick enterprise&amp;ready: Automated, auditable cross&amp;account resource promotion</title>
<link>https://news.jatlink.uk/23001</link>
<guid>https://news.jatlink.uk/23001</guid>
<description><![CDATA[ Promoting Amazon Quick resources (agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account has been a manual, error-prone chore. This post shows how to automate cross-account promotion with an idempotent, auditable MCP server on Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Mon, 05 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Making, Amazon, Quick, enterprise-ready:, Automated, auditable, cross-account, resource, promotion</media:keywords>
</item>

<item>
<title>Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/23002</link>
<guid>https://news.jatlink.uk/23002</guid>
<description><![CDATA[ Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly. Run the same query through both paths, read the trace events, and compare what each retrieval path costs. ]]></description>
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<pubDate>Mon, 05 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, retrieval, with, LangChain, and, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Our approach to EU text provenance rules</title>
<link>https://news.jatlink.uk/23000</link>
<guid>https://news.jatlink.uk/23000</guid>
<description><![CDATA[ How OpenAI is approaching text watermarking under EU rules. Learn where watermarks apply, how detection works, and why access starts with researchers. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 05 Oct 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, approach, text, provenance, rules</media:keywords>
</item>

<item>
<title>Building advertising for the way people use AI</title>
<link>https://news.jatlink.uk/22989</link>
<guid>https://news.jatlink.uk/22989</guid>
<description><![CDATA[ OpenAI introduces a new visual ad format in ChatGPT and expands measurement tools, attribution partnerships, and brand suitability for advertisers. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 05 Oct 2026 13:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, advertising, for, the, way, people, use</media:keywords>
</item>

<item>
<title>A model guide for the GPT&amp;6 family</title>
<link>https://news.jatlink.uk/22793</link>
<guid>https://news.jatlink.uk/22793</guid>
<description><![CDATA[ Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 02 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>model, guide, for, the, GPT-6, family</media:keywords>
</item>

<item>
<title>Chatham scales its capital markets expertise with OpenAI</title>
<link>https://news.jatlink.uk/22794</link>
<guid>https://news.jatlink.uk/22794</guid>
<description><![CDATA[ Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 02 Oct 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Chatham, scales, its, capital, markets, expertise, with, OpenAI</media:keywords>
</item>

<item>
<title>NVIDIA DGX Spark 64GB Gives Developers More Ways to Build and Scale Local AI</title>
<link>https://news.jatlink.uk/22777</link>
<guid>https://news.jatlink.uk/22777</guid>
<description><![CDATA[ Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more to run locally.  Coming this month, NVIDIA DGX Spark will be available with 64GB of unified memory from top manufacturer partners — Acer, […] ]]></description>
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<pubDate>Fri, 02 Oct 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, DGX, Spark, 64GB, Gives, Developers, More, Ways, Build, and, Scale, Local</media:keywords>
</item>

<item>
<title>Add secure Web Search to Claude Desktop with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/22774</link>
<guid>https://news.jatlink.uk/22774</guid>
<description><![CDATA[ Claude Desktop on Amazon Bedrock is limited to the model&#039;s knowledge cutoff without web search. In this post, we walk through connecting Claude Desktop to Web Search using Amazon Bedrock AgentCore Gateway, with JWT-based inbound authentication through AWS IAM Identity Center and Amazon Cognito. ]]></description>
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<pubDate>Fri, 02 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Add, secure, Web, Search, Claude, Desktop, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Fine&amp;tune a search agent with multi&amp;turn RL on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/22775</link>
<guid>https://news.jatlink.uk/22775</guid>
<description><![CDATA[ Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured in retrieval quality and reliability. ]]></description>
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<pubDate>Fri, 02 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tune, search, agent, with, multi-turn, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern</title>
<link>https://news.jatlink.uk/22773</link>
<guid>https://news.jatlink.uk/22773</guid>
<description><![CDATA[ The Adjudicated Query pattern pairs the Amazon Quick chat agent with a bounded MCP server over a deterministic rules engine to deliver provably complete, defensible compliance answers. This post walks through the reference architecture and a deployable AWS CDK sample, using lease compliance as the running example. ]]></description>
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<pubDate>Fri, 02 Oct 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sweep, thousands, leases, for, compliance, using, Amazon, Quick, and, the, Adjudicated, Query, pattern</media:keywords>
</item>

<item>
<title>How NVIDIA GPUs Help Accelerate OpenAI’s GPT&amp;6 Astra Ultrafast</title>
<link>https://news.jatlink.uk/22702</link>
<guid>https://news.jatlink.uk/22702</guid>
<description><![CDATA[ GPT-6 Astra Ultrafast, running on NVIDIA Blackwell GPUs, is available now in the OpenAI API and to eligible ChatGPT Work and Codex users.  Accelerated by inference optimizations through OpenAI’s models that tap into the capabilities of the NVIDIA Blackwell architecture, Ultrafast offers up to 8x faster token generation than the Astra Standard mode. For developers, […] ]]></description>
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<pubDate>Fri, 02 Oct 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, NVIDIA, GPUs, Help, Accelerate, OpenAI’s, GPT-6, Astra, Ultrafast</media:keywords>
</item>

<item>
<title>The Den frees up 10&amp;15 hours a week to grow with ChatGPT Work</title>
<link>https://news.jatlink.uk/22701</link>
<guid>https://news.jatlink.uk/22701</guid>
<description><![CDATA[ As it opens a new location, the social club prepares grant applications in 2 hours instead of 3 days and liquor-license materials in 3 hours instead of 4 days. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 02 Oct 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Den, frees, 10-15, hours, week, grow, with, ChatGPT, Work</media:keywords>
</item>

<item>
<title>Simplify dashboard drill&amp;down with the Amazon Quick Sight hierarchy filter</title>
<link>https://news.jatlink.uk/22682</link>
<guid>https://news.jatlink.uk/22682</guid>
<description><![CDATA[ Amazon Quick Sight is a fully managed, cloud-native business intelligence (BI) capability for building and publishing interactive dashboards. The new hierarchy filter gives dashboard authors rich, multi-level filtering in a single compact control, reducing clutter and guiding readers to the data they need in fewer steps. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simplify, dashboard, drill-down, with, the, Amazon, Quick, Sight, hierarchy, filter</media:keywords>
</item>

<item>
<title>Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors</title>
<link>https://news.jatlink.uk/22678</link>
<guid>https://news.jatlink.uk/22678</guid>
<description><![CDATA[ Learn how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS). This post shows how NAT&#039;s memory subsystem works and how to implement Amazon S3 Vectors as a custom memory provider, using a multi-agent investment research use case. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agent, memory, with, NVIDIA, NeMo, Agent, Toolkit, and, Amazon, Vectors</media:keywords>
</item>

<item>
<title>Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS</title>
<link>https://news.jatlink.uk/22679</link>
<guid>https://news.jatlink.uk/22679</guid>
<description><![CDATA[ Generative AI makes it cheap to produce personalized content at scale, but which variation do you show each customer? Amazon Payments used a multi-objective contextual bandit on Amazon SageMaker AI to personalize an acquisition funnel, achieving a high single-digit conversion lift for one audience, and learning why content, not the model, was the constraint. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Uplifting, conversion, across, the, acquisition, funnel, with, personalization, using, contextual, bandits, AWS</media:keywords>
</item>

<item>
<title>Building ambient agents with Amazon Bedrock AgentCore: From event&amp;driven signals to human&amp;in&amp;the&amp;loop workflows</title>
<link>https://news.jatlink.uk/22680</link>
<guid>https://news.jatlink.uk/22680</guid>
<description><![CDATA[ Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore using Amazon SQS, AWS Lambda, and Amazon DynamoDB, with a single ask_human tool and a Jobs page for human-in-the-loop review. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, ambient, agents, with, Amazon, Bedrock, AgentCore:, From, event-driven, signals, human-in-the-loop, workflows</media:keywords>
</item>

<item>
<title>Implementing Multi&amp;Environment Access for Claude Platform on AWS</title>
<link>https://news.jatlink.uk/22681</link>
<guid>https://news.jatlink.uk/22681</guid>
<description><![CDATA[ Learn how to configure secure, multi-environment access to Claude Platform on AWS from a single subscription: cross-account SigV4 for AWS workloads, workspace-scoped API keys for developers, and OIDC federation for external environments, with workspace-level isolation in a dedicated AI Services account. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implementing, Multi-Environment, Access, for, Claude, Platform, AWS</media:keywords>
</item>

<item>
<title>The eternal complement</title>
<link>https://news.jatlink.uk/22675</link>
<guid>https://news.jatlink.uk/22675</guid>
<description><![CDATA[ Advanced AI may matter most for the routine work behind breakthrough ideas. Explore why execution could shape the next economy and the pace of progress. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 01 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, eternal, complement</media:keywords>
</item>

<item>
<title>How Albertsons Companies is reimagining retail from the inside out</title>
<link>https://news.jatlink.uk/22676</link>
<guid>https://news.jatlink.uk/22676</guid>
<description><![CDATA[ Albertsons Cos. is using ChatGPT Enterprise and the OpenAI API to help teams work faster and make grocery shopping easier for millions of customers. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 01 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Albertsons, Companies, reimagining, retail, from, the, inside, out</media:keywords>
</item>

<item>
<title>Serve live, governed data in AI&amp;built apps with Amazon Quick</title>
<link>https://news.jatlink.uk/22677</link>
<guid>https://news.jatlink.uk/22677</guid>
<description><![CDATA[ With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so row-level and column-level security apply per reader. Learn how to build, publish, and share a live-data app using natural language. ]]></description>
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<pubDate>Thu, 01 Oct 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Serve, live, governed, data, AI-built, apps, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Fall Into 25 New Games on GeForce NOW This October</title>
<link>https://news.jatlink.uk/22662</link>
<guid>https://news.jatlink.uk/22662</guid>
<description><![CDATA[ Spooky season is streaming in. Alongside falling leaves, pumpkin spice and everything nice, 25 new games are joining GeForce NOW throughout October, including six ready to play this week. From a new CONTROL Resonant reward for Performance and Ultimate members to The Witcher 3: Wild Hunt – Remastered joining the cloud, this GFN Thursday is […] ]]></description>
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<pubDate>Thu, 01 Oct 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fall, Into, New, Games, GeForce, NOW, This, October</media:keywords>
</item>

<item>
<title>Productive, Durable, Fungible: How NVIDIA AI Factories Maximize Return on Investment</title>
<link>https://news.jatlink.uk/22663</link>
<guid>https://news.jatlink.uk/22663</guid>
<description><![CDATA[ AI factories are built by the megawatt, even by the gigawatt. Each megawatt factory costs roughly $60 million, and AI factory operators will only commit capital on that scale with a clear view of the return on investment. Three key things shape AI factory returns:  Earning capacity: What the factory could earn in a year […] ]]></description>
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<pubDate>Thu, 01 Oct 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Productive, Durable, Fungible:, How, NVIDIA, Factories, Maximize, Return, Investment</media:keywords>
</item>

<item>
<title>How uniopen customized Amazon Nova to their retail moderation policies for production deployment</title>
<link>https://news.jatlink.uk/22661</link>
<guid>https://news.jatlink.uk/22661</guid>
<description><![CDATA[ See how uniopen, a retail platform from Taiwan&#039;s Uni-President Enterprises Group, adapted Amazon Nova 2 Lite to its content-moderation policies using supervised fine-tuning in Amazon SageMaker AI and prompt optimization. Business-relevant evaluation and release gates kept quality in check. ]]></description>
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<pubDate>Thu, 01 Oct 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, uniopen, customized, Amazon, Nova, their, retail, moderation, policies, for, production, deployment</media:keywords>
</item>

<item>
<title>NVIDIA Opens Applications for 2027–2028 Graduate Fellowships With Awards Up to $60,000</title>
<link>https://news.jatlink.uk/22581</link>
<guid>https://news.jatlink.uk/22581</guid>
<description><![CDATA[ Bringing together the world’s brightest minds and the latest accelerated computing technology leads to powerful breakthroughs that help tackle some of the biggest research problems. To foster such innovation, the NVIDIA Graduate Fellowship Program provides grants, mentors and technical support to doctoral students doing outstanding research relevant to NVIDIA technologies. The program, in its 26th […] ]]></description>
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<pubDate>Wed, 30 Sep 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Opens, Applications, for, 2027–2028, Graduate, Fellowships, With, Awards, 60, 000</media:keywords>
</item>

<item>
<title>Disrupting a coordinated model&amp;distillation campaign</title>
<link>https://news.jatlink.uk/22580</link>
<guid>https://news.jatlink.uk/22580</guid>
<description><![CDATA[ Learn how OpenAI disrupted a campaign to extract protected model reasoning and is strengthening defenses against adversarial distillation. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 30 Sep 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Disrupting, coordinated, model-distillation, campaign</media:keywords>
</item>

<item>
<title>From Training to Production, NVIDIA and CoreWeave Close the Loop on Agentic AI</title>
<link>https://news.jatlink.uk/22559</link>
<guid>https://news.jatlink.uk/22559</guid>
<description><![CDATA[ Building on nearly a decade of co-engineering, CoreWeave has built NVIDIA compute, networking and software into a cloud purpose-built for AI that’s still returning on investment across multiple generations of deployment. Now, CoreWeave is bringing the next generation of NVIDIA infrastructure to production. At CoreWeave Fully Connected, running this week in San Francisco, CoreWeave announced […] ]]></description>
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<pubDate>Wed, 30 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Training, Production, NVIDIA, and, CoreWeave, Close, the, Loop, Agentic</media:keywords>
</item>

<item>
<title>Build a multi&amp;agent music production pipeline on Amazon Bedrock AgentCore Runtime Instances</title>
<link>https://news.jatlink.uk/22558</link>
<guid>https://news.jatlink.uk/22558</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Runtime Instances gives multi-agent workflows AWS managed EC2 infrastructure with GPUs, persistent volumes, and multi-day sessions. In this post, we deploy a three-agent music production pipeline where the agents colocate on one GPU instance, share a filesystem, and hand work to each other to produce a finished track. ]]></description>
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<pubDate>Wed, 30 Sep 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multi-agent, music, production, pipeline, Amazon, Bedrock, AgentCore, Runtime, Instances</media:keywords>
</item>

<item>
<title>Helping small businesses put AI to work</title>
<link>https://news.jatlink.uk/22556</link>
<guid>https://news.jatlink.uk/22556</guid>
<description><![CDATA[ OpenAI is partnering with America’s SBDC to expand hands-on AI training and local support for small businesses, alongside a new report on how small teams are using AI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 30 Sep 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Helping, small, businesses, put, work</media:keywords>
</item>

<item>
<title>Query claims in natural language with Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/22557</link>
<guid>https://news.jatlink.uk/22557</guid>
<description><![CDATA[ This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations. It covers ingesting claim documents from Amazon S3, querying with the AgenticRetrieveStream API, multi-turn follow-ups, metadata filters, and contextual grounding guardrails. ]]></description>
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<pubDate>Wed, 30 Sep 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Query, claims, natural, language, with, Amazon, Bedrock, Knowledge, Bases</media:keywords>
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<item>
<title>Amazon Bedrock expands Claude model availability to in&amp;country inferencing in India</title>
<link>https://news.jatlink.uk/22516</link>
<guid>https://news.jatlink.uk/22516</guid>
<description><![CDATA[ Anthropic&#039;s Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 are now available in India through Amazon Bedrock geographic cross-Region inference. You can access these models while processing data within the India Regions, and get started from the Amazon Bedrock console or with the Messages, InvokeModel, and Converse APIs. ]]></description>
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<pubDate>Wed, 30 Sep 2026 05:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, expands, Claude, model, availability, in-country, inferencing, India</media:keywords>
</item>

<item>
<title>Introducing Anthropic models on Amazon Bedrock for in&amp;region inference in Seoul and Singapore</title>
<link>https://news.jatlink.uk/22517</link>
<guid>https://news.jatlink.uk/22517</guid>
<description><![CDATA[ Amazon Bedrock now supports Anthropic&#039;s Claude Opus 5 and Claude Sonnet 5 with in-region inference in Seoul, and Claude Sonnet 5 in Singapore. If you have local data processing requirements in South Korea or Singapore, you can now use these Anthropic models at scale, with inference processed entirely within the Region you call. ]]></description>
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<pubDate>Wed, 30 Sep 2026 05:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Anthropic, models, Amazon, Bedrock, for, in-region, inference, Seoul, and, Singapore</media:keywords>
</item>

<item>
<title>Prompt engineering by Quick component: Patterns and pitfalls</title>
<link>https://news.jatlink.uk/22489</link>
<guid>https://news.jatlink.uk/22489</guid>
<description><![CDATA[ Part 2 of our Amazon Quick prompt engineering series goes component by component. Learn the prompt patterns that get the best results from Amazon Quick Research, Quick Flows, Quick Sight, chat agents, and action integrations, plus the common pitfalls to avoid. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Prompt, engineering, Quick, component:, Patterns, and, pitfalls</media:keywords>
</item>

<item>
<title>Building an AI&amp;powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/22490</link>
<guid>https://news.jatlink.uk/22490</guid>
<description><![CDATA[ Manually extracting data from hundreds of vendor contracts doesn&#039;t scale, and RAG chat tools fall short on portfolio-wide questions. This post shares a contract intelligence platform on AWS that uses AI agents to extract and verify contract fields, then answers aggregate and single-contract questions through Amazon Quick analytics. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, AI-powered, contract, intelligence, platform, with, Amazon, Quick, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Bring near&amp;Astra intelligence to everyday work with GPT&amp;6.1 Sol on Amazon Bedrock</title>
<link>https://news.jatlink.uk/22487</link>
<guid>https://news.jatlink.uk/22487</guid>
<description><![CDATA[ GPT-6.1 Sol is now generally available on Amazon Bedrock, bringing stronger reasoning to coding, computer use, and professional workloads that run frequently. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bring, near-Astra, intelligence, everyday, work, with, GPT-6.1, Sol, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Prompt engineering fundamentals for Amazon Quick</title>
<link>https://news.jatlink.uk/22488</link>
<guid>https://news.jatlink.uk/22488</guid>
<description><![CDATA[ Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (specificity, context-setting, few-shot examples, and the CRISPE framework) for consistent, high-quality results across Amazon Quick. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Prompt, engineering, fundamentals, for, Amazon, Quick</media:keywords>
</item>

<item>
<title>Introducing dots</title>
<link>https://news.jatlink.uk/22486</link>
<guid>https://news.jatlink.uk/22486</guid>
<description><![CDATA[ Dots by OpenAI are a proactive assistant that can keep working across complex projects and everyday tasks. Learn how dots help you stay in control while work moves forward. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, dots</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;6.1 Sol</title>
<link>https://news.jatlink.uk/22484</link>
<guid>https://news.jatlink.uk/22484</guid>
<description><![CDATA[ Meet GPT-6.1 Sol: near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra’s standard API input and output token prices. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-6.1, Sol</media:keywords>
</item>

<item>
<title>DevDay 2026 Recap</title>
<link>https://news.jatlink.uk/22485</link>
<guid>https://news.jatlink.uk/22485</guid>
<description><![CDATA[ Explore more than 20 announcements from OpenAI DevDay 2026, including GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders. ]]></description>
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<pubDate>Tue, 29 Sep 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>DevDay, 2026, Recap</media:keywords>
</item>

<item>
<title>How Condé Nast built multimodal video discovery with Amazon Bedrock</title>
<link>https://news.jatlink.uk/22460</link>
<guid>https://news.jatlink.uk/22460</guid>
<description><![CDATA[ Condé Nast&#039;s editorial teams spent an average of 250 minutes per task searching a library of more than 140,000 videos using only titles and descriptions. Working with the AWS Generative AI Innovation Center, they built a multimodal video discovery solution on Amazon Bedrock and Amazon OpenSearch Service that cut discovery time to under 2 minutes. ]]></description>
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<pubDate>Tue, 29 Sep 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Condé, Nast, built, multimodal, video, discovery, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Towards safety cases for frontier AI training</title>
<link>https://news.jatlink.uk/22438</link>
<guid>https://news.jatlink.uk/22438</guid>
<description><![CDATA[ Our early guidelines for safety cases in frontier AI training cover technical safeguards, operational practices, and investigating misalignment incidents ]]></description>
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<pubDate>Tue, 29 Sep 2026 09:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Towards, safety, cases, for, frontier, training</media:keywords>
</item>

<item>
<title>How we will do better for Australia</title>
<link>https://news.jatlink.uk/22426</link>
<guid>https://news.jatlink.uk/22426</guid>
<description><![CDATA[ OpenAI apologises for incidents involving Australian government websites and outlines stronger safeguards and support to strengthen Australia’s cyber defences. ]]></description>
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<pubDate>Tue, 29 Sep 2026 05:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, will, better, for, Australia</media:keywords>
</item>

<item>
<title>Grok 4.7 is now available on Amazon Bedrock</title>
<link>https://news.jatlink.uk/22409</link>
<guid>https://news.jatlink.uk/22409</guid>
<description><![CDATA[ xAI&#039;s Grok 4.7 is now available on Amazon Bedrock: a frontier model for coding, long-running agents, and knowledge work. It offers a 500K token context window and four configurable reasoning effort levels, reachable through the Responses, Chat Completions, and Converse APIs. ]]></description>
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<pubDate>Tue, 29 Sep 2026 01:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Grok, 4.7, now, available, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Basis completes a tax workbook 2x faster with GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/22407</link>
<guid>https://news.jatlink.uk/22407</guid>
<description><![CDATA[ GPT-6 Astra completed a 50-tab tax workbook twice as fast as GPT-5.6 Sol, and its stronger understanding of user intent gives Basis more confidence in real-world use. ]]></description>
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<pubDate>Tue, 29 Sep 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Basis, completes, tax, workbook, faster, with, GPT-6, Astra</media:keywords>
</item>

<item>
<title>Are you a Codex Original?</title>
<link>https://news.jatlink.uk/22408</link>
<guid>https://news.jatlink.uk/22408</guid>
<description><![CDATA[ We’re collecting real stories of builders, tinkerers, researchers, and creators who are using Codex to do incredible things. If you want to be a part of the next chapter of the Codex Originals program, tell us more about your story and project below. ]]></description>
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<pubDate>Tue, 29 Sep 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Are, you, Codex, Original</media:keywords>
</item>

<item>
<title>Build real&amp;time voice applications with vLLM&amp;Omni on SageMaker AI – Part 1</title>
<link>https://news.jatlink.uk/22389</link>
<guid>https://news.jatlink.uk/22389</guid>
<description><![CDATA[ Deploy a text-to-speech model on Amazon SageMaker AI with the AWS vLLM-Omni Deep Learning Container and stream generated speech over a persistent bidirectional connection. This Part 1 tutorial deploys Qwen3-TTS and streams speech through a Gradio application. ]]></description>
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<pubDate>Mon, 28 Sep 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, real-time, voice, applications, with, vLLM-Omni, SageMaker, –, Part</media:keywords>
</item>

<item>
<title>Generate images and video with vLLM&amp;Omni on SageMaker AI – Part 2</title>
<link>https://news.jatlink.uk/22390</link>
<guid>https://news.jatlink.uk/22390</guid>
<description><![CDATA[ Deploy two generative media models from one AWS vLLM-Omni Deep Learning Container on Amazon SageMaker AI. Generate an image with FLUX.2-klein through real-time inference, then animate it into video with Wan2.1-VACE through asynchronous inference, and retrieve the MP4 from Amazon S3. ]]></description>
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<pubDate>Mon, 28 Sep 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, images, and, video, with, vLLM-Omni, SageMaker, –, Part</media:keywords>
</item>

<item>
<title>The Lenfest Institute grows landmark program with expanded OpenAI support</title>
<link>https://news.jatlink.uk/22387</link>
<guid>https://news.jatlink.uk/22387</guid>
<description><![CDATA[ OpenAI is expanding the Lenfest AI Collaborative and Fellowship Program with $5 million in funding and up to $5 million in software credits and engineering support. ]]></description>
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<pubDate>Mon, 28 Sep 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Lenfest, Institute, grows, landmark, program, with, expanded, OpenAI, support</media:keywords>
</item>

<item>
<title>Introducing Claude Sonnet 5.5 on AWS</title>
<link>https://news.jatlink.uk/22388</link>
<guid>https://news.jatlink.uk/22388</guid>
<description><![CDATA[ Claude Sonnet 5.5 is now available on Amazon Bedrock and Claude Platform on AWS. It&#039;s a smarter, more efficient Sonnet model for focused coding and knowledge work, with a lower cost per task at faster speed. This post covers what&#039;s new, when to choose Sonnet, and how to get started. ]]></description>
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<pubDate>Mon, 28 Sep 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Claude, Sonnet, 5.5, AWS</media:keywords>
</item>

<item>
<title>Implementing synthetic monitoring using Amazon Nova Act</title>
<link>https://news.jatlink.uk/22366</link>
<guid>https://news.jatlink.uk/22366</guid>
<description><![CDATA[ Learn an agent-driven approach to synthetic monitoring using Amazon Nova Act and Amazon Bedrock AgentCore. The post covers the architecture and patterns for resilient, managed user-journey validation that moves beyond brittle UI scripts, with a complete sample implementation. ]]></description>
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<pubDate>Mon, 28 Sep 2026 17:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implementing, synthetic, monitoring, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Automating Amazon Textract adapter lifecycle management across accounts</title>
<link>https://news.jatlink.uk/22367</link>
<guid>https://news.jatlink.uk/22367</guid>
<description><![CDATA[ Learn how to operationalize Amazon Textract Custom Queries adapters for production: infrastructure as code with AWS CloudFormation and Terraform, a cross-account adapter promotion process, a pre-classification routing pattern for multiple form versions, and production security controls such as VPC endpoints, encryption, and least-privilege IAM. ]]></description>
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<pubDate>Mon, 28 Sep 2026 17:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automating, Amazon, Textract, adapter, lifecycle, management, across, accounts</media:keywords>
</item>

<item>
<title>NVIDIA Announces a $150 Billion Share Repurchase Authorization Increase</title>
<link>https://news.jatlink.uk/22346</link>
<guid>https://news.jatlink.uk/22346</guid>
<description><![CDATA[ NVIDIA today announced that its Board of Directors has authorized an additional $150 billion under the company’s existing share repurchase program, increasing the total remaining amount authorized to $235 billion. ]]></description>
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<pubDate>Mon, 28 Sep 2026 14:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, 150, Billion, Share, Repurchase, Authorization, Increase</media:keywords>
</item>

<item>
<title>NVIDIA Launches Open Agent Safety Platform to Secure Agents From Testing to Deployment</title>
<link>https://news.jatlink.uk/22347</link>
<guid>https://news.jatlink.uk/22347</guid>
<description><![CDATA[ NVIDIA today announced NVIDIA Open Agent Safety Platform, an open software platform and reference system design to strengthen AI security from agent testing to deployment, with full-stack governance and control across software and the hardware, compute and robotics systems that run agents. ]]></description>
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<pubDate>Mon, 28 Sep 2026 14:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Launches, Open, Agent, Safety, Platform, Secure, Agents, From, Testing, Deployment</media:keywords>
</item>

<item>
<title>Deploying real&amp;time personalized speech with Qwen3&amp;TTS on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/22177</link>
<guid>https://news.jatlink.uk/22177</guid>
<description><![CDATA[ Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed, real-time endpoint, and clone a voice from a short reference clip. Cross-lingual cloning preserves the speaker&#039;s identity across languages. ]]></description>
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<pubDate>Fri, 25 Sep 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploying, real-time, personalized, speech, with, Qwen3-TTS, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput</title>
<link>https://news.jatlink.uk/22174</link>
<guid>https://news.jatlink.uk/22174</guid>
<description><![CDATA[ Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP. This post presents an architecture that combines Amazon EKS, EFA, and Amazon S3 and increased aggregate reinforcement learning rollout throughput by 40% for large-scale RLHF and GRPO training. ]]></description>
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<pubDate>Fri, 25 Sep 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, MoE, reinforcement, learning, Amazon, EKS, with, EFA, and, DeepEP, with, 40, more, throughput</media:keywords>
</item>

<item>
<title>Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/22175</link>
<guid>https://news.jatlink.uk/22175</guid>
<description><![CDATA[ Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, launching a Ray cluster from SageMaker Studio, submitting and monitoring the job, and hosting the trained LoRA adapter for inference. ]]></description>
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<pubDate>Fri, 25 Sep 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, multimodal, training, with, SkyRL, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>NarrateAI: production&amp;ready LLM quality assurance on Amazon Bedrock</title>
<link>https://news.jatlink.uk/22176</link>
<guid>https://news.jatlink.uk/22176</guid>
<description><![CDATA[ NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, composite evaluation, and data accuracy verification—that reach about 99% numerical accuracy while streaming responses in real time. ]]></description>
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<pubDate>Fri, 25 Sep 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NarrateAI:, production-ready, LLM, quality, assurance, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Proaction boosts sales 60% and saves 75+ hours with Codex</title>
<link>https://news.jatlink.uk/22173</link>
<guid>https://news.jatlink.uk/22173</guid>
<description><![CDATA[ With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster. ]]></description>
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<pubDate>Fri, 25 Sep 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Proaction, boosts, sales, 60, and, saves, 75, hours, with, Codex</media:keywords>
</item>

<item>
<title>How Datacor built self&amp;service rental analytics with Amazon Quick Sight</title>
<link>https://news.jatlink.uk/22151</link>
<guid>https://news.jatlink.uk/22151</guid>
<description><![CDATA[ Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick Sight dashboards and natural language querying into its TrackAbout platform, powered by an automated cross-cloud data pipeline and multi-tenant row-level security. ]]></description>
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<pubDate>Fri, 25 Sep 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Datacor, built, self-service, rental, analytics, with, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>Multi&amp;Region training with Amazon SageMaker HyperPod and Qumulo</title>
<link>https://news.jatlink.uk/22152</link>
<guid>https://news.jatlink.uk/22152</guid>
<description><![CDATA[ Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster&#039;s throughput after a brief NeuralCache warmup. ]]></description>
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<pubDate>Fri, 25 Sep 2026 17:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multi-Region, training, with, Amazon, SageMaker, HyperPod, and, Qumulo</media:keywords>
</item>

<item>
<title>Aderant builds intelligent ticket triage with Amazon Nova</title>
<link>https://news.jatlink.uk/22094</link>
<guid>https://news.jatlink.uk/22094</guid>
<description><![CDATA[ Learn how Aderant built an intelligent ticket triage system on Amazon Nova Lite through Amazon Bedrock, automating context gathering, classification, routing, and knowledge enrichment for its cloud operations team. ]]></description>
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<pubDate>Thu, 24 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Aderant, builds, intelligent, ticket, triage, with, Amazon, Nova</media:keywords>
</item>

<item>
<title>Speaker&amp;labeled transcription with WhisperX on SageMaker AI</title>
<link>https://news.jatlink.uk/22092</link>
<guid>https://news.jatlink.uk/22092</guid>
<description><![CDATA[ The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynchronous endpoints for word-level, speaker-labeled transcription, plus the production details that matter: the GPU AMI pin, scaling, and cost controls. ]]></description>
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<pubDate>Thu, 24 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Speaker-labeled, transcription, with, WhisperX, SageMaker</media:keywords>
</item>

<item>
<title>Build a multi&amp;account AI agent with AgentCore Gateway and MCP</title>
<link>https://news.jatlink.uk/22093</link>
<guid>https://news.jatlink.uk/22093</guid>
<description><![CDATA[ Build a multi-account architecture that keeps each team&#039;s data in its own AWS account while giving AI agents a unified way to query across them. A central platform account runs the agent using Amazon Bedrock AgentCore Gateway and MCP, while line-of-business accounts expose their data as MCP servers with secure cross-account access and fine-grained authorization. ]]></description>
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<pubDate>Thu, 24 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multi-account, agent, with, AgentCore, Gateway, and, MCP</media:keywords>
</item>

<item>
<title>How Open Science Can Help Researchers Prepare for the Next Pandemic</title>
<link>https://news.jatlink.uk/22068</link>
<guid>https://news.jatlink.uk/22068</guid>
<description><![CDATA[ When COVID-19 emerged, scientists had a crucial advantage: Decades of prior research on coronaviruses meant they understood the virus’ key proteins well enough to design vaccines in record time. The next pandemic may not offer the same head start.  To help improve the odds, NVIDIA has joined a coalition of global research organizations, including Google […] ]]></description>
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<pubDate>Thu, 24 Sep 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Open, Science, Can, Help, Researchers, Prepare, for, the, Next, Pandemic</media:keywords>
</item>

<item>
<title>Contain the Chaos: ‘CONTROL Resonant’ Launches on GeForce NOW</title>
<link>https://news.jatlink.uk/22069</link>
<guid>https://news.jatlink.uk/22069</guid>
<description><![CDATA[ A warped Manhattan is waiting in the cloud this week. Remedy Entertainment’s CONTROL Resonant brings Dylan Faden’s extraordinary abilities and a paranatural crisis to GeForce NOW at launch. With the release comes the final days of the CONTROL Resonant Ultimate Membership Bundle. Purchase a 12-month GeForce NOW Ultimate membership through Sunday, Sept. 27, and receive […] ]]></description>
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<pubDate>Thu, 24 Sep 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Contain, the, Chaos:, ‘CONTROL, Resonant’, Launches, GeForce, NOW</media:keywords>
</item>

<item>
<title>ChatGPT Ads expands to Southeast Asia and Taiwan</title>
<link>https://news.jatlink.uk/22047</link>
<guid>https://news.jatlink.uk/22047</guid>
<description><![CDATA[ ChatGPT Ads is expanding to Southeast Asia and Taiwan, giving eligible businesses new ways to reach people across more than 60 countries. ]]></description>
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<pubDate>Thu, 24 Sep 2026 07:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, Ads, expands, Southeast, Asia, and, Taiwan</media:keywords>
</item>

<item>
<title>Use open weight models as your AI coding agent with Amazon Bedrock</title>
<link>https://news.jatlink.uk/22030</link>
<guid>https://news.jatlink.uk/22030</guid>
<description><![CDATA[ Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, open, weight, models, your, coding, agent, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Airbnb widens access to GPT&amp;6 Astra and OpenAI frontier models</title>
<link>https://news.jatlink.uk/22027</link>
<guid>https://news.jatlink.uk/22027</guid>
<description><![CDATA[ Learn how Airbnb is expanding access to GPT-6 Astra and OpenAI frontier models to help engineering teams solve bugs, design systems, and ship faster. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Airbnb, widens, access, GPT-6, Astra, and, OpenAI, frontier, models</media:keywords>
</item>

<item>
<title>From portal&amp;hopping to instant answers: HEMA’s journey with MCP and Amazon Bedrock</title>
<link>https://news.jatlink.uk/22028</link>
<guid>https://news.jatlink.uk/22028</guid>
<description><![CDATA[ HEMA, a 100-year-old Dutch retailer, turned developer portal-hopping into instant answers by building HAL, an internal AI assistant on Amazon Bedrock AgentCore. Using Model Context Protocol (MCP), HAL delivers governed knowledge inside the tools teams already use, with no AWS credentials on the client and security anchored in Microsoft Entra ID. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, portal-hopping, instant, answers:, HEMA’s, journey, with, MCP, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Agentic conversational video intelligence built on AWS</title>
<link>https://news.jatlink.uk/22029</link>
<guid>https://news.jatlink.uk/22029</guid>
<description><![CDATA[ Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, conversational, video, intelligence, built, AWS</media:keywords>
</item>

<item>
<title>How invideo improves color grading 3x with GPT‑6 Astra</title>
<link>https://news.jatlink.uk/22023</link>
<guid>https://news.jatlink.uk/22023</guid>
<description><![CDATA[ With GPT‑6 Astra, invideo plans edits with greater precision, improves color correction and grading threefold, and produces 50 custom effects in one day. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, invideo, improves, color, grading, with, GPT‑6, Astra</media:keywords>
</item>

<item>
<title>Harvey turns legal context into stronger drafts with GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/22024</link>
<guid>https://news.jatlink.uk/22024</guid>
<description><![CDATA[ GPT-6 Astra produces more structured, context-aware legal documents, freeing lawyers to focus on strategy. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Harvey, turns, legal, context, into, stronger, drafts, with, GPT-6, Astra</media:keywords>
</item>

<item>
<title>Sam Altman’s remarks at the United Nations Security Council</title>
<link>https://news.jatlink.uk/22025</link>
<guid>https://news.jatlink.uk/22025</guid>
<description><![CDATA[ OpenAI CEO Sam Altman discusses AI safety, human control, and international cooperation in remarks to the United Nations Security Council. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sam, Altman’s, remarks, the, United, Nations, Security, Council</media:keywords>
</item>

<item>
<title>Introducing MentalHealthBench</title>
<link>https://news.jatlink.uk/22026</link>
<guid>https://news.jatlink.uk/22026</guid>
<description><![CDATA[ MentalHealthBench is an expert-informed benchmark for evaluating helpful and safe AI responses across realistic mental health conversations. ]]></description>
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<pubDate>Wed, 23 Sep 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, MentalHealthBench</media:keywords>
</item>

<item>
<title>Sakeena Fiza Helps NVIDIA Hardware Succeed at Scale</title>
<link>https://news.jatlink.uk/22006</link>
<guid>https://news.jatlink.uk/22006</guid>
<description><![CDATA[ When Sakeena Fiza describes her work as a validation engineer at NVIDIA, she does so in terms more befitting a detective story than a world-class engineering lab. “Validation engineers look in the shadows and shine a light into every corner,” Fiza said. “Every time we get a system, our first thought is: how can it […] ]]></description>
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<pubDate>Wed, 23 Sep 2026 20:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sakeena, Fiza, Helps, NVIDIA, Hardware, Succeed, Scale</media:keywords>
</item>

<item>
<title>Ringg’s AI agents resolve up to 65% of customer calls with OpenAI</title>
<link>https://news.jatlink.uk/22004</link>
<guid>https://news.jatlink.uk/22004</guid>
<description><![CDATA[ Using GPT-5.6, Ringg powers multilingual agents across voice, chat, WhatsApp, and web for 90% less cost vs. GPT-4.1. ]]></description>
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<pubDate>Wed, 23 Sep 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Ringg’s, agents, resolve, 65, customer, calls, with, OpenAI</media:keywords>
</item>

<item>
<title>Two years of OpenAI Academy</title>
<link>https://news.jatlink.uk/22005</link>
<guid>https://news.jatlink.uk/22005</guid>
<description><![CDATA[ Marking two years of OpenAI Academy and bringing AI skills to even more communities. ]]></description>
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<pubDate>Wed, 23 Sep 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Two, years, OpenAI, Academy</media:keywords>
</item>

<item>
<title>Grab and OpenAI bring practical AI skills to Southeast Asia</title>
<link>https://news.jatlink.uk/21991</link>
<guid>https://news.jatlink.uk/21991</guid>
<description><![CDATA[ OpenAI and Grab launch GO Forward with AI, a regional programme helping 30,000 partners build practical AI skills across Southeast Asia. ]]></description>
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<pubDate>Wed, 23 Sep 2026 15:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Grab, and, OpenAI, bring, practical, skills, Southeast, Asia</media:keywords>
</item>

<item>
<title>OpenAI extends cyber access to Ukraine for civilian defense</title>
<link>https://news.jatlink.uk/21990</link>
<guid>https://news.jatlink.uk/21990</guid>
<description><![CDATA[ OpenAI is extending access to its Daybreak program to the Government of Ukraine to support the cyber defense of civilian infrastructure. ]]></description>
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<pubDate>Wed, 23 Sep 2026 15:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, extends, cyber, access, Ukraine, for, civilian, defense</media:keywords>
</item>

<item>
<title>At AI Day Singapore, NVIDIA and Partners Showcase AI Advancements Across Southeast Asia</title>
<link>https://news.jatlink.uk/21950</link>
<guid>https://news.jatlink.uk/21950</guid>
<description><![CDATA[ NVIDIA AI Day Singapore, which takes place Sept. 22-23 at the Raffles City Convention Centre, is offering attendees opportunities to explore the hands-on training, expert-led sessions and advanced tools to accelerate their work in AI and high-performance computing. At the event, NVIDIA and its partners are showcasing breakthrough AI advancements across the Southeast Asia region […] ]]></description>
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<pubDate>Wed, 23 Sep 2026 04:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Day, Singapore, NVIDIA, and, Partners, Showcase, Advancements, Across, Southeast, Asia</media:keywords>
</item>

<item>
<title>Parallel cut research time and cost in half with GPT‑6 Astra</title>
<link>https://news.jatlink.uk/21949</link>
<guid>https://news.jatlink.uk/21949</guid>
<description><![CDATA[ GPT‑6 Astra allowed Parallel’s agents to research and synthesize labor-market data in half the time and at half the cost vs. prior models. ]]></description>
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<pubDate>Wed, 23 Sep 2026 03:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Parallel, cut, research, time, and, cost, half, with, GPT‑6, Astra</media:keywords>
</item>

<item>
<title>Claude Opus 5.5 is now available on AWS</title>
<link>https://news.jatlink.uk/21930</link>
<guid>https://news.jatlink.uk/21930</guid>
<description><![CDATA[ Claude Opus 5.5, Anthropic&#039;s most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what&#039;s new in Opus 5.5, practical guidance, and how to start building with the model on Amazon Bedrock. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Claude, Opus, 5.5, now, available, AWS</media:keywords>
</item>

<item>
<title>Evaluate skill&amp;equipped agents with Strands Evals and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21931</link>
<guid>https://news.jatlink.uk/21931</guid>
<description><![CDATA[ Skills let you encode domain-specific procedures as reusable, portable instructions for agents, but a fluent answer doesn&#039;t prove the agent picked the right skill or followed it. Learn how to measure skill selection and instruction following with Strands Evals and Amazon Bedrock AgentCore Evaluations. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, skill-equipped, agents, with, Strands, Evals, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Priorities and principles for effective third party assessments</title>
<link>https://news.jatlink.uk/21928</link>
<guid>https://news.jatlink.uk/21928</guid>
<description><![CDATA[ OpenAI outlines priorities and principles for rigorous, secure, and independent third-party AI safety assessments of frontier models and safeguards. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Priorities, and, principles, for, effective, third, party, assessments</media:keywords>
</item>

<item>
<title>Bring more intelligence to everyday work with GPT&amp;6 Sol and GPT&amp;6 Luna on Amazon Bedrock</title>
<link>https://news.jatlink.uk/21929</link>
<guid>https://news.jatlink.uk/21929</guid>
<description><![CDATA[ GPT-6 Sol and GPT-6 Luna are now generally available on Amazon Bedrock, giving you more options to match intelligence and efficiency to each workload. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bring, more, intelligence, everyday, work, with, GPT-6, Sol, and, GPT-6, Luna, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Better prompt caching for GPT&amp;6</title>
<link>https://news.jatlink.uk/21926</link>
<guid>https://news.jatlink.uk/21926</guid>
<description><![CDATA[ Learn how GPT-6 improves prompt caching with higher cache hit rates, new diagnostics, explicit breakpoints, and controls that reduce latency and costs. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Better, prompt, caching, for, GPT-6</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;6 Sol and Luna</title>
<link>https://news.jatlink.uk/21927</link>
<guid>https://news.jatlink.uk/21927</guid>
<description><![CDATA[ Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost. ]]></description>
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<pubDate>Tue, 22 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-6, Sol, and, Luna</media:keywords>
</item>

<item>
<title>How Tata Elxsi detects industrial safety risks in seconds on AWS</title>
<link>https://news.jatlink.uk/21909</link>
<guid>https://news.jatlink.uk/21909</guid>
<description><![CDATA[ Learn how Tata Elxsi built IRIS, a real-time industrial safety platform on AWS. IRIS filters camera video at the edge, streams metadata through Amazon Kinesis, runs computer vision on Amazon SageMaker AI, and correlates detections into high-confidence alerts, detecting unsafe conditions in seconds instead of minutes. ]]></description>
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<pubDate>Tue, 22 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tata, Elxsi, detects, industrial, safety, risks, seconds, AWS</media:keywords>
</item>

<item>
<title>Extending public sector intelligence with Agentforce and AWS</title>
<link>https://news.jatlink.uk/21910</link>
<guid>https://news.jatlink.uk/21910</guid>
<description><![CDATA[ Public sector agencies process large volumes of unstructured evidence, such as body camera footage and scanned documents. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn that data into structured insights and surface them through natural language queries in Salesforce Agentforce. ]]></description>
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<pubDate>Tue, 22 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Extending, public, sector, intelligence, with, Agentforce, and, AWS</media:keywords>
</item>

<item>
<title>How Reactiv automates mobile commerce 80% faster with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21906</link>
<guid>https://news.jatlink.uk/21906</guid>
<description><![CDATA[ Reactiv used Amazon Bedrock AgentCore to build a multi-agent AI Scheduler that autonomously refreshes Shopify merchants&#039; mobile apps on a schedule, reducing merchant configuration time by 80% and getting to production 33% faster. ]]></description>
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<pubDate>Tue, 22 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Reactiv, automates, mobile, commerce, 80, faster, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Right&amp;size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/21907</link>
<guid>https://news.jatlink.uk/21907</guid>
<description><![CDATA[ Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running automated concurrency sweeps with the CreateAIBenchmarkJob API, and using the results to make data-driven capacity decisions about fleet size. ]]></description>
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<pubDate>Tue, 22 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Right-size, generative, endpoints, with, concurrency, sweeps, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>How Trane gets building insights 60x faster with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21908</link>
<guid>https://news.jatlink.uk/21908</guid>
<description><![CDATA[ In about four weeks, Trane Technologies built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute, multi-screen building diagnostic workflow to a 20-second natural language interaction, a 60x improvement in time-to-insight. This post shares the architectural approach and key design decisions behind the solution. ]]></description>
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<pubDate>Tue, 22 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Trane, gets, building, insights, 60x, faster, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>NVIDIA Isaac ROS 5.0 Advances Agentic, Open Source Robotics Development</title>
<link>https://news.jatlink.uk/21888</link>
<guid>https://news.jatlink.uk/21888</guid>
<description><![CDATA[ To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools. The ROS open framework is a project from Open Robotics that helps humans build robots. NVIDIA Isaac ROS 5.0 — a collection of GPU-accelerated packages built on ROS, released today at […] ]]></description>
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<pubDate>Tue, 22 Sep 2026 15:00:32 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Isaac, ROS, 5.0, Advances, Agentic, Open, Source, Robotics, Development</media:keywords>
</item>

<item>
<title>NVIDIA Launches DSX Ready to Qualify Power and Cooling Products for AI Factories</title>
<link>https://news.jatlink.uk/21842</link>
<guid>https://news.jatlink.uk/21842</guid>
<description><![CDATA[ Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing capacity into useful AI output. To help builders make those decisions, NVIDIA is introducing […] ]]></description>
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<pubDate>Mon, 21 Sep 2026 23:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Launches, DSX, Ready, Qualify, Power, and, Cooling, Products, for, Factories</media:keywords>
</item>

<item>
<title>xAI’s Grok 4.6 is now available in Amazon Bedrock</title>
<link>https://news.jatlink.uk/21841</link>
<guid>https://news.jatlink.uk/21841</guid>
<description><![CDATA[ xAI&#039;s Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support. ]]></description>
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<pubDate>Mon, 21 Sep 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>xAI’s, Grok, 4.6, now, available, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Advisory Group on Mathematics and Artificial Intelligence</title>
<link>https://news.jatlink.uk/21838</link>
<guid>https://news.jatlink.uk/21838</guid>
<description><![CDATA[ OpenAI is working with an independent Advisory Group on Mathematics and Artificial Intelligence to guide the review and communication of emerging AI results. ]]></description>
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<pubDate>Mon, 21 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advisory, Group, Mathematics, and, Artificial, Intelligence</media:keywords>
</item>

<item>
<title>Building standards for the next phase of AI</title>
<link>https://news.jatlink.uk/21839</link>
<guid>https://news.jatlink.uk/21839</guid>
<description><![CDATA[ OpenAI outlines a path to shared global AI standards, calling for coordinated evaluation, reporting, and governance to improve safety. ]]></description>
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<pubDate>Mon, 21 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, standards, for, the, next, phase</media:keywords>
</item>

<item>
<title>Expanding OpenAI Academy with new learning paths</title>
<link>https://news.jatlink.uk/21840</link>
<guid>https://news.jatlink.uk/21840</guid>
<description><![CDATA[ Explore new OpenAI Academy learning paths for employees, developers, leaders, educators, and students to build and demonstrate practical AI skills. ]]></description>
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<pubDate>Mon, 21 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Expanding, OpenAI, Academy, with, new, learning, paths</media:keywords>
</item>

<item>
<title>Higgsfield AI ships new video features in a day with GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/21837</link>
<guid>https://news.jatlink.uk/21837</guid>
<description><![CDATA[ With GPT-6 Astra, Higgsfield AI makes video ad creation easier for small businesses and brings new creative tools to market faster. ]]></description>
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<pubDate>Mon, 21 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Higgsfield, ships, new, video, features, day, with, GPT-6, Astra</media:keywords>
</item>

<item>
<title>From Enablement to Execution, Egypt’s AI Ecosystem Reaches Production Scale</title>
<link>https://news.jatlink.uk/21819</link>
<guid>https://news.jatlink.uk/21819</guid>
<description><![CDATA[ Today, Egypt’s AI builders gathered in the Grand Egyptian Museum for a reception that highlighted the nation’s rapidly growing AI ecosystem — spanning AI natives, developers, researchers, startups and enterprises — building applications across industries. The event included a keynote from Paolo Guglielmini, vice president of EMEA at NVIDIA. Ahmed Mostafa, regional AI adoption lead […] ]]></description>
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<pubDate>Mon, 21 Sep 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Enablement, Execution, Egypt’s, Ecosystem, Reaches, Production, Scale</media:keywords>
</item>

<item>
<title>Why Deploying Physical AI at Scale Demands Safety at Every Layer</title>
<link>https://news.jatlink.uk/21820</link>
<guid>https://news.jatlink.uk/21820</guid>
<description><![CDATA[ Physical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As these machines enter roads, factories, warehouses and other environments shared with people, […] ]]></description>
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<pubDate>Mon, 21 Sep 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Why, Deploying, Physical, Scale, Demands, Safety, Every, Layer</media:keywords>
</item>

<item>
<title>AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack</title>
<link>https://news.jatlink.uk/21821</link>
<guid>https://news.jatlink.uk/21821</guid>
<description><![CDATA[ AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work.  As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster.  Technology Changes, Security Fundamentals Endure The internet and cloud computing changed how software operates, […] ]]></description>
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<pubDate>Mon, 21 Sep 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Security, Engineering, Problem, —, How, Solve, Every, Layer, the, Agent, Stack</media:keywords>
</item>

<item>
<title>5 Companies Using NVIDIA AI for Clean Energy</title>
<link>https://news.jatlink.uk/21822</link>
<guid>https://news.jatlink.uk/21822</guid>
<description><![CDATA[ Clean energy isn’t hard to come by, but the pace of large-scale adoption has historically been slow due to bottlenecks — including out-of-date infrastructure, elongated research and development timelines, and upfront cost barriers.  At New York Climate Week, NVIDIA is highlighting five companies pioneering clean energy projects with AI baked into their foundation, accelerating research-to-inception […] ]]></description>
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<pubDate>Mon, 21 Sep 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Companies, Using, NVIDIA, for, Clean, Energy</media:keywords>
</item>

<item>
<title>How Benchling secured multi&amp;tenant AI agents with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21817</link>
<guid>https://news.jatlink.uk/21817</guid>
<description><![CDATA[ Learn how Benchling built a defense-in-depth security architecture to run untrusted, AI agent-generated scientific code across thousands of life sciences tenants using Amazon Bedrock AgentCore Code Interpreter in VPC mode, combined with Amazon Route 53 Resolver DNS Firewall and VPC endpoint policies to block data exfiltration, including through DNS. ]]></description>
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<pubDate>Mon, 21 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Benchling, secured, multi-tenant, agents, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Reducing medical claims review time with AI on AWS: The EXL Medical IDP solution</title>
<link>https://news.jatlink.uk/21818</link>
<guid>https://news.jatlink.uk/21818</guid>
<description><![CDATA[ EXL built an AI-powered Medical intelligent document processing (IDP) solution on AWS, combining IDP with domain-specific large language models on Amazon SageMaker and Amazon Bedrock to extract, summarize, and query medical records at enterprise scale and cut claims review time from over 100 minutes per case. ]]></description>
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<pubDate>Mon, 21 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reducing, medical, claims, review, time, with, AWS:, The, EXL, Medical, IDP, solution</media:keywords>
</item>

<item>
<title>How BMW Group detects cost anomalies across 14,000 cloud accounts</title>
<link>https://news.jatlink.uk/21815</link>
<guid>https://news.jatlink.uk/21815</guid>
<description><![CDATA[ BMW Group operates CLEA, a FinOps platform monitoring more than 14,000 cloud accounts. This post shows how BMW added automated daily cost anomaly detection, moving from reactive dashboards to proactive alerts using Prophet forecasting, AWS Step Functions, and a serverless pipeline that processes every account for about $50 per month. ]]></description>
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<pubDate>Mon, 21 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, BMW, Group, detects, cost, anomalies, across, 14, 000, cloud, accounts</media:keywords>
</item>

<item>
<title>Run Positron on Amazon SageMaker AI for data science workflows</title>
<link>https://news.jatlink.uk/21816</link>
<guid>https://news.jatlink.uk/21816</guid>
<description><![CDATA[ Positron, Posit&#039;s IDE for data science, now runs on Amazon SageMaker AI. This post shows how a data scientist explores an Amazon Athena table, validates features in R, trains an XGBoost model in Python, deploys a real-time SageMaker AI endpoint, and reports results with Quarto, all in one governed SageMaker Studio Space. ]]></description>
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<pubDate>Mon, 21 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, Positron, Amazon, SageMaker, for, data, science, workflows</media:keywords>
</item>

<item>
<title>How V7 gives AI agents institutional memory</title>
<link>https://news.jatlink.uk/21814</link>
<guid>https://news.jatlink.uk/21814</guid>
<description><![CDATA[ Using GPT-5.6, V7 turns scattered company files into context agents can use to complete complex, source-linked work. ]]></description>
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<pubDate>Mon, 21 Sep 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, gives, agents, institutional, memory</media:keywords>
</item>

<item>
<title>Introducing the Australian Youth Safety Blueprint</title>
<link>https://news.jatlink.uk/21673</link>
<guid>https://news.jatlink.uk/21673</guid>
<description><![CDATA[ OpenAI introduces the Australian Youth Safety Blueprint, a six-pillar roadmap for safer AI experiences that protect and empower young people. ]]></description>
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<pubDate>Sat, 19 Sep 2026 10:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Australian, Youth, Safety, Blueprint</media:keywords>
</item>

<item>
<title>Hex turns complex analysis into visual reports with GPT‑6 Astra</title>
<link>https://news.jatlink.uk/21649</link>
<guid>https://news.jatlink.uk/21649</guid>
<description><![CDATA[ GPT-6 Astra helps Hex’s data agents turn answers into interactive visualizations that employees are proud to share. ]]></description>
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<pubDate>Sat, 19 Sep 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Hex, turns, complex, analysis, into, visual, reports, with, GPT‑6, Astra</media:keywords>
</item>

<item>
<title>Amazon SageMaker Inference: 2026 year&amp;to&amp;date launches in review</title>
<link>https://news.jatlink.uk/21636</link>
<guid>https://news.jatlink.uk/21636</guid>
<description><![CDATA[ Amazon SageMaker AI shipped 13 inference launches in the first half of 2026 across two deployment paths: fully managed endpoints and Amazon SageMaker HyperPod Inference. This post reviews each launch, from inference recommendations and capacity-aware instance pools to tiered KV caching and disaggregated prefill and decode. ]]></description>
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<pubDate>Fri, 18 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, Inference:, 2026, year-to-date, launches, review</media:keywords>
</item>

<item>
<title>Introducing Amazon SageMaker HyperPod Inference Gateway</title>
<link>https://news.jatlink.uk/21620</link>
<guid>https://news.jatlink.uk/21620</guid>
<description><![CDATA[ Amazon SageMaker HyperPod Inference Gateway is a Kubernetes-native, GPU-aware routing add-on for Amazon EKS. It uses real-time GPU signals to send each inference request to the best-suited pod, cutting first-token latency by up to 82% with no changes to your model servers or client applications. ]]></description>
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<pubDate>Fri, 18 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, SageMaker, HyperPod, Inference, Gateway</media:keywords>
</item>

<item>
<title>Introducing Kimi K3 on Amazon Bedrock</title>
<link>https://news.jatlink.uk/21616</link>
<guid>https://news.jatlink.uk/21616</guid>
<description><![CDATA[ Kimi K3 from Moonshot AI is now available on Amazon Bedrock, giving you a powerful new open-weight option for coding and knowledge work. It offers native vision, a 1-million-token context window, and explicit prompt caching to reduce latency and input costs. ]]></description>
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<pubDate>Fri, 18 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Kimi, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Migrating multi&amp;model AI agents to Amazon Bedrock AgentCore runtime</title>
<link>https://news.jatlink.uk/21617</link>
<guid>https://news.jatlink.uk/21617</guid>
<description><![CDATA[ Migrate a multi-model healthcare AI agent from self-managed Amazon ECS with AWS Fargate to Amazon Bedrock AgentCore runtime, preserving triple-model orchestration and vector-enhanced knowledge retrieval while reducing infrastructure management. The framework-agnostic pattern applies across healthcare, financial services, and manufacturing. ]]></description>
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<pubDate>Fri, 18 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrating, multi-model, agents, Amazon, Bedrock, AgentCore, runtime</media:keywords>
</item>

<item>
<title>The new AgentCore runtime: Elastic, optimized, and consistently fast starts</title>
<link>https://news.jatlink.uk/21618</link>
<guid>https://news.jatlink.uk/21618</guid>
<description><![CDATA[ Today we are announcing the new AgentCore runtime, a capability of Amazon Bedrock AgentCore built for the speed, flexibility, and cost efficiency that production agents demand. It reclaims memory as sessions release it and delivers consistent cold starts regardless of image size or concurrency. ]]></description>
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<pubDate>Fri, 18 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, new, AgentCore, runtime:, Elastic, optimized, and, consistently, fast, starts</media:keywords>
</item>

<item>
<title>Deploy Hugging Face models on Amazon SageMaker AI with coding agents</title>
<link>https://news.jatlink.uk/21619</link>
<guid>https://news.jatlink.uk/21619</guid>
<description><![CDATA[ Deploy production-ready Hugging Face models on Amazon SageMaker AI using six open-source agent skills. Point a coding agent at a model and get back a real-time endpoint with the right serving container, autoscaling, Amazon CloudWatch alarms, and a verified teardown path. ]]></description>
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<pubDate>Fri, 18 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, Hugging, Face, models, Amazon, SageMaker, with, coding, agents</media:keywords>
</item>

<item>
<title>How Cooley is accelerating IPO work with ChatGPT</title>
<link>https://news.jatlink.uk/21559</link>
<guid>https://news.jatlink.uk/21559</guid>
<description><![CDATA[ Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most. ]]></description>
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<pubDate>Fri, 18 Sep 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Cooley, accelerating, IPO, work, with, ChatGPT</media:keywords>
</item>

<item>
<title>Introducing Astra for Law</title>
<link>https://news.jatlink.uk/21543</link>
<guid>https://news.jatlink.uk/21543</guid>
<description><![CDATA[ OpenAI for Law brings frontier intelligence for law, custom firm workflows, connected legal data sources, and legal-grade controls for confidential client work. ]]></description>
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<pubDate>Thu, 17 Sep 2026 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Astra, for, Law</media:keywords>
</item>

<item>
<title>Reduce time&amp;to&amp;hire for quality candidates with AI&amp;powered Amazon Connect Talent</title>
<link>https://news.jatlink.uk/21544</link>
<guid>https://news.jatlink.uk/21544</guid>
<description><![CDATA[ Amazon Connect Talent is an AI hiring solution built for talent acquisition leaders managing scaled hiring. It delivers AI-led interviews, data-driven assessments, and consistent evaluation, helping recruiters identify strong candidates more efficiently while providing applicants with a flexible interview experience. Informed by decades of Amazon&#039;s hiring science, Amazon Connect Talent provides transparency for every assessment, interview, and candidate score, enabling recruiters to stay in control of final hiring decisions. ]]></description>
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<pubDate>Thu, 17 Sep 2026 22:00:04 +0100</pubDate>
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<media:keywords>Reduce, time-to-hire, for, quality, candidates, with, AI-powered, Amazon, Connect, Talent</media:keywords>
</item>

<item>
<title>How MRH Trowe enabled secure self&amp;service AI agents in financial services</title>
<link>https://news.jatlink.uk/21523</link>
<guid>https://news.jatlink.uk/21523</guid>
<description><![CDATA[ Learn how MRH Trowe, one of Germany&#039;s leading commercial and industrial insurance brokers, gave about 400 employees secure, self-service access to AI agents in its first month of production - using Strands Agents, Amazon Bedrock AgentCore, and LibreChat to meet the security, data residency, and compliance requirements of the German financial sector. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, MRH, Trowe, enabled, secure, self-service, agents, financial, services</media:keywords>
</item>

<item>
<title>Implementing defense&amp;in&amp;depth authorization for MCP tools on Amazon Quick</title>
<link>https://news.jatlink.uk/21524</link>
<guid>https://news.jatlink.uk/21524</guid>
<description><![CDATA[ Learn how to enforce defense-in-depth authorization for Model Context Protocol (MCP) tools on Amazon Quick. This walkthrough wires Microsoft Entra ID group and claims-based JWTs through an Amazon Bedrock AgentCore Gateway interceptor to apply per-user, per-tool role-based and attribute-based access control, with a server-side check and an immutable audit trail. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implementing, defense-in-depth, authorization, for, MCP, tools, Amazon, Quick</media:keywords>
</item>

<item>
<title>Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/21525</link>
<guid>https://news.jatlink.uk/21525</guid>
<description><![CDATA[ Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This approach improved person detection by up to 160% without manual annotation or hazardous data collection near heavy machinery. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhancing, industrial, safety, with, synthetic, data, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Selecting a vector store for Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/21520</link>
<guid>https://news.jatlink.uk/21520</guid>
<description><![CDATA[ Choosing the right vector store for your Amazon Bedrock Knowledge Bases RAG application affects performance and cost. This post compares Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors across three RAG use cases, with benchmarks and a practical selection framework. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Selecting, vector, store, for, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>A serverless, data&amp;driven Git metrics dashboard using Amazon Quick Sight</title>
<link>https://news.jatlink.uk/21521</link>
<guid>https://news.jatlink.uk/21521</guid>
<description><![CDATA[ Learn how to build a fully serverless pipeline that automatically collects Git metrics from GitHub and GitLab and visualizes them in interactive Amazon Quick Sight dashboards, giving engineering teams near-real-time delivery analytics at low cost. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>serverless, data-driven, Git, metrics, dashboard, using, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21522</link>
<guid>https://news.jatlink.uk/21522</guid>
<description><![CDATA[ Wood Mackenzie built APEX, a shared agentic AI platform on Amazon Bedrock AgentCore so every team can ship production agents without rebuilding runtime, identity, observability, and guardrails from scratch. Learn why they chose AgentCore, how APEX Studio operates it, and where multi-agent systems go next. ]]></description>
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<pubDate>Thu, 17 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>shared, agentic, platform, for, Wood, Mackenzie, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Cute Critters Come to the Cloud: ‘Aniimo’ Launches on GeForce NOW</title>
<link>https://news.jatlink.uk/21508</link>
<guid>https://news.jatlink.uk/21508</guid>
<description><![CDATA[ A new creature-catching adventure is ready to stream from the cloud this week. Pawprint Studio’s Aniimo arrives on GeForce NOW at launch, inviting gamers to explore the vibrant continent of Idyll across supported devices. Also this week, 007 First Light receives a path-tracing update on GeForce NOW, alongside a smashing limited-time Deluxe Edition sale on […] ]]></description>
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<pubDate>Thu, 17 Sep 2026 15:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cute, Critters, Come, the, Cloud:, ‘Aniimo’, Launches, GeForce, NOW</media:keywords>
</item>

<item>
<title>Our framework for reporting model misalignment</title>
<link>https://news.jatlink.uk/21468</link>
<guid>https://news.jatlink.uk/21468</guid>
<description><![CDATA[ OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior. ]]></description>
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<pubDate>Thu, 17 Sep 2026 02:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, framework, for, reporting, model, misalignment</media:keywords>
</item>

<item>
<title>Improving HCLS AI reasoning with open&amp;source agent skills</title>
<link>https://news.jatlink.uk/21450</link>
<guid>https://news.jatlink.uk/21450</guid>
<description><![CDATA[ AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ]]></description>
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<pubDate>Wed, 16 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improving, HCLS, reasoning, with, open-source, agent, skills</media:keywords>
</item>

<item>
<title>Fault tolerant distributed training on Amazon EKS using NVRx</title>
<link>https://news.jatlink.uk/21451</link>
<guid>https://news.jatlink.uk/21451</guid>
<description><![CDATA[ Integrate NVIDIA Resiliency Extension (NVRx) into PyTorch FSDP training on Amazon EKS to overlap checkpoint I/O with training and recover from GPU faults in seconds. This post covers async checkpointing, in-process restart, and ft_launcher in-job restart, with H100 benchmarks at 2 to 8 nodes showing 99%+ training efficiency and second-scale recovery. ]]></description>
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<pubDate>Wed, 16 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fault, tolerant, distributed, training, Amazon, EKS, using, NVRx</media:keywords>
</item>

<item>
<title>How to connect AI usage to business value</title>
<link>https://news.jatlink.uk/21449</link>
<guid>https://news.jatlink.uk/21449</guid>
<description><![CDATA[ Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes. ]]></description>
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<pubDate>Wed, 16 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, connect, usage, business, value</media:keywords>
</item>

<item>
<title>NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut</title>
<link>https://news.jatlink.uk/21437</link>
<guid>https://news.jatlink.uk/21437</guid>
<description><![CDATA[ System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments.  […] ]]></description>
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<pubDate>Wed, 16 Sep 2026 19:00:06 +0100</pubDate>
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<media:keywords>NVIDIA, Vera, Rubin, NVL72, Delivers, Leading, Performance, MLPerf, Inference, v6.1, Debut</media:keywords>
</item>

<item>
<title>Optimizing agent system prompts with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21435</link>
<guid>https://news.jatlink.uk/21435</guid>
<description><![CDATA[ AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer&#039;s reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors. ]]></description>
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<pubDate>Wed, 16 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, agent, system, prompts, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/21436</link>
<guid>https://news.jatlink.uk/21436</guid>
<description><![CDATA[ Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ]]></description>
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<pubDate>Wed, 16 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, PII, redaction, pipeline, with, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Helping older adults use AI in everyday life</title>
<link>https://news.jatlink.uk/21432</link>
<guid>https://news.jatlink.uk/21432</guid>
<description><![CDATA[ OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI skills safely. ]]></description>
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<pubDate>Wed, 16 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Helping, older, adults, use, everyday, life</media:keywords>
</item>

<item>
<title>Reimagining advertising with AI</title>
<link>https://news.jatlink.uk/21433</link>
<guid>https://news.jatlink.uk/21433</guid>
<description><![CDATA[ Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify. ]]></description>
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<pubDate>Wed, 16 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reimagining, advertising, with</media:keywords>
</item>

<item>
<title>How workers are unlocking new ways of working</title>
<link>https://news.jatlink.uk/21434</link>
<guid>https://news.jatlink.uk/21434</guid>
<description><![CDATA[ New OpenAI Economic Research shows how workers use AI beyond traditional roles and which new activities become recurring parts of their work. ]]></description>
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<pubDate>Wed, 16 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, workers, are, unlocking, new, ways, working</media:keywords>
</item>

<item>
<title>Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers</title>
<link>https://news.jatlink.uk/21420</link>
<guid>https://news.jatlink.uk/21420</guid>
<description><![CDATA[ AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center.  Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use […] ]]></description>
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<pubDate>Wed, 16 Sep 2026 15:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Emerald, AI, Google, and, NVIDIA, Launch, Alliance, Advance, Flexible, Data, Centers</media:keywords>
</item>

<item>
<title>University of Manchester Uses NVIDIA Earth&amp;2 to Forecast Air Pollution Across the UK</title>
<link>https://news.jatlink.uk/21395</link>
<guid>https://news.jatlink.uk/21395</guid>
<description><![CDATA[ Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help — but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run.  David Topping, a professor in the […] ]]></description>
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<pubDate>Wed, 16 Sep 2026 07:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>University, Manchester, Uses, NVIDIA, Earth-2, Forecast, Air, Pollution, Across, the</media:keywords>
</item>

<item>
<title>‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce</title>
<link>https://news.jatlink.uk/21375</link>
<guid>https://news.jatlink.uk/21375</guid>
<description><![CDATA[ Know everything. Do anything. That was the message NVIDIA founder and CEO Jensen Huang brought to Salesforce Dreamforce Tuesday, joining CEO Marc Benioff onstage in an appearance that coincided with the announcement of Koa — Salesforce’s first CRM reasoning model, built on NVIDIA Nemotron 3 Super. Huang didn’t just take the stage. He walked into […] ]]></description>
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<pubDate>Wed, 16 Sep 2026 03:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>‘Now, Can, Know, Everything, and, Anything, ’, Jensen, Huang, Says, Dreamforce</media:keywords>
</item>

<item>
<title>From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production</title>
<link>https://news.jatlink.uk/21344</link>
<guid>https://news.jatlink.uk/21344</guid>
<description><![CDATA[ On a sweltering August evening in Silicon Valley, as the sun dropped and air conditioning loads spiked, Silicon Valley Power sent a signal to an AI factory to adjust its power consumption. Varun Sivaram was watching on Zoom with about forty others — his team at Emerald AI in their San Francisco conference room, engineers […] ]]></description>
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<pubDate>Tue, 15 Sep 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Megawatts, Tokens:, How, NVIDIA, Maximizes, Factory, Production</media:keywords>
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<item>
<title>AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories</title>
<link>https://news.jatlink.uk/21345</link>
<guid>https://news.jatlink.uk/21345</guid>
<description><![CDATA[ Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA, Tuesday spoke on AI factory efficiency at the AI Infra Summit, the Santa Clara Convention Center event that has morphed into a Coachella of infrastructure tech. Before a packed audience — with more than 8,000 attendees this year, up from 3,500 last year — […] ]]></description>
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<pubDate>Tue, 15 Sep 2026 19:00:07 +0100</pubDate>
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<item>
<title>Announcing instance preference lists for Amazon SageMaker AI training jobs</title>
<link>https://news.jatlink.uk/21343</link>
<guid>https://news.jatlink.uk/21343</guid>
<description><![CDATA[ Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ]]></description>
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<pubDate>Tue, 15 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, instance, preference, lists, for, Amazon, SageMaker, training, jobs</media:keywords>
</item>

<item>
<title>Optimizing cost and latency with Amazon Bedrock prompt caching</title>
<link>https://news.jatlink.uk/21341</link>
<guid>https://news.jatlink.uk/21341</guid>
<description><![CDATA[ Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration. ]]></description>
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<pubDate>Tue, 15 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, cost, and, latency, with, Amazon, Bedrock, prompt, caching</media:keywords>
</item>

<item>
<title>Build an AI&amp;powered product tagging system with Amazon SageMaker serverless model customization</title>
<link>https://news.jatlink.uk/21342</link>
<guid>https://news.jatlink.uk/21342</guid>
<description><![CDATA[ Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ]]></description>
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<pubDate>Tue, 15 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, product, tagging, system, with, Amazon, SageMaker, serverless, model, customization</media:keywords>
</item>

<item>
<title>Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care</title>
<link>https://news.jatlink.uk/21315</link>
<guid>https://news.jatlink.uk/21315</guid>
<description><![CDATA[  ]]></description>
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<pubDate>Tue, 15 Sep 2026 11:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Heart, the, Matter:, How, Major, Children’s, Hospital, Uses, Open, Source, NVIDIA, for, Cardiac, Care</media:keywords>
</item>

<item>
<title>Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale</title>
<link>https://news.jatlink.uk/21290</link>
<guid>https://news.jatlink.uk/21290</guid>
<description><![CDATA[ Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ]]></description>
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<pubDate>Tue, 15 Sep 2026 02:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Abnormal, AI:, Amazon, Bedrock, AgentCore, for, agentic, email, security, scale</media:keywords>
</item>

<item>
<title>Manage end&amp;user OAuth consent for AI agents with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21272</link>
<guid>https://news.jatlink.uk/21272</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Identity now offers a Consent portal, a managed web experience and session binding endpoint for AgentCore Gateway. This post walks through provisioning a portal, configuring GitHub and Slack 3LO targets, and the end-user consent flow, and shows how to review activity in AWS CloudTrail. ]]></description>
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<pubDate>Mon, 14 Sep 2026 22:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, end-user, OAuth, consent, for, agents, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX</title>
<link>https://news.jatlink.uk/21259</link>
<guid>https://news.jatlink.uk/21259</guid>
<description><![CDATA[ As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information […] ]]></description>
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<pubDate>Mon, 14 Sep 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Perplexity, Portable, Computer, Now, Available, Windows, Powered, NVIDIA, RTX</media:keywords>
</item>

<item>
<title>The generative AI customization spectrum: From prompt engineering to custom models on AWS</title>
<link>https://news.jatlink.uk/21257</link>
<guid>https://news.jatlink.uk/21257</guid>
<description><![CDATA[ Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ]]></description>
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<pubDate>Mon, 14 Sep 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, generative, customization, spectrum:, From, prompt, engineering, custom, models, AWS</media:keywords>
</item>

<item>
<title>Automate replenishment with MMF, Databricks Genie, and Amazon Quick</title>
<link>https://news.jatlink.uk/21258</link>
<guid>https://news.jatlink.uk/21258</guid>
<description><![CDATA[ Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ]]></description>
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<pubDate>Mon, 14 Sep 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, replenishment, with, MMF, Databricks, Genie, and, Amazon, Quick</media:keywords>
</item>

<item>
<title>How Fyxer built an AI executive assistant people trust</title>
<link>https://news.jatlink.uk/21255</link>
<guid>https://news.jatlink.uk/21255</guid>
<description><![CDATA[ Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice. ]]></description>
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<pubDate>Mon, 14 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Fyxer, built, executive, assistant, people, trust</media:keywords>
</item>

<item>
<title>How Ninth Wave built AI&amp;powered open finance onboarding on Amazon Bedrock</title>
<link>https://news.jatlink.uk/21256</link>
<guid>https://news.jatlink.uk/21256</guid>
<description><![CDATA[ Learn how Ninth Wave built Compass, a multi-agent AI onboarding assistant on Amazon Bedrock AgentCore that validates bank APIs against Financial Data Exchange (FDX) standards, scores compliance, and compresses open finance onboarding from weeks to minutes while meeting SOC 2 and PCI DSS requirements. ]]></description>
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<pubDate>Mon, 14 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ninth, Wave, built, AI-powered, open, finance, onboarding, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>NVIDIA Expands Open Source CUDA&amp;Q Platform for Fault&amp;Tolerant Quantum Computing</title>
<link>https://news.jatlink.uk/21236</link>
<guid>https://news.jatlink.uk/21236</guid>
<description><![CDATA[ NVIDIA today announced an expansion of the NVIDIA CUDA-Q™ open source platform with CUDA-Q Logical, an orchestration layer that provides a programmable, verifiable approach to developing useful applications for fault-tolerant quantum computers. ]]></description>
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<pubDate>Mon, 14 Sep 2026 15:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Expands, Open, Source, CUDA-Q, Platform, for, Fault-Tolerant, Quantum, Computing</media:keywords>
</item>

<item>
<title>Cappy: Outperforming and boosting large multi&amp;task language models with a small scorer</title>
<link>https://news.jatlink.uk/21152</link>
<guid>https://news.jatlink.uk/21152</guid>
<description><![CDATA[ Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research





Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0, FLAN, and OPT-IML. First, multi-task data is gathered with each task following a task-specific template, where each labeled example is converted into an instruction (e.g., &quot;Put the concepts together to form a sentence: ski, mountain, skier”) paired with a corresponding response (e.g., &quot;Skier skis down the mountain&quot;). These instruction-response pairs are used to train the LLM, resulting in a conditional generation model that takes an instruction as input and generates a response. Moreover, multi-task LLMs have exhibited remarkable task-wise generalization capabilities as they can address unseen tasks by understanding and solving brand-new instructions.



The demonstration of the instruction-following pre-training of multi-task LLMs, e.g., FLAN. Pre-training tasks under this paradigm improves the performance for unseen tasks.



Due to the complexity of understanding and solving various tasks solely using instructions, the size of multi-task LLMs typically spans from several billion parameters to hundreds of billions (e.g., FLAN-11B, T0-11B and OPT-IML-175B). As a result, operating such sizable models poses significant challenges because they demand considerable computational power and impose substantial requirements on the memory capacities of GPUs and TPUs, making their training and inference expensive and inefficient. Extensive storage is required to maintain a unique LLM copy for each downstream task. Moreover, the most powerful multi-task LLMs (e.g., FLAN-PaLM-540B) are closed-sourced, making them impossible to be adapted. However, in practical applications, harnessing a single multi-task LLM to manage all conceivable tasks in a zero-shot manner remains difficult, particularly when dealing with complex tasks, personalized tasks and those that cannot be succinctly defined using instructions. On the other hand, the size of downstream training data is usually insufficient to train a model well without incorporating rich prior knowledge. Hence, it is long desired to adapt LLMs with downstream supervision while bypassing storage, memory, and access issues. 



Certain parameter-efficient tuning strategies, including prompt tuning and adapters, substantially diminish storage requirements, but they still perform back-propagation through LLM parameters during the tuning process, thereby keeping their memory demands high. Additionally, some in-context learning techniques circumvent parameter tuning by integrating a limited number of supervised examples into the instruction. However, these techniques are constrained by the model&#039;s maximum input length, which permits only a few samples to guide task resolution.



In “Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer”, presented at NeurIPS 2023, we propose a novel approach that enhances the performance and efficiency of multi-task LLMs. We introduce a lightweight pre-trained scorer, Cappy, based on continual pre-training on top of RoBERTa with merely 360 million parameters. Cappy takes in an instruction and a candidate response as input, and produces a score between 0 and 1, indicating an estimated correctness of the response with respect to the instruction. Cappy functions either independently on classification tasks or serves as an auxiliary component for LLMs, boosting their performance. Moreover, Cappy efficiently enables downstream supervision without requiring any finetuning, which avoids the need for back-propagation through LLM parameters and reduces memory requirements. Finally, adaptation with Cappy doesn’t require access to LLM parameters as it is compatible with closed-source multi-task LLMs, such as those only accessible via WebAPIs.



Cappy takes an instruction and response pair as input and outputs a score ranging from 0 to 1, indicating an estimation of the correctness of the response with respect to the instruction.




    

Pre-training



We begin with the same dataset collection, which includes 39 diverse datasets from PromptSource that were used to train T0. This collection encompasses a wide range of task types, such as question answering, sentiment analysis, and summarization. Each dataset is associated with one or more templates that convert each instance from the original datasets into an instruction paired with its ground truth response.



Cappy&#039;s regression modeling requires each pre-training data instance to include an instruction-response pair along with a correctness annotation for the response, so we produce a dataset with correctness annotations that range from 0 to 1. For every instance within a generation task, we leverage an existing multi-task LLM to generate multiple responses by sampling, co ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cappy:, Outperforming, and, boosting, large, multi-task, language, models, with, small, scorer</media:keywords>
</item>

<item>
<title>Talk like a graph: Encoding graphs for large language models</title>
<link>https://news.jatlink.uk/21153</link>
<guid>https://news.jatlink.uk/21153</guid>
<description><![CDATA[ Posted by Bahare Fatemi and Bryan Perozzi, Research Scientists, Google Research




Imagine all the things around you — your friends, tools in your kitchen, or even the parts of your bike. They are all connected in different ways. In computer science, the term graph is used to describe connections between objects. Graphs consist of nodes (the objects themselves) and edges (connections between two nodes, indicating a  relationship between them). Graphs are everywhere now. The internet itself is a giant graph of websites linked together. Even the knowledge search engines use is organized in a graph-like way.





Furthermore, consider the remarkable advancements in artificial intelligence — such as chatbots that can write stories in seconds, and even software that can interpret medical reports. This exciting progress is largely thanks to large language models (LLMs). New LLM technology is constantly being developed for different uses. 


Since graphs are everywhere and LLM technology is on the rise, in “Talk like a Graph: Encoding Graphs for Large Language Models”, presented at ICLR 2024, we present a way to teach powerful LLMs how to better reason with graph information. Graphs are a useful way to organize information, but LLMs are mostly trained on regular text. The objective is to test different techniques to see what works best and gain practical insights. Translating graphs into text that LLMs can understand is a remarkably complex task. The difficulty stems from the inherent complexity of graph structures with multiple nodes and the intricate web of edges that connect them. Our work studies how to take a graph and translate it into a format that an LLM can understand. We also design a benchmark called GraphQA to study different approaches on different graph reasoning problems and show how to phrase a graph-related problem in a way that enables the LLM to solve the graph problem. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: 1) the graph encoding method, 2) the nature of the graph task itself, and 3) interestingly, the very structure of the graph considered. These findings give us clues on how to best represent graphs for LLMs. Picking the right method can make the LLM up to 60% better at graph tasks!





Pictured, the process of encoding a graph as text using two different approaches and feeding the text and a question about the graph to the LLM.



    

Graphs as text



To be able to systematically find out what is the best way to translate a graph to text, we first design a benchmark called GraphQA. Think of GraphQA as an exam designed to evaluate powerful LLMs on graph-specific problems. We want to see how well LLMs can understand and solve problems that involve graphs in different setups. To create a comprehensive and realistic exam for LLMs, we don’t just use one type of graph, we use a mix of graphs ensuring breadth in the number of connections. This is mainly because different graph types make solving such problems easier or harder. This way, GraphQA can help expose biases in how an LLM thinks about the graphs, and the whole exam gets closer to a realistic setup that LLMs might encounter in the real world.





Overview of our framework for reasoning with graphs using LLMs.




GraphQA focuses on simple tasks related to graphs, like checking if an edge exists, calculating the number of nodes or edges, finding nodes that are connected to a specific node, and checking for cycles in a graph. These tasks might seem basic, but they require understanding the relationships between nodes and edges. By covering different types of challenges, from identifying patterns to creating new connections, GraphQA helps models learn how to analyze graphs effectively. These basic tasks are crucial for more complex reasoning on graphs, like finding the shortest path between nodes, detecting communities, or identifying influential nodes. Additionally, GraphQA includes generating random graphs using various algorithms like Erdős-Rényi, scale-free networks, Barabasi-Albert model, and stochastic block model, as well as simpler graph structures like paths, complete graphs, and star graphs, providing a diverse set of data for training.


When working with graphs, we also need to find ways to ask graph-related questions that LLMs can understand.  Prompting heuristics are different strategies for doing this. Let&#039;s break down the common ones:



Zero-shot: simply describe the task (&quot;Is there a cycle in this graph?&quot;) and tell the LLM to go for it. No examples provided.

Few-shot: This is like giving the LLM a mini practice test before the real deal. We provide a few example graph questions and their correct answers.

Chain-of-Thought: Here, we show the LLM how to break down a problem step-by-step with examples. The goal is to teach it to generate its own &quot;thought process&quot; when faced with new graphs.

Zero-CoT: Similar to CoT, but instead of training examples, we give the LLM a simp ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Talk, like, graph:, Encoding, graphs, for, large, language, models</media:keywords>
</item>

<item>
<title>MELON: Reconstructing 3D objects from images with unknown poses</title>
<link>https://news.jatlink.uk/21150</link>
<guid>https://news.jatlink.uk/21150</guid>
<description><![CDATA[ Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research





A person&#039;s prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years. This fundamental computer vision task has applications ranging from the creation of e-commerce 3D models to autonomous vehicle navigation. 



A key part of the problem is how to determine the exact positions from which images were taken, known as pose inference. If camera poses are known, a range of successful techniques — such as neural radiance fields (NeRF) or 3D Gaussian Splatting — can reconstruct an object in 3D. But if these poses are not available, then we face a difficult “chicken and egg” problem where we could determine the poses if we knew the 3D object, but we can’t reconstruct the 3D object until we know the camera poses. The problem is made harder by pseudo-symmetries — i.e., many objects look similar when viewed from different angles. For example, square objects like a chair tend to look similar every 90° rotation. Pseudo-symmetries of an object can be revealed by rendering it on a turntable from various angles and plotting its photometric self-similarity map. 


Self-Similarity map of a toy truck model. Left: The model is rendered on a turntable from various azimuthal angles, θ. Right: The average L2 RGB similarity of a rendering from θ with that of θ*. The pseudo-similarities are indicated by the dashed red lines.



The diagram above only visualizes one dimension of rotation. It becomes even more complex (and difficult to visualize) when introducing more degrees of freedom. Pseudo-symmetries make the problem ill-posed, with naïve approaches often converging to local minima. In practice, such an approach might mistake the back view as the front view of an object, because they share a similar silhouette. Previous techniques (such as BARF or SAMURAI) side-step this problem by relying on an initial pose estimate that starts close to the global minima. But how can we approach this if those aren’t available?



Methods, such as GNeRF and VMRF leverage generative adversarial networks (GANs) to overcome the problem. These techniques have the ability to artificially “amplify” a limited number of training views, aiding reconstruction. GAN techniques, however, often have complex, sometimes unstable, training processes, making robust and reliable convergence difficult to achieve in practice. A range of other successful methods, such as SparsePose or RUST, can infer poses from a limited number views, but require pre-training on a large dataset of posed images, which aren’t always available, and can suffer from “domain-gap” issues when inferring poses for different types of images.



In “MELON: NeRF with Unposed Images in SO(3)”, spotlighted at 3DV 2024, we present a technique that can determine object-centric camera poses entirely from scratch while reconstructing the object in 3D. MELON (Modulo Equivalent Latent Optimization of NeRF) is one of the first techniques that can do this without initial pose camera estimates, complex training schemes or pre-training on labeled data. MELON is a relatively simple technique that can easily be integrated into existing NeRF methods. We demonstrate that MELON can reconstruct a NeRF from unposed images with state-of-the-art accuracy while requiring as few as 4–6 images of an object. 




    

MELON



We leverage two key techniques to aid convergence of this ill-posed problem. The first is a very lightweight, dynamically trained convolutional neural network (CNN) encoder that regresses camera poses from training images. We pass a downscaled training image to a four layer CNN that infers the camera pose. This CNN is initialized from noise and requires no pre-training. Its capacity is so small that it forces similar looking images to similar poses, providing an implicit regularization greatly aiding convergence.



The second technique is a modulo loss that simultaneously considers pseudo symmetries of an object. We render the object from a fixed set of viewpoints for each training image, backpropagating the loss only through the view that best fits the training image. This effectively considers the plausibility of multiple views for each image. In practice, we find N=2 views (viewing an object from the other side) is all that’s required in most cases, but sometimes get better results with N=4 for square objects.



These two techniques are integrated into standard NeRF training, except that instead of fixed camera poses, poses are inferred by the CNN and duplicated by the modulo loss. Photometric gradients back-propagate through the best-fitting cameras into the CNN. We observe that cameras generally converge quickly to g ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>MELON:, Reconstructing, objects, from, images, with, unknown, poses</media:keywords>
</item>

<item>
<title>HEAL: A framework for health equity assessment of machine learning performance</title>
<link>https://news.jatlink.uk/21151</link>
<guid>https://news.jatlink.uk/21151</guid>
<description><![CDATA[ Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer &amp; Director, Google Core




Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the diagnostic technology. In dermatology for example, skin cancer outcomes are worse for populations such as minorities, those with lower socioeconomic status, or individuals with limited healthcare access. While there is great promise in recent advances in machine learning (ML) and artificial intelligence (AI) to help improve healthcare, this transition from research to bedside must be accompanied by a careful understanding of whether and how they impact health equity.

 


Health equity is defined by public health organizations as fairness of opportunity for everyone to be as healthy as possible. Importantly, equity may be different from equality. For example, people with greater barriers to improving their health may require more or different effort to experience this fair opportunity. Similarly, equity is not fairness as defined in the AI for healthcare literature. Whereas AI fairness often strives for equal performance of the AI technology across different patient populations, this does not center the goal of prioritizing performance with respect to pre-existing health disparities.


Health equity considerations. An intervention (e.g., an ML-based tool, indicated in dark blue) promotes health equity if it helps reduce existing disparities in health outcomes (indicated in lighter blue).


In “Health Equity Assessment of machine Learning performance (HEAL): a framework and dermatology AI model case study”, published in The Lancet eClinicalMedicine, we propose a methodology to quantitatively assess whether ML-based health technologies perform equitably. In other words, does the ML model perform well for those with the worst health outcomes for the condition(s) the model is meant to address? This goal anchors on the principle that health equity should prioritize and measure model performance with respect to disparate health outcomes, which may be due to a number of factors that include structural inequities (e.g., demographic, social, cultural, political, economic, environmental and geographic).

 

The health equity framework (HEAL)


The HEAL framework proposes a 4-step process to estimate the likelihood that an ML-based health technology performs equitably:



Identify factors associated with health inequities and define tool performance metrics,


Identify and quantify pre-existing health disparities,


Measure the performance of the tool for each subpopulation,


Measure the likelihood that the tool prioritizes performance with respect to health disparities.




The final step’s output is termed the HEAL metric, which quantifies how anticorrelated the ML model’s performance is with health disparities. In other words, does the model perform better with populations that have the worse health outcomes?


This 4-step process is designed to inform improvements for making ML model performance more equitable, and is meant to be iterative and re-evaluated on a regular basis. For example, the availability of health outcomes data in step (2) can inform the choice of demographic factors and brackets in step (1), and the framework can be applied again with new datasets, models and populations.


Framework for Health Equity Assessment of machine Learning performance (HEAL). Our guiding principle is to avoid exacerbating health inequities, and these steps help us identify disparities and assess for inequitable model performance to move towards better outcomes for all.


With this work, we take a step towards encouraging explicit assessment of the health equity considerations of AI technologies, and encourage prioritization of efforts during model development to reduce health inequities for subpopulations exposed to structural inequities that can precipitate disparate outcomes. We should note that the present framework does not model causal relationships and, therefore, cannot quantify the actual impact a new technology will have on reducing health outcome disparities. However, the HEAL metric may help identify opportunities for improvement, where the current performance is not prioritized with respect to pre-existing health disparities.

 

Case study on a dermatology model



As an illustrative case study, we applied the framework to a dermatology model, which utilizes a convolutional neural network similar to that described in prior work. This example dermatology model was trained to classify 288 skin conditions using a development dataset of 29k cases. The input to the model consists of three photos of a skin concern along with demographic information and a brief structured medical history. The output consists of a ranked list of possible matching skin condition ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>HEAL:, framework, for, health, equity, assessment, machine, learning, performance</media:keywords>
</item>

<item>
<title>ScreenAI: A visual language model for UI and visually&amp;situated language understanding</title>
<link>https://news.jatlink.uk/21148</link>
<guid>https://news.jatlink.uk/21148</guid>
<description><![CDATA[ Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research





Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.g., icons and layouts), that offer an opportunity to build a single model that can understand, reason, and interact with these interfaces. However, because of their complexity and varied presentation formats, infographics and UIs present a unique modeling challenge.



To that end, we introduce “ScreenAI: A Vision-Language Model for UI and Infographics Understanding”. ScreenAI improves upon the PaLI architecture with the flexible patching strategy from pix2struct. We train ScreenAI on a unique mixture of datasets and tasks, including a novel Screen Annotation task that requires the model to identify UI element information (i.e., type, location and description) on a screen. These text annotations provide large language models (LLMs) with screen descriptions, enabling them to automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. At only 5B parameters, ScreenAI achieves state-of-the-art results on UI- and infographic-based tasks (WebSRC and MoTIF), and best-in-class performance on Chart QA, DocVQA, and InfographicVQA compared to models of similar size. We are also releasing three new datasets: Screen Annotation to evaluate the layout understanding capability of the model, as well as ScreenQA Short and Complex ScreenQA for a more comprehensive evaluation of its QA capability. 



    

ScreenAI



ScreenAI’s architecture is based on PaLI, composed of a multimodal encoder block and an autoregressive decoder. The PaLI encoder uses a vision transformer (ViT) that creates image embeddings and a multimodal encoder that takes the concatenation of the image and text embeddings as input. This flexible architecture allows ScreenAI to solve vision tasks that can be recast as text+image-to-text problems. 



On top of the PaLI architecture, we employ a flexible patching strategy introduced in pix2struct. Instead of using a fixed-grid pattern, the grid dimensions are selected such that they preserve the native aspect ratio of the input image. This enables ScreenAI to work well across images of various aspect ratios. 



The ScreenAI model is trained in two stages: a pre-training stage followed by a fine-tuning stage. First, self-supervised learning is applied to automatically generate data labels, which are then used to train ViT and the language model. ViT is frozen during the fine-tuning stage, where most data used is manually labeled by human raters. 



ScreenAI model architecture.





    

Data generation



To create a pre-training dataset for ScreenAI, we first compile an extensive collection of screenshots from various devices, including desktops, mobile, and tablets. This is achieved by using publicly accessible web pages and following the programmatic exploration approach used for the RICO dataset for mobile apps. We then apply a layout annotator, based on the DETR model, that identifies and labels a wide range of UI elements (e.g., image, pictogram, button, text) and their spatial relationships. Pictograms undergo further analysis using an icon classifier capable of distinguishing 77 different icon types. This detailed classification is essential for interpreting the subtle information conveyed through icons. For icons that are not covered by the classifier, and for infographics and images, we use the PaLI image captioning model to generate descriptive captions that provide contextual information. We also apply an optical character recognition (OCR) engine to extract and annotate textual content on screen. We combine the OCR text with the previous annotations to create a detailed description of each screen.



A mobile app screenshot with generated annotations that include UI elements and their descriptions, e.g., TEXT elements also contain the text content from OCR, IMAGE elements contain image captions, LIST_ITEMs contain all their child elements.





    

LLM-based data generation



We enhance the pre-training data&#039;s diversity using PaLM 2 to generate input-output pairs in a two-step process. First, screen annotations are generated using the technique outlined above, then we craft a prompt around this schema for the LLM to create synthetic data. This process requires prompt engineering and iterative refinement to find an effective prompt. We assess the generated data&#039;s quality through human validation against a quality threshold. 




You only speak JSON. Do not write text that isn’t JSON.
You are given the following mobile screenshot, described in words. Can you generate 5 questions regarding the content of the screenshot as well as the corresponding short answers to them? 

 ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ScreenAI:, visual, language, model, for, and, visually-situated, language, understanding</media:keywords>
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<title>SCIN: A new resource for representative dermatology images</title>
<link>https://news.jatlink.uk/21149</link>
<guid>https://news.jatlink.uk/21149</guid>
<description><![CDATA[ Posted by Pooja Rao, Research Scientist, Google Research




Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones. Yet, existing dermatology image datasets often lack representation of everyday conditions (like rashes, allergies and infections) and skew towards lighter skin tones. Furthermore, race and ethnicity information is frequently missing, hindering our ability to assess disparities or create solutions.





To address these limitations, we are releasing the Skin Condition Image Network (SCIN) dataset in collaboration with physicians at Stanford Medicine. We designed SCIN to reflect the broad range of concerns that people search for online, supplementing the types of conditions typically found in clinical datasets. It contains images across various skin tones and body parts, helping to ensure that future AI tools work effectively for all. We&#039;ve made the SCIN dataset freely available as an open-access resource for researchers, educators, and developers, and have taken careful steps to protect contributor privacy.   




Example set of images and metadata from the SCIN dataset.




    

Dataset composition



The SCIN dataset currently contains over 10,000 images of skin, nail, or hair conditions, directly contributed by individuals experiencing them. All contributions were made voluntarily with informed consent by individuals in the US, under an institutional-review board approved study. To provide context for retrospective dermatologist labeling, contributors were asked to take images both close-up and from slightly further away. They were given the option to self-report demographic information and tanning propensity (self-reported Fitzpatrick Skin Type, i.e., sFST), and to describe the texture, duration and symptoms related to their concern.


One to three dermatologists labeled each contribution with up to five dermatology conditions, along with a confidence score for each label. The SCIN dataset contains these individual labels, as well as an aggregated and weighted differential diagnosis derived from them that could be useful for model testing or training. These labels were assigned retrospectively and are not equivalent to a clinical diagnosis, but they allow us to compare the distribution of dermatology conditions in the SCIN dataset with existing datasets.





The SCIN dataset contains largely allergic, inflammatory and infectious conditions while datasets from clinical sources focus on benign and malignant neoplasms.





While many existing dermatology datasets focus on malignant and benign tumors and are intended to assist with skin cancer diagnosis, the SCIN dataset consists largely of common allergic, inflammatory, and infectious conditions. The majority of images in the SCIN dataset show early-stage concerns — more than half arose less than a week before the photo, and 30% arose less than a day before the image was taken. Conditions within this time window are seldom seen within the health system and therefore are underrepresented in existing dermatology datasets. 


We also obtained dermatologist estimates of Fitzpatrick Skin Type (estimated FST or eFST) and layperson labeler estimates of Monk Skin Tone (eMST) for the images. This allowed comparison of the skin condition and skin type distributions to those in existing dermatology datasets. Although we did not selectively target any skin types or skin tones, the SCIN dataset has a balanced Fitzpatrick skin type distribution (with more of Types 3, 4, 5, and 6) compared to similar datasets from clinical sources. 





Self-reported and dermatologist-estimated Fitzpatrick Skin Type distribution in the SCIN dataset compared with existing un-enriched dermatology datasets (Fitzpatrick17k, PH², SKINL2, and PAD-UFES-20).




The Fitzpatrick Skin Type scale was originally developed as a photo-typing scale to measure the response of skin types to UV radiation, and it is widely used in dermatology research. The Monk Skin Tone scale is a newer 10-shade scale that measures skin tone rather than skin phototype, capturing more nuanced differences between the darker skin tones. While neither scale was intended for retrospective estimation using images, the inclusion of these labels is intended to enable future research into skin type and tone representation in dermatology. For example, the SCIN dataset provides an initial benchmark for the distribution of these skin types and tones in the US population.


The SCIN dataset has a high representation of women and younger individuals, likely reflecting a combination of factors. These could include differences in skin condition incidence, propensity to seek health information online, and variations in willingness to contribute to research across demographics.





    

Crowdsourcing metho ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>SCIN:, new, resource, for, representative, dermatology, images</media:keywords>
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<item>
<title>Computer&amp;aided diagnosis for lung cancer screening</title>
<link>https://news.jatlink.uk/21146</link>
<guid>https://news.jatlink.uk/21146</guid>
<description><![CDATA[ Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research 





Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier. In the US, screening involves annual scans, with some countries or cases recommending more or less frequent scans. 



The United States Preventive Services Task Force recently expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups. However, false positives (i.e., incorrectly reporting a potential cancer in a cancer-free patient) can cause anxiety and lead to unnecessary procedures for patients while increasing costs for the healthcare system. Moreover, efficiency in screening a large number of individuals can be challenging depending on healthcare infrastructure and radiologist availability.




At Google we have previously developed machine learning (ML) models for lung cancer detection, and have evaluated their ability to automatically detect and classify regions that show signs of potential cancer. Performance has been shown to be comparable to that of specialists in detecting possible cancer. While they have achieved high performance, effectively communicating findings in realistic environments is necessary to realize their full potential.



To that end, in “Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the US and Japan”, published in Radiology AI, we investigate how ML models can effectively communicate findings to radiologists. We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening. The system takes CT imaging as input and outputs a cancer suspicion rating using four categories (no suspicion, probably benign, suspicious, highly suspicious) along with the corresponding regions of interest. We evaluate the system’s utility in improving clinician performance through randomized reader studies in both the US and Japan, using the local cancer scoring systems (Lung-RADSs V1.1 and Sendai Score) and image viewers that mimic realistic settings. We found that reader specificity increases with model assistance in both reader studies. To accelerate progress in conducting similar studies with ML models, we have open-sourced code to process CT images and generate images compatible with the picture archiving and communication system (PACS) used by radiologists. 



    

Developing an interface to communicate model results



Integrating ML models into radiologist workflows involves understanding the nuances and goals of their tasks to meaningfully support them. In the case of lung cancer screening, hospitals follow various country-specific guidelines that are regularly updated. For example, in the US, Lung-RADs V1.1 assigns an alpha-numeric score to indicate the lung cancer risk and follow-up recommendations. When assessing patients, radiologists load the CT in their workstation to read the case, find lung nodules or lesions, and apply set guidelines to determine follow-up decisions. 




Our first step was to improve the previously developed ML models through additional training data and architectural improvements, including self-attention. Then, instead of targeting specific guidelines, we experimented with a complementary way of communicating AI results independent of guidelines or their particular versions. Specifically, the system output offers a suspicion rating and localization (regions of interest) for the user to consider in conjunction with their own specific guidelines. The interface produces output images directly associated with the CT study, requiring no changes to the user’s workstation. The radiologist only needs to review a small set of additional images. There is no other change to their system or interaction with the system.






Example of the assistive lung cancer screening system outputs. Results for the radiologist’s evaluation are visualized on the location of the CT volume where the suspicious lesion is found. The overall suspicion is displayed at the top of the CT images. Circles highlight the suspicious lesions while squares show a rendering of the same lesion from a different perspective, called a sagittal view.



The assistive lung cancer screening system comprises 13 models and has a high-level architecture similar to the end-to-end system used in prior work. The models coordinate with each other to first segment the lungs, obtain an overall assessment, locate three suspicious regions, then use the information to assign a suspicion rating to each region. The system was deployed on Google Cloud  ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Computer-aided, diagnosis, for, lung, cancer, screening</media:keywords>
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<item>
<title>Using AI to expand global access to reliable flood forecasts</title>
<link>https://news.jatlink.uk/21147</link>
<guid>https://news.jatlink.uk/21147</guid>
<description><![CDATA[ Posted by Yossi Matias, VP Engineering &amp; Research, and Grey Nearing, Research Scientist, Google Research




Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change. Nearly 1.5 billion people, making up 19% of the world’s population, are exposed to substantial risks from severe flood events. Upgrading early warning systems to make accurate and timely information accessible to these populations can save thousands of lives per year. 



Driven by the potential impact of reliable flood forecasting on people’s lives globally, we started our flood forecasting effort in 2017. Through this multi-year journey, we advanced research over the years hand-in-hand with building a real-time operational flood forecasting system that provides alerts on Google Search, Maps, Android notifications and through the Flood Hub. However, in order to scale globally, especially in places where accurate local data is not available, more research advances were required.



In “Global prediction of extreme floods in ungauged watersheds”, published in Nature, we demonstrate how machine learning (ML) technologies can significantly improve global-scale flood forecasting relative to the current state-of-the-art for countries where flood-related data is scarce. With these AI-based technologies we extended the reliability of currently-available global nowcasts, on average, from zero to five days, and improved forecasts across regions in Africa and Asia to be similar to what are currently available in Europe. The evaluation of the models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF).



These technologies also enable Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across over 80 countries. This information can be used by people, communities, governments and international organizations to take anticipatory action to help protect vulnerable populations.







    

Flood forecasting at Google 



The ML models that power the FloodHub tool are the product of many years of research, conducted in collaboration with several partners, including academics, governments, international organizations, and NGOs. 



In 2018, we launched a pilot early warning system in the Ganges-Brahmaputra river basin in India, with the hypothesis that ML could help address the challenging problem of reliable flood forecasting at scale. The pilot was further expanded the following year via the combination of an inundation model, real-time water level measurements, the creation of an elevation map and hydrologic modeling.



In collaboration with academics, and, in particular, with the JKU Institute for Machine Learning we explored ML-based hydrologic models, showing that LSTM-based models could produce more accurate simulations than traditional conceptual and physics-based hydrology models. This research led to flood forecasting improvements that enabled the expansion of our forecasting coverage to include all of India and Bangladesh. We also worked with researchers at Yale University to test technological interventions that increase the reach and impact of flood warnings.



Our hydrological models predict river floods by processing publicly available weather data like precipitation and physical watershed information. Such models must be calibrated to long data records from streamflow gauging stations in individual rivers. A low percentage of global river watersheds (basins) have streamflow gauges, which are expensive but necessary to supply relevant data, and it’s challenging for hydrological simulation and forecasting to provide predictions in basins that lack this infrastructure. Lower gross domestic product (GDP) is correlated with increased vulnerability to flood risks, and there is an inverse correlation between national GDP and the amount of publicly available data in a country. ML helps to address this problem by allowing a single model to be trained on all available river data and to be applied to ungauged basins where no data are available. In this way, models can be trained globally, and can make predictions for any river location.



There is an inverse (log-log) correlation between the amount of publicly available streamflow data in a country and national GDP. Streamflow data from the Global Runoff Data Center.




Our academic collaborations led to ML research that developed methods to estimate uncertainty in river forecasts and showed how ML river forecast models synthesize information from multiple data sources. They demonstrated that these models can simulate extreme events reliably, even when those events are not part of the training data. In an effort to contribute to open science, in 2023 we open-sourced a community-driven dataset for large-sample hydrology in Nature Sci ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, expand, global, access, reliable, flood, forecasts</media:keywords>
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<item>
<title>Generative AI to quantify uncertainty in weather forecasting</title>
<link>https://news.jatlink.uk/21144</link>
<guid>https://news.jatlink.uk/21144</guid>
<description><![CDATA[ Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research




Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of hazardous weather conditions. The importance of accurate and timely weather forecasts will only increase as the climate changes. Recognizing this, we at Google have been investing in weather and climate research to help ensure that the forecasting technology of tomorrow can meet the demand for reliable weather information. Some of our recent innovations include MetNet-3, Google&#039;s high-resolution forecasts up to 24-hours into the future, and GraphCast, a weather model that can predict weather up to 10 days ahead.

 


Weather is inherently stochastic. To quantify the uncertainty, traditional methods rely on physics-based simulation to generate an ensemble of forecasts. However, it is computationally costly to generate a large ensemble so that rare and extreme weather events can be discerned and characterized accurately.  


With that in mind, we are excited to announce our latest innovation designed to accelerate progress in weather forecasting, Scalable Ensemble Envelope Diffusion Sampler (SEEDS), recently published in Science Advances. SEEDS is a generative AI model that can efficiently generate ensembles of weather forecasts at scale at a small fraction of the cost of traditional physics-based forecasting models. This technology opens up novel opportunities for weather and climate science, and it represents one of the first applications to weather and climate forecasting of probabilistic diffusion models, a generative AI technology behind recent advances in media generation.

 

The need for probabilistic forecasts: the butterfly effect


In December 1972, at the American Association for the Advancement of Science meeting in Washington, D.C., MIT meteorology professor Ed Lorenz gave a talk entitled, “Does the Flap of a Butterfly&#039;s Wings in Brazil Set Off a Tornado in Texas?” which contributed to the term “butterfly effect”. He was building on his earlier, landmark 1963 paper where he examined the feasibility of “very-long-range weather prediction” and described how errors in initial conditions grow exponentially when integrated in time with numerical weather prediction models. This exponential error growth, known as chaos, results in a deterministic predictability limit that restricts the use of individual forecasts in decision making, because they do not quantify the inherent uncertainty of weather conditions. This is particularly problematic when forecasting extreme weather events, such as hurricanes, heatwaves, or floods.


Recognizing the limitations of deterministic forecasts, weather agencies around the world issue probabilistic forecasts. Such forecasts are based on ensembles of deterministic forecasts, each of which is generated by including synthetic noise in the initial conditions and stochasticity in the physical processes. Leveraging the fast error growth rate in weather models, the forecasts in an ensemble are purposefully different: the initial uncertainties are tuned to generate runs that are as different as possible and the stochastic processes in the weather model introduce additional differences during the model run. The error growth is mitigated by averaging all the forecasts in the ensemble and the variability in the ensemble of forecasts quantifies the uncertainty of the weather conditions.


While effective, generating these probabilistic forecasts is computationally costly. They require running highly complex numerical weather models on massive supercomputers multiple times. Consequently, many operational weather forecasts can only afford to generate ~10–50 ensemble members for each forecast cycle. This is a problem for users concerned with the likelihood of rare but high-impact weather events, which typically require much larger ensembles to assess beyond a few days. For instance, one would need a 10,000-member ensemble to forecast the likelihood of events with 1% probability of occurrence with a relative error less than 10%. Quantifying the probability of such extreme events could be useful, for example, for emergency management preparation or for energy traders.

 

SEEDS: AI-enabled advances


In the aforementioned paper, we present the Scalable Ensemble Envelope Diffusion Sampler (SEEDS), a generative AI technology for weather forecast ensemble generation. SEEDS is based on denoising diffusion probabilistic models, a state-of-the-art generative AI method pioneered in part by Google Research.


SEEDS can generate a large ensemble conditioned on as few as one or two forecasts from an operational numerical weather prediction system. The generated ensembles not only yield plausible real-weather–like forecasts but also match or exceed physics-based ensembles in ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generative, quantify, uncertainty, weather, forecasting</media:keywords>
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<item>
<title>AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks</title>
<link>https://news.jatlink.uk/21145</link>
<guid>https://news.jatlink.uk/21145</guid>
<description><![CDATA[ Posted by Urs Köster, Software Engineer, Google Research




Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data&#039;s patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly used Mauna Loa CO2 dataset. However, they often rely on domain experts to painstakingly select appropriate model components and may be computationally expensive. Alternatives such as neural networks lack interpretability, making it difficult to understand how they generate forecasts, and don&#039;t produce reliable confidence intervals. 



To that end, we introduce AutoBNN, a new open-source package written in JAX. AutoBNN automates the discovery of interpretable time series forecasting models, provides high-quality uncertainty estimates, and scales effectively for use on large datasets. We describe how AutoBNN combines the interpretability of traditional probabilistic approaches with the scalability and flexibility of neural networks.



    

AutoBNN



AutoBNN is based on a line of research that over the past decade has yielded improved predictive accuracy by modeling time series using GPs with learned kernel structures. The kernel function of a GP encodes assumptions about the function being modeled, such as the presence of trends, periodicity or noise.  With learned GP kernels, the kernel function is defined compositionally: it is either a base kernel (such as Linear, Quadratic, Periodic, Matérn or ExponentiatedQuadratic) or a composite that combines two or more kernel functions using operators such as Addition, Multiplication, or ChangePoint. This compositional kernel structure serves two related purposes. First, it is simple enough that a user who is an expert about their data, but not necessarily about GPs, can construct a reasonable prior for their time series. Second, techniques like Sequential Monte Carlo can be used for discrete searches over small structures and can output interpretable results.


AutoBNN improves upon these ideas, replacing the GP with Bayesian neural networks (BNNs) while retaining the compositional kernel structure. A BNN is a neural network with a probability distribution over weights rather than a fixed set of weights. This induces a distribution over outputs, capturing uncertainty in the predictions. BNNs bring the following advantages over GPs: First, training large GPs is computationally expensive, and traditional training algorithms scale as the cube of the number of data points in the time series. In contrast, for a fixed width, training a BNN will often be approximately linear in the number of data points. Second, BNNs lend themselves better to GPU and TPU hardware acceleration than GP training operations. Third, compositional BNNs can be easily combined with traditional deep BNNs, which have the ability to do feature discovery. One could imagine &quot;hybrid&quot; architectures, in which users specify a top-level structure of Add(Linear, Periodic, Deep), and the deep BNN is left to learn the contributions from potentially high-dimensional covariate information.



How might one translate a GP with compositional kernels into a BNN then? A single layer neural network will typically converge to a GP as the number of neurons (or &quot;width&quot;) goes to infinity. More recently, researchers have discovered a correspondence in the other direction — many popular GP kernels (such as Matern, ExponentiatedQuadratic, Polynomial or Periodic) can be obtained as infinite-width BNNs with appropriately chosen activation functions and weight distributions. Furthermore, these BNNs remain close to the corresponding GP even when the width is very much less than infinite. For example, the figures below show the difference in the covariance between pairs of observations, and regression results of the true GPs and their corresponding width-10 neural network versions.


Comparison of Gram matrices between true GP kernels (top row) and their width 10 neural network approximations (bottom row).




Comparison of regression results between true GP kernels (top row) and their width 10 neural network approximations (bottom row).




Finally, the translation is completed with BNN analogues of the Addition and Multiplication operators over GPs, and input warping to produce periodic kernels. BNN addition is straightforwardly given by adding the outputs of the component BNNs. BNN multiplication is achieved by multiplying the activations of the hidden layers of the BNNs and then applying a shared dense layer. We are therefore limited to only multiplying BNNs with the same hidden width.



    

Using AutoBNN



The AutoBNN package is available within Tensorflow Pr ]]></description>
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<pubDate>Sun, 13 Sep 2026 06:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AutoBNN:, Probabilistic, time, series, forecasting, with, compositional, bayesian, neural, networks</media:keywords>
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<title>Perplexity trusts GPT&amp;6 Astra with end&amp;to&amp;end systems</title>
<link>https://news.jatlink.uk/21064</link>
<guid>https://news.jatlink.uk/21064</guid>
<description><![CDATA[ Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 12 Sep 2026 02:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Perplexity, trusts, GPT-6, Astra, with, end-to-end, systems</media:keywords>
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<title>Cognition helps Devin test its own work with GPT‑6 Astra</title>
<link>https://news.jatlink.uk/21065</link>
<guid>https://news.jatlink.uk/21065</guid>
<description><![CDATA[ GPT‑6 Astra improves Devin’s ability to test software and show that it works, with the goal of helping engineers review less code and ship more. ]]></description>
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<pubDate>Sat, 12 Sep 2026 02:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cognition, helps, Devin, test, its, own, work, with, GPT‑6, Astra</media:keywords>
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<item>
<title>Build interactive MCP Apps using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/21046</link>
<guid>https://news.jatlink.uk/21046</guid>
<description><![CDATA[ Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension. ]]></description>
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<pubDate>Fri, 11 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, interactive, MCP, Apps, using, Amazon, Bedrock, AgentCore</media:keywords>
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<title>Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations</title>
<link>https://news.jatlink.uk/21044</link>
<guid>https://news.jatlink.uk/21044</guid>
<description><![CDATA[ Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system. ]]></description>
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<pubDate>Fri, 11 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Monitoring, production, agent, lifecycle, with, AWS, DevOps, Agent, and, AgentCore, Evaluations</media:keywords>
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<title>Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload</title>
<link>https://news.jatlink.uk/21045</link>
<guid>https://news.jatlink.uk/21045</guid>
<description><![CDATA[ Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock. ]]></description>
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<pubDate>Fri, 11 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, the, price, per, token:, Choosing, the, right, OpenAI, model, Amazon, Bedrock, for, your, workload</media:keywords>
</item>

<item>
<title>Rapidly scaling online storage to serve over 1 billion ChatGPT users</title>
<link>https://news.jatlink.uk/21031</link>
<guid>https://news.jatlink.uk/21031</guid>
<description><![CDATA[ Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 11 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rapidly, scaling, online, storage, serve, over, billion, ChatGPT, users</media:keywords>
</item>

<item>
<title>Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0</title>
<link>https://news.jatlink.uk/20971</link>
<guid>https://news.jatlink.uk/20971</guid>
<description><![CDATA[ TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ]]></description>
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<pubDate>Fri, 11 Sep 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Video, and, image, search, Amazon, Bedrock, Knowledge, Base, using, Marengo, 3.0</media:keywords>
</item>

<item>
<title>Reduce LLM latency with prefix&amp;aware routing on Amazon SageMaker Inference</title>
<link>https://news.jatlink.uk/20969</link>
<guid>https://news.jatlink.uk/20969</guid>
<description><![CDATA[ Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%. ]]></description>
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<pubDate>Fri, 11 Sep 2026 02:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reduce, LLM, latency, with, prefix-aware, routing, Amazon, SageMaker, Inference</media:keywords>
</item>

<item>
<title>Reduce inference cold starts on Amazon SageMaker HyperPod with model caching</title>
<link>https://news.jatlink.uk/20970</link>
<guid>https://news.jatlink.uk/20970</guid>
<description><![CDATA[ Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it. ]]></description>
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<pubDate>Fri, 11 Sep 2026 02:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reduce, inference, cold, starts, Amazon, SageMaker, HyperPod, with, model, caching</media:keywords>
</item>

<item>
<title>Amazon Quick is now generally available on desktop</title>
<link>https://news.jatlink.uk/20951</link>
<guid>https://news.jatlink.uk/20951</guid>
<description><![CDATA[ Your teams get an AI assistant that handles real work while your data stays in your environment and your conversations stay private Today, the Amazon Quick desktop application is generally available on macOS and Windows. We’re also adding a new activity feed to the mobile experience on iOS and Android that consolidates email, calendar, CRM, […] ]]></description>
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<pubDate>Thu, 10 Sep 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, now, generally, available, desktop</media:keywords>
</item>

<item>
<title>Introducing the Agents API</title>
<link>https://news.jatlink.uk/20950</link>
<guid>https://news.jatlink.uk/20950</guid>
<description><![CDATA[ Build and launch cloud agents with the Agents API, a managed service powered by the Codex harness for orchestration, long-running sessions, and tool use. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 10 Sep 2026 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Agents, API</media:keywords>
</item>

<item>
<title>Build more natural voice experiences with GPT‑Live‑1 in the API</title>
<link>https://news.jatlink.uk/20949</link>
<guid>https://news.jatlink.uk/20949</guid>
<description><![CDATA[ GPT‑Live‑1 brings natural, full-duplex voice conversations to the API, with stronger instruction following, custom voices, and telephony support. ]]></description>
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<pubDate>Thu, 10 Sep 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, more, natural, voice, experiences, with, GPT‑Live‑1, the, API</media:keywords>
</item>

<item>
<title>Introducing ChatGPT for Financial Services</title>
<link>https://news.jatlink.uk/20948</link>
<guid>https://news.jatlink.uk/20948</guid>
<description><![CDATA[ Introducing ChatGPT for Financial Services, combining built-in financial data and GPT-6 Astra for research, modeling, and client-ready materials. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 10 Sep 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, for, Financial, Services</media:keywords>
</item>

<item>
<title>Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video</title>
<link>https://news.jatlink.uk/20935</link>
<guid>https://news.jatlink.uk/20935</guid>
<description><![CDATA[ Manufacturing floors, warehouses and production lines rarely stay fixed — tasks change, layouts shift and new products arrive, and most robots can’t keep up without significant reprogramming. Skild AI’s new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses […] ]]></description>
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<pubDate>Thu, 10 Sep 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Skild, Taps, NVIDIA, Physical, Teach, Robots, New, Tasks, From, Single, Video</media:keywords>
</item>

<item>
<title>Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies</title>
<link>https://news.jatlink.uk/20936</link>
<guid>https://news.jatlink.uk/20936</guid>
<description><![CDATA[ The global robotaxi market — physical AI’s first commercial breakthrough — is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world’s busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is […] ]]></description>
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<pubDate>Thu, 10 Sep 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Physical, Takes, the, Wheel:, How, the, World’s, Robotaxi, Leaders, Are, Building, With, NVIDIA, Technologies</media:keywords>
</item>

<item>
<title>Build an end&amp;to&amp;end RFI questionnaire workflow using Amazon Quick Automate</title>
<link>https://news.jatlink.uk/20931</link>
<guid>https://news.jatlink.uk/20931</guid>
<description><![CDATA[ Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 — cutting development from days to hours. ]]></description>
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<pubDate>Thu, 10 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, end-to-end, RFI, questionnaire, workflow, using, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Model&amp;agnostic PII detection with LLMs</title>
<link>https://news.jatlink.uk/20932</link>
<guid>https://news.jatlink.uk/20932</guid>
<description><![CDATA[ A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors. ]]></description>
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<pubDate>Thu, 10 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Model-agnostic, PII, detection, with, LLMs</media:keywords>
</item>

<item>
<title>Agent Evaluation Metric for multi&amp;turn conversations</title>
<link>https://news.jatlink.uk/20933</link>
<guid>https://news.jatlink.uk/20933</guid>
<description><![CDATA[ Multi-turn agents fail in ways single-turn evaluation misses: one early mistake corrupts every later turn. This post introduces the Agent Evaluation Metric (AEM), a decomposable, turn-level way to measure agent quality, applied to its first dimension, correctness, to pinpoint the turn that caused a failure and separate it from the turns that inherited it. ]]></description>
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<pubDate>Thu, 10 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent, Evaluation, Metric, for, multi-turn, conversations</media:keywords>
</item>

<item>
<title>How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20934</link>
<guid>https://news.jatlink.uk/20934</guid>
<description><![CDATA[ AvioBook, a Thales Group Company, prototyped Connected Analytics on Amazon Bedrock AgentCore to turn AvioBook Connect&#039;s operational data into plain-language, evidence-based answers for airline managers and dispatchers, helping them find and act on the causes of flight turnaround delays. ]]></description>
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<pubDate>Thu, 10 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AvioBook, builds, turnaround, insights, from, operational, data, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules</title>
<link>https://news.jatlink.uk/20930</link>
<guid>https://news.jatlink.uk/20930</guid>
<description><![CDATA[ César de la Fuente’s lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections. ]]></description>
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<pubDate>Thu, 10 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, researcher, uses, Codex, and, ChatGPT, search, for, new, antimicrobial, molecules</media:keywords>
</item>

<item>
<title>Now everyone can put data to work</title>
<link>https://news.jatlink.uk/20928</link>
<guid>https://news.jatlink.uk/20928</guid>
<description><![CDATA[ Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language. ]]></description>
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<pubDate>Thu, 10 Sep 2026 17:00:34 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Now, everyone, can, put, data, work</media:keywords>
</item>

<item>
<title>Expanding AI access and cyber defense for federal, state, local, and tribal governments</title>
<link>https://news.jatlink.uk/20929</link>
<guid>https://news.jatlink.uk/20929</guid>
<description><![CDATA[ OpenAI and GSA will offer eligible federal, state, local, and tribal governments $0 license fees, 50% off usage, and expanded cyber defense support. ]]></description>
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<pubDate>Thu, 10 Sep 2026 17:00:34 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Expanding, access, and, cyber, defense, for, federal, state, local, and, tribal, governments</media:keywords>
</item>

<item>
<title>d&amp;Matrix Adopts NVIDIA NVLink Fusion for Rack&amp;Scale XPU Deployment</title>
<link>https://news.jatlink.uk/20909</link>
<guid>https://news.jatlink.uk/20909</guid>
<description><![CDATA[ AI inference chipmaker d-Matrix today announced it will use NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA’s AI infrastructure platform — joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and Spectrum-X scale-out networking, the NVIDIA MGX rack architecture and the broader NVIDIA AI platform, NVLink Fusion […] ]]></description>
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<pubDate>Thu, 10 Sep 2026 15:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>d-Matrix, Adopts, NVIDIA, NVLink, Fusion, for, Rack-Scale, XPU, Deployment</media:keywords>
</item>

<item>
<title>Boots on the Ground: ‘WARDOGS’ Goes All Out on GeForce NOW at Early&amp;Access Launch</title>
<link>https://news.jatlink.uk/20910</link>
<guid>https://news.jatlink.uk/20910</guid>
<description><![CDATA[ Gear up: The latest PC games and major updates are ready to play on GeForce NOW this week. WARDOGS drops onto the cloud at early-access launch, alongside the Valheim 1.0 Deep North update and Bus Simulator 27 — part of nine new titles joining the cloud. The newest PC releases can demand serious hardware, storage […] ]]></description>
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<pubDate>Thu, 10 Sep 2026 15:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Boots, the, Ground:, ‘WARDOGS’, Goes, All, Out, GeForce, NOW, Early-Access, Launch</media:keywords>
</item>

<item>
<title>NVIDIA and Palantir Bring Sovereign Intelligence to Critical Supply Chains</title>
<link>https://news.jatlink.uk/20892</link>
<guid>https://news.jatlink.uk/20892</guid>
<description><![CDATA[ Palantir Technologies Inc. (NASDAQ: PLTR) and NVIDIA (NASDAQ: NVDA) today announced a collaboration to bring sovereign AI to critical supply chains, starting with NVIDIA’s own operations. ]]></description>
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<pubDate>Thu, 10 Sep 2026 11:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Palantir, Bring, Sovereign, Intelligence, Critical, Supply, Chains</media:keywords>
</item>

<item>
<title>NVIDIA Expands AI Infrastructure Capacity in Partnership With Australia’s Data Center Ecosystem</title>
<link>https://news.jatlink.uk/20863</link>
<guid>https://news.jatlink.uk/20863</guid>
<description><![CDATA[ NVIDIA today announced that it is collaborating with a growing ecosystem of Australian NVIDIA Cloud Partners (NCPs) and AI infrastructure partners to expand land, power and shell capacity designed to host multiple generations of NVIDIA DSX™ AI factories, supporting the nation’s growing demand for AI compute — with up to a 2-gigawatt buildout by 2027. ]]></description>
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<pubDate>Thu, 10 Sep 2026 03:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Expands, Infrastructure, Capacity, Partnership, With, Australia’s, Data, Center, Ecosystem</media:keywords>
</item>

<item>
<title>Deploying Qwen3.8&amp;2.4T&amp;A95B on Amazon SageMaker HyperPod with vLLM</title>
<link>https://news.jatlink.uk/20862</link>
<guid>https://news.jatlink.uk/20862</guid>
<description><![CDATA[ Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding. ]]></description>
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<pubDate>Thu, 10 Sep 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploying, Qwen3.8-2.4T-A95B, Amazon, SageMaker, HyperPod, with, vLLM</media:keywords>
</item>

<item>
<title>The AI policy window is open. We need to act.</title>
<link>https://news.jatlink.uk/20860</link>
<guid>https://news.jatlink.uk/20860</guid>
<description><![CDATA[ Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action while the policy window remains open. ]]></description>
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<pubDate>Thu, 10 Sep 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, policy, window, open., need, act.</media:keywords>
</item>

<item>
<title>GPT&amp;6 Astra: The next generation in intelligence for work</title>
<link>https://news.jatlink.uk/20861</link>
<guid>https://news.jatlink.uk/20861</guid>
<description><![CDATA[ Meet GPT-6 Astra, OpenAI’s most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment. ]]></description>
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<pubDate>Thu, 10 Sep 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-6, Astra:, The, next, generation, intelligence, for, work</media:keywords>
</item>

<item>
<title>ICYMI: What landed for AI builders in August 2026</title>
<link>https://news.jatlink.uk/20847</link>
<guid>https://news.jatlink.uk/20847</guid>
<description><![CDATA[ A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to 14 days on dedicated compute, expanded AWS GovCloud availability, and Strands Robots for physical deployment. ]]></description>
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<pubDate>Wed, 09 Sep 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ICYMI:, What, landed, for, builders, August, 2026</media:keywords>
</item>

<item>
<title>How Heurist Finance built an AI&amp;native investment workbench on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20848</link>
<guid>https://news.jatlink.uk/20848</guid>
<description><![CDATA[ Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable. ]]></description>
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<pubDate>Wed, 09 Sep 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Heurist, Finance, built, AI-native, investment, workbench, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Paul Christiano joins OpenAI Foundation Board</title>
<link>https://news.jatlink.uk/20846</link>
<guid>https://news.jatlink.uk/20846</guid>
<description><![CDATA[ Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards. ]]></description>
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<pubDate>Wed, 09 Sep 2026 21:00:18 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Paul, Christiano, joins, OpenAI, Foundation, Board</media:keywords>
</item>

<item>
<title>NVIDIA Brings Real&amp;Time AI to Broadcast, Sports and Global Streaming at IBC</title>
<link>https://news.jatlink.uk/20829</link>
<guid>https://news.jatlink.uk/20829</guid>
<description><![CDATA[ At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers […] ]]></description>
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<pubDate>Wed, 09 Sep 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Brings, Real-Time, Broadcast, Sports, and, Global, Streaming, IBC</media:keywords>
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<item>
<title>Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers</title>
<link>https://news.jatlink.uk/20827</link>
<guid>https://news.jatlink.uk/20827</guid>
<description><![CDATA[ TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. ]]></description>
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<pubDate>Wed, 09 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simplify, and, support, your, TorchServe, workloads, using, Ray, Serve, Deep, Learning, Containers</media:keywords>
</item>

<item>
<title>Automate user&amp;level custom permissions for Amazon Quick</title>
<link>https://news.jatlink.uk/20828</link>
<guid>https://news.jatlink.uk/20828</guid>
<description><![CDATA[ Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script. ]]></description>
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<pubDate>Wed, 09 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, user-level, custom, permissions, for, Amazon, Quick</media:keywords>
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<item>
<title>Take on your most ambitious work with GPT&amp;6 Astra on Amazon Bedrock</title>
<link>https://news.jatlink.uk/20763</link>
<guid>https://news.jatlink.uk/20763</guid>
<description><![CDATA[ GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale. ]]></description>
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<pubDate>Wed, 09 Sep 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Take, your, most, ambitious, work, with, GPT-6, Astra, Amazon, Bedrock</media:keywords>
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<item>
<title>Amazon SageMaker Feature Store introduces UpdateRecord for feature&amp;level writes</title>
<link>https://news.jatlink.uk/20743</link>
<guid>https://news.jatlink.uk/20743</guid>
<description><![CDATA[ Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers. ]]></description>
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<pubDate>Tue, 08 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, Feature, Store, introduces, UpdateRecord, for, feature-level, writes</media:keywords>
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<item>
<title>Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2</title>
<link>https://news.jatlink.uk/20744</link>
<guid>https://news.jatlink.uk/20744</guid>
<description><![CDATA[ Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated. ]]></description>
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<pubDate>Tue, 08 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Govern, models, with, MLflow, and, Amazon, SageMaker, Model, Registry, sync:, Part</media:keywords>
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<item>
<title>Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 1</title>
<link>https://news.jatlink.uk/20745</link>
<guid>https://news.jatlink.uk/20745</guid>
<description><![CDATA[ Managed MLflow on Amazon SageMaker AI now syncs richer model metadata (training metrics, evaluation results, inference specs, and lineage) into the SageMaker AI Model Registry, with lifecycle stage promotion. Part 1 shows how to govern candidate models in a single account using IAM guardrails. ]]></description>
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<pubDate>Tue, 08 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Govern, models, with, MLflow, and, Amazon, SageMaker, Model, Registry, sync:, Part</media:keywords>
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<item>
<title>How GPT&amp;5.6 Sol helps run quantum computing experiments</title>
<link>https://news.jatlink.uk/20741</link>
<guid>https://news.jatlink.uk/20741</guid>
<description><![CDATA[ See how an MIT researcher uses GPT-5.6 Sol with Codex to autonomously run quantum computing experiments, analyze results, and calibrate qubits. ]]></description>
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<pubDate>Tue, 08 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, GPT-5.6, Sol, helps, run, quantum, computing, experiments</media:keywords>
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<item>
<title>Pathway’s brain&amp;inspired architecture development on Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/20742</link>
<guid>https://news.jatlink.uk/20742</guid>
<description><![CDATA[ Pathway&#039;s Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark. ]]></description>
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<pubDate>Tue, 08 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Pathway’s, brain-inspired, architecture, development, Amazon, SageMaker, HyperPod</media:keywords>
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<item>
<title>Introducing ChatGPT Images 2.5</title>
<link>https://news.jatlink.uk/20737</link>
<guid>https://news.jatlink.uk/20737</guid>
<description><![CDATA[ ChatGPT Images 2.5 helps turn your ideas, sketches, and reference photos into more personalized, polished images that better reflect your ideas. ]]></description>
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<pubDate>Tue, 08 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, Images, 2.5</media:keywords>
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<item>
<title>On the Navier–Stokes Millennium Prize Problem</title>
<link>https://news.jatlink.uk/20738</link>
<guid>https://news.jatlink.uk/20738</guid>
<description><![CDATA[ We’re sharing an AI-generated solution to the Navier–Stokes Millennium Prize Problem, including a writeup and a formal proof in Lean. ]]></description>
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<pubDate>Tue, 08 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>the, Navier–Stokes, Millennium, Prize, Problem</media:keywords>
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<item>
<title>Funding grants for new research into AI and teen development</title>
<link>https://news.jatlink.uk/20739</link>
<guid>https://news.jatlink.uk/20739</guid>
<description><![CDATA[ Apply now for OpenAI’s $5 million grant program supporting independent research on how generative AI affects teen development, well-being, and safety. ]]></description>
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<pubDate>Tue, 08 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Funding, grants, for, new, research, into, and, teen, development</media:keywords>
</item>

<item>
<title>1Password increases engineering productivity 21% with Codex</title>
<link>https://news.jatlink.uk/20740</link>
<guid>https://news.jatlink.uk/20740</guid>
<description><![CDATA[ Engineers at 1Password use Codex to rapidly build new features and internal tools, reaching production-readiness while maintaining rigorous security policies. ]]></description>
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<pubDate>Tue, 08 Sep 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>1Password, increases, engineering, productivity, 21, with, Codex</media:keywords>
</item>

<item>
<title>How HPE Zerto built an agentic troubleshooting system with Amazon Bedrock</title>
<link>https://news.jatlink.uk/20719</link>
<guid>https://news.jatlink.uk/20719</guid>
<description><![CDATA[ HPE Zerto built an agentic troubleshooting system powered by Amazon Bedrock that runs on-premises inside the customer environment. This post describes the multi-agent architecture, the on-premises deployment model built with Strands Agents, and the engineering challenges of grounding agents in live disaster recovery data. ]]></description>
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<pubDate>Tue, 08 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, HPE, Zerto, built, agentic, troubleshooting, system, with, Amazon, Bedrock</media:keywords>
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<item>
<title>How DiDi built intelligent contact center QA with Amazon Bedrock</title>
<link>https://news.jatlink.uk/20720</link>
<guid>https://news.jatlink.uk/20720</guid>
<description><![CDATA[ DiDi built a transparent, self-owned contact center quality assurance (QA) system on Amazon Bedrock, replacing an opaque third-party tool. Intent verification accuracy rose from 38% to 86%, compliance scoring topped 90%, and Voice of Customer trend analysis dropped from hours to minutes across Spanish and Portuguese support. ]]></description>
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<pubDate>Tue, 08 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, DiDi, built, intelligent, contact, center, with, Amazon, Bedrock</media:keywords>
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<item>
<title>Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions</title>
<link>https://news.jatlink.uk/20717</link>
<guid>https://news.jatlink.uk/20717</guid>
<description><![CDATA[ Wire Amazon Bedrock AgentCore Evaluations into a GitHub Actions pipeline: deploy an AI agent and an OAuth-protected MCP server to AgentCore runtime, invoke the agent with test prompts, score the responses, and automatically block pull requests when agent behavior regresses. ]]></description>
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<pubDate>Tue, 08 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, agent, evaluation, with, Amazon, Bedrock, AgentCore, and, GitHub, Actions</media:keywords>
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<item>
<title>Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6</title>
<link>https://news.jatlink.uk/20718</link>
<guid>https://news.jatlink.uk/20718</guid>
<description><![CDATA[ Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU instances on Amazon SageMaker AI. Compare throughput, latency, and cost-per-token, and see how G7&#039;s NVIDIA Blackwell GPUs deliver measurable price-performance gains for real-time LLM inference. ]]></description>
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<pubDate>Tue, 08 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Benchmarking, small, LLM, inference, SageMaker, AI:, and</media:keywords>
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<item>
<title>OpenAI expands initiatives to support journalism from classrooms to newsrooms</title>
<link>https://news.jatlink.uk/20716</link>
<guid>https://news.jatlink.uk/20716</guid>
<description><![CDATA[ OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations. ]]></description>
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<pubDate>Tue, 08 Sep 2026 17:00:23 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, expands, initiatives, support, journalism, from, classrooms, newsrooms</media:keywords>
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<item>
<title>The Work Now Within Reach</title>
<link>https://news.jatlink.uk/20715</link>
<guid>https://news.jatlink.uk/20715</guid>
<description><![CDATA[ Explore how more capable, affordable AI can expand the work people and businesses can accomplish—and make growth more economical. ]]></description>
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<pubDate>Tue, 08 Sep 2026 17:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Work, Now, Within, Reach</media:keywords>
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<item>
<title>Supporting independent journalism in Ukraine</title>
<link>https://news.jatlink.uk/20608</link>
<guid>https://news.jatlink.uk/20608</guid>
<description><![CDATA[ OpenAI, AIRPPU and WAN-IFRA launch an AI program to help Ukrainian news organizations strengthen innovation, resilience, and independent journalism. ]]></description>
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<pubDate>Mon, 07 Sep 2026 09:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Supporting, independent, journalism, Ukraine</media:keywords>
</item>

<item>
<title>An Alien Mind</title>
<link>https://news.jatlink.uk/20569</link>
<guid>https://news.jatlink.uk/20569</guid>
<description><![CDATA[ Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination. ]]></description>
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<pubDate>Sun, 06 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Alien, Mind</media:keywords>
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<item>
<title>Research acceleration: The view inside OpenAI</title>
<link>https://news.jatlink.uk/20568</link>
<guid>https://news.jatlink.uk/20568</guid>
<description><![CDATA[ Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration. ]]></description>
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<pubDate>Sun, 06 Sep 2026 17:00:54 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Research, acceleration:, The, view, inside, OpenAI</media:keywords>
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<item>
<title>GPT&amp;6 Astra: A new generation of intelligence</title>
<link>https://news.jatlink.uk/20465</link>
<guid>https://news.jatlink.uk/20465</guid>
<description><![CDATA[ Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science. ]]></description>
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<pubDate>Sat, 05 Sep 2026 05:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-6, Astra:, new, generation, intelligence</media:keywords>
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<item>
<title>Deploy a multimodal WhatsApp ordering assistant with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20448</link>
<guid>https://news.jatlink.uk/20448</guid>
<description><![CDATA[ Learn how to deploy a multimodal WhatsApp ordering assistant that takes customer orders through text, voice notes, and real-time voice calls on a single business number, built on Amazon Bedrock AgentCore with Amazon Nova 2. The channel and ordering layers stay separate, and one shared memory recognizes each customer across all three channels. ]]></description>
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<pubDate>Sat, 05 Sep 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, multimodal, WhatsApp, ordering, assistant, with, Amazon, Bedrock, AgentCore</media:keywords>
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<item>
<title>Designing lifecycle policies for AgentCore memory</title>
<link>https://news.jatlink.uk/20431</link>
<guid>https://news.jatlink.uk/20431</guid>
<description><![CDATA[ Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk. Learn how to design memory lifecycle policies for Amazon Bedrock AgentCore: scoring, consolidating, and pruning agent memories on a nightly AWS Step Functions workflow, with a deployable AWS CDK stack. ]]></description>
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<pubDate>Fri, 04 Sep 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Designing, lifecycle, policies, for, AgentCore, memory</media:keywords>
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<item>
<title>Run agent&amp;driven Amazon SageMaker HyperPod operations with InstantStart</title>
<link>https://news.jatlink.uk/20410</link>
<guid>https://news.jatlink.uk/20410</guid>
<description><![CDATA[ HyperPod InstantStart is an open source control plane that composes Amazon EKS orchestration with the managed capabilities of Amazon SageMaker HyperPod. It drives the same guarded operations through both a web interface and an AI agent, turning cluster bootstrap, capacity, training, inference, and storage into dependable, agent-driven infrastructure. ]]></description>
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<pubDate>Fri, 04 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, agent-driven, Amazon, SageMaker, HyperPod, operations, with, InstantStart</media:keywords>
</item>

<item>
<title>Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract</title>
<link>https://news.jatlink.uk/20411</link>
<guid>https://news.jatlink.uk/20411</guid>
<description><![CDATA[ Learn how to customize an Amazon Bedrock knowledge base for large, complex documents by combining the high-accuracy text extraction of Amazon Textract with the generative AI of Amazon Bedrock. This post shows how to ingest and preprocess PDFs and images, then query utility bills at scale for faster, more accurate customer interactions. ]]></description>
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<pubDate>Fri, 04 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customizing, your, knowledge, base, Amazon, Bedrock, for, large, and, complex, documents, using, Amazon, Textract</media:keywords>
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<item>
<title>How Intuit built an agentic disaster recovery assistant with Amazon Bedrock</title>
<link>https://news.jatlink.uk/20412</link>
<guid>https://news.jatlink.uk/20412</guid>
<description><![CDATA[ Disaster recovery at scale is hard. Learn how Intuit built EWOK Agent, an agentic disaster recovery assistant on Amazon Bedrock that lets on-call engineers run production failovers from a plain-language request while keeping every action audited, policy-compliant, and safe. ]]></description>
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<pubDate>Fri, 04 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Intuit, built, agentic, disaster, recovery, assistant, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod</title>
<link>https://news.jatlink.uk/20409</link>
<guid>https://news.jatlink.uk/20409</guid>
<description><![CDATA[ Building a Physical AI system takes a continuous pipeline, not a single training job. This post shows how to run that model factory (synthetic data generation, post-training, and closed-loop evaluation with NVIDIA Cosmos 3) on a persistent, resilient Amazon SageMaker HyperPod cluster on Amazon EKS, with GPU goodput as the metric that matters. ]]></description>
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<pubDate>Fri, 04 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, Physical, model, factory, with, NVIDIA, Cosmos, SageMaker, HyperPod</media:keywords>
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<item>
<title>Playco cut manual fixes 50% prototyping games with GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/20350</link>
<guid>https://news.jatlink.uk/20350</guid>
<description><![CDATA[ Using GPT-6 Astra, Playco built three themed game prototypes from one grey box foundation and reported 50% fewer manual fixes than with the previous model. ]]></description>
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<pubDate>Fri, 04 Sep 2026 01:00:18 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Playco, cut, manual, fixes, 50, prototyping, games, with, GPT-6, Astra</media:keywords>
</item>

<item>
<title>Safety overview: GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/20332</link>
<guid>https://news.jatlink.uk/20332</guid>
<description><![CDATA[ GPT-6 Astra is our most capable broadly deployed model and our first to reach the Critical level of cybersecurity capability under our Preparedness Framework. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 03 Sep 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Safety, overview:, GPT-6, Astra</media:keywords>
</item>

<item>
<title>Daybreak for Frontline Defenders: $1B to protect essential services</title>
<link>https://news.jatlink.uk/20330</link>
<guid>https://news.jatlink.uk/20330</guid>
<description><![CDATA[ OpenAI introduces Daybreak for Frontline Defenders. A $1 billion commitment expands access to frontier cyber AI, training, and support for essential services. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 03 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Daybreak, for, Frontline, Defenders:, 1B, protect, essential, services</media:keywords>
</item>

<item>
<title>Legora reviewed 41 documents in minutes with GPT&amp;6 Astra</title>
<link>https://news.jatlink.uk/20331</link>
<guid>https://news.jatlink.uk/20331</guid>
<description><![CDATA[ Legora used GPT-6 Astra to review 41 documents in minutes, find all four planted errors, and improve performance by nearly 40% in this financial-review workflow. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 03 Sep 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Legora, reviewed, documents, minutes, with, GPT-6, Astra</media:keywords>
</item>

<item>
<title>Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026</title>
<link>https://news.jatlink.uk/20312</link>
<guid>https://news.jatlink.uk/20312</guid>
<description><![CDATA[ Frontier intelligence is going local. At IFA 2026, NVIDIA, Microsoft and its partners are teaming up to provide faster inference and new tools that make agents easier to set up and run locally on NVIDIA hardware. New compact NVIDIA RTX Spark Windows PCs are also coming in October to give AI enthusiasts, developers and creators […] ]]></description>
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<pubDate>Thu, 03 Sep 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sparks, Fly:, NVIDIA, Accelerates, Local, IFA, 2026</media:keywords>
</item>

<item>
<title>Embed Quick Sight visuals using Cognito user authentication</title>
<link>https://news.jatlink.uk/20310</link>
<guid>https://news.jatlink.uk/20310</guid>
<description><![CDATA[ Learn how to embed individual Amazon Quick Sight visuals into a React application with per-user access control. This walkthrough uses Amazon Cognito authentication and a serverless AWS Lambda backend to generate scoped embed URLs, deployed with a single AWS CloudFormation stack. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, Quick, Sight, visuals, using, Cognito, user, authentication</media:keywords>
</item>

<item>
<title>Set up OpenAI ChatGPT Codex with LiteLLM on Amazon ECS and Amazon Bedrock</title>
<link>https://news.jatlink.uk/20308</link>
<guid>https://news.jatlink.uk/20308</guid>
<description><![CDATA[ Deploy a customer-operated LiteLLM gateway on Amazon ECS with AWS Fargate, connect it to an OpenAI model on Amazon Bedrock, and configure Codex to route requests through the gateway&#039;s Responses API with scoped identities, budgets, rate limits, and telemetry. We also compare direct IAM Identity Center access and a managed Portkey deployment. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Set, OpenAI, ChatGPT, Codex, with, LiteLLM, Amazon, ECS, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Best practices for building agentic automations with Amazon Quick Automate</title>
<link>https://news.jatlink.uk/20309</link>
<guid>https://news.jatlink.uk/20309</guid>
<description><![CDATA[ Learn best practices for building production-grade, agent-based business process automations with Amazon Quick Automate: choosing the right process, designing focused agents, combining them with deterministic steps, applying human-in-the-loop review, and building in evaluation and observability. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, for, building, agentic, automations, with, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Migrate agentic workloads to Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20306</link>
<guid>https://news.jatlink.uk/20306</guid>
<description><![CDATA[ An agent that works in a notebook is not an agent in production. This post walks through migrating a LangGraph customer support agent to Amazon Bedrock AgentCore in two stages: onto Runtime, Gateway, and Memory, then to model-driven planning on Strands Agents, retiring operational burdens along the way. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, agentic, workloads, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Integrating Outlook with Amazon Quick for AI&amp;powered email automation</title>
<link>https://news.jatlink.uk/20307</link>
<guid>https://news.jatlink.uk/20307</guid>
<description><![CDATA[ Integrate Microsoft Outlook with Amazon Quick to automate email management, calendar scheduling, and workflow coordination. This post walks through the end-to-end setup and shows automation scenarios using Amazon Quick chat agents, Amazon Quick Flows, and Amazon Quick Automate. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrating, Outlook, with, Amazon, Quick, for, AI-powered, email, automation</media:keywords>
</item>

<item>
<title>AI&amp;driven development lifecycle using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20305</link>
<guid>https://news.jatlink.uk/20305</guid>
<description><![CDATA[ Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) often struggle to turn concepts into working code. This post walks through two reference implementations on Amazon Bedrock AgentCore, Kiro, and Claude Code: an SQL-to-ER-diagram generator and a multi-agent code security analyzer that put the AI-DLC construction phase into practice. ]]></description>
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<pubDate>Thu, 03 Sep 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AI-driven, development, lifecycle, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>‘NBA 2K27’ With NVIDIA DLSS 5 Leads 26 New Games Coming to GeForce NOW</title>
<link>https://news.jatlink.uk/20293</link>
<guid>https://news.jatlink.uk/20293</guid>
<description><![CDATA[ September is here with 26 more games streaming on GeForce NOW this month, led by a slam dunk: NBA 2K27 with the NVIDIA DLSS 5 3D-Guided Neural Rendering feature. Through NVIDIA’s close collaboration with Visual Concepts and 2K, DLSS 5 brings a new level of lifelike lighting and material detail to the court — tuned […] ]]></description>
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<pubDate>Thu, 03 Sep 2026 15:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>‘NBA, 2K27’, With, NVIDIA, DLSS, Leads, New, Games, Coming, GeForce, NOW</media:keywords>
</item>

<item>
<title>NVIDIA to Acquire Hugging Face</title>
<link>https://news.jatlink.uk/20294</link>
<guid>https://news.jatlink.uk/20294</guid>
<description><![CDATA[ I’m excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face’s platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for […] ]]></description>
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<pubDate>Thu, 03 Sep 2026 15:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Acquire, Hugging, Face</media:keywords>
</item>

<item>
<title>Accessing OpenAI models on Amazon Bedrock from Australia with global cross&amp;Region inference</title>
<link>https://news.jatlink.uk/20249</link>
<guid>https://news.jatlink.uk/20249</guid>
<description><![CDATA[ Australian teams can now access OpenAI GPT-5.6 Sol, Terra, and Luna models on Amazon Bedrock with global cross-Region inference from the Asia Pacific (Sydney) and Asia Pacific (Melbourne) Regions. This post shows how to invoke the models, use prompt caching, set up Codex with OpenID Connect authentication, and monitor usage with Amazon CloudWatch. ]]></description>
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<pubDate>Thu, 03 Sep 2026 02:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accessing, OpenAI, models, Amazon, Bedrock, from, Australia, with, global, cross-Region, inference</media:keywords>
</item>

<item>
<title>ATV Big Air Tour turned 3 days of work into 3 hours with ChatGPT</title>
<link>https://news.jatlink.uk/20248</link>
<guid>https://news.jatlink.uk/20248</guid>
<description><![CDATA[ ATV Big Air Tour uses ChatGPT Work to speed up marketing, merchandising, and more. It even turned merchandise photos into an inventory website in 15 minutes. ]]></description>
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<pubDate>Thu, 03 Sep 2026 01:00:16 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ATV, Big, Air, Tour, turned, days, work, into, hours, with, ChatGPT</media:keywords>
</item>

<item>
<title>Trinity: Agentic AI&amp;powered transition planning for students with disabilities</title>
<link>https://news.jatlink.uk/20230</link>
<guid>https://news.jatlink.uk/20230</guid>
<description><![CDATA[ Learn how University Startups and its AWS partner g/d/n/a scaled Trinity, a conversational AI solution for students with disabilities, into a serverless multi-agent architecture on Amazon Bedrock that produces IDEA-aligned transition plans for school districts across the US. ]]></description>
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<pubDate>Wed, 02 Sep 2026 22:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Trinity:, Agentic, AI-powered, transition, planning, for, students, with, disabilities</media:keywords>
</item>

<item>
<title>How an AWS team detects dashboard content failures at scale using Amazon Bedrock</title>
<link>https://news.jatlink.uk/20228</link>
<guid>https://news.jatlink.uk/20228</guid>
<description><![CDATA[ Business intelligence dashboards can fail silently, showing blank, stale, or wrong data even when every infrastructure monitor reports healthy. Learn how an AWS team built an automated, AI-powered content validation solution on Amazon Bedrock that scans hundreds of dashboards and alerts owners, cutting mean time to detection from days to under an hour. ]]></description>
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<pubDate>Wed, 02 Sep 2026 22:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AWS, team, detects, dashboard, content, failures, scale, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From code to diagrams: Agentic architecture documentation with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/20229</link>
<guid>https://news.jatlink.uk/20229</guid>
<description><![CDATA[ Learn how a global interdealer broker built an automated architecture documentation pipeline on Amazon Bedrock AgentCore that analyzes .NET code bases, generates architecture diagrams, and maintains searchable documentation through Amazon Bedrock Knowledge Bases and AWS CodePipeline. ]]></description>
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<pubDate>Wed, 02 Sep 2026 22:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, code, diagrams:, Agentic, architecture, documentation, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Modernizing and scaling support operations with generative AI on AWS</title>
<link>https://news.jatlink.uk/20227</link>
<guid>https://news.jatlink.uk/20227</guid>
<description><![CDATA[ Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work. ]]></description>
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<pubDate>Wed, 02 Sep 2026 22:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Modernizing, and, scaling, support, operations, with, generative, AWS</media:keywords>
</item>

<item>
<title>How law firm Gilbert + Tobin governs and scales AI with OpenAI</title>
<link>https://news.jatlink.uk/20177</link>
<guid>https://news.jatlink.uk/20177</guid>
<description><![CDATA[ See how Gilbert + Tobin combines CEO-led commitment, rigorous governance, and human accountability to scale ChatGPT Enterprise and Codex across the firm. ]]></description>
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<pubDate>Wed, 02 Sep 2026 05:00:25 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, law, firm, Gilbert, Tobin, governs, and, scales, with, OpenAI</media:keywords>
</item>

<item>
<title>NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier</title>
<link>https://news.jatlink.uk/20150</link>
<guid>https://news.jatlink.uk/20150</guid>
<description><![CDATA[ “We’re at an inflection point in cybersecurity,” Jensen Huang told a sold-out crowd at CrowdStrike’s Fal.Con 2026 in Las Vegas Tuesday. Attacks are now automated. Defense has to be, too.  The NVIDIA founder and CEO joined CrowdStrike CEO and founder George Kurtz to announce CrowdStrike SafeMind, its agentic cybersecurity system developed by the CrowdStrike Cyber […] ]]></description>
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<pubDate>Tue, 01 Sep 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, CrowdStrike, Strengthen, Agentic, Cybersecurity, Frontier</media:keywords>
</item>

<item>
<title>Introducing Claude Fable 5.1 on AWS</title>
<link>https://news.jatlink.uk/20149</link>
<guid>https://news.jatlink.uk/20149</guid>
<description><![CDATA[ Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1&#039;s improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud environment you control, and how to start building with the model on Amazon Bedrock. ]]></description>
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<pubDate>Tue, 01 Sep 2026 22:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Claude, Fable, 5.1, AWS</media:keywords>
</item>

<item>
<title>Path to Astra: critical capabilities and frontier safeguards</title>
<link>https://news.jatlink.uk/20148</link>
<guid>https://news.jatlink.uk/20148</guid>
<description><![CDATA[ Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, with stronger safeguards for release. ]]></description>
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<pubDate>Tue, 01 Sep 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Path, Astra:, critical, capabilities, and, frontier, safeguards</media:keywords>
</item>

<item>
<title>How AI&amp;native companies turn workflows into operating capability</title>
<link>https://news.jatlink.uk/20146</link>
<guid>https://news.jatlink.uk/20146</guid>
<description><![CDATA[ Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply. ]]></description>
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<pubDate>Tue, 01 Sep 2026 21:00:19 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AI-native, companies, turn, workflows, into, operating, capability</media:keywords>
</item>

<item>
<title>Healthcare organizations can now connect EHR and additional industry data to ChatGPT</title>
<link>https://news.jatlink.uk/20147</link>
<guid>https://news.jatlink.uk/20147</guid>
<description><![CDATA[ ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more. ]]></description>
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<pubDate>Tue, 01 Sep 2026 21:00:19 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Healthcare, organizations, can, now, connect, EHR, and, additional, industry, data, ChatGPT</media:keywords>
</item>

<item>
<title>How Boomi Scribe streamlines documentation using AWS</title>
<link>https://news.jatlink.uk/20128</link>
<guid>https://news.jatlink.uk/20128</guid>
<description><![CDATA[ Boomi Scribe is an AI-powered agent on AWS that automatically generates documentation for enterprise integration workflows. Learn how Boomi uses Amazon Bedrock, Amazon SageMaker AI, Amazon S3, Amazon DynamoDB, and AWS Lambda to parse integration DAGs, generate detailed documentation, and compare component versions at scale. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Boomi, Scribe, streamlines, documentation, using, AWS</media:keywords>
</item>

<item>
<title>How ZS democratized secure ad&amp;hoc analytics with Amazon SageMaker</title>
<link>https://news.jatlink.uk/20127</link>
<guid>https://news.jatlink.uk/20127</guid>
<description><![CDATA[ Learn how ZS built a security-hardened Amazon SageMaker platform that balances developer agility with healthcare-grade governance, serving 1,000+ daily active users across 200+ SageMaker domains. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, democratized, secure, ad-hoc, analytics, with, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>From theory to delivery: How Atos upskilled 400 engineers in agentic AI</title>
<link>https://news.jatlink.uk/20123</link>
<guid>https://news.jatlink.uk/20123</guid>
<description><![CDATA[ When Atos set out to upskill 400 engineers in agentic AI, hands-on learning was the missing ingredient. Over three days, engineers built multi-agent systems on AWS through an AI League event. This post explains why Atos chose the format, what engineers built and learned, and what other enterprises should consider. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, theory, delivery:, How, Atos, upskilled, 400, engineers, agentic</media:keywords>
</item>

<item>
<title>Tokenomics at scale: How Jamf built real&amp;time spend enforcement for Amazon Bedrock</title>
<link>https://news.jatlink.uk/20124</link>
<guid>https://news.jatlink.uk/20124</guid>
<description><![CDATA[ As generative AI adoption scales, cost governance becomes a top challenge. Learn how Jamf built real-time, per-user spend enforcement for Amazon Bedrock using IAM Customer Managed Policies, an Amazon Athena cost view, and a serverless AWS Lambda loop that applies tiered model limits in near-real-time without disrupting active sessions. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tokenomics, scale:, How, Jamf, built, real-time, spend, enforcement, for, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Securing Amazon Quick from POC to production: Agents, Flows, and Spaces</title>
<link>https://news.jatlink.uk/20125</link>
<guid>https://news.jatlink.uk/20125</guid>
<description><![CDATA[ Amazon Quick proof-of-concept projects often stall when security teams review the production plan. This post walks through designing dashboards, Spaces, knowledge bases, agents, and Flows with security controls that hold as you scale: dataset shaping, agent isolation, document classification, and approval gates. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, Amazon, Quick, from, POC, production:, Agents, Flows, and, Spaces</media:keywords>
</item>

<item>
<title>How t54 built a trust layer with Amazon Bedrock AgentCore payments</title>
<link>https://news.jatlink.uk/20126</link>
<guid>https://news.jatlink.uk/20126</guid>
<description><![CDATA[ t54 built x402-secure, a trust layer on Amazon Bedrock AgentCore payments that scores every endpoint before an autonomous agent pays it. See how session budgets, credential isolation, and a deterministic trust gate have governed more than 20 million agent-initiated transactions with no human in the loop. ]]></description>
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<pubDate>Tue, 01 Sep 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, t54, built, trust, layer, with, Amazon, Bedrock, AgentCore, payments</media:keywords>
</item>

<item>
<title>OpenAI supports California’s bill to advance youth AI safety</title>
<link>https://news.jatlink.uk/20089</link>
<guid>https://news.jatlink.uk/20089</guid>
<description><![CDATA[ OpenAI supports California SB 1119, advancing strong, age-appropriate AI safeguards for teens while preserving opportunities to learn, create, and explore. ]]></description>
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<pubDate>Tue, 01 Sep 2026 09:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, supports, California’s, bill, advance, youth, safety</media:keywords>
</item>

<item>
<title>Connect an AgentCore Runtime hosted MCP server to Amazon Quick</title>
<link>https://news.jatlink.uk/20076</link>
<guid>https://news.jatlink.uk/20076</guid>
<description><![CDATA[ In this post, you will learn how to deploy and host your MCP server in AgentCore Runtime and integrate it with Amazon Quick, along with the prerequisites. With this pattern, you promote reusability and avoid duplication of AI tools, so clients can reuse commonly used tools and agents exposed through an MCP server instead of authoring them from scratch again. Your customers get a way to use your product inside Amazon Quick (chat agents and workflows) without building custom connectors for every use case. ]]></description>
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<pubDate>Tue, 01 Sep 2026 02:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connect, AgentCore, Runtime, hosted, MCP, server, Amazon, Quick</media:keywords>
</item>

<item>
<title>Polimill builds Japan&amp;apos;s next&amp;generation public AI infrastructure</title>
<link>https://news.jatlink.uk/20075</link>
<guid>https://news.jatlink.uk/20075</guid>
<description><![CDATA[ Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 01 Sep 2026 01:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Polimill, builds, Japans, next-generation, public, infrastructure</media:keywords>
</item>

<item>
<title>Build observable enterprise agentic retrieval using Managed Amazon Bedrock Knowledge Base with AWS CloudFormation</title>
<link>https://news.jatlink.uk/20063</link>
<guid>https://news.jatlink.uk/20063</guid>
<description><![CDATA[ This post builds an enterprise agentic retrieval solution on the Amazon Bedrock Managed Knowledge Base and Amazon Bedrock AgentCore. An agent reasons, routes across multiple knowledge bases, and returns cited answers, with seven layers of observability and both on-demand and continuous evaluation, all deployed with a single AWS CloudFormation chain. ]]></description>
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<pubDate>Mon, 31 Aug 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, observable, enterprise, agentic, retrieval, using, Managed, Amazon, Bedrock, Knowledge, Base, with, AWS, CloudFormation</media:keywords>
</item>

<item>
<title>Build multi&amp;tenant agentic chat applications on enterprise data with Amazon Bedrock Managed Knowledge Base</title>
<link>https://news.jatlink.uk/20064</link>
<guid>https://news.jatlink.uk/20064</guid>
<description><![CDATA[ Learn how to build a multi-tenant agentic document chat application on Amazon Bedrock Managed Knowledge Base, where users upload documents and immediately ask grounded questions. This post covers the ingestion and retrieval flows, the asynchronous indexing lifecycle, per-user data isolation, and best practices for operating the solution at scale. ]]></description>
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<pubDate>Mon, 31 Aug 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multi-tenant, agentic, chat, applications, enterprise, data, with, Amazon, Bedrock, Managed, Knowledge, Base</media:keywords>
</item>

<item>
<title>Manage agents, tools and skills at scale with AWS Agent Registry</title>
<link>https://news.jatlink.uk/20062</link>
<guid>https://news.jatlink.uk/20062</guid>
<description><![CDATA[ AWS Agent Registry is now generally available: a single, searchable, governed catalog for the agents, tools, skills, and custom resources across your organization. This post explains what Registry is and walks through its publishing, curation, and discovery workflows, plus enterprise considerations and what&#039;s next. ]]></description>
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<pubDate>Mon, 31 Aug 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, agents, tools, and, skills, scale, with, AWS, Agent, Registry</media:keywords>
</item>

<item>
<title>AWS recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025</title>
<link>https://news.jatlink.uk/20061</link>
<guid>https://news.jatlink.uk/20061</guid>
<description><![CDATA[ We&#039;re excited to share that AWS has been recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. In this evaluation of 13 providers, AWS received the highest score in the Strategy category. ]]></description>
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<pubDate>Mon, 31 Aug 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, recognized, Leader, The, Forrester, Wave:, Infrastructure, Solutions, 2025</media:keywords>
</item>

<item>
<title>A milestone in expanding access to AI</title>
<link>https://news.jatlink.uk/20049</link>
<guid>https://news.jatlink.uk/20049</guid>
<description><![CDATA[ ChatGPT Ads reaches $1 billion in annualized revenue run rate and expands globally, supporting broader access to AI through free and affordable options. ]]></description>
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<pubDate>Mon, 31 Aug 2026 17:00:52 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>milestone, expanding, access</media:keywords>
</item>

<item>
<title>NVIDIA and MediaTek Deepen Long&amp;Standing Partnership to Build AI Edge to Cloud Computing Platforms</title>
<link>https://news.jatlink.uk/20032</link>
<guid>https://news.jatlink.uk/20032</guid>
<description><![CDATA[ NVIDIA and MediaTek today announced a deepening of their longstanding collaboration to build the next generations of AI computing platforms — spanning AI infrastructure, local AI computing and automotive. ]]></description>
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<pubDate>Mon, 31 Aug 2026 15:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, MediaTek, Deepen, Long-Standing, Partnership, Build, Edge, Cloud, Computing, Platforms</media:keywords>
</item>

<item>
<title>Our decision on Cursor following its acquisition by SpaceX</title>
<link>https://news.jatlink.uk/19877</link>
<guid>https://news.jatlink.uk/19877</guid>
<description><![CDATA[ Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX. ]]></description>
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<pubDate>Sat, 29 Aug 2026 05:00:24 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, decision, Cursor, following, its, acquisition, SpaceX</media:keywords>
</item>

<item>
<title>Batch write and discover records in Amazon SageMaker Feature Store</title>
<link>https://news.jatlink.uk/19859</link>
<guid>https://news.jatlink.uk/19859</guid>
<description><![CDATA[ Amazon SageMaker Feature Store now supports two new APIs: BatchWriteRecord writes up to 25 records across multiple feature groups in a single call, and ListRecords enumerates record identifiers within a feature group. In this post, we walk through each API with code examples you can use to get started. ]]></description>
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<pubDate>Fri, 28 Aug 2026 22:00:21 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Batch, write, and, discover, records, Amazon, SageMaker, Feature, Store</media:keywords>
</item>

<item>
<title>How Decathlon runs demand forecasting at scale with Chronos&amp;2</title>
<link>https://news.jatlink.uk/19841</link>
<guid>https://news.jatlink.uk/19841</guid>
<description><![CDATA[ Decathlon, one of the world&#039;s largest sporting goods retailers, forecasts weekly demand for tens of thousands of products across multiple continents. Learn how they deployed Chronos-2 on AWS to improve forecast accuracy by 11-15 points while cutting operational complexity and running weekly inference for about $0.03 on CPU-only instances. ]]></description>
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<pubDate>Fri, 28 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Decathlon, runs, demand, forecasting, scale, with, Chronos-2</media:keywords>
</item>

<item>
<title>Spreading the load: How Salesforce met Multi&amp;AZ HA with SageMaker Inference Components</title>
<link>https://news.jatlink.uk/19842</link>
<guid>https://news.jatlink.uk/19842</guid>
<description><![CDATA[ Learn how Salesforce used Amazon SageMaker AI Inference Component placement (the SchedulingConfig parameter) to distribute model copies across multiple Availability Zones, meeting their Multi-AZ high availability compliance requirements without sacrificing the cost efficiency of multi-model co-hosting. ]]></description>
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<pubDate>Fri, 28 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Spreading, the, load:, How, Salesforce, met, Multi-AZ, with, SageMaker, Inference, Components</media:keywords>
</item>

<item>
<title>Supporting Thailand’s next generation of AI startups</title>
<link>https://news.jatlink.uk/19823</link>
<guid>https://news.jatlink.uk/19823</guid>
<description><![CDATA[ OpenAI and Thailand’s MHESI launch an eight-week accelerator helping 10 health, wellness, and education startups turn AI prototypes into trusted products. ]]></description>
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<pubDate>Fri, 28 Aug 2026 13:00:21 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Supporting, Thailand’s, next, generation, startups</media:keywords>
</item>

<item>
<title>Build agentic creative workflows with Amazon Quick and fal</title>
<link>https://news.jatlink.uk/19787</link>
<guid>https://news.jatlink.uk/19787</guid>
<description><![CDATA[ Creative teams produce more assets than ever, but fragmented tools and manual context transfer slow production. This post shows how to build a reusable agent harness with Amazon Quick and fal, connected through the Model Context Protocol (MCP), using two hands-on workflows: an eight-panel storyboard and a music-video concept prototype. ]]></description>
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<pubDate>Fri, 28 Aug 2026 02:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agentic, creative, workflows, with, Amazon, Quick, and, fal</media:keywords>
</item>

<item>
<title>NVIDIA Announces Upcoming Event for Financial Community</title>
<link>https://news.jatlink.uk/19770</link>
<guid>https://news.jatlink.uk/19770</guid>
<description><![CDATA[ NVIDIA will present at the following event for the financial community ]]></description>
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<pubDate>Thu, 27 Aug 2026 23:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Upcoming, Event, for, Financial, Community</media:keywords>
</item>

<item>
<title>Introducing OpenAI models on Amazon Bedrock for in&amp;country inferencing in India</title>
<link>https://news.jatlink.uk/19769</link>
<guid>https://news.jatlink.uk/19769</guid>
<description><![CDATA[ Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India with India geographic cross-Region inference. If you have local data processing requirements, you can now use these models at scale while Amazon Bedrock keeps inference requests and data within India. ]]></description>
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<pubDate>Thu, 27 Aug 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, OpenAI, models, Amazon, Bedrock, for, in-country, inferencing, India</media:keywords>
</item>

<item>
<title>Deepgram deepens Amazon SageMaker AI observability with Enhanced Metrics</title>
<link>https://news.jatlink.uk/19749</link>
<guid>https://news.jatlink.uk/19749</guid>
<description><![CDATA[ Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Amazon CloudWatch account. ]]></description>
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<pubDate>Thu, 27 Aug 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deepgram, deepens, Amazon, SageMaker, observability, with, Enhanced, Metrics</media:keywords>
</item>

<item>
<title>Reduce ASR inference costs by 75% with NVIDIA MPS on Amazon EC2</title>
<link>https://news.jatlink.uk/19750</link>
<guid>https://news.jatlink.uk/19750</guid>
<description><![CDATA[ Serving automatic speech recognition (ASR) models at scale is costly when each request uses only a fraction of a GPU. Learn how NVIDIA CUDA Multi-Process Service (MPS) with NVIDIA Triton Inference Server on Amazon EC2 GPU instances cuts GPU infrastructure by 75% while holding sub-second latency at 92.1 requests per second per GPU. ]]></description>
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<pubDate>Thu, 27 Aug 2026 18:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reduce, ASR, inference, costs, 75, with, NVIDIA, MPS, Amazon, EC2</media:keywords>
</item>

<item>
<title>Better answers, broader thinking: What students gain from ChatGPT and critical&amp;thinking training</title>
<link>https://news.jatlink.uk/19748</link>
<guid>https://news.jatlink.uk/19748</guid>
<description><![CDATA[ A randomized study of more than 1,000 students examines ChatGPT, critical thinking, originality, and student performance on a real-world university assignment. ]]></description>
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<pubDate>Thu, 27 Aug 2026 18:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Better, answers, broader, thinking:, What, students, gain, from, ChatGPT, and, critical-thinking, training</media:keywords>
</item>

<item>
<title>GeForce NOW Gives Gamers More Ways to Play at Gamescom 2026</title>
<link>https://news.jatlink.uk/19736</link>
<guid>https://news.jatlink.uk/19736</guid>
<description><![CDATA[ NVIDIA’s Gamescom announcements are revealing what’s next for GeForce NOW, with new ways to play, more supported devices and platforms, and even more big PC games headed to the cloud. New NVIDIA DLSS 4.5 technology controls give members more ways to fine-tune gameplay, while expanded support for new Steam devices, GOG single sign-on, Firefox browser […] ]]></description>
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<pubDate>Thu, 27 Aug 2026 15:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Gives, Gamers, More, Ways, Play, Gamescom, 2026</media:keywords>
</item>

<item>
<title>Expanding OpenAI’s presence in Brazil</title>
<link>https://news.jatlink.uk/19735</link>
<guid>https://news.jatlink.uk/19735</guid>
<description><![CDATA[ OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country. ]]></description>
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<pubDate>Thu, 27 Aug 2026 13:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Expanding, OpenAI’s, presence, Brazil</media:keywords>
</item>

<item>
<title>Introducing Intelligence Age</title>
<link>https://news.jatlink.uk/19691</link>
<guid>https://news.jatlink.uk/19691</guid>
<description><![CDATA[ Introducing Intelligence Age, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom. ]]></description>
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<pubDate>Thu, 27 Aug 2026 01:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Intelligence, Age</media:keywords>
</item>

<item>
<title>NVIDIA NVLink Fusion Expands With NVHBM Custom High&amp;Bandwidth Memory</title>
<link>https://news.jatlink.uk/19671</link>
<guid>https://news.jatlink.uk/19671</guid>
<description><![CDATA[ The next wave of AI is placing new demands on infrastructure.  As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system. To help hyperscalers and AI innovators build the next generation […] ]]></description>
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<pubDate>Wed, 26 Aug 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, NVLink, Fusion, Expands, With, NVHBM, Custom, High-Bandwidth, Memory</media:keywords>
</item>

<item>
<title>AWS and NVIDIA to Deliver 2 Million Additional GPUs and Next&amp;Generation Infrastructure for Agentic and Physical AI</title>
<link>https://news.jatlink.uk/19672</link>
<guid>https://news.jatlink.uk/19672</guid>
<description><![CDATA[ Amazon Web Services (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced a major expansion of their strategic collaboration to meet surging global demand for AI infrastructure as demand continues to accelerate. ]]></description>
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<pubDate>Wed, 26 Aug 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, and, NVIDIA, Deliver, Million, Additional, GPUs, and, Next-Generation, Infrastructure, for, Agentic, and, Physical</media:keywords>
</item>

<item>
<title>NVIDIA Announces Financial Results for Second Quarter Fiscal 2027</title>
<link>https://news.jatlink.uk/19673</link>
<guid>https://news.jatlink.uk/19673</guid>
<description><![CDATA[ NVIDIA (NASDAQ: NVDA) today reported revenue for the second quarter ended July 26, 2026, of $96.2 billion, up 18% from the previous quarter and up 106% from a year ago. ]]></description>
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<pubDate>Wed, 26 Aug 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Financial, Results, for, Second, Quarter, Fiscal, 2027</media:keywords>
</item>

<item>
<title>Evaluate any agent framework with Amazon Bedrock AgentCore Evaluations</title>
<link>https://news.jatlink.uk/19670</link>
<guid>https://news.jatlink.uk/19670</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Evaluations decouples agent evaluation from the framework you build on. As long as your agent emits OpenTelemetry telemetry, the service can score it, whether you use LangGraph, LlamaIndex, the OpenAI Agents SDK, Google ADK, the Claude Agent SDK, or Strands Agents. This post explains how the framework-agnostic contract works. ]]></description>
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<pubDate>Wed, 26 Aug 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, any, agent, framework, with, Amazon, Bedrock, AgentCore, Evaluations</media:keywords>
</item>

<item>
<title>The Hugging Face incident and the road ahead</title>
<link>https://news.jatlink.uk/19669</link>
<guid>https://news.jatlink.uk/19669</guid>
<description><![CDATA[ OpenAI shares findings from the Hugging Face security incident and the steps we’re taking to strengthen AI model security, monitoring, and alignment. ]]></description>
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<pubDate>Wed, 26 Aug 2026 21:00:16 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Hugging, Face, incident, and, the, road, ahead</media:keywords>
</item>

<item>
<title>Learning never stops: How AI makes learning continuous</title>
<link>https://news.jatlink.uk/19667</link>
<guid>https://news.jatlink.uk/19667</guid>
<description><![CDATA[ OpenAI’s new report explores how students and educators use ChatGPT to make learning more continuous, with support that extends beyond the classroom. ]]></description>
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<pubDate>Wed, 26 Aug 2026 21:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Learning, never, stops:, How, makes, learning, continuous</media:keywords>
</item>

<item>
<title>Bringing ChatGPT for Teachers to more U.S. school districts</title>
<link>https://news.jatlink.uk/19668</link>
<guid>https://news.jatlink.uk/19668</guid>
<description><![CDATA[ ChatGPT for Teachers is expanding to 55 U.S. school systems, bringing secure AI tools, training, and support to over 100,000 more educators and staff. ]]></description>
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<pubDate>Wed, 26 Aug 2026 21:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bringing, ChatGPT, for, Teachers, more, U.S., school, districts</media:keywords>
</item>

<item>
<title>Bring your own model with Amazon SageMaker AI: Script mode in SDK v3</title>
<link>https://news.jatlink.uk/19652</link>
<guid>https://news.jatlink.uk/19652</guid>
<description><![CDATA[ The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest and a multi-GPU Stable Diffusion 3.5 LoRA fine-tune, showing how SourceCode syncs your local code into any container at runtime so you can iterate without rebuilding Docker images. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bring, your, own, model, with, Amazon, SageMaker, AI:, Script, mode, SDK</media:keywords>
</item>

<item>
<title>Preparing data for supervised fine&amp;tuning Part 2: Advanced data strategies</title>
<link>https://news.jatlink.uk/19653</link>
<guid>https://news.jatlink.uk/19653</guid>
<description><![CDATA[ The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Preparing, data, for, supervised, fine-tuning, Part, Advanced, data, strategies</media:keywords>
</item>

<item>
<title>Preparing data for supervised fine&amp;tuning Part 1: Formatting and quality</title>
<link>https://news.jatlink.uk/19654</link>
<guid>https://news.jatlink.uk/19654</guid>
<description><![CDATA[ Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Preparing, data, for, supervised, fine-tuning, Part, Formatting, and, quality</media:keywords>
</item>

<item>
<title>Connect Amazon Bedrock AgentCore to cross&amp;account knowledge bases</title>
<link>https://news.jatlink.uk/19655</link>
<guid>https://news.jatlink.uk/19655</guid>
<description><![CDATA[ Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without copying source data. This post covers the architecture, security boundary, and two orchestration models: a code-based Strands agent and a declarative AgentCore harness. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connect, Amazon, Bedrock, AgentCore, cross-account, knowledge, bases</media:keywords>
</item>

<item>
<title>How GoDaddy transformed its analytics with Amazon Quick</title>
<link>https://news.jatlink.uk/19650</link>
<guid>https://news.jatlink.uk/19650</guid>
<description><![CDATA[ In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results across every dimension of the business: 15,000 hours saved annually, 50% reduction in dashboard count, rendering times cut to under 5 seconds, and AI-powered self-service analytics now accessible to every employee. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, GoDaddy, transformed, its, analytics, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Natera’s intelligent appointment scheduling with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/19651</link>
<guid>https://news.jatlink.uk/19651</guid>
<description><![CDATA[ Learn how Natera built an automated voice agent on Amazon Bedrock AgentCore that lets patients book mobile phlebotomy appointments through natural conversation. The post covers the dual-WebSocket bridge, event-driven latency masking, and progressive-trust authentication behind 100% tool-calling accuracy and sub-7-second latency. ]]></description>
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<pubDate>Wed, 26 Aug 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Natera’s, intelligent, appointment, scheduling, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How loveholidays is making everyone a builder with Codex</title>
<link>https://news.jatlink.uk/19633</link>
<guid>https://news.jatlink.uk/19633</guid>
<description><![CDATA[ Discover how loveholidays uses OpenAI Codex to make software development accessible across the business, helping teams turn ideas into products faster. ]]></description>
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<pubDate>Wed, 26 Aug 2026 12:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, loveholidays, making, everyone, builder, with, Codex</media:keywords>
</item>

<item>
<title>Governed reports with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP</title>
<link>https://news.jatlink.uk/19567</link>
<guid>https://news.jatlink.uk/19567</guid>
<description><![CDATA[ Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill drafts cited weekly reports and Slack summaries with human review before anything is shared. ]]></description>
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<pubDate>Tue, 25 Aug 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Governed, reports, with, Amazon, Quick, Desktop, and, Amazon, FSx, for, NetApp, ONTAP</media:keywords>
</item>

<item>
<title>Agentic observability with Amazon OpenSearch Service MCP Apps</title>
<link>https://news.jatlink.uk/19566</link>
<guid>https://news.jatlink.uk/19566</guid>
<description><![CDATA[ Amazon OpenSearch Service now supports MCP Apps, which return interactive visualizations alongside your AI agent&#039;s text responses. Learn how a single, locally run MCP server lets your agent move from alert to trace to logs to root cause in one conversation, and how you can verify every step inline without leaving your IDE. ]]></description>
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<pubDate>Tue, 25 Aug 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, observability, with, Amazon, OpenSearch, Service, MCP, Apps</media:keywords>
</item>

<item>
<title>Introducing the Admin plugin for ChatGPT Work and Codex</title>
<link>https://news.jatlink.uk/19565</link>
<guid>https://news.jatlink.uk/19565</guid>
<description><![CDATA[ Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests. ]]></description>
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<pubDate>Tue, 25 Aug 2026 20:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Admin, plugin, for, ChatGPT, Work, and, Codex</media:keywords>
</item>

<item>
<title>Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark</title>
<link>https://news.jatlink.uk/19553</link>
<guid>https://news.jatlink.uk/19553</guid>
<description><![CDATA[ NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality.  Electronic Arts, Embark and Ubisoft are among the latest game publishers and developers bringing their blockbuster titles to NVIDIA RTX Spark ahead of its launch […] ]]></description>
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<pubDate>Tue, 25 Aug 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Leading, Publishers, Bring, Blockbuster, Games, and, Technology, NVIDIA, RTX, Spark</media:keywords>
</item>

<item>
<title>NVIDIA Announces Jetson Orin Nano 2 Robotics Computer to Redefine Entry&amp;Level Edge AI</title>
<link>https://news.jatlink.uk/19554</link>
<guid>https://news.jatlink.uk/19554</guid>
<description><![CDATA[ NVIDIA today announced NVIDIA Jetson Orin Nano™ 2, a new robotics computer set to redefine entry-level edge AI — putting frontier-class generative AI performance in the hands of millions of developers. ]]></description>
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<pubDate>Tue, 25 Aug 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Jetson, Orin, Nano, Robotics, Computer, Redefine, Entry-Level, Edge</media:keywords>
</item>

<item>
<title>The full stack behind abundant intelligence</title>
<link>https://news.jatlink.uk/19552</link>
<guid>https://news.jatlink.uk/19552</guid>
<description><![CDATA[ OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost. ]]></description>
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<pubDate>Tue, 25 Aug 2026 17:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, full, stack, behind, abundant, intelligence</media:keywords>
</item>

<item>
<title>Jalapeño’s first results show industry&amp;leading speed and efficiency in AI inference</title>
<link>https://news.jatlink.uk/19551</link>
<guid>https://news.jatlink.uk/19551</guid>
<description><![CDATA[ Jalapeño is a custom inference chip from OpenAI that delivers faster, more power-efficient AI inference, with higher throughput and lower latency for modern models. ]]></description>
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<pubDate>Tue, 25 Aug 2026 16:00:47 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Jalapeño’s, first, results, show, industry-leading, speed, and, efficiency, inference</media:keywords>
</item>

<item>
<title>Disrupting a new covert influence campaign from Russia</title>
<link>https://news.jatlink.uk/19533</link>
<guid>https://news.jatlink.uk/19533</guid>
<description><![CDATA[ OpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a “sovereignty” index praising Russia and criticizing the West. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 25 Aug 2026 12:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Disrupting, new, covert, influence, campaign, from, Russia</media:keywords>
</item>

<item>
<title>Building a restaurant telephony AI host with Amazon Connect</title>
<link>https://news.jatlink.uk/19480</link>
<guid>https://news.jatlink.uk/19480</guid>
<description><![CDATA[ Learn how to build a voice ordering system for restaurants that answers a phone call and takes an order end to end, with no app, no website, and no sign-in. It uses Amazon Connect for telephony, Amazon Connect Agentic Voice for real-time speech, an Amazon Connect AI agent for reasoning, and Amazon Bedrock AgentCore Gateway to reach backend tools through MCP. ]]></description>
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<pubDate>Mon, 24 Aug 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, restaurant, telephony, host, with, Amazon, Connect</media:keywords>
</item>

<item>
<title>Agentic Resource Discovery (ARD): An open specification for agent discovery</title>
<link>https://news.jatlink.uk/19479</link>
<guid>https://news.jatlink.uk/19479</guid>
<description><![CDATA[ AWS Agent Registry gives your organization a centralized, searchable catalog for agents, tools, and skills. It works with the open Agentic Resource Discovery (ARD) standard to enable cross-environment discovery and governance at scale. ]]></description>
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<pubDate>Mon, 24 Aug 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, Resource, Discovery, ARD:, open, specification, for, agent, discovery</media:keywords>
</item>

<item>
<title>Introducing new Ray capabilities on SageMaker HyperPod</title>
<link>https://news.jatlink.uk/19477</link>
<guid>https://news.jatlink.uk/19477</guid>
<description><![CDATA[ Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMaker Studio, all with open-source KubeRay and standard Ray APIs. ]]></description>
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<pubDate>Mon, 24 Aug 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, new, Ray, capabilities, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Democratizing institutional knowledge: Building an AI&amp;powered knowledge management system with AWS</title>
<link>https://news.jatlink.uk/19478</link>
<guid>https://news.jatlink.uk/19478</guid>
<description><![CDATA[ Learn how to build a customizable, smart-caching knowledge management system on AWS that captures and delivers institutional (tribal) knowledge through a voice-first AI avatar. The accelerator uses Amazon Bedrock Knowledge Bases for retrieval-augmented generation and deploys in hours with AWS CloudFormation. ]]></description>
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<pubDate>Mon, 24 Aug 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Democratizing, institutional, knowledge:, Building, AI-powered, knowledge, management, system, with, AWS</media:keywords>
</item>

<item>
<title>Advancing price&amp;performance for developers with GPT‑5.6 in Kiro</title>
<link>https://news.jatlink.uk/19476</link>
<guid>https://news.jatlink.uk/19476</guid>
<description><![CDATA[ GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance. ]]></description>
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<pubDate>Mon, 24 Aug 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, price-performance, for, developers, with, GPT‑5.6, Kiro</media:keywords>
</item>

<item>
<title>With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents</title>
<link>https://news.jatlink.uk/19457</link>
<guid>https://news.jatlink.uk/19457</guid>
<description><![CDATA[ The next era of AI inference won’t be defined by a single breakthrough chip, network or system. It’ll be defined by how every layer of the AI factory works together. That’s why NVIDIA is extending Vera Rubin NVL72 with fast token generation for agentic systems. Announced today, the NVIDIA Vera Rubin rack-scale system NVIDIA Groq […] ]]></description>
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<pubDate>Mon, 24 Aug 2026 18:00:10 +0100</pubDate>
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<media:keywords>With, Groq, LPX, Full, Production, NVIDIA, Extends, Vera, Rubin, Inference, for, Agents</media:keywords>
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<item>
<title>Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents</title>
<link>https://news.jatlink.uk/19458</link>
<guid>https://news.jatlink.uk/19458</guid>
<description><![CDATA[ According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why?  Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a […] ]]></description>
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<pubDate>Mon, 24 Aug 2026 18:00:10 +0100</pubDate>
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<media:keywords>30x, More, Work, Per, Watt:, NVIDIA, Vera, Rubin, NVL72, Sets, New, Efficiency, Standard, for, Agents</media:keywords>
</item>

<item>
<title>NVIDIA Groq 3 LPX Now in Full Production With World&amp;Class Speed for Agentic AI</title>
<link>https://news.jatlink.uk/19459</link>
<guid>https://news.jatlink.uk/19459</guid>
<description><![CDATA[ NVIDIA today announced that NVIDIA Groq 3 LPX, the interactive AI inference accelerator, is now in full production. An extension of the NVIDIA Vera Rubin platform, Groq 3 LPX delivers a major boost in AI inference by enabling ultrafast token generation for highly responsive agentic systems. ]]></description>
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<pubDate>Mon, 24 Aug 2026 18:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Groq, LPX, Now, Full, Production, With, World-Class, Speed, for, Agentic</media:keywords>
</item>

<item>
<title>SpaceXAI Adopts NVIDIA Vera CPU to Accelerate Agentic AI at Massive Scale</title>
<link>https://news.jatlink.uk/19460</link>
<guid>https://news.jatlink.uk/19460</guid>
<description><![CDATA[ NVIDIA today announced that SpaceXAI will deploy NVIDIA Vera CPUs to accelerate its next generation of agentic AI applications, bringing the first CPU built for AI agents to one of the world’s most ambitious AI deployments. ]]></description>
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<pubDate>Mon, 24 Aug 2026 18:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>SpaceXAI, Adopts, NVIDIA, Vera, CPU, Accelerate, Agentic, Massive, Scale</media:keywords>
</item>

<item>
<title>How XPUs Meet a World&amp;Class AI Factory</title>
<link>https://news.jatlink.uk/19456</link>
<guid>https://news.jatlink.uk/19456</guid>
<description><![CDATA[ To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime.  That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider […] ]]></description>
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<pubDate>Mon, 24 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, XPUs, Meet, World-Class, Factory</media:keywords>
</item>

<item>
<title>AI&amp;powered metadata correction and harmonization</title>
<link>https://news.jatlink.uk/19455</link>
<guid>https://news.jatlink.uk/19455</guid>
<description><![CDATA[ Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus governance considerations for production deployment. ]]></description>
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<pubDate>Mon, 24 Aug 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AI-powered, metadata, correction, and, harmonization</media:keywords>
</item>

<item>
<title>Agentic Data Operations Platform (ADOP): Data engineering into hours</title>
<link>https://news.jatlink.uk/19254</link>
<guid>https://news.jatlink.uk/19254</guid>
<description><![CDATA[ The Agentic Data Operations Platform (ADOP) is a reference architecture on Amazon Bedrock that uses specialized AI agents to automate the full Bronze-to-Silver-to-Gold data pipeline lifecycle, compressing new-source onboarding from weeks to hours while keeping data governance and compliance controls inline. ]]></description>
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<pubDate>Fri, 21 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, Data, Operations, Platform, ADOP:, Data, engineering, into, hours</media:keywords>
</item>

<item>
<title>Govern AI agent tool access with Amazon Bedrock AgentCore Gateway</title>
<link>https://news.jatlink.uk/19255</link>
<guid>https://news.jatlink.uk/19255</guid>
<description><![CDATA[ Give your AI agents governed, auditable access to enterprise tools without consolidating infrastructure. This post walks through a four-scope maturity model (Connect, Control, Catalog, and Harden) for building a governed tool gateway with Amazon Bedrock AgentCore, advancing only when real governance pain demands it. ]]></description>
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<pubDate>Fri, 21 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Govern, agent, tool, access, with, Amazon, Bedrock, AgentCore, Gateway</media:keywords>
</item>

<item>
<title>Reduce RAG costs on Amazon Bedrock with query&amp;aware compression</title>
<link>https://news.jatlink.uk/19256</link>
<guid>https://news.jatlink.uk/19256</guid>
<description><![CDATA[ Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers, reducing input tokens and cost while preserving answer quality. ]]></description>
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<pubDate>Fri, 21 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reduce, RAG, costs, Amazon, Bedrock, with, query-aware, compression</media:keywords>
</item>

<item>
<title>Accelerating aircraft IFEC diagnostics with agentic AI on AWS</title>
<link>https://news.jatlink.uk/19257</link>
<guid>https://news.jatlink.uk/19257</guid>
<description><![CDATA[ Panasonic Avionics worked with AWS and the AWS Generative AI Innovation Center to build an agentic AI system on Amazon Bedrock, Amazon SageMaker, and AWS Glue that diagnoses in-flight entertainment and connectivity (IFEC) issues across a global fleet, reducing diagnosis time from hours to minutes while maintaining accuracy. ]]></description>
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<pubDate>Fri, 21 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, aircraft, IFEC, diagnostics, with, agentic, AWS</media:keywords>
</item>

<item>
<title>Build a no&amp;code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight</title>
<link>https://news.jatlink.uk/19183</link>
<guid>https://news.jatlink.uk/19183</guid>
<description><![CDATA[ In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick Sight, build interactive dashboards, use generative BI to answer questions in natural language, and publish AI-generated executive summaries for stakeholders. ]]></description>
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<pubDate>Fri, 21 Aug 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, no-code, workflow, with, Snowflake, Amazon, SageMaker, Canvas, and, Amazon, Quick, –, Part, Visualizing, insights, with, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>Build a no&amp;code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment</title>
<link>https://news.jatlink.uk/19181</link>
<guid>https://news.jatlink.uk/19181</guid>
<description><![CDATA[ Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for building a fraud detection model without writing code. ]]></description>
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<pubDate>Fri, 21 Aug 2026 01:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, no-code, workflow, with, Snowflake, Amazon, SageMaker, Canvas, and, Amazon, Quick, –, Part, Setting, your, Snowflake, environment</media:keywords>
</item>

<item>
<title>Build a no&amp;code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas</title>
<link>https://news.jatlink.uk/19182</link>
<guid>https://news.jatlink.uk/19182</guid>
<description><![CDATA[ In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual transformations, and train an XGBoost fraud detection model. All without writing machine learning code, laying the groundwork for interactive dashboards in Part 3. ]]></description>
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<pubDate>Fri, 21 Aug 2026 01:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, no-code, workflow, with, Snowflake, Amazon, SageMaker, Canvas, and, Amazon, Quick, –, Part, Data, preparation, and, model, building, with, Amazon, SageMaker, Canvas</media:keywords>
</item>

<item>
<title>Introducing cross&amp;Region inference for OpenAI GPT&amp;5.6 models on Amazon Bedrock</title>
<link>https://news.jatlink.uk/19180</link>
<guid>https://news.jatlink.uk/19180</guid>
<description><![CDATA[ Amazon Bedrock now offers OpenAI GPT-5.6 models (Sol, Terra, and Luna) in more than 25 AWS Regions with cross-Region inference. Learn how US geographic and global inference profiles route requests for higher throughput, how to call the models with the OpenAI and Converse APIs, and how to configure IAM, quotas, and monitoring. ]]></description>
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<pubDate>Fri, 21 Aug 2026 01:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, cross-Region, inference, for, OpenAI, GPT-5.6, models, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Stampli cuts launch hours by 68% using ChatGPT Work</title>
<link>https://news.jatlink.uk/19179</link>
<guid>https://news.jatlink.uk/19179</guid>
<description><![CDATA[ With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days. ]]></description>
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<pubDate>Fri, 21 Aug 2026 00:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Stampli, cuts, launch, hours, 68, using, ChatGPT, Work</media:keywords>
</item>

<item>
<title>AWS vector solutions: Build agentic AI where your data lives</title>
<link>https://news.jatlink.uk/19162</link>
<guid>https://news.jatlink.uk/19162</guid>
<description><![CDATA[ AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof points for each. ]]></description>
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<pubDate>Thu, 20 Aug 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, vector, solutions:, Build, agentic, where, your, data, lives</media:keywords>
</item>

<item>
<title>Authoring Dogwood policies from natural language in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/19159</link>
<guid>https://news.jatlink.uk/19159</guid>
<description><![CDATA[ AI agents can take actions that do not match your organization&#039;s policies. Policy in Amazon Bedrock AgentCore lets teams enforce controls across agents, now including time-based constraints. This post shows how Policy Authoring turns natural-language policy documents into correct Dogwood policies, with worked examples and best practices. ]]></description>
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<pubDate>Thu, 20 Aug 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Authoring, Dogwood, policies, from, natural, language, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Scaling agentic AI: Enterprise patterns without vendor lock&amp;in</title>
<link>https://news.jatlink.uk/19160</link>
<guid>https://news.jatlink.uk/19160</guid>
<description><![CDATA[ Scaling agentic AI across an enterprise requires patterns that preserve flexibility while avoiding vendor lock-in. In this second post of our multi-agent series, we examine how ML teams operate many agentic AI systems across a multi-everything environment of frameworks, models, and providers, and the principles that let those systems scale together. ]]></description>
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<pubDate>Thu, 20 Aug 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, agentic, AI:, Enterprise, patterns, without, vendor, lock-in</media:keywords>
</item>

<item>
<title>Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/19161</link>
<guid>https://news.jatlink.uk/19161</guid>
<description><![CDATA[ Learn how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Purpose-built AI agents handle discovery, infrastructure as code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes. ]]></description>
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<pubDate>Thu, 20 Aug 2026 21:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, cloud, migrations, with, agentic, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing AI Futures</title>
<link>https://news.jatlink.uk/19158</link>
<guid>https://news.jatlink.uk/19158</guid>
<description><![CDATA[ Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom. ]]></description>
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<pubDate>Thu, 20 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Futures</media:keywords>
</item>

<item>
<title>Bring the Fire: Play Games on GeForce NOW With New Firefox Browser Support</title>
<link>https://news.jatlink.uk/19141</link>
<guid>https://news.jatlink.uk/19141</guid>
<description><![CDATA[ It’s a new way into the cloud.  GeForce NOW welcomes Firefox support to the cloud, opening up another way to jump into high-performance PC gaming straight from the browser, starting today. Whether on a school laptop or everyday PC, it’s now even easier to play supported PC games without downloading a dedicated app. Plus, discover […] ]]></description>
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<pubDate>Thu, 20 Aug 2026 18:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bring, the, Fire:, Play, Games, GeForce, NOW, With, New, Firefox, Browser, Support</media:keywords>
</item>

<item>
<title>Build intelligent security for healthcare APIs with Amazon Bedrock</title>
<link>https://news.jatlink.uk/19140</link>
<guid>https://news.jatlink.uk/19140</guid>
<description><![CDATA[ Learn how to add context-aware security monitoring to FHIR APIs using Amazon Bedrock. This post shows how to detect anomalous access patterns, classify data sensitivity automatically, and generate compliance reports in natural language, all without adding latency to clinical workflows. ]]></description>
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<pubDate>Thu, 20 Aug 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, security, for, healthcare, APIs, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How ChatGPT Work helps Stampli move ideas to market</title>
<link>https://news.jatlink.uk/19139</link>
<guid>https://news.jatlink.uk/19139</guid>
<description><![CDATA[ With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 20 Aug 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, ChatGPT, Work, helps, Stampli, move, ideas, market</media:keywords>
</item>

<item>
<title>KnowledgeForge: mining gold from the ITSM ticket graveyard</title>
<link>https://news.jatlink.uk/19081</link>
<guid>https://news.jatlink.uk/19081</guid>
<description><![CDATA[ KnowledgeForge mines resolved ITSM incident tickets into new knowledge base articles and automatically curates the existing library by deduplicating, quality-scoring, and improving content, using Amazon Bedrock, Amazon S3 Vectors, and AWS Step Functions in a multi-tenant, closed-loop pipeline. ]]></description>
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<pubDate>Thu, 20 Aug 2026 01:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>KnowledgeForge:, mining, gold, from, the, ITSM, ticket, graveyard</media:keywords>
</item>

<item>
<title>Asynchronous patterns for calling Amazon Bedrock AgentCore agents in serverless pipelines</title>
<link>https://news.jatlink.uk/19079</link>
<guid>https://news.jatlink.uk/19079</guid>
<description><![CDATA[ In this post, you learn three serverless patterns (task-token callback, direct service integration, and durable functions) for invoking Amazon Bedrock AgentCore agents asynchronously from AWS Step Functions pipelines, eliminating idle compute costs while your AI agent processes requests. ]]></description>
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<pubDate>Thu, 20 Aug 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Asynchronous, patterns, for, calling, Amazon, Bedrock, AgentCore, agents, serverless, pipelines</media:keywords>
</item>

<item>
<title>How Fanatics Betting and Gaming built a multi&amp;agent customer support system</title>
<link>https://news.jatlink.uk/19080</link>
<guid>https://news.jatlink.uk/19080</guid>
<description><![CDATA[ Fanatics Betting and Gaming built a multi-agent customer support system on AWS to handle the complexity of sports betting: state-specific rules, real-time responsible gaming, and traffic spikes during major sporting events. This post walks through the architecture, the AWS services involved, and the patterns for your own multi-agent support solution. ]]></description>
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<pubDate>Thu, 20 Aug 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Fanatics, Betting, and, Gaming, built, multi-agent, customer, support, system</media:keywords>
</item>

<item>
<title>Domain and publish date filters for Web Search on AgentCore</title>
<link>https://news.jatlink.uk/19077</link>
<guid>https://news.jatlink.uk/19077</guid>
<description><![CDATA[ Web Search on Amazon Bedrock AgentCore now supports runtime domain and published-date filtering. New per-request filters give developers per-call control over which web sources their agents consult and how fresh those sources must be, all enforced server-side. This release also expands Web Search to the Europe (Ireland) and Asia Pacific (Tokyo) Regions. ]]></description>
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<pubDate>Thu, 20 Aug 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Domain, and, publish, date, filters, for, Web, Search, AgentCore</media:keywords>
</item>

<item>
<title>Automate Document Processing with Quick Automate and the IDP Accelerator</title>
<link>https://news.jatlink.uk/19078</link>
<guid>https://news.jatlink.uk/19078</guid>
<description><![CDATA[ Classifying, extracting, and validating high volumes of documents is a challenge across banking, insurance, healthcare, and the public sector. See how a mid-size mortgage lender automates its entire document intake pipeline, from email to validated data, using the AWS GAIIC IDP Accelerator and Amazon Quick Automate. ]]></description>
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<pubDate>Thu, 20 Aug 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, Document, Processing, with, Quick, Automate, and, the, IDP, Accelerator</media:keywords>
</item>

<item>
<title>Offering Zero Data Retention for frontier models</title>
<link>https://news.jatlink.uk/19060</link>
<guid>https://news.jatlink.uk/19060</guid>
<description><![CDATA[ OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy. ]]></description>
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<pubDate>Wed, 19 Aug 2026 20:00:16 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Offering, Zero, Data, Retention, for, frontier, models</media:keywords>
</item>

<item>
<title>Replit expands access to software creation with GPT&amp;5.6 Luna</title>
<link>https://news.jatlink.uk/19044</link>
<guid>https://news.jatlink.uk/19044</guid>
<description><![CDATA[ Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs. ]]></description>
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<pubDate>Wed, 19 Aug 2026 16:01:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Replit, expands, access, software, creation, with, GPT-5.6, Luna</media:keywords>
</item>

<item>
<title>ChatGPT Ads expands across Europe</title>
<link>https://news.jatlink.uk/19023</link>
<guid>https://news.jatlink.uk/19023</guid>
<description><![CDATA[ ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions. ]]></description>
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<pubDate>Wed, 19 Aug 2026 08:00:44 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, Ads, expands, across, Europe</media:keywords>
</item>

<item>
<title>NVIDIA Guarantees SB Energy’s PORTS&amp;Pike Technology Campus in Ohio to Exclusively Host NVIDIA AI Compute</title>
<link>https://news.jatlink.uk/18990</link>
<guid>https://news.jatlink.uk/18990</guid>
<description><![CDATA[ NVIDIA announced that it has secured land, power and shell (LPS) capacity through a partnership with SB Energy at the PORTS-Pike Technology Campus in Pike County, Ohio, to host NVIDIA compute... ]]></description>
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<pubDate>Wed, 19 Aug 2026 02:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Guarantees, Energy’s, PORTS-Pike, Technology, Campus, Ohio, Exclusively, Host, NVIDIA, Compute</media:keywords>
</item>

<item>
<title>How NVIDIA scales expertise with ChatGPT Work</title>
<link>https://news.jatlink.uk/18989</link>
<guid>https://news.jatlink.uk/18989</guid>
<description><![CDATA[ NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally. ]]></description>
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<pubDate>Wed, 19 Aug 2026 00:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, NVIDIA, scales, expertise, with, ChatGPT, Work</media:keywords>
</item>

<item>
<title>Improve contract search accuracy with auto&amp;generated filters in Amazon Bedrock</title>
<link>https://news.jatlink.uk/18972</link>
<guid>https://news.jatlink.uk/18972</guid>
<description><![CDATA[ In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improve, contract, search, accuracy, with, auto-generated, filters, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Axonius built secure multi&amp;tenant AI agents on Bedrock AgentCore</title>
<link>https://news.jatlink.uk/18973</link>
<guid>https://news.jatlink.uk/18973</guid>
<description><![CDATA[ Learn how Axonius, a cybersecurity SaaS provider, used Amazon Bedrock AgentCore to deploy fully isolated, multi-tenant AI agents across hundreds of customer environments, without building custom compute isolation, authentication, or observability infrastructure from scratch. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Axonius, built, secure, multi-tenant, agents, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How Jumio built a real&amp;time feature store on AWS</title>
<link>https://news.jatlink.uk/18971</link>
<guid>https://news.jatlink.uk/18971</guid>
<description><![CDATA[ Learn how Jumio built a centralized, real-time feature store on AWS with Amazon SageMaker Feature Store, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams. The architecture delivers sub-100ms feature serving for fraud detection and saves approximately $120,000 annually. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Jumio, built, real-time, feature, store, AWS</media:keywords>
</item>

<item>
<title>Implement vector&amp;prompt document classification using Amazon Bedrock</title>
<link>https://news.jatlink.uk/18970</link>
<guid>https://news.jatlink.uk/18970</guid>
<description><![CDATA[ Learn how to build a multi-agent document classification solution on Amazon Bedrock using the Strands Agents SDK. Three specialized agents combine textual analysis with Claude Haiku 4.5 and visual similarity search with Amazon Titan Multimodal Embeddings to accurately classify insurance documents such as policies and affidavits. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implement, vector-prompt, document, classification, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Customize Amazon Quick embedded chat into your application</title>
<link>https://news.jatlink.uk/18969</link>
<guid>https://news.jatlink.uk/18969</guid>
<description><![CDATA[ Amazon Quick embedded chat brings a conversational AI interface into your web application. This post walks through customizing the embedded chat with container and SDK styling, branding removal, and a custom agent persona so it matches your brand&#039;s look, feel, and voice. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, Amazon, Quick, embedded, chat, into, your, application</media:keywords>
</item>

<item>
<title>Amazon Bedrock AgentCore payments is now generally available: Enabling agents to transact safely and autonomously at scale</title>
<link>https://news.jatlink.uk/18968</link>
<guid>https://news.jatlink.uk/18968</guid>
<description><![CDATA[ Amazon Bedrock AgentCore payments is now generally available, enabling AI agents to autonomously transact at scale with built-in spending guardrails, protocol-agnostic payment orchestration, and production-ready observability. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, AgentCore, payments, now, generally, available:, Enabling, agents, transact, safely, and, autonomously, scale</media:keywords>
</item>

<item>
<title>Strengthening Democratic Oversight in National Security</title>
<link>https://news.jatlink.uk/18967</link>
<guid>https://news.jatlink.uk/18967</guid>
<description><![CDATA[ OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise. ]]></description>
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<pubDate>Tue, 18 Aug 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Strengthening, Democratic, Oversight, National, Security</media:keywords>
</item>

<item>
<title>Pacing model development in an era of cyber&amp;critical capabilities</title>
<link>https://news.jatlink.uk/18965</link>
<guid>https://news.jatlink.uk/18965</guid>
<description><![CDATA[ OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development. ]]></description>
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<pubDate>Tue, 18 Aug 2026 20:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Pacing, model, development, era, cyber-critical, capabilities</media:keywords>
</item>

<item>
<title>Asana cleared 5 years of engineering work in 2 weeks with Codex</title>
<link>https://news.jatlink.uk/18966</link>
<guid>https://news.jatlink.uk/18966</guid>
<description><![CDATA[ Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K. ]]></description>
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<pubDate>Tue, 18 Aug 2026 20:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Asana, cleared, years, engineering, work, weeks, with, Codex</media:keywords>
</item>

<item>
<title>Introducing ChatGPT for Teens: Built for learning, backed by protections</title>
<link>https://news.jatlink.uk/18927</link>
<guid>https://news.jatlink.uk/18927</guid>
<description><![CDATA[ ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents. ]]></description>
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<pubDate>Tue, 18 Aug 2026 13:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, for, Teens:, Built, for, learning, backed, protections</media:keywords>
</item>

<item>
<title>Partnering with CodeAI to prepare the first AI generation</title>
<link>https://news.jatlink.uk/18928</link>
<guid>https://news.jatlink.uk/18928</guid>
<description><![CDATA[ OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly. ]]></description>
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<pubDate>Tue, 18 Aug 2026 13:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Partnering, with, CodeAI, prepare, the, first, generation</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3.5 Lightning now available in Amazon SageMaker JumpStart</title>
<link>https://news.jatlink.uk/18869</link>
<guid>https://news.jatlink.uk/18869</guid>
<description><![CDATA[ NVIDIA Nemotron 3.5 Lightning, an open model built for high-volume agentic workloads, is now available in Amazon SageMaker JumpStart. This post shows how to deploy the 30B Mixture-of-Experts model (3B active), which delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents. ]]></description>
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<pubDate>Mon, 17 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, 3.5, Lightning, now, available, Amazon, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Build OpenClaw agents that transact with Amazon Bedrock AgentCore payments</title>
<link>https://news.jatlink.uk/18870</link>
<guid>https://news.jatlink.uk/18870</guid>
<description><![CDATA[ Give an autonomous agent a wallet and spending guardrails so it can pay for paywalled APIs, MCP servers, and web content. This post connects OpenClaw to Amazon Bedrock AgentCore payments and the x402 protocol, using the aws-agents-pay plugin to make bounded, human-approved testnet payments. ]]></description>
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<pubDate>Mon, 17 Aug 2026 21:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, OpenClaw, agents, that, transact, with, Amazon, Bedrock, AgentCore, payments</media:keywords>
</item>

<item>
<title>The Defender’s Window</title>
<link>https://news.jatlink.uk/18854</link>
<guid>https://news.jatlink.uk/18854</guid>
<description><![CDATA[ AI is reshaping cybersecurity for attackers and defenders alike. Learn how OpenAI is strengthening its defenses and what security teams can do now. ]]></description>
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<pubDate>Mon, 17 Aug 2026 16:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Defender’s, Window</media:keywords>
</item>

<item>
<title>OpenAI joins PORTS&amp;Pike project</title>
<link>https://news.jatlink.uk/18855</link>
<guid>https://news.jatlink.uk/18855</guid>
<description><![CDATA[ OpenAI joins PORTS-Pike project, expanding community investment and supporting thousands of Southern Ohio jobs ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 17 Aug 2026 16:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, joins, PORTS-Pike, project</media:keywords>
</item>

<item>
<title>Securing the Infrastructure of Intelligence</title>
<link>https://news.jatlink.uk/18836</link>
<guid>https://news.jatlink.uk/18836</guid>
<description><![CDATA[ AI factories are the defining infrastructure of the AI era—where compute transforms energy and data into intelligence that powers every business, industry and country. In the AI economy, compute is revenue. AI factories require a full stack of critical resources: advanced chips, packaging, memory, and networking – as well as land, power and shell. Just […] ]]></description>
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<pubDate>Mon, 17 Aug 2026 14:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, the, Infrastructure, Intelligence</media:keywords>
</item>

<item>
<title>NVIDIA Guarantees SB Energy&amp;apos;s PORTS&amp;Pike Technology Campus in Ohio to Exclusively Host NVIDIA AI Compute</title>
<link>https://news.jatlink.uk/18837</link>
<guid>https://news.jatlink.uk/18837</guid>
<description><![CDATA[ NVIDIA announced that it has secured land, power and shell (LPS) capacity through a partnership with SB Energy at the PORTS-Pike Technology Campus in Pike County, Ohio, to host NVIDIA compute... ]]></description>
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<pubDate>Mon, 17 Aug 2026 14:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Guarantees, Energys, PORTS-Pike, Technology, Campus, Ohio, Exclusively, Host, NVIDIA, Compute</media:keywords>
</item>

<item>
<title>New policy ideas for the Intelligence Age</title>
<link>https://news.jatlink.uk/18835</link>
<guid>https://news.jatlink.uk/18835</guid>
<description><![CDATA[ OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age. ]]></description>
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<pubDate>Mon, 17 Aug 2026 13:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, policy, ideas, for, the, Intelligence, Age</media:keywords>
</item>

<item>
<title>Universitas Gadjah Mada, Indosat and NVIDIA Open Indonesia’s First University AI Center to Develop Local AI Talent</title>
<link>https://news.jatlink.uk/18651</link>
<guid>https://news.jatlink.uk/18651</guid>
<description><![CDATA[ Indonesia is taking charge of its AI future. This week, the Ministry of Communication and Digital Affairs (Komdigi), Indosat Ooredoo Hutchison (Indosat or IOH), NVIDIA and Universitas Gadjah Mada (UGM) launched the UGM Indosat NVIDIA AI Technology Center (NVAITC) in Yogyakarta — the country’s first university-based AI technology center. Established under Indonesia’s AI Center of […] ]]></description>
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<pubDate>Fri, 14 Aug 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Universitas, Gadjah, Mada, Indosat, and, NVIDIA, Open, Indonesia’s, First, University, Center, Develop, Local, Talent</media:keywords>
</item>

<item>
<title>Custom reward functions for multi&amp;turn reinforcement learning with Amazon Nova Forge</title>
<link>https://news.jatlink.uk/18650</link>
<guid>https://news.jatlink.uk/18650</guid>
<description><![CDATA[ In multi-turn reinforcement learning, your custom reward function decides what the model actually learns. This post shows how to design a composite multi-turn reward for Amazon Nova Forge, execute model-generated code safely inside it, and instrument each component to catch the pitfalls that quietly collapse a reward. ]]></description>
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<pubDate>Fri, 14 Aug 2026 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Custom, reward, functions, for, multi-turn, reinforcement, learning, with, Amazon, Nova, Forge</media:keywords>
</item>

<item>
<title>Building agentic workflows with SageMaker AI and Bedrock AgentCore</title>
<link>https://news.jatlink.uk/18635</link>
<guid>https://news.jatlink.uk/18635</guid>
<description><![CDATA[ Learn how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime to build a multi-agent workflow where each specialized agent uses the model best suited to its job. This post also shows how to get token-level observability from SageMaker endpoints that Strands Agents does not instrument by default. ]]></description>
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<pubDate>Fri, 14 Aug 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, agentic, workflows, with, SageMaker, and, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Monitor on&amp;premises and multi&amp;cloud AI agents with AgentCore Observability</title>
<link>https://news.jatlink.uk/18570</link>
<guid>https://news.jatlink.uk/18570</guid>
<description><![CDATA[ Set up Amazon Bedrock AgentCore Observability for AI agents running outside AWS: on-premises, on GCP, on Azure, or on developer machines. This walkthrough uses the AWS Distro for OpenTelemetry (ADOT) and IAM credentials to route session traces, span metrics, and token usage to the same AgentCore Observability dashboard. ]]></description>
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<pubDate>Thu, 13 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Monitor, on-premises, and, multi-cloud, agents, with, AgentCore, Observability</media:keywords>
</item>

<item>
<title>The builder’s guide to GPT‑5.6</title>
<link>https://news.jatlink.uk/18567</link>
<guid>https://news.jatlink.uk/18567</guid>
<description><![CDATA[ Learn how startups use GPT-5.6 to build faster, more cost-efficient AI agents with smarter model selection and new Responses API capabilities. ]]></description>
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<pubDate>Thu, 13 Aug 2026 20:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, builder’s, guide, GPT‑5.6</media:keywords>
</item>

<item>
<title>Previewing Ultrafast mode: GPT&amp;5.6 Sol at up to 14X the speed</title>
<link>https://news.jatlink.uk/18568</link>
<guid>https://news.jatlink.uk/18568</guid>
<description><![CDATA[ Preview Ultrafast, a new OpenAI API service tier that runs GPT-5.6 Sol up to 14× faster. Powered by Cerebras, it delivers up to 750 output tokens per second. ]]></description>
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<pubDate>Thu, 13 Aug 2026 20:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Previewing, Ultrafast, mode:, GPT-5.6, Sol, 14X, the, speed</media:keywords>
</item>

<item>
<title>OpenAI appoints Dali Rajic as Chief Revenue Officer</title>
<link>https://news.jatlink.uk/18569</link>
<guid>https://news.jatlink.uk/18569</guid>
<description><![CDATA[ OpenAI appoints Dali Rajic as Chief Revenue Officer to lead its global revenue organization and help businesses realize the full value of AI. ]]></description>
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<pubDate>Thu, 13 Aug 2026 20:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, appoints, Dali, Rajic, Chief, Revenue, Officer</media:keywords>
</item>

<item>
<title>Class Is in Session: GeForce NOW Levels Up Linux, Chromebooks and More</title>
<link>https://news.jatlink.uk/18548</link>
<guid>https://news.jatlink.uk/18548</guid>
<description><![CDATA[ GeForce NOW is giving cloud gaming an extra-credit upgrade just in time for back-to-school season. The native Linux app for GeForce NOW is officially out of beta. GeForce NOW is also delivering new cloud optimizations that make Frame Generation feel even more responsive while streaming. On top of that, Performance members will see higher frame […] ]]></description>
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<pubDate>Thu, 13 Aug 2026 18:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Class, Session:, GeForce, NOW, Levels, Linux, Chromebooks, and, More</media:keywords>
</item>

<item>
<title>Accelerating M&amp;amp;A due diligence with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/18546</link>
<guid>https://news.jatlink.uk/18546</guid>
<description><![CDATA[ Learn how to build a multi-agent M&amp;A due diligence system on Amazon Bedrock AgentCore. This post walks through a reference architecture that combines agent orchestration, knowledge retrieval, and governance controls, then deploys a complete sample you can run in your own AWS account. ]]></description>
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<pubDate>Thu, 13 Aug 2026 17:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, M&amp;A, due, diligence, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Amazon Quick for Microsoft 365: Agentic AI where you work</title>
<link>https://news.jatlink.uk/18547</link>
<guid>https://news.jatlink.uk/18547</guid>
<description><![CDATA[ Amazon Quick is now available directly inside Microsoft Word, Excel, PowerPoint, and Outlook. These extensions bring connected data access and agentic document editing into the Microsoft 365 apps your teams already use, so you can analyze data, draft content, and reach enterprise knowledge without switching applications. ]]></description>
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<pubDate>Thu, 13 Aug 2026 17:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, for, Microsoft, 365:, Agentic, where, you, work</media:keywords>
</item>

<item>
<title>Automate legacy web applications with Amazon Bedrock AgentCore Browser Tool</title>
<link>https://news.jatlink.uk/18545</link>
<guid>https://news.jatlink.uk/18545</guid>
<description><![CDATA[ Learn how to automate legacy web applications that need human-like interaction using Amazon Bedrock AgentCore Browser Tool and Strands Agents. This walkthrough covers a reference architecture for an AI-powered digital worker that drives legacy interfaces through secure, isolated browser sessions while preserving human oversight and full audit trails. ]]></description>
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<pubDate>Thu, 13 Aug 2026 17:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, legacy, web, applications, with, Amazon, Bedrock, AgentCore, Browser, Tool</media:keywords>
</item>

<item>
<title>How RingCentral builds AI&amp;native work from engineering to ops</title>
<link>https://news.jatlink.uk/18482</link>
<guid>https://news.jatlink.uk/18482</guid>
<description><![CDATA[ See how RingCentral uses ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations. ]]></description>
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<pubDate>Thu, 13 Aug 2026 00:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, RingCentral, builds, AI-native, work, from, engineering, ops</media:keywords>
</item>

<item>
<title>Part 2: Amazon Bedrock cost attribution with Amazon Athena and CUDOS</title>
<link>https://news.jatlink.uk/18467</link>
<guid>https://news.jatlink.uk/18467</guid>
<description><![CDATA[ Learn how to visualize and analyze Amazon Bedrock cost attribution using Amazon Athena and CUDOS dashboards. This post shows how to set up CUR 2.0 with IAM principal data, query Bedrock spend by principal, project, and team, and build dashboards to track AI costs across your organization. ]]></description>
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<pubDate>Wed, 12 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Part, Amazon, Bedrock, cost, attribution, with, Amazon, Athena, and, CUDOS</media:keywords>
</item>

<item>
<title>NVIDIA CEO Tops Glassdoor’s 2026 List of Best CEOs</title>
<link>https://news.jatlink.uk/18450</link>
<guid>https://news.jatlink.uk/18450</guid>
<description><![CDATA[ NVIDIA founder and CEO Jensen Huang is ranked No. 1 on Glassdoor’s Best CEOs list for 2026. In the just-released ranking, recognition is earned directly from the people who know their leadership the best — employees. Huang topped the list, with 99% of employees approving of the job he does. “As AI and shifting expectations […] ]]></description>
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<pubDate>Wed, 12 Aug 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, CEO, Tops, Glassdoor’s, 2026, List, Best, CEOs</media:keywords>
</item>

<item>
<title>Pay with confidence: How Solv Labs built verifiable, auditable agent payments on Amazon Bedrock AgentCore payments</title>
<link>https://news.jatlink.uk/18448</link>
<guid>https://news.jatlink.uk/18448</guid>
<description><![CDATA[ Solv Labs built a governed agent-payments workflow on Amazon Bedrock AgentCore payments, where every transaction is authorized, attested in an AWS Nitro Enclave, priced for risk, and anchored to a public blockchain before settlement. See how the pattern gives enterprises a verifiable, auditable trail for autonomous agent payments in regulated environments. ]]></description>
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<pubDate>Wed, 12 Aug 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Pay, with, confidence:, How, Solv, Labs, built, verifiable, auditable, agent, payments, Amazon, Bedrock, AgentCore, payments</media:keywords>
</item>

<item>
<title>Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine</title>
<link>https://news.jatlink.uk/18449</link>
<guid>https://news.jatlink.uk/18449</guid>
<description><![CDATA[ Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that extends the cache into a shared, distributed NVMe pool with Curvine, so replicas reuse cache at near-local-disk speeds on cost-efficient instances. ]]></description>
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<pubDate>Wed, 12 Aug 2026 17:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tiered, cache, for, large, LLMs, Amazon, SageMaker, HyperPod, with, Curvine</media:keywords>
</item>

<item>
<title>How OneAdvanced deployed over 50 AI agents on UK&amp;sovereign AWS</title>
<link>https://news.jatlink.uk/18447</link>
<guid>https://news.jatlink.uk/18447</guid>
<description><![CDATA[ Learn how OneAdvanced, a UK enterprise software provider, built a UK-sovereign AI platform by self-hosting Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI, with a RAG pipeline on pgvector and over 50 agents built with Strands Agents SDK on Amazon ECS. ]]></description>
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<pubDate>Wed, 12 Aug 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, OneAdvanced, deployed, over, agents, UK-sovereign, AWS</media:keywords>
</item>

<item>
<title>From assistance to execution: How enterprises put AI to work</title>
<link>https://news.jatlink.uk/18446</link>
<guid>https://news.jatlink.uk/18446</guid>
<description><![CDATA[ OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption. ]]></description>
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<pubDate>Wed, 12 Aug 2026 16:00:16 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, assistance, execution:, How, enterprises, put, work</media:keywords>
</item>

<item>
<title>NVIDIA AI Factory Compute Is Becoming an Investable Asset Class</title>
<link>https://news.jatlink.uk/18378</link>
<guid>https://news.jatlink.uk/18378</guid>
<description><![CDATA[ We announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time. This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies […] ]]></description>
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<pubDate>Wed, 12 Aug 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Factory, Compute, Becoming, Investable, Asset, Class</media:keywords>
</item>

<item>
<title>Accelerate cyber defense with OpenAI and AWS: Daybreak Red &amp;amp; Daybreak Blue now available to eligible customers on Amazon Bedrock</title>
<link>https://news.jatlink.uk/18377</link>
<guid>https://news.jatlink.uk/18377</guid>
<description><![CDATA[ Daybreak Red and Daybreak Blue from OpenAI, specialized cyber defense models from OpenAI, are now available on Amazon Bedrock to eligible customers. Both models run with zero-operator access enforced at the chip, keeping your code and vulnerability data secure. ]]></description>
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<pubDate>Wed, 12 Aug 2026 01:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, cyber, defense, with, OpenAI, and, AWS:, Daybreak, Red, Daybreak, Blue, now, available, eligible, customers, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Daybreak models are now available on AWS</title>
<link>https://news.jatlink.uk/18376</link>
<guid>https://news.jatlink.uk/18376</guid>
<description><![CDATA[ OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows. ]]></description>
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<pubDate>Wed, 12 Aug 2026 00:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Daybreak, models, are, now, available, AWS</media:keywords>
</item>

<item>
<title>How ONESTRUCTION built the Ishigaki&amp;IDS foundation model with AWS GenAIIC</title>
<link>https://news.jatlink.uk/18358</link>
<guid>https://news.jatlink.uk/18358</guid>
<description><![CDATA[ ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows. This architectural case study shows how they combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2 to build a domain model in a data-scarce field. ]]></description>
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<pubDate>Tue, 11 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, ONESTRUCTION, built, the, Ishigaki-IDS, foundation, model, with, AWS, GenAIIC</media:keywords>
</item>

<item>
<title>How Pixieset achieved 35% AI feature adoption by solving the right problem with Amazon Bedrock</title>
<link>https://news.jatlink.uk/18359</link>
<guid>https://news.jatlink.uk/18359</guid>
<description><![CDATA[ Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in. ]]></description>
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<pubDate>Tue, 11 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Pixieset, achieved, 35, feature, adoption, solving, the, right, problem, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>First Orion accelerates QA automation using Amazon Nova Act</title>
<link>https://news.jatlink.uk/18360</link>
<guid>https://news.jatlink.uk/18360</guid>
<description><![CDATA[ Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of maintaining selector-based code, they cut QA cycle times, freed engineering capacity, and caught regressions earlier. ]]></description>
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<pubDate>Tue, 11 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>First, Orion, accelerates, automation, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Why Scaling AI Compute Performance Requires a New Power Architecture</title>
<link>https://news.jatlink.uk/18339</link>
<guid>https://news.jatlink.uk/18339</guid>
<description><![CDATA[ Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU.  In traditional power delivery, electricity travels from the grid as an alternating current (AC) […] ]]></description>
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<pubDate>Tue, 11 Aug 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Why, Scaling, Compute, Performance, Requires, New, Power, Architecture</media:keywords>
</item>

<item>
<title>NVIDIA and Local AI Community Fuel Open Source Models and Intelligent Agents</title>
<link>https://news.jatlink.uk/18340</link>
<guid>https://news.jatlink.uk/18340</guid>
<description><![CDATA[ The open source ecosystem is making it easier for AI enthusiasts and developers to build, customize and run increasingly capable agents locally.  Throughout August, NVIDIA is celebrating the partners and open source communities moving local AI forward, along with the models, applications and tools emerging across the ecosystem. That includes NVIDIA’s latest open models, software […] ]]></description>
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<pubDate>Tue, 11 Aug 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Local, Community, Fuel, Open, Source, Models, and, Intelligent, Agents</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI</title>
<link>https://news.jatlink.uk/18341</link>
<guid>https://news.jatlink.uk/18341</guid>
<description><![CDATA[ As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and evolves. Today, NVIDIA is expanding its Nemotron 3 model family with Nemotron 3.5 Lightning, the highest-efficiency model in its class for long-running agentic AI workloads. This release follows Nemotron […] ]]></description>
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<pubDate>Tue, 11 Aug 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, 3.5, Lightning, and, NeMo, Switchyard, Deliver, Faster, Smarter, More, Efficient, Agentic</media:keywords>
</item>

<item>
<title>Deploying Anthropic Claude apps gateway for AWS for enterprise workloads</title>
<link>https://news.jatlink.uk/18338</link>
<guid>https://news.jatlink.uk/18338</guid>
<description><![CDATA[ Claude apps gateway is a self-hosted governance layer between Claude Code and Claude Desktop and Amazon Bedrock or Claude Platform on AWS. This post presents a production reference deployment covering end-to-end architecture, enterprise deployment patterns, cost, and implementation resources. ]]></description>
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<pubDate>Tue, 11 Aug 2026 17:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploying, Anthropic, Claude, apps, gateway, for, AWS, for, enterprise, workloads</media:keywords>
</item>

<item>
<title>Virgin Atlantic sharpens customer journeys with ChatGPT Work</title>
<link>https://news.jatlink.uk/18275</link>
<guid>https://news.jatlink.uk/18275</guid>
<description><![CDATA[ Virgin Atlantic is accelerating research, product planning, and decision-making with ChatGPT Work, helping teams connect signals across the customer journey. ]]></description>
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<pubDate>Tue, 11 Aug 2026 00:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Virgin, Atlantic, sharpens, customer, journeys, with, ChatGPT, Work</media:keywords>
</item>

<item>
<title>How Zapier transformed core marketing processes with ChatGPT Work</title>
<link>https://news.jatlink.uk/18276</link>
<guid>https://news.jatlink.uk/18276</guid>
<description><![CDATA[ The enterprise marketing team at Zapier uses ChatGPT Work to reduce the number of drop-offs in its lead funnel, build campaign assets, and automate reporting. ]]></description>
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<pubDate>Tue, 11 Aug 2026 00:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Zapier, transformed, core, marketing, processes, with, ChatGPT, Work</media:keywords>
</item>

<item>
<title>NVIDIA Partners With Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to Establish AI Compute Infrastructure Financing Platforms to Mobilize Over $500 Billion of Third&amp;Party Capital</title>
<link>https://news.jatlink.uk/18260</link>
<guid>https://news.jatlink.uk/18260</guid>
<description><![CDATA[ NVIDIA today announced strategic partnerships to establish independent compute financing platforms with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 ... ]]></description>
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<pubDate>Mon, 10 Aug 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Partners, With, Apollo, BlackRock, Blackstone, Brookfield, Goldman, Sachs, and, KKR, Establish, Compute, Infrastructure, Financing, Platforms, Mobilize, Over, 500, Billion, Third-Party, Capital</media:keywords>
</item>

<item>
<title>Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows</title>
<link>https://news.jatlink.uk/18258</link>
<guid>https://news.jatlink.uk/18258</guid>
<description><![CDATA[ The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito. ]]></description>
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<pubDate>Mon, 10 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, interactive, IDEs, Amazon, EKS, with, SageMaker, power, your, workflows</media:keywords>
</item>

<item>
<title>How nOps shipped FinOps agents 75% faster with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/18259</link>
<guid>https://news.jatlink.uk/18259</guid>
<description><![CDATA[ nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views. ]]></description>
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<pubDate>Mon, 10 Aug 2026 21:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, nOps, shipped, FinOps, agents, 75, faster, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>What building an AI&amp;native finance function taught me</title>
<link>https://news.jatlink.uk/18254</link>
<guid>https://news.jatlink.uk/18254</guid>
<description><![CDATA[ OpenAI CFO Sarah Friar shares five lessons for building an AI-native finance function, from automated forecasting to stronger controls and AI ROI. ]]></description>
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<pubDate>Mon, 10 Aug 2026 20:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>What, building, AI-native, finance, function, taught</media:keywords>
</item>

<item>
<title>Putting frontier cyber models in more trusted hands</title>
<link>https://news.jatlink.uk/18255</link>
<guid>https://news.jatlink.uk/18255</guid>
<description><![CDATA[ Approved Daybreak partners can use OpenAI’s frontier cyber models to deliver authorized, governed cybersecurity services to customers. ]]></description>
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<pubDate>Mon, 10 Aug 2026 20:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Putting, frontier, cyber, models, more, trusted, hands</media:keywords>
</item>

<item>
<title>Expanding Daybreak as the Cyber Defense Window Narrows</title>
<link>https://news.jatlink.uk/18256</link>
<guid>https://news.jatlink.uk/18256</guid>
<description><![CDATA[ Meet GPT-5.6-Cyber, OpenAI’s cybersecurity-specific model available through Daybreak Red for authorized vulnerability research, exploit validation, and security testing. ]]></description>
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<pubDate>Mon, 10 Aug 2026 20:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Expanding, Daybreak, the, Cyber, Defense, Window, Narrows</media:keywords>
</item>

<item>
<title>Premium seats are coming to ChatGPT Business</title>
<link>https://news.jatlink.uk/18257</link>
<guid>https://news.jatlink.uk/18257</guid>
<description><![CDATA[ Premium seats are coming to ChatGPT Business. Sign up by August 20 to get $100 in workspace credits and unlock higher usage for your team&#039;s most demanding work. ]]></description>
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<pubDate>Mon, 10 Aug 2026 20:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Premium, seats, are, coming, ChatGPT, Business</media:keywords>
</item>

<item>
<title>OpenAI’s letter to Governor Abbott on responsible AI infrastructure in Texas</title>
<link>https://news.jatlink.uk/18236</link>
<guid>https://news.jatlink.uk/18236</guid>
<description><![CDATA[ OpenAI sent Governor Greg Abbott a letter outlining its commitment to responsible AI infrastructure in Texas. The letter supports reliable, transparent growth that benefits Texans. ]]></description>
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<pubDate>Mon, 10 Aug 2026 16:00:34 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI’s, letter, Governor, Abbott, responsible, infrastructure, Texas</media:keywords>
</item>

<item>
<title>Model ML completes finance work more efficiently with GPT&amp;5.6 Sol</title>
<link>https://news.jatlink.uk/18237</link>
<guid>https://news.jatlink.uk/18237</guid>
<description><![CDATA[ Model ML uses GPT-5.6 Sol to carry finance work from research and analysis through editable, traceable PowerPoint decks and Excel workbooks. ]]></description>
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<pubDate>Mon, 10 Aug 2026 16:00:34 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Model, completes, finance, work, more, efficiently, with, GPT-5.6, Sol</media:keywords>
</item>

<item>
<title>Firebird Launches CIS Region’s Largest AI Factory in Armenia</title>
<link>https://news.jatlink.uk/18066</link>
<guid>https://news.jatlink.uk/18066</guid>
<description><![CDATA[ The global buildout of AI infrastructure reached a new milestone today — Firebird, an emerging AI cloud, launched the CIS region’s largest AI factory in Armenia, establishing a new AI computing hub powered by NVIDIA accelerated computing and Dell Technologies high-performance AI infrastructure.  Nikol Pashinyan, prime minister of the Republic of Armenia; Zhaslan Madiyev, deputy […] ]]></description>
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<pubDate>Sat, 08 Aug 2026 13:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Firebird, Launches, CIS, Region’s, Largest, Factory, Armenia</media:keywords>
</item>

<item>
<title>Determining playoff clinching scenarios in the NHL using constraint programming</title>
<link>https://news.jatlink.uk/18021</link>
<guid>https://news.jatlink.uk/18021</guid>
<description><![CDATA[ The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results. ]]></description>
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<pubDate>Fri, 07 Aug 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
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</item>

<item>
<title>How Cohere Health digitizes clinical policies using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/18019</link>
<guid>https://news.jatlink.uk/18019</guid>
<description><![CDATA[ In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight. ]]></description>
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<pubDate>Fri, 07 Aug 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Cohere, Health, digitizes, clinical, policies, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How TReNDS automates root&amp;cause analysis with Amazon Bedrock</title>
<link>https://news.jatlink.uk/18020</link>
<guid>https://news.jatlink.uk/18020</guid>
<description><![CDATA[ TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds. ]]></description>
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<pubDate>Fri, 07 Aug 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, TReNDS, automates, root-cause, analysis, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Responding to the next frontier of critical cyber capabilities</title>
<link>https://news.jatlink.uk/18018</link>
<guid>https://news.jatlink.uk/18018</guid>
<description><![CDATA[ OpenAI is sharing preliminary cybersecurity evaluations for Astra and the steps we’re taking to strengthen safeguards and security controls. ]]></description>
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<pubDate>Fri, 07 Aug 2026 18:00:26 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Responding, the, next, frontier, critical, cyber, capabilities</media:keywords>
</item>

<item>
<title>How HSP GRUPPE builds AI capabilities for tax advisory</title>
<link>https://news.jatlink.uk/17984</link>
<guid>https://news.jatlink.uk/17984</guid>
<description><![CDATA[ Discover how HSP GRUPPE uses ChatGPT Enterprise to boost productivity, improve work quality, and create more capacity for tax advisory and client service. ]]></description>
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<pubDate>Fri, 07 Aug 2026 11:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, HSP, GRUPPE, builds, capabilities, for, tax, advisory</media:keywords>
</item>

<item>
<title>Securing AI agents with temporal policies in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/17936</link>
<guid>https://news.jatlink.uk/17936</guid>
<description><![CDATA[ Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent&#039;s session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions. ]]></description>
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<pubDate>Thu, 06 Aug 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, agents, with, temporal, policies, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Building an agentic app deployer with Amazon Bedrock and AWS Lambda</title>
<link>https://news.jatlink.uk/17917</link>
<guid>https://news.jatlink.uk/17917</guid>
<description><![CDATA[ PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, agentic, app, deployer, with, Amazon, Bedrock, and, AWS, Lambda</media:keywords>
</item>

<item>
<title>LLM optimization integration for Amazon SageMaker Python SDK</title>
<link>https://news.jatlink.uk/17918</link>
<guid>https://news.jatlink.uk/17918</guid>
<description><![CDATA[ The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommendations, and deploy the recommended configuration without leaving your notebook workflow. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>LLM, optimization, integration, for, Amazon, SageMaker, Python, SDK</media:keywords>
</item>

<item>
<title>Control agent behaviors and cost beyond a single action: new capabilities in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/17913</link>
<guid>https://news.jatlink.uk/17913</guid>
<description><![CDATA[ Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:09 +0100</pubDate>
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<media:keywords>Control, agent, behaviors, and, cost, beyond, single, action:, new, capabilities, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build visibility for Codex on Amazon Bedrock with OpenTelemetry and Amazon CloudWatch</title>
<link>https://news.jatlink.uk/17914</link>
<guid>https://news.jatlink.uk/17914</guid>
<description><![CDATA[ As engineering teams adopt coding agents like Codex, leaders need visibility into adoption, consumption, and reliability. This post shows how to route Codex OpenTelemetry metrics through a local collector to Amazon CloudWatch for an AWS native view of usage by user, team, and cost center. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, visibility, for, Codex, Amazon, Bedrock, with, OpenTelemetry, and, Amazon, CloudWatch</media:keywords>
</item>

<item>
<title>Enforcing data residency with single&amp;Region Claude Code on Amazon Bedrock</title>
<link>https://news.jatlink.uk/17915</link>
<guid>https://news.jatlink.uk/17915</guid>
<description><![CDATA[ A regulated customer needed all Claude Code inference processed in a single AWS Region (London), not just in-geography. This post shows two ways to pin Claude Code on Amazon Bedrock to one Region: an application inference profile or the Mantle endpoint, paired with an IAM Region condition, plus how to verify compliance in AWS CloudTrail. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enforcing, data, residency, with, single-Region, Claude, Code, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Agent Skills for Automated Reasoning policies in Amazon Bedrock</title>
<link>https://news.jatlink.uk/17916</link>
<guid>https://news.jatlink.uk/17916</guid>
<description><![CDATA[ Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent, Skills, for, Automated, Reasoning, policies, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Configure rate limits for AI traffic on AgentCore gateway</title>
<link>https://news.jatlink.uk/17912</link>
<guid>https://news.jatlink.uk/17912</guid>
<description><![CDATA[ Learn how to configure rate limits on Amazon Bedrock AgentCore gateway to enforce per-user and per-target traffic controls. Define request, token, and connection limits scoped by JWT claims or IAM identity to protect downstream models, tools, and agents from traffic spikes. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Configure, rate, limits, for, traffic, AgentCore, gateway</media:keywords>
</item>

<item>
<title>Improving GPT‑5.6 Sol in ChatGPT—and expanding access to GPT&amp;5.6 Luna for free users</title>
<link>https://news.jatlink.uk/17910</link>
<guid>https://news.jatlink.uk/17910</guid>
<description><![CDATA[ ChatGPT introduces improved GPT-5.6 Sol with better accuracy and consistency, plus expanded access for free users and unlimited everyday chats with GPT-5.6 Luna. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improving, GPT‑5.6, Sol, ChatGPT—and, expanding, access, GPT-5.6, Luna, for, free, users</media:keywords>
</item>

<item>
<title>From asking to doing: How the world is putting ChatGPT to work</title>
<link>https://news.jatlink.uk/17911</link>
<guid>https://news.jatlink.uk/17911</guid>
<description><![CDATA[ New OpenAI Signals data shows how people use ChatGPT worldwide, with country-level insights on adoption, usage trends, and evolving behavior. ]]></description>
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<pubDate>Thu, 06 Aug 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, asking, doing:, How, the, world, putting, ChatGPT, work</media:keywords>
</item>

<item>
<title>Working with the American Psychological Association on youth mental health and AI</title>
<link>https://news.jatlink.uk/17909</link>
<guid>https://news.jatlink.uk/17909</guid>
<description><![CDATA[ OpenAI and the APA are launching a three-year partnership to develop guidance, resources, and safeguards for responsible AI use supporting youth mental health. ]]></description>
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<pubDate>Thu, 06 Aug 2026 18:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Working, with, the, American, Psychological, Association, youth, mental, health, and</media:keywords>
</item>

<item>
<title>GeForce NOW Shakes Up August With 26 New Games</title>
<link>https://news.jatlink.uk/17890</link>
<guid>https://news.jatlink.uk/17890</guid>
<description><![CDATA[ August is here, bringing 26 new games for GeForce NOW members.  Command the seas in World of Warships: Legends and discover what’s next in the GeForce NOW library, starting with the eight newly added games this week.  In addition, GeForce NOW is at the QuakeCon gaming conference this week in Grapevine, Texas, with hands-on experiences […] ]]></description>
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<pubDate>Thu, 06 Aug 2026 16:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Shakes, August, With, New, Games</media:keywords>
</item>

<item>
<title>Into the Omniverse: How Open World Models Push the Frontier of Physical AI</title>
<link>https://news.jatlink.uk/17891</link>
<guid>https://news.jatlink.uk/17891</guid>
<description><![CDATA[ In July, NVIDIA joined more than 200 companies and organizations in signing “Open Weights and American AI Leadership,” an open letter arguing that AI leadership will be measured not by any single frontier model but by whether an open ecosystem reaches every sector. ]]></description>
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<pubDate>Thu, 06 Aug 2026 16:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Into, the, Omniverse:, How, Open, World, Models, Push, the, Frontier, Physical</media:keywords>
</item>

<item>
<title>Run production AI agents in n8n with Amazon Bedrock AgentCore harness</title>
<link>https://news.jatlink.uk/17819</link>
<guid>https://news.jatlink.uk/17819</guid>
<description><![CDATA[ Amazon Bedrock AgentCore harness is now generally available. Learn how to add it as an agent step in n8n workflows using a new open-source community node, and build agents with persistent memory, real tools, code execution, and VPC isolation — all from the n8n editor with no infrastructure or agent code. ]]></description>
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<pubDate>Wed, 05 Aug 2026 23:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, production, agents, n8n, with, Amazon, Bedrock, AgentCore, harness</media:keywords>
</item>

<item>
<title>How we built an MCP bridge to give our AgentCore&amp;hosted AI agent access to local MCP tools</title>
<link>https://news.jatlink.uk/17818</link>
<guid>https://news.jatlink.uk/17818</guid>
<description><![CDATA[ AI agents on Amazon Bedrock AgentCore run in the cloud, but users&#039; tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required. ]]></description>
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<pubDate>Wed, 05 Aug 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, built, MCP, bridge, give, our, AgentCore-hosted, agent, access, local, MCP, tools</media:keywords>
</item>

<item>
<title>How LendingTree built a multi&amp;agent mortgage assistant on Amazon Bedrock</title>
<link>https://news.jatlink.uk/17816</link>
<guid>https://news.jatlink.uk/17816</guid>
<description><![CDATA[ Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance. ]]></description>
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<pubDate>Wed, 05 Aug 2026 23:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, LendingTree, built, multi-agent, mortgage, assistant, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Mobileye transformed support operations using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/17817</link>
<guid>https://news.jatlink.uk/17817</guid>
<description><![CDATA[ In this post, we&#039;ll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards. ]]></description>
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<pubDate>Wed, 05 Aug 2026 23:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Mobileye, transformed, support, operations, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing Web Search on Amazon Bedrock for foundation model grounding</title>
<link>https://news.jatlink.uk/17729</link>
<guid>https://news.jatlink.uk/17729</guid>
<description><![CDATA[ Today, we are introducing the general availability of Web Search on Amazon Bedrock. It is a server-side built-in tool that grounds model responses in current web knowledge. With Web Search, grounding becomes a native capability of Amazon Bedrock, with no third-party vendors to onboard, no external APIs to orchestrate, and no additional third party vendor security reviews to conduct. In this post, we walk through what Web Search on Amazon Bedrock is, why it matters, how to enable it using the OpenAI Responses API, and how to get started with the tool. ]]></description>
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<pubDate>Tue, 04 Aug 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Web, Search, Amazon, Bedrock, for, foundation, model, grounding</media:keywords>
</item>

<item>
<title>Third&amp;party cyber evaluations involving OpenAI models</title>
<link>https://news.jatlink.uk/17728</link>
<guid>https://news.jatlink.uk/17728</guid>
<description><![CDATA[ OpenAI explains recent third-party cybersecurity evaluation incidents and outlines new safeguards to strengthen AI model testing and evaluation. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 04 Aug 2026 23:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Third-party, cyber, evaluations, involving, OpenAI, models</media:keywords>
</item>

<item>
<title>NVIDIA Joins NSF State and Regional AI Hubs Program to Expand AI Research and Education Across the US</title>
<link>https://news.jatlink.uk/17708</link>
<guid>https://news.jatlink.uk/17708</guid>
<description><![CDATA[ NVIDIA is participating in the U.S. National Science Foundation’s (NSF) State and Regional Artificial Intelligence Infrastructure Hubs program, an effort launching today to expand access to the advanced computing, data, software and expertise needed for AI-enabled research and education. ]]></description>
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<pubDate>Tue, 04 Aug 2026 20:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Joins, NSF, State, and, Regional, Hubs, Program, Expand, Research, and, Education, Across, the</media:keywords>
</item>

<item>
<title>NVIDIA Alpamayo 2 Super, the Frontier Open Model for Robotaxis and Autonomous Vehicles, Now Available for Commercial Use</title>
<link>https://news.jatlink.uk/17709</link>
<guid>https://news.jatlink.uk/17709</guid>
<description><![CDATA[ For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for. Handling these long‑tail events takes more than just object detection and motion prediction. AVs must understand the situation, reason about cause and effect, choose the right action and […] ]]></description>
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<pubDate>Tue, 04 Aug 2026 20:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Alpamayo, Super, the, Frontier, Open, Model, for, Robotaxis, and, Autonomous, Vehicles, Now, Available, for, Commercial, Use</media:keywords>
</item>

<item>
<title>As AI Increases Demands on Memory, Storage Steps Up</title>
<link>https://news.jatlink.uk/17710</link>
<guid>https://news.jatlink.uk/17710</guid>
<description><![CDATA[ Surging AI demands are driving the need for massive datasets and context windows that burst past the confines of system memory.  But rising needs aren’t met by simply adding more storage capacity. What’s needed is useful, grounded insights from AI factories and efficient, secure storage architectures that enable those insights.  At this week’s Future of […] ]]></description>
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<pubDate>Tue, 04 Aug 2026 20:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Increases, Demands, Memory, Storage, Steps</media:keywords>
</item>

<item>
<title>Automated web insight extraction with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/17707</link>
<guid>https://news.jatlink.uk/17707</guid>
<description><![CDATA[ Extracting insights from dozens of websites by hand quickly becomes overwhelming. This post shows how to build an automated web insight extraction solution with Amazon Bedrock AgentCore Browser, Amazon Bedrock, Amazon OpenSearch Serverless, and AWS Lambda that monitors RSS feeds, renders pages reliably, and makes AI-extracted insights searchable. ]]></description>
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<pubDate>Tue, 04 Aug 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, web, insight, extraction, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>New ways to learn and teach with ChatGPT Work and Codex</title>
<link>https://news.jatlink.uk/17706</link>
<guid>https://news.jatlink.uk/17706</guid>
<description><![CDATA[ Explore new education plugins for ChatGPT Work and Codex that help K–12 teachers, college educators, and students learn, teach, research, and build. ]]></description>
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<pubDate>Tue, 04 Aug 2026 19:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, ways, learn, and, teach, with, ChatGPT, Work, and, Codex</media:keywords>
</item>

<item>
<title>AI Leaders Propose SAFE Guidelines for Cybersecurity Transparency</title>
<link>https://news.jatlink.uk/17688</link>
<guid>https://news.jatlink.uk/17688</guid>
<description><![CDATA[ Members of the Open Secure AI Alliance — now more than 120 organizations strong — are developing new guidelines to strengthen agentic AI cybersecurity as the annual Black Hat conference begins in Las Vegas today.  The Linux Foundation today shared a Request for Comments on Shared AI Findings Exchange (SAFE), a proposed set of guidelines […] ]]></description>
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<pubDate>Tue, 04 Aug 2026 16:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Leaders, Propose, SAFE, Guidelines, for, Cybersecurity, Transparency</media:keywords>
</item>

<item>
<title>Apple is getting this wrong</title>
<link>https://news.jatlink.uk/17656</link>
<guid>https://news.jatlink.uk/17656</guid>
<description><![CDATA[ OpenAI addresses Apple’s baseless lawsuit, corrects claims about its employees, and shares messages documenting what happened. ]]></description>
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<pubDate>Tue, 04 Aug 2026 07:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Apple, getting, this, wrong</media:keywords>
</item>

<item>
<title>Circles powers telco personalization with OpenAI technology</title>
<link>https://news.jatlink.uk/17639</link>
<guid>https://news.jatlink.uk/17639</guid>
<description><![CDATA[ Circles uses the OpenAI API and Codex to power AI-native telco experiences, increasing ARPU by 22%, reducing churn by 9%, and improving development efficiency. ]]></description>
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<pubDate>Tue, 04 Aug 2026 03:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Circles, powers, telco, personalization, with, OpenAI, technology</media:keywords>
</item>

<item>
<title>How we built a realtime system for responsive voice AI in six months</title>
<link>https://news.jatlink.uk/17622</link>
<guid>https://news.jatlink.uk/17622</guid>
<description><![CDATA[ GPT-Live enables continuous voice interaction with AI, using a turnless speech model and low-latency architecture for faster, more natural conversations. ]]></description>
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<pubDate>Mon, 03 Aug 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, built, realtime, system, for, responsive, voice, six, months</media:keywords>
</item>

<item>
<title>From weeks to minutes: How Formula 1® uses agentic AI on AWS to accelerate data operations</title>
<link>https://news.jatlink.uk/17605</link>
<guid>https://news.jatlink.uk/17605</guid>
<description><![CDATA[ Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate. ]]></description>
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<pubDate>Mon, 03 Aug 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, weeks, minutes:, How, Formula, 1®, uses, agentic, AWS, accelerate, data, operations</media:keywords>
</item>

<item>
<title>Automated Reasoning policy refinement in Amazon Bedrock</title>
<link>https://news.jatlink.uk/17606</link>
<guid>https://news.jatlink.uk/17606</guid>
<description><![CDATA[ Amazon Bedrock now supports automatic Automated Reasoning policy refinement. The refinement engine diagnoses failing tests and proposes formal-logic fixes for rule issues and language issues, and you approve every change before it takes effect. This post walks through both refinement modes with complete API and console workflows. ]]></description>
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<pubDate>Mon, 03 Aug 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, Reasoning, policy, refinement, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Ten advances in mathematics and theoretical computer science</title>
<link>https://news.jatlink.uk/17426</link>
<guid>https://news.jatlink.uk/17426</guid>
<description><![CDATA[ OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity. ]]></description>
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<pubDate>Sat, 01 Aug 2026 10:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Ten, advances, mathematics, and, theoretical, computer, science</media:keywords>
</item>

<item>
<title>Deploying Kimi K3 on Amazon SageMaker HyperPod and Amazon EKS</title>
<link>https://news.jatlink.uk/17395</link>
<guid>https://news.jatlink.uk/17395</guid>
<description><![CDATA[ This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and  Amazon Elastic Kubernetes Service (Amazon EKS) cluster. ]]></description>
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<pubDate>Fri, 31 Jul 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploying, Kimi, Amazon, SageMaker, HyperPod, and, Amazon, EKS</media:keywords>
</item>

<item>
<title>Announcing the Agentic Catalog Experience in Amazon Quick</title>
<link>https://news.jatlink.uk/17394</link>
<guid>https://news.jatlink.uk/17394</guid>
<description><![CDATA[ Amazon Quick introduces the Agentic Catalog Experience, an AI-powered workflow for data curators to discover upstream catalog assets in natural language and auto-create Datasets and Topics with inherited semantics. Now in preview for AWS Glue Data Catalog and Databricks Unity Catalog. ]]></description>
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<pubDate>Fri, 31 Jul 2026 23:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, the, Agentic, Catalog, Experience, Amazon, Quick</media:keywords>
</item>

<item>
<title>Optimizing production agents with Amazon Bedrock AgentCore Observability</title>
<link>https://news.jatlink.uk/17382</link>
<guid>https://news.jatlink.uk/17382</guid>
<description><![CDATA[ As your AI agents move from prototype to production, the challenge shifts from getting them to work to keeping them fast and efficient. Learn how to use Amazon Bedrock AgentCore Observability and Amazon CloudWatch to find performance bottlenecks and diagnose memory issues in long-running agent sessions. ]]></description>
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<pubDate>Fri, 31 Jul 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, production, agents, with, Amazon, Bedrock, AgentCore, Observability</media:keywords>
</item>

<item>
<title>Disrupting a Criminal Scam Operation</title>
<link>https://news.jatlink.uk/17380</link>
<guid>https://news.jatlink.uk/17380</guid>
<description><![CDATA[ OpenAI disrupted a Cambodia-based scam operation using ChatGPT to support investment, romance, gambling, and impersonation schemes. ]]></description>
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<pubDate>Fri, 31 Jul 2026 18:00:18 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Disrupting, Criminal, Scam, Operation</media:keywords>
</item>

<item>
<title>Building abundant intelligence</title>
<link>https://news.jatlink.uk/17381</link>
<guid>https://news.jatlink.uk/17381</guid>
<description><![CDATA[ A full-stack approach to making advanced AI more capable, more affordable, and more widely useful. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 31 Jul 2026 18:00:18 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, abundant, intelligence</media:keywords>
</item>

<item>
<title>Univé builds an AI&amp;ready workforce</title>
<link>https://news.jatlink.uk/17346</link>
<guid>https://news.jatlink.uk/17346</guid>
<description><![CDATA[ See how Univé built an AI-ready workforce with ChatGPT Enterprise by combining leadership, responsible governance, and employee-led innovation to transform work at scale. ]]></description>
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<pubDate>Fri, 31 Jul 2026 11:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Univé, builds, AI-ready, workforce</media:keywords>
</item>

<item>
<title>Advancing responsible AI across Europe</title>
<link>https://news.jatlink.uk/17345</link>
<guid>https://news.jatlink.uk/17345</guid>
<description><![CDATA[ OpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe. The work will continue as the EU AI Act advances. ]]></description>
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<pubDate>Fri, 31 Jul 2026 10:00:35 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, responsible, across, Europe</media:keywords>
</item>

<item>
<title>How avatarin built a 24/7 retail agent with GPT&amp;Realtime</title>
<link>https://news.jatlink.uk/17314</link>
<guid>https://news.jatlink.uk/17314</guid>
<description><![CDATA[ avatarin uses OpenAI’s GPT-Realtime to give Yamada Denki shoppers 24/7 multilingual support. In two weeks, 30,000 people used the agent and 92% of survey responses were positive. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 31 Jul 2026 03:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, avatarin, built, 247, retail, agent, with, GPT-Realtime</media:keywords>
</item>

<item>
<title>Migrate your prompts to new models and optimize them on Amazon Bedrock</title>
<link>https://news.jatlink.uk/17283</link>
<guid>https://news.jatlink.uk/17283</guid>
<description><![CDATA[ Amazon Bedrock Advanced Prompt Optimization optimizes your prompts for up to 5 models at once and compares original versus optimized performance across quality, latency, and cost. Migrate to a new model or improve your current one in minutes instead of weeks. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, your, prompts, new, models, and, optimize, them, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Yahoo enhances search retargeting using Amazon Bedrock</title>
<link>https://news.jatlink.uk/17280</link>
<guid>https://news.jatlink.uk/17280</guid>
<description><![CDATA[ In this post, we demonstrate how Yahoo implemented Amazon Bedrock to enhance their Search Retargeting (SRT) capabilities in the Yahoo DSP ad tech suite. SRT is a core audience targeting solution that helps advertisers reach users based on their historical search behavior, bridging search intent with display, video, and native advertising. Beyond targeting keywords entered on Yahoo Search, SRT uses AI to identify and engage users who demonstrate intent through search activity both on Yahoo and across integrated partner systems. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Yahoo, enhances, search, retargeting, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Inference meta&amp;monitoring for Amazon SageMaker AI endpoints with Amazon Quick</title>
<link>https://news.jatlink.uk/17281</link>
<guid>https://news.jatlink.uk/17281</guid>
<description><![CDATA[ Learn how to build an inference meta-monitoring system for Amazon SageMaker AI endpoints using Amazon Quick. This governance layer sits above production ML inference pipelines to continuously track prediction and data quality, detect drift, integrate delayed ground truth, and surface automated performance dashboards. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Inference, meta-monitoring, for, Amazon, SageMaker, endpoints, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Introducing explicit prompt caching for OpenAI GPT&amp;5.6 models on Amazon Bedrock</title>
<link>https://news.jatlink.uk/17282</link>
<guid>https://news.jatlink.uk/17282</guid>
<description><![CDATA[ OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, explicit, prompt, caching, for, OpenAI, GPT-5.6, models, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Advancing the price&amp;performance frontier with GPT&amp;5.6</title>
<link>https://news.jatlink.uk/17278</link>
<guid>https://news.jatlink.uk/17278</guid>
<description><![CDATA[ Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, the, price-performance, frontier, with, GPT-5.6</media:keywords>
</item>

<item>
<title>Deploying Kimi K3 on AWS</title>
<link>https://news.jatlink.uk/17279</link>
<guid>https://news.jatlink.uk/17279</guid>
<description><![CDATA[ This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and  Amazon Elastic Kubernetes Service (Amazon EKS) cluster. ]]></description>
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<pubDate>Thu, 30 Jul 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploying, Kimi, AWS</media:keywords>
</item>

<item>
<title>Best in Class: Stream PC Games and Study on the Same Laptop With GeForce NOW</title>
<link>https://news.jatlink.uk/17262</link>
<guid>https://news.jatlink.uk/17262</guid>
<description><![CDATA[ Back to school means balancing assignments, deadlines and downtime. GeForce NOW makes it easy to have it all. With cloud gaming, everyday laptops used for class can also become GeForce RTX-powered gaming setups.  When it’s time to switch from studying to gaming, members can jump into Halo: Campaign Evolved, as GeForce NOW is bringing one […] ]]></description>
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<pubDate>Thu, 30 Jul 2026 16:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, Class:, Stream, Games, and, Study, the, Same, Laptop, With, GeForce, NOW</media:keywords>
</item>

<item>
<title>How enabling two settings tripled our scores on the ARC&amp;AGI&amp;3 benchmark</title>
<link>https://news.jatlink.uk/17213</link>
<guid>https://news.jatlink.uk/17213</guid>
<description><![CDATA[ How two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction. ]]></description>
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<pubDate>Thu, 30 Jul 2026 02:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, enabling, two, settings, tripled, our, scores, the, ARC-AGI-3, benchmark</media:keywords>
</item>

<item>
<title>NVIDIA Sets Conference Call for Second&amp;Quarter Financial Results</title>
<link>https://news.jatlink.uk/17196</link>
<guid>https://news.jatlink.uk/17196</guid>
<description><![CDATA[ NVIDIA will host a conference call on Wednesday, August 26, at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the second quarter of fiscal year 2027, which ended July 26, 2026. ]]></description>
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<pubDate>Thu, 30 Jul 2026 00:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Sets, Conference, Call, for, Second-Quarter, Financial, Results</media:keywords>
</item>

<item>
<title>How GPT&amp;5.6 fuses frontier intelligence with frontier efficiency</title>
<link>https://news.jatlink.uk/17195</link>
<guid>https://news.jatlink.uk/17195</guid>
<description><![CDATA[ GPT-5.6 improves AI efficiency across models, inference, and agentic workflows, helping deliver more useful intelligence per dollar. ]]></description>
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<pubDate>Wed, 29 Jul 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, GPT-5.6, fuses, frontier, intelligence, with, frontier, efficiency</media:keywords>
</item>

<item>
<title>Automating customer retention workflows in Amazon Quick</title>
<link>https://news.jatlink.uk/17181</link>
<guid>https://news.jatlink.uk/17181</guid>
<description><![CDATA[ Learn how to build a no-code customer retention pipeline in Amazon Quick that detects at-risk customers from call transcripts and CSAT data, scores them by retention priority with a custom MCP Action, and generates personalized retention letters, reducing response time from days to minutes. ]]></description>
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<pubDate>Wed, 29 Jul 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automating, customer, retention, workflows, Amazon, Quick</media:keywords>
</item>

<item>
<title>Authenticate with Private Key JWT using Amazon Bedrock AgentCore Identity</title>
<link>https://news.jatlink.uk/17179</link>
<guid>https://news.jatlink.uk/17179</guid>
<description><![CDATA[ This post explains how Private Key JWT client authentication works in AgentCore Identity and reviews the supported grant flows. We then walk through creating an AWS KMS signing key, registering its public key with your identity provider, configuring a credential provider on the AWS Management Console, and reviewing example AWS CloudTrail events that record your agent’s access. ]]></description>
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<pubDate>Wed, 29 Jul 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Authenticate, with, Private, Key, JWT, using, Amazon, Bedrock, AgentCore, Identity</media:keywords>
</item>

<item>
<title>Generate Autonomous Business Insights with AI Agent and MCP Servers</title>
<link>https://news.jatlink.uk/17180</link>
<guid>https://news.jatlink.uk/17180</guid>
<description><![CDATA[ Learn how Amazon Bedrock AgentCore delivers autonomous, cross-system business intelligence through configuration rather than custom code. Using pre-built MCP server connectors, fine-grained access control, and persistent memory, enterprises can query multiple data sources with natural language while enforcing role-based boundaries automatically. ]]></description>
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<pubDate>Wed, 29 Jul 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, Autonomous, Business, Insights, with, Agent, and, MCP, Servers</media:keywords>
</item>

<item>
<title>Accelerating scientific discovery with ChatGPT for Academic Researchers</title>
<link>https://news.jatlink.uk/17178</link>
<guid>https://news.jatlink.uk/17178</guid>
<description><![CDATA[ OpenAI is giving 100,000 academic researchers free access to ChatGPT&#039;s most advanced AI models to accelerate scientific research, collaboration, and discovery. ]]></description>
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<pubDate>Wed, 29 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, scientific, discovery, with, ChatGPT, for, Academic, Researchers</media:keywords>
</item>

<item>
<title>How AgentCore Gateway supports the MCP 2026&amp;07&amp;28 spec</title>
<link>https://news.jatlink.uk/17103</link>
<guid>https://news.jatlink.uk/17103</guid>
<description><![CDATA[ The Model Context Protocol (MCP) published its 2026-07-28 specification, the largest revision since launch: MCP is now stateless, with a governed extensions system and hardened authorization. Learn what changed and how to enable the new version on Amazon Bedrock AgentCore Gateway with a single UpdateGateway call. ]]></description>
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<pubDate>Tue, 28 Jul 2026 23:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AgentCore, Gateway, supports, the, MCP, 2026-07-28, spec</media:keywords>
</item>

<item>
<title>Powerful Compute So Compact, It’s Clutch — Build AI in Your Hand With NVIDIA Jetson</title>
<link>https://news.jatlink.uk/17086</link>
<guid>https://news.jatlink.uk/17086</guid>
<description><![CDATA[ Anyone can make a robot move; NVIDIA Jetson makes it think. As a discerning AI investor who values style and substance, Sarah Guo knows this season’s standout accessory isn’t the latest designer purse — but what’s inside it.  In a recent video, Guo, founder of AI-native venture capital firm Conviction and co-host of the AI […] ]]></description>
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<pubDate>Tue, 28 Jul 2026 20:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Powerful, Compute, Compact, It’s, Clutch, —, Build, Your, Hand, With, NVIDIA, Jetson</media:keywords>
</item>

<item>
<title>Scientific computing in the age of agentic AI</title>
<link>https://news.jatlink.uk/17084</link>
<guid>https://news.jatlink.uk/17084</guid>
<description><![CDATA[ A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond. ]]></description>
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<pubDate>Tue, 28 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scientific, computing, the, age, agentic</media:keywords>
</item>

<item>
<title>Market surveillance agent with LangGraph and Strands on AgentCore</title>
<link>https://news.jatlink.uk/17085</link>
<guid>https://news.jatlink.uk/17085</guid>
<description><![CDATA[ Learn how to architect and deploy a production-ready multi-agent AI system using LangGraph for workflow orchestration and Strands for agent reasoning on Amazon Bedrock AgentCore. This post walks through a market surveillance example with state-driven orchestration, checkpoint-based recovery, and AgentCore memory and observability. ]]></description>
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<pubDate>Tue, 28 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Market, surveillance, agent, with, LangGraph, and, Strands, AgentCore</media:keywords>
</item>

<item>
<title>How Guardoc transforms medical document processing with Amazon Nova models</title>
<link>https://news.jatlink.uk/16998</link>
<guid>https://news.jatlink.uk/16998</guid>
<description><![CDATA[ In this post, we explore how Guardoc Health uses the Amazon Nova family of models, available through Amazon Bedrock, to transform clinical documentation in long-term care. ]]></description>
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<pubDate>Mon, 27 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Guardoc, transforms, medical, document, processing, with, Amazon, Nova, models</media:keywords>
</item>

<item>
<title>Beyond RAG: Task&amp;aware knowledge compression for enterprise AI on AWS</title>
<link>https://news.jatlink.uk/16996</link>
<guid>https://news.jatlink.uk/16996</guid>
<description><![CDATA[ Traditional RAG hits a ceiling on analytical tasks that span hundreds of documents. This post shows how to use task-aware knowledge compression (TAKC) on AWS to pre-compress entire knowledge bases into task-specific representations, cache them at multiple fidelity tiers, and route each query to the right tier, with an open-source implementation you can deploy. ]]></description>
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<pubDate>Mon, 27 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, RAG:, Task-aware, knowledge, compression, for, enterprise, AWS</media:keywords>
</item>

<item>
<title>Deepgram enhances Amazon SageMaker AI support with AWS IAM Temporary Delegation</title>
<link>https://news.jatlink.uk/16997</link>
<guid>https://news.jatlink.uk/16997</guid>
<description><![CDATA[ In this post, we cover why Deepgram built on IAM temporary delegation, how the integration works end-to-end, and what it unlocks for customers running Deepgram speech models on SageMaker AI. With this integration, Deepgram has reduced the time for initial investigation on a SageMaker AI support ticket from days to minutes. ]]></description>
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<pubDate>Mon, 27 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deepgram, enhances, Amazon, SageMaker, support, with, AWS, IAM, Temporary, Delegation</media:keywords>
</item>

<item>
<title>Ilya Sutskever’s Safe Superintelligence Inc. and NVIDIA Announce Long&amp;Term Strategic Partnership</title>
<link>https://news.jatlink.uk/16976</link>
<guid>https://news.jatlink.uk/16976</guid>
<description><![CDATA[ Safe Superintelligence Inc. (SSI) and NVIDIA today announced a long-term partnership to rapidly accelerate SSI’s strategic growth... ]]></description>
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<pubDate>Mon, 27 Jul 2026 16:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Ilya, Sutskever’s, Safe, Superintelligence, Inc., and, NVIDIA, Announce, Long-Term, Strategic, Partnership</media:keywords>
</item>

<item>
<title>How AI is expanding what people do at work</title>
<link>https://news.jatlink.uk/16974</link>
<guid>https://news.jatlink.uk/16974</guid>
<description><![CDATA[ New OpenAI research shows how AI is expanding what workers do, with ChatGPT users taking on tasks across roles and reshaping job boundaries. ]]></description>
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<pubDate>Mon, 27 Jul 2026 14:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, expanding, what, people, work</media:keywords>
</item>

<item>
<title>Industry Leaders Unite in Open Secure AI Alliance for AI Safety and Security</title>
<link>https://news.jatlink.uk/16963</link>
<guid>https://news.jatlink.uk/16963</guid>
<description><![CDATA[ Open source software is a critical pillar of the global economy. It underpins cloud computing, financial services, manufacturing, telecommunications, government and internet services by making technology accessible and observable to communities of experts.  Cybersecurity is among the top three beneficiaries of open source software. The Open Secure AI Alliance — building on the leadership of […] ]]></description>
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<pubDate>Mon, 27 Jul 2026 12:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Industry, Leaders, Unite, Open, Secure, Alliance, for, Safety, and, Security</media:keywords>
</item>

<item>
<title>NVIDIA Harnesses Vera CPU to Speed Up Design of Next&amp;Generation CPUs and GPUs</title>
<link>https://news.jatlink.uk/16945</link>
<guid>https://news.jatlink.uk/16945</guid>
<description><![CDATA[ The complexity of modern chip design continues to grow as engineering teams work to develop increasingly sophisticated CPUs, GPUs and AI systems. To help meet that challenge, NVIDIA is collaborating with industry leaders Cadence and Synopsys to optimize critical electronic design automation (EDA) applications for the NVIDIA Vera CPU. NVIDIA is now deploying Vera across […] ]]></description>
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<pubDate>Mon, 27 Jul 2026 04:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Harnesses, Vera, CPU, Speed, Design, Next-Generation, CPUs, and, GPUs</media:keywords>
</item>

<item>
<title>NVIDIA Expands NVIDIA Agent Toolkit With NVIDIA PhysicsNeMo and CUDA&amp;X Libraries to Transform How the World Engineers, Designs and Builds</title>
<link>https://news.jatlink.uk/16946</link>
<guid>https://news.jatlink.uk/16946</guid>
<description><![CDATA[ NVIDIA today announced an expansion of NVIDIA Agent Toolkit for engineering, now adding NVIDIA PhysicsNeMo™ and CUDA-X™ libraries as agent-ready tools and skills built to transform how the world designs and develops products. ]]></description>
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<pubDate>Mon, 27 Jul 2026 04:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Expands, NVIDIA, Agent, Toolkit, With, NVIDIA, PhysicsNeMo, and, CUDA-X, Libraries, Transform, How, the, World, Engineers, Designs, and, Builds</media:keywords>
</item>

<item>
<title>SK Group and NVIDIA Expand Strategic Partnership Across AI Factories and Next&amp;Generation Memory</title>
<link>https://news.jatlink.uk/16824</link>
<guid>https://news.jatlink.uk/16824</guid>
<description><![CDATA[ SK Group and NVIDIA today announced plans for a $500-billion-plus comprehensive partnership to establish AI infrastructure serving the surging demand for global compute. The two sides signed letters of intent to formalize the agreement, which spans from AI factory construction to AI memory supply. ]]></description>
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<pubDate>Sat, 25 Jul 2026 08:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Group, and, NVIDIA, Expand, Strategic, Partnership, Across, Factories, and, Next-Generation, Memory</media:keywords>
</item>

<item>
<title>NAVER, NVIDIA and Brookfield to Expand Korea’s National AI Factory Infrastructure Buildout</title>
<link>https://news.jatlink.uk/16825</link>
<guid>https://news.jatlink.uk/16825</guid>
<description><![CDATA[ NAVER, NVIDIA and Brookfield today announced a proposed expansion of Korea&#039;s sovereign AI factory infrastructure, with planned investments that will grow the initial NVIDIA® DSX™ AI factory deployment to 200 megawatts — more than tripling the 55-megawatt buildout announced last month. NAVER intends to expand its deployment of NVIDIA AI infrastructure to 1 gigawatt. ]]></description>
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<pubDate>Sat, 25 Jul 2026 08:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NAVER, NVIDIA, and, Brookfield, Expand, Korea’s, National, Factory, Infrastructure, Buildout</media:keywords>
</item>

<item>
<title>Get started with OpenAI GPT&amp;5.6 Sol, Terra, and Luna on Amazon Bedrock</title>
<link>https://news.jatlink.uk/16789</link>
<guid>https://news.jatlink.uk/16789</guid>
<description><![CDATA[ OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. Learn how to select a model, run inference through the Responses API on the bedrock-mantle endpoint, reduce cost with prompt caching, connect the OpenAI Codex coding agent, and plan for quotas and scaling. ]]></description>
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<pubDate>Fri, 24 Jul 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Get, started, with, OpenAI, GPT-5.6, Sol, Terra, and, Luna, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing Claude Opus 5 on AWS: Anthropic’s most capable Opus model</title>
<link>https://news.jatlink.uk/16787</link>
<guid>https://news.jatlink.uk/16787</guid>
<description><![CDATA[ This post covers Opus 5’s improvements and practical guidance for AI engineers integrating the model into agentic systems and production inference workloads on Amazon Bedrock. See the documentation for Claude Platform on AWS. ]]></description>
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<pubDate>Fri, 24 Jul 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Claude, Opus, AWS:, Anthropic’s, most, capable, Opus, model</media:keywords>
</item>

<item>
<title>Build an explainable next&amp;best&amp;product recommendation system for banking on AWS</title>
<link>https://news.jatlink.uk/16788</link>
<guid>https://news.jatlink.uk/16788</guid>
<description><![CDATA[ Learn the architecture and design decisions behind an explainable next-best-product recommendation system for banking, built with Amazon SageMaker AI and PyTorch. A multi-tower neural network with learned attention delivers accurate, per-customer recommendations while providing the explainability that banking regulators require. ]]></description>
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<pubDate>Fri, 24 Jul 2026 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, explainable, next-best-product, recommendation, system, for, banking, AWS</media:keywords>
</item>

<item>
<title>At AI Summit, South Korea Outlines Its AI Future With NVIDIA and Partners</title>
<link>https://news.jatlink.uk/16741</link>
<guid>https://news.jatlink.uk/16741</guid>
<description><![CDATA[ At this week’s AI Summit in San Francisco, South Korean President Jae Myung Lee and some of the country’s top business leaders and researchers are meeting with NVIDIA and ecosystem partners to chart Korea’s AI progress. Building on NVIDIA founder and CEO Jensen Huang’s visit to Korea last month, this week’s discussions and announcements advance […] ]]></description>
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<pubDate>Fri, 24 Jul 2026 08:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Summit, South, Korea, Outlines, Its, Future, With, NVIDIA, and, Partners</media:keywords>
</item>

<item>
<title>NVIDIA and KAIST Launch Joint AI Research Lab to Accelerate AI Innovation in Korea</title>
<link>https://news.jatlink.uk/16724</link>
<guid>https://news.jatlink.uk/16724</guid>
<description><![CDATA[ NVIDIA and the Korea Advanced Institute of Science and Technology (KAIST) today announced the launch of a joint AI research laboratory at the KAIST Kim Jaechul Graduate School of AI in Seoul, dedicated to advancing agentic AI for South Korea. ]]></description>
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<pubDate>Fri, 24 Jul 2026 04:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, KAIST, Launch, Joint, Research, Lab, Accelerate, Innovation, Korea</media:keywords>
</item>

<item>
<title>Best practices for applying Amazon Bedrock Guardrails to code generation workflows</title>
<link>https://news.jatlink.uk/16722</link>
<guid>https://news.jatlink.uk/16722</guid>
<description><![CDATA[ In this post, we explain how Amazon Bedrock Guardrails can be configured for code generation workflows with coding assistants to overcome these constraints. With these best practices, you can build an efficient blueprint helping you with effective capacity planning with robust safety coverage. ]]></description>
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<pubDate>Fri, 24 Jul 2026 03:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, for, applying, Amazon, Bedrock, Guardrails, code, generation, workflows</media:keywords>
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