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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>Building Workforce AI Agents with Visier and Amazon Quick</title>
<link>https://news.jatlink.uk/9698</link>
<guid>https://news.jatlink.uk/9698</guid>
<description><![CDATA[ In this post, we show how connecting the Visier Workforce AI platform with Amazon Quick through Model Context Protocol (MCP) gives every knowledge worker a unified agentic workspace to ask questions in. Visier helps ground the workspace in live workforce data and the organizational context that surrounds it while letting your users act on the conversational results without switching tools. ]]></description>
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<pubDate>Fri, 24 Apr 2026 23:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Workforce, Agents, with, Visier, and, Amazon, Quick</media:keywords>
</item>

<item>
<title>OpenAI’s New GPT&amp;5.5 Powers Codex on NVIDIA Infrastructure — and NVIDIA Is Already Putting It to Work</title>
<link>https://news.jatlink.uk/9621</link>
<guid>https://news.jatlink.uk/9621</guid>
<description><![CDATA[ AI agents have revolutionized developer workflows, and their next frontier is knowledge work: processing information, solving complex problems, coming up with new ideas and driving innovation.  Codex, OpenAI’s agentic coding application, is enabling this new frontier. It’s now powered by GPT-5.5, OpenAI’s latest frontier model, which runs on NVIDIA GB200 NVL72 rack-scale systems.  Over 10,000 […] ]]></description>
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<pubDate>Fri, 24 Apr 2026 00:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI’s, New, GPT-5.5, Powers, Codex, NVIDIA, Infrastructure, —, and, NVIDIA, Already, Putting, Work</media:keywords>
</item>

<item>
<title>What is Codex?</title>
<link>https://news.jatlink.uk/9619</link>
<guid>https://news.jatlink.uk/9619</guid>
<description><![CDATA[ Learn how Codex helps you go beyond chat by automating tasks, connecting tools, and producing real outputs like docs and dashboards. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>What, Codex</media:keywords>
</item>

<item>
<title>GPT&amp;5.5 Bio Bug Bounty</title>
<link>https://news.jatlink.uk/9620</link>
<guid>https://news.jatlink.uk/9620</guid>
<description><![CDATA[ Explore the GPT-5.5 Bio Bug Bounty: a red-teaming challenge to find universal jailbreaks for bio safety risks, with rewards up to $25,000. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.5, Bio, Bug, Bounty</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.5</title>
<link>https://news.jatlink.uk/9611</link>
<guid>https://news.jatlink.uk/9611</guid>
<description><![CDATA[ Introducing GPT-5.5, our smartest model yet—faster, more capable, and built for complex tasks like coding, research, and data analysis across tools. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.5</media:keywords>
</item>

<item>
<title>GPT&amp;5.5 System Card</title>
<link>https://news.jatlink.uk/9612</link>
<guid>https://news.jatlink.uk/9612</guid>
<description><![CDATA[  ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.5, System, Card</media:keywords>
</item>

<item>
<title>Codex settings</title>
<link>https://news.jatlink.uk/9613</link>
<guid>https://news.jatlink.uk/9613</guid>
<description><![CDATA[ Learn how to configure Codex settings, including personalization, detail level, and permissions, to run tasks smoothly and customize your workflow. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Codex, settings</media:keywords>
</item>

<item>
<title>Automations</title>
<link>https://news.jatlink.uk/9614</link>
<guid>https://news.jatlink.uk/9614</guid>
<description><![CDATA[ Learn how to automate tasks in Codex using schedules and triggers to create reports, summaries, and recurring workflows without manual effort. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automations</media:keywords>
</item>

<item>
<title>Top 10 uses for Codex at work</title>
<link>https://news.jatlink.uk/9615</link>
<guid>https://news.jatlink.uk/9615</guid>
<description><![CDATA[ Explore 10 practical Codex use cases to automate tasks, create deliverables, and turn real inputs into outputs across tools, files, and workflows. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Top, uses, for, Codex, work</media:keywords>
</item>

<item>
<title>How to get started with Codex</title>
<link>https://news.jatlink.uk/9616</link>
<guid>https://news.jatlink.uk/9616</guid>
<description><![CDATA[ Learn how to get started with Codex by setting up projects, creating threads, and completing your first tasks with step-by-step guidance. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, get, started, with, Codex</media:keywords>
</item>

<item>
<title>Working with Codex</title>
<link>https://news.jatlink.uk/9617</link>
<guid>https://news.jatlink.uk/9617</guid>
<description><![CDATA[ Learn how to set up your Codex workspace, create threads and projects, manage files, and start completing tasks with step-by-step guidance. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Working, with, Codex</media:keywords>
</item>

<item>
<title>Plugins and skills</title>
<link>https://news.jatlink.uk/9618</link>
<guid>https://news.jatlink.uk/9618</guid>
<description><![CDATA[ Learn how to use Codex plugins and skills to connect tools, access data, and follow repeatable workflows to automate tasks and improve results. ]]></description>
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<pubDate>Thu, 23 Apr 2026 22:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Plugins, and, skills</media:keywords>
</item>

<item>
<title>Amazon Quick for marketing: From scattered data to strategic action</title>
<link>https://news.jatlink.uk/9596</link>
<guid>https://news.jatlink.uk/9596</guid>
<description><![CDATA[ Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, for, marketing:, From, scattered, data, strategic, action</media:keywords>
</item>

<item>
<title>Applying multimodal biological foundation models across therapeutics and patient care</title>
<link>https://news.jatlink.uk/9597</link>
<guid>https://news.jatlink.uk/9597</guid>
<description><![CDATA[ In this post, we&#039;ll explore how multimodal BioFMs work, showcase real-world applications in drug discovery and clinical development, and contextualize how AWS enables organizations to build and deploy multimodal BioFMs. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applying, multimodal, biological, foundation, models, across, therapeutics, and, patient, care</media:keywords>
</item>

<item>
<title>Making Sense of the Early Universe</title>
<link>https://news.jatlink.uk/9583</link>
<guid>https://news.jatlink.uk/9583</guid>
<description><![CDATA[ This Spring Astronomy Day, here’s a look at how AI and GPUs are helping astronomers work through unprecedented volumes of cosmic data. ]]></description>
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<pubDate>Thu, 23 Apr 2026 16:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Making, Sense, the, Early, Universe</media:keywords>
</item>

<item>
<title>Tag, You’re It: GeForce NOW Levels Up Game Discovery With Xbox Game Pass and Ubisoft+ Labels</title>
<link>https://news.jatlink.uk/9582</link>
<guid>https://news.jatlink.uk/9582</guid>
<description><![CDATA[ GeForce NOW is doubling down on what matters most: gamers. This week’s upgrades bring smarter libraries, making it easier than ever for gamers to turn a PC collection into a cloud-powered flex. It starts with giving existing libraries time to shine. Gamers can bring the games they love to the cloud, stream them with high […] ]]></description>
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<pubDate>Thu, 23 Apr 2026 16:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tag, You’re, It:, GeForce, NOW, Levels, Game, Discovery, With, Xbox, Game, Pass, and, Ubisoft, Labels</media:keywords>
</item>

<item>
<title>Making ChatGPT better for clinicians</title>
<link>https://news.jatlink.uk/9546</link>
<guid>https://news.jatlink.uk/9546</guid>
<description><![CDATA[ OpenAI makes ChatGPT for Clinicians free for verified U.S. physicians, nurse practitioners, and pharmacists, supporting clinical care, documentation, and research. ]]></description>
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<pubDate>Thu, 23 Apr 2026 02:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Making, ChatGPT, better, for, clinicians</media:keywords>
</item>

<item>
<title>Introducing ChatGPT Images 2.0</title>
<link>https://news.jatlink.uk/9529</link>
<guid>https://news.jatlink.uk/9529</guid>
<description><![CDATA[ ChatGPT Images 2.0 introduces a state-of-the-art image generation model with improved text rendering, multilingual support, and advanced visual reasoning. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, Images, 2.0</media:keywords>
</item>

<item>
<title>Cost&amp;effective multilingual audio transcription at scale with Parakeet&amp;TDT and AWS Batch</title>
<link>https://news.jatlink.uk/9530</link>
<guid>https://news.jatlink.uk/9530</guid>
<description><![CDATA[ In this post, we walk through building a scalable, event-driven transcription pipeline that automatically processes audio files uploaded to Amazon Simple Storage Service (Amazon S3), and show you how to use Amazon EC2 Spot Instances and buffered streaming inference to further reduce costs. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-effective, multilingual, audio, transcription, scale, with, Parakeet-TDT, and, AWS, Batch</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI now supports optimized generative AI inference recommendations</title>
<link>https://news.jatlink.uk/9531</link>
<guid>https://news.jatlink.uk/9531</guid>
<description><![CDATA[ Today, Amazon SageMaker AI  supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, now, supports, optimized, generative, inference, recommendations</media:keywords>
</item>

<item>
<title>Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/9532</link>
<guid>https://news.jatlink.uk/9532</guid>
<description><![CDATA[ Today, we&#039;re introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Get, your, first, working, agent, minutes:, Announcing, new, features, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Workspace agents</title>
<link>https://news.jatlink.uk/9515</link>
<guid>https://news.jatlink.uk/9515</guid>
<description><![CDATA[ Learn how to build, use, and scale workspace agents in ChatGPT to automate repeatable workflows, connect tools, and streamline team operations. ]]></description>
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<pubDate>Wed, 22 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Workspace, agents</media:keywords>
</item>

<item>
<title>Company&amp;wise memory in Amazon Bedrock with Amazon Neptune and Mem0</title>
<link>https://news.jatlink.uk/9516</link>
<guid>https://news.jatlink.uk/9516</guid>
<description><![CDATA[ Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions ]]></description>
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<pubDate>Wed, 22 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Company-wise, memory, Amazon, Bedrock, with, Amazon, Neptune, and, Mem0</media:keywords>
</item>

<item>
<title>Introducing workspace agents in ChatGPT</title>
<link>https://news.jatlink.uk/9514</link>
<guid>https://news.jatlink.uk/9514</guid>
<description><![CDATA[ Workspace agents in ChatGPT are Codex-powered agents that automate complex workflows, run in the cloud, and help teams scale work across tools securely. ]]></description>
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<pubDate>Wed, 22 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, workspace, agents, ChatGPT</media:keywords>
</item>

<item>
<title>Speeding up agentic workflows with WebSockets in the Responses API</title>
<link>https://news.jatlink.uk/9512</link>
<guid>https://news.jatlink.uk/9512</guid>
<description><![CDATA[ A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency. ]]></description>
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<pubDate>Wed, 22 Apr 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Speeding, agentic, workflows, with, WebSockets, the, Responses, API</media:keywords>
</item>

<item>
<title>Introducing OpenAI Privacy Filter</title>
<link>https://news.jatlink.uk/9513</link>
<guid>https://news.jatlink.uk/9513</guid>
<description><![CDATA[ OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy ]]></description>
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<pubDate>Wed, 22 Apr 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, OpenAI, Privacy, Filter</media:keywords>
</item>

<item>
<title>From Rainforests to Recycling Plants: 5 Ways NVIDIA AI Is Protecting the Planet</title>
<link>https://news.jatlink.uk/9499</link>
<guid>https://news.jatlink.uk/9499</guid>
<description><![CDATA[ Across climate, conservation, disaster monitoring and recycling, NVIDIA AI is enabling applications protecting the planet. ]]></description>
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<pubDate>Wed, 22 Apr 2026 16:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Rainforests, Recycling, Plants:, Ways, NVIDIA, Protecting, the, Planet</media:keywords>
</item>

<item>
<title>NVIDIA and Google Cloud Collaborate to Advance Agentic and Physical AI</title>
<link>https://news.jatlink.uk/9500</link>
<guid>https://news.jatlink.uk/9500</guid>
<description><![CDATA[ NVIDIA and Google Cloud have collaborated for more than a decade, co‑engineering a full‑stack AI platform that spans every technology layer — from performance‑optimized libraries and frameworks to enterprise‑grade cloud services.  This foundation enables developers, startups and enterprises to push agentic and physical AI out of the lab and into production — from agents that […] ]]></description>
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<pubDate>Wed, 22 Apr 2026 16:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Google, Cloud, Collaborate, Advance, Agentic, and, Physical</media:keywords>
</item>

<item>
<title>From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock</title>
<link>https://news.jatlink.uk/9447</link>
<guid>https://news.jatlink.uk/9447</guid>
<description><![CDATA[ Today, we&#039;re excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. In this post, we walk through how Claude Cowork integrates with Amazon Bedrock and show an example of how knowledge workers use it in practice. ]]></description>
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<pubDate>Tue, 21 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, developer, desks, the, whole, organization:, Running, Claude, Cowork, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>End&amp;to&amp;end lineage with DVC and Amazon SageMaker AI MLflow apps</title>
<link>https://news.jatlink.uk/9433</link>
<guid>https://news.jatlink.uk/9433</guid>
<description><![CDATA[ In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks. ]]></description>
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<pubDate>Tue, 21 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>End-to-end, lineage, with, DVC, and, Amazon, SageMaker, MLflow, apps</media:keywords>
</item>

<item>
<title>Scaling Codex to enterprises worldwide</title>
<link>https://news.jatlink.uk/9418</link>
<guid>https://news.jatlink.uk/9418</guid>
<description><![CDATA[ OpenAI launches Codex Transformation Partners, a program with Accenture, PwC, Infosys, and others to help enterprises deploy and scale Codex across the software development lifecycle. ]]></description>
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<pubDate>Tue, 21 Apr 2026 14:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, Codex, enterprises, worldwide</media:keywords>
</item>

<item>
<title>Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances</title>
<link>https://news.jatlink.uk/9362</link>
<guid>https://news.jatlink.uk/9362</guid>
<description><![CDATA[ Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, 4, and 8 RTX PRO 6000 GPU instances, with each GPU providing 96 GB of GDDR7 memory. This launch provides the capability to use a single-node GPU, G7e.2xlarge instance to host powerful open source foundation models (FMs) like GPT-OSS-120B, Nemotron-3-Super-120B-A12B (NVFP4 variant), and Qwen3.5-35B-A3B, offering organizations a cost-effective and high-performing option. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Generative, Inference, Amazon, SageMaker, with, G7e, Instances</media:keywords>
</item>

<item>
<title>ToolSimulator: scalable tool testing for AI agents</title>
<link>https://news.jatlink.uk/9363</link>
<guid>https://news.jatlink.uk/9363</guid>
<description><![CDATA[ You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator&#039;s large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ToolSimulator:, scalable, tool, testing, for, agents</media:keywords>
</item>

<item>
<title>Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/9348</link>
<guid>https://news.jatlink.uk/9348</guid>
<description><![CDATA[ In this post, we&#039;ll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic. ]]></description>
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<pubDate>Mon, 20 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Omnichannel, ordering, with, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>OpenAI helps Hyatt advance AI among colleagues</title>
<link>https://news.jatlink.uk/9347</link>
<guid>https://news.jatlink.uk/9347</guid>
<description><![CDATA[ Hyatt deploys ChatGPT Enterprise across its global workforce, using GPT-5.4 and Codex to improve productivity, operations, and guest experiences. ]]></description>
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<pubDate>Mon, 20 Apr 2026 17:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, helps, Hyatt, advance, among, colleagues</media:keywords>
</item>

<item>
<title>Autonomous AI at Scale: Adobe Agents Unlock Breakthrough Creative Intelligence With NVIDIA and WPP</title>
<link>https://news.jatlink.uk/9333</link>
<guid>https://news.jatlink.uk/9333</guid>
<description><![CDATA[ AI agents are transforming how work gets done across all industries, accelerating everything from content creation to decision-making. NVIDIA’s expanded strategic collaborations with Adobe and WPP are bringing agentic AI to the center of enterprise marketing operations across creative production and customer experience orchestration.  As demand for personalized customer experiences surges, brands require intelligent systems […] ]]></description>
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<pubDate>Mon, 20 Apr 2026 15:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Autonomous, Scale:, Adobe, Agents, Unlock, Breakthrough, Creative, Intelligence, With, NVIDIA, and, WPP</media:keywords>
</item>

<item>
<title>NVIDIA and Partners Showcase the Future of AI&amp;Driven Manufacturing at Hannover Messe 2026</title>
<link>https://news.jatlink.uk/9316</link>
<guid>https://news.jatlink.uk/9316</guid>
<description><![CDATA[ Manufacturing is at an inflection point. Across every major industrial economy, the pressure to do more with less — due to faster design cycles, leaner operations and strain on skilled labor pools — is accelerating the shift to AI-driven production.  The question is no longer whether to adopt AI, but how fast and at what […] ]]></description>
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<pubDate>Mon, 20 Apr 2026 11:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Partners, Showcase, the, Future, AI-Driven, Manufacturing, Hannover, Messe, 2026</media:keywords>
</item>

<item>
<title>Introducing granular cost attribution for Amazon Bedrock</title>
<link>https://news.jatlink.uk/9154</link>
<guid>https://news.jatlink.uk/9154</guid>
<description><![CDATA[ In this post, we share how Amazon Bedrock&#039;s granular cost attribution works and walk through example cost tracking scenarios. ]]></description>
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<pubDate>Sat, 18 Apr 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, granular, cost, attribution, for, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From hours to minutes: How Agentic AI gave marketers time back for what matters</title>
<link>https://news.jatlink.uk/9139</link>
<guid>https://news.jatlink.uk/9139</guid>
<description><![CDATA[ In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, hours, minutes:, How, Agentic, gave, marketers, time, back, for, what, matters</media:keywords>
</item>

<item>
<title>Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock</title>
<link>https://news.jatlink.uk/9136</link>
<guid>https://news.jatlink.uk/9136</guid>
<description><![CDATA[ In this post, we show you how to use Model Distillation, a model customization technique on Amazon Bedrock, to transfer routing intelligence from a large teacher model (Amazon Nova Premier) into a much smaller student model (Amazon Nova Micro). This approach cuts inference cost by over 95% and reduces latency by 50% while maintaining the nuanced routing quality that the task demands. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimize, video, semantic, search, intent, with, Amazon, Nova, Model, Distillation, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Power video semantic search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/9137</link>
<guid>https://news.jatlink.uk/9137</guid>
<description><![CDATA[ In this post, we show you how to build a video semantic search solution on Amazon Bedrock using Nova Multimodal Embeddings that intelligently understands user intent and retrieves accurate video results across all signal types simultaneously. We also share a reference implementation you can deploy and explore with your own content. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Power, video, semantic, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>Nova Forge SDK series part 2: Practical guide to fine&amp;tune Nova models using data mixing capabilities</title>
<link>https://news.jatlink.uk/9138</link>
<guid>https://news.jatlink.uk/9138</guid>
<description><![CDATA[ This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Nova, Forge, SDK, series, part, Practical, guide, fine-tune, Nova, models, using, data, mixing, capabilities</media:keywords>
</item>

<item>
<title>Cost&amp;efficient custom text&amp;to&amp;SQL using Amazon Nova Micro and Amazon Bedrock on&amp;demand inference</title>
<link>https://news.jatlink.uk/9049</link>
<guid>https://news.jatlink.uk/9049</guid>
<description><![CDATA[ In this post, we demonstrate two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production ready performance. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-efficient, custom, text-to-SQL, using, Amazon, Nova, Micro, and, Amazon, Bedrock, on-demand, inference</media:keywords>
</item>

<item>
<title>Transform retail with AWS generative AI services</title>
<link>https://news.jatlink.uk/9050</link>
<guid>https://news.jatlink.uk/9050</guid>
<description><![CDATA[ Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on […] ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, retail, with, AWS, generative, services</media:keywords>
</item>

<item>
<title>How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance</title>
<link>https://news.jatlink.uk/9051</link>
<guid>https://news.jatlink.uk/9051</guid>
<description><![CDATA[ In this post, you&#039;ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You&#039;ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Automated, Reasoning, checks, Amazon, Bedrock, transform, generative, compliance</media:keywords>
</item>

<item>
<title>Codex for (almost) everything</title>
<link>https://news.jatlink.uk/9046</link>
<guid>https://news.jatlink.uk/9046</guid>
<description><![CDATA[ The updated Codex app for macOS and Windows adds computer use, in-app browsing, image generation, memory, and plugins to accelerate developer workflows. ]]></description>
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<pubDate>Thu, 16 Apr 2026 21:00:19 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Codex, for, almost, everything</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;Rosalind for life sciences research</title>
<link>https://news.jatlink.uk/9047</link>
<guid>https://news.jatlink.uk/9047</guid>
<description><![CDATA[ OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows. ]]></description>
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<pubDate>Thu, 16 Apr 2026 21:00:19 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-Rosalind, for, life, sciences, research</media:keywords>
</item>

<item>
<title>Responsible and safe use of AI</title>
<link>https://news.jatlink.uk/9048</link>
<guid>https://news.jatlink.uk/9048</guid>
<description><![CDATA[ Learn how to use AI responsibly with best practices for safety, accuracy, and transparency when using tools like ChatGPT. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 16 Apr 2026 21:00:19 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Responsible, and, safe, use</media:keywords>
</item>

<item>
<title>No Need for Space Gear — Capcom’s ‘PRAGMATA’ Joins GeForce NOW on Launch Day</title>
<link>https://news.jatlink.uk/9018</link>
<guid>https://news.jatlink.uk/9018</guid>
<description><![CDATA[ Head straight for orbit with GeForce NOW — no space helmet required.  PRAGMATA, Capcom’s long-awaited sci-fi action adventure, touches down on GeForce NOW the same day it launches worldwide. The futuristic journey through a cold lunar station in the near future can be streamed instantly from the cloud to almost any device, no console or […] ]]></description>
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<pubDate>Thu, 16 Apr 2026 15:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Need, for, Space, Gear, —, Capcom’s, ‘PRAGMATA’, Joins, GeForce, NOW, Launch, Day</media:keywords>
</item>

<item>
<title>Our response to the Axios developer tool compromise</title>
<link>https://news.jatlink.uk/9017</link>
<guid>https://news.jatlink.uk/9017</guid>
<description><![CDATA[ OpenAI responds to the Axios supply chain attack by rotating macOS code signing certificates, updating apps, and confirming no user data was compromised. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 16 Apr 2026 13:00:21 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, response, the, Axios, developer, tool, compromise</media:keywords>
</item>

<item>
<title>Accelerating the cyber defense ecosystem that protects us all</title>
<link>https://news.jatlink.uk/9016</link>
<guid>https://news.jatlink.uk/9016</guid>
<description><![CDATA[ Leading security firms and enterprises join OpenAI’s Trusted Access for Cyber, using GPT-5.4-Cyber and $10M in API grants to strengthen global cyber defense. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 16 Apr 2026 13:00:20 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, the, cyber, defense, ecosystem, that, protects, all</media:keywords>
</item>

<item>
<title>The next evolution of the Agents SDK</title>
<link>https://news.jatlink.uk/8958</link>
<guid>https://news.jatlink.uk/8958</guid>
<description><![CDATA[ OpenAI updates the Agents SDK with native sandbox execution and a model-native harness, helping developers build secure, long-running agents across files and tools. ]]></description>
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<pubDate>Wed, 15 Apr 2026 21:00:24 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, next, evolution, the, Agents, SDK</media:keywords>
</item>

<item>
<title>Rethinking AI TCO: Why Cost per Token Is the Only Metric That Matters</title>
<link>https://news.jatlink.uk/8939</link>
<guid>https://news.jatlink.uk/8939</guid>
<description><![CDATA[ Traditional data centers only stored, retrieved and processed data. In the generative and agentic AI era, these facilities have evolved into AI token factories. With AI inference becoming their primary workload, their primary output is intelligence manufactured in the form of tokens.  This transformation demands a corresponding shift in how the economics of AI infrastructure, […] ]]></description>
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<pubDate>Wed, 15 Apr 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rethinking, TCO:, Why, Cost, per, Token, the, Only, Metric, That, Matters</media:keywords>
</item>

<item>
<title>Accelerating decode&amp;heavy LLM inference with speculative decoding on AWS Trainium and vLLM</title>
<link>https://news.jatlink.uk/8937</link>
<guid>https://news.jatlink.uk/8937</guid>
<description><![CDATA[ In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, decode-heavy, LLM, inference, with, speculative, decoding, AWS, Trainium, and, vLLM</media:keywords>
</item>

<item>
<title>Create rich, custom tooltips in Amazon Quick Sight</title>
<link>https://news.jatlink.uk/8936</link>
<guid>https://news.jatlink.uk/8936</guid>
<description><![CDATA[ Today, we&#039;re announcing sheet tooltips in Amazon Quick Sight. Dashboard authors can now design custom tooltip layouts using free-form layout sheets. These layouts combine charts, key performance indicator (KPI) metrics, text, and other visuals into a single tooltip that renders dynamically when readers hover over data points. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, rich, custom, tooltips, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>Rede Mater Dei de Saúde: Monitoring AI agents in the revenue cycle with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8938</link>
<guid>https://news.jatlink.uk/8938</guid>
<description><![CDATA[ This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […] ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rede, Mater, Dei, Saúde:, Monitoring, agents, the, revenue, cycle, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>New Adobe Premiere Color Grading Mode Accelerated on NVIDIA GPUs</title>
<link>https://news.jatlink.uk/8924</link>
<guid>https://news.jatlink.uk/8924</guid>
<description><![CDATA[ The NAB Show 2026 trade show, running April 18-22 in Las Vegas, is set to showcase a wave of new features and optimizations for top video editing applications. Bringing together over 60,000 content professionals from across the broadcast and media and entertainment industries, the event highlights how video editors, livestreamers and professional creators are exploring […] ]]></description>
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<pubDate>Wed, 15 Apr 2026 15:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, Adobe, Premiere, Color, Grading, Mode, Accelerated, NVIDIA, GPUs</media:keywords>
</item>

<item>
<title>Navigating the generative AI journey: The Path&amp;to&amp;Value framework from AWS</title>
<link>https://news.jatlink.uk/8864</link>
<guid>https://news.jatlink.uk/8864</guid>
<description><![CDATA[ In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Navigating, the, generative, journey:, The, Path-to-Value, framework, from, AWS</media:keywords>
</item>

<item>
<title>Use&amp;case based deployments on SageMaker JumpStart</title>
<link>https://news.jatlink.uk/8865</link>
<guid>https://news.jatlink.uk/8865</guid>
<description><![CDATA[ We&#039;re excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed deployments, but now deployments are optimized for their specific use case and performance constraint. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use-case, based, deployments, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Best practices to run inference on Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/8866</link>
<guid>https://news.jatlink.uk/8866</guid>
<description><![CDATA[ This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating your generative AI deployments from concept to production. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, run, inference, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How Guidesly built AI&amp;generated trip reports for outdoor guides on AWS</title>
<link>https://news.jatlink.uk/8867</link>
<guid>https://news.jatlink.uk/8867</guid>
<description><![CDATA[ In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely, reliably, and at scale. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Guidesly, built, AI-generated, trip, reports, for, outdoor, guides, AWS</media:keywords>
</item>

<item>
<title>Trusted access for the next era of cyber defense</title>
<link>https://news.jatlink.uk/8863</link>
<guid>https://news.jatlink.uk/8863</guid>
<description><![CDATA[ OpenAI expands its Trusted Access for Cyber program, introducing GPT-5.4-Cyber to vetted defenders and strengthening safeguards as AI cybersecurity capabilities advance. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Trusted, access, for, the, next, era, cyber, defense</media:keywords>
</item>

<item>
<title>NVIDIA Launches Ising, the World’s First Open AI Models to Accelerate the Path to Useful Quantum Computers</title>
<link>https://news.jatlink.uk/8845</link>
<guid>https://news.jatlink.uk/8845</guid>
<description><![CDATA[ NVIDIA today announced the world’s first family of open source quantum AI models, NVIDIA Ising, designed to help researchers and enterprises build quantum processors capable of running useful applications. ]]></description>
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<pubDate>Tue, 14 Apr 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Launches, Ising, the, World’s, First, Open, Models, Accelerate, the, Path, Useful, Quantum, Computers</media:keywords>
</item>

<item>
<title>Spring AI SDK for Amazon Bedrock AgentCore is now Generally Available</title>
<link>https://news.jatlink.uk/8827</link>
<guid>https://news.jatlink.uk/8827</guid>
<description><![CDATA[ With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI. In this post, we build an AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution. ]]></description>
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<pubDate>Tue, 14 Apr 2026 14:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Spring, SDK, for, Amazon, Bedrock, AgentCore, now, Generally, Available</media:keywords>
</item>

<item>
<title>How to build effective reward functions with AWS Lambda for Amazon Nova model customization</title>
<link>https://news.jatlink.uk/8762</link>
<guid>https://news.jatlink.uk/8762</guid>
<description><![CDATA[ This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You&#039;ll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting. ]]></description>
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<pubDate>Mon, 13 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, build, effective, reward, functions, with, AWS, Lambda, for, Amazon, Nova, model, customization</media:keywords>
</item>

<item>
<title>Enterprises power agentic workflows in Cloudflare Agent Cloud with OpenAI</title>
<link>https://news.jatlink.uk/8761</link>
<guid>https://news.jatlink.uk/8761</guid>
<description><![CDATA[ Cloudflare brings OpenAI’s GPT-5.4 and Codex to Agent Cloud, enabling enterprises to build, deploy, and scale AI agents for real-world tasks with speed and security. ]]></description>
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<pubDate>Mon, 13 Apr 2026 17:00:28 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enterprises, power, agentic, workflows, Cloudflare, Agent, Cloud, with, OpenAI</media:keywords>
</item>

<item>
<title>Research with ChatGPT</title>
<link>https://news.jatlink.uk/8734</link>
<guid>https://news.jatlink.uk/8734</guid>
<description><![CDATA[ Learn how to research with ChatGPT using search and deep research to find up-to-date information, analyze sources, and generate structured insights. ]]></description>
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<pubDate>Mon, 13 Apr 2026 10:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Research, with, ChatGPT</media:keywords>
</item>

<item>
<title>Using custom GPTs</title>
<link>https://news.jatlink.uk/8733</link>
<guid>https://news.jatlink.uk/8733</guid>
<description><![CDATA[ Learn how to build and use custom GPTs to automate workflows, maintain consistent outputs, and create purpose-built AI assistants. ]]></description>
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<pubDate>Mon, 13 Apr 2026 09:00:35 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, custom, GPTs</media:keywords>
</item>

<item>
<title>Applications of AI at OpenAI</title>
<link>https://news.jatlink.uk/8655</link>
<guid>https://news.jatlink.uk/8655</guid>
<description><![CDATA[ Explore how OpenAI products like ChatGPT, Codex, and APIs bring AI into real-world use for work, development, and everyday tasks. ]]></description>
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<pubDate>Sun, 12 Apr 2026 05:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applications, OpenAI</media:keywords>
</item>

<item>
<title>Analyzing data with ChatGPT</title>
<link>https://news.jatlink.uk/8656</link>
<guid>https://news.jatlink.uk/8656</guid>
<description><![CDATA[ Learn how to analyze data with ChatGPT by exploring datasets, generating insights, creating visualizations, and turning findings into actionable decisions. ]]></description>
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<pubDate>Sun, 12 Apr 2026 05:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Analyzing, data, with, ChatGPT</media:keywords>
</item>

<item>
<title>Brainstorming with ChatGPT</title>
<link>https://news.jatlink.uk/8635</link>
<guid>https://news.jatlink.uk/8635</guid>
<description><![CDATA[ Learn how to use ChatGPT to brainstorm ideas, organize thinking, and turn rough concepts into structured, actionable plans. ]]></description>
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<pubDate>Sat, 11 Apr 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Brainstorming, with, ChatGPT</media:keywords>
</item>

<item>
<title>Prompting fundamentals</title>
<link>https://news.jatlink.uk/8596</link>
<guid>https://news.jatlink.uk/8596</guid>
<description><![CDATA[ Learn prompting fundamentals and how to write clear, effective prompts to get better, more useful responses from ChatGPT. ]]></description>
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<pubDate>Sat, 11 Apr 2026 05:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Prompting, fundamentals</media:keywords>
</item>

<item>
<title>Working with files in ChatGPT</title>
<link>https://news.jatlink.uk/8597</link>
<guid>https://news.jatlink.uk/8597</guid>
<description><![CDATA[ Learn how to upload and work with files in ChatGPT to analyze data, summarize documents, and generate content from PDFs, spreadsheets, and more. ]]></description>
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<pubDate>Sat, 11 Apr 2026 05:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Working, with, files, ChatGPT</media:keywords>
</item>

<item>
<title>ChatGPT for customer success teams</title>
<link>https://news.jatlink.uk/8598</link>
<guid>https://news.jatlink.uk/8598</guid>
<description><![CDATA[ Learn how customer success teams use ChatGPT to manage accounts, improve communication, reduce churn, and drive adoption and renewals. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 11 Apr 2026 05:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, customer, success, teams</media:keywords>
</item>

<item>
<title>Creating images with ChatGPT</title>
<link>https://news.jatlink.uk/8588</link>
<guid>https://news.jatlink.uk/8588</guid>
<description><![CDATA[ Learn how to create and refine images with ChatGPT using clear prompts, iterate on designs, and generate high-quality visuals in minutes. ]]></description>
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<pubDate>Sat, 11 Apr 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Creating, images, with, ChatGPT</media:keywords>
</item>

<item>
<title>Healthcare</title>
<link>https://news.jatlink.uk/8589</link>
<guid>https://news.jatlink.uk/8589</guid>
<description><![CDATA[ Explore how clinicians use ChatGPT to support diagnosis, documentation, and patient care with secure, HIPAA-compliant AI tools. ]]></description>
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<pubDate>Sat, 11 Apr 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Healthcare</media:keywords>
</item>

<item>
<title>Using skills</title>
<link>https://news.jatlink.uk/8590</link>
<guid>https://news.jatlink.uk/8590</guid>
<description><![CDATA[ Learn how to create and use ChatGPT skills to build reusable workflows, automate recurring tasks, and ensure consistent, high-quality outputs. ]]></description>
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<pubDate>Sat, 11 Apr 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, skills</media:keywords>
</item>

<item>
<title>Using projects in ChatGPT</title>
<link>https://news.jatlink.uk/8591</link>
<guid>https://news.jatlink.uk/8591</guid>
<description><![CDATA[ Learn how to use orojects in ChatGPT to organize chats, files, and instructions, manage ongoing work, and collaborate more effectively. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 11 Apr 2026 01:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, projects, ChatGPT</media:keywords>
</item>

<item>
<title>Financial services</title>
<link>https://news.jatlink.uk/8587</link>
<guid>https://news.jatlink.uk/8587</guid>
<description><![CDATA[ Explore AI resources for financial services, including prompt packs, GPTs, guides, and tools to help institutions deploy and scale AI securely. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 11 Apr 2026 01:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Financial, services</media:keywords>
</item>

<item>
<title>Personalizing ChatGPT</title>
<link>https://news.jatlink.uk/8568</link>
<guid>https://news.jatlink.uk/8568</guid>
<description><![CDATA[ Learn how to personalize ChatGPT using custom instructions and memory to get more relevant, consistent, and tailored responses. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Personalizing, ChatGPT</media:keywords>
</item>

<item>
<title>ChatGPT for finance teams</title>
<link>https://news.jatlink.uk/8569</link>
<guid>https://news.jatlink.uk/8569</guid>
<description><![CDATA[ Learn how finance teams use ChatGPT to streamline reporting, analyze data, improve forecasts, and communicate insights more clearly. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, finance, teams</media:keywords>
</item>

<item>
<title>ChatGPT for operations teams</title>
<link>https://news.jatlink.uk/8570</link>
<guid>https://news.jatlink.uk/8570</guid>
<description><![CDATA[ Learn how operations teams use ChatGPT to streamline workflows, improve coordination, standardize processes, and drive faster execution. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, operations, teams</media:keywords>
</item>

<item>
<title>ChatGPT for sales teams</title>
<link>https://news.jatlink.uk/8571</link>
<guid>https://news.jatlink.uk/8571</guid>
<description><![CDATA[ Learn how sales teams use ChatGPT to research accounts, personalize outreach, manage deals, and improve pipeline and conversion. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, sales, teams</media:keywords>
</item>

<item>
<title>ChatGPT for research</title>
<link>https://news.jatlink.uk/8572</link>
<guid>https://news.jatlink.uk/8572</guid>
<description><![CDATA[ Learn how to use ChatGPT for research to gather sources, analyze information, and create structured, citation-backed insights. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, research</media:keywords>
</item>

<item>
<title>Writing with ChatGPT</title>
<link>https://news.jatlink.uk/8573</link>
<guid>https://news.jatlink.uk/8573</guid>
<description><![CDATA[ Learn how to use ChatGPT for writing to draft, revise, and refine content with clear structure, tone, and intent. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Writing, with, ChatGPT</media:keywords>
</item>

<item>
<title>Getting started with ChatGPT</title>
<link>https://news.jatlink.uk/8574</link>
<guid>https://news.jatlink.uk/8574</guid>
<description><![CDATA[ Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI. ]]></description>
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<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Getting, started, with, ChatGPT</media:keywords>
</item>

<item>
<title>ChatGPT for marketing teams</title>
<link>https://news.jatlink.uk/8575</link>
<guid>https://news.jatlink.uk/8575</guid>
<description><![CDATA[ Learn how marketing teams use ChatGPT to plan campaigns, generate content, analyze performance, and move from ideas to execution faster. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, marketing, teams</media:keywords>
</item>

<item>
<title>AI fundamentals</title>
<link>https://news.jatlink.uk/8576</link>
<guid>https://news.jatlink.uk/8576</guid>
<description><![CDATA[ Learn what AI is, how it works, and how tools like ChatGPT use large language models. A clear, beginner-friendly guide to understanding artificial intelligence. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:22 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>fundamentals</media:keywords>
</item>

<item>
<title>ChatGPT for managers</title>
<link>https://news.jatlink.uk/8567</link>
<guid>https://news.jatlink.uk/8567</guid>
<description><![CDATA[ Learn how managers use ChatGPT to prepare for conversations, write clear feedback, stay organized, and improve team effectiveness. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 10 Apr 2026 21:00:21 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ChatGPT, for, managers</media:keywords>
</item>

<item>
<title>CyberAgent moves faster with ChatGPT Enterprise and Codex</title>
<link>https://news.jatlink.uk/8488</link>
<guid>https://news.jatlink.uk/8488</guid>
<description><![CDATA[ CyberAgent uses ChatGPT Enterprise and Codex to securely scale AI adoption, improve quality, and accelerate decisions across advertising, media, and gaming. ]]></description>
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<pubDate>Fri, 10 Apr 2026 01:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>CyberAgent, moves, faster, with, ChatGPT, Enterprise, and, Codex</media:keywords>
</item>

<item>
<title>The future of managing agents at scale: AWS Agent Registry now in preview</title>
<link>https://news.jatlink.uk/8472</link>
<guid>https://news.jatlink.uk/8472</guid>
<description><![CDATA[ Today, we&#039;re announcing AWS Agent Registry (preview) in AgentCore, a single place to discover, share, and reuse AI agents, tools, and agent skills across your enterprise. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, future, managing, agents, scale:, AWS, Agent, Registry, now, preview</media:keywords>
</item>

<item>
<title>Understanding Amazon Bedrock model lifecycle</title>
<link>https://news.jatlink.uk/8471</link>
<guid>https://news.jatlink.uk/8471</guid>
<description><![CDATA[ This post shows you how to manage FM transitions in Amazon Bedrock, so you can make sure your AI applications remain operational as models evolve. We discuss the three lifecycle states, how to plan migrations with the new extended access feature, and practical strategies to transition your applications to newer models without disruption. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Understanding, Amazon, Bedrock, model, lifecycle</media:keywords>
</item>

<item>
<title>Embed a live AI browser agent in your React app with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8473</link>
<guid>https://news.jatlink.uk/8473</guid>
<description><![CDATA[ This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, live, browser, agent, your, React, app, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/8453</link>
<guid>https://news.jatlink.uk/8453</guid>
<description><![CDATA[ In this post, you will learn how to build stateful MCP servers that request user input during execution, invoke LLM sampling for dynamic content generation, and stream progress updates for long-running tasks. You will see code examples for each capability and deploy a working stateful MCP server to Amazon Bedrock AgentCore Runtime. ]]></description>
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<pubDate>Thu, 09 Apr 2026 18:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, stateful, MCP, client, capabilities, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Strength and Destiny Collide: ‘Samson: A Tyndalston Story’ Arrives in the Cloud</title>
<link>https://news.jatlink.uk/8435</link>
<guid>https://news.jatlink.uk/8435</guid>
<description><![CDATA[ A timeless story of grit, faith and rebellion takes center stage as Samson: A Tyndalston Story joins the GeForce NOW library today.  The highly anticipated release from Liquid Swords can now be streamed on nearly any device with GeForce NOW bringing cinematic intensity and mythic storytelling to the cloud. Catch it as part of four […] ]]></description>
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<pubDate>Thu, 09 Apr 2026 15:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Strength, and, Destiny, Collide:, ‘Samson:, Tyndalston, Story’, Arrives, the, Cloud</media:keywords>
</item>

<item>
<title>OpenAI Full Fan Mode Contest: Terms &amp;amp; Conditions</title>
<link>https://news.jatlink.uk/8434</link>
<guid>https://news.jatlink.uk/8434</guid>
<description><![CDATA[ Explore the official terms and conditions for the OpenAI Full Fan Mode Contest, including eligibility, entry steps, judging criteria, and prize details. Learn how to participate, submit your entry on Instagram, and win IPL match tickets. ]]></description>
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<pubDate>Thu, 09 Apr 2026 14:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, Full, Fan, Mode, Contest:, Terms, Conditions</media:keywords>
</item>

<item>
<title>The next phase of enterprise AI</title>
<link>https://news.jatlink.uk/8392</link>
<guid>https://news.jatlink.uk/8392</guid>
<description><![CDATA[ OpenAI outlines the next phase of enterprise AI, as adoption accelerates across industries with Frontier, ChatGPT Enterprise, Codex, and company-wide AI agents. ]]></description>
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<pubDate>Thu, 09 Apr 2026 01:00:21 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, next, phase, enterprise</media:keywords>
</item>

<item>
<title>Human&amp;in&amp;the&amp;loop constructs for agentic workflows in healthcare and life sciences</title>
<link>https://news.jatlink.uk/8374</link>
<guid>https://news.jatlink.uk/8374</guid>
<description><![CDATA[ In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential. In this post, you will learn four practical approaches to implementing human-in-the-loop constructs using AWS services. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Human-in-the-loop, constructs, for, agentic, workflows, healthcare, and, life, sciences</media:keywords>
</item>

<item>
<title>Building intelligent audio search with Amazon Nova Embeddings: A deep dive into semantic audio understanding</title>
<link>https://news.jatlink.uk/8375</link>
<guid>https://news.jatlink.uk/8375</guid>
<description><![CDATA[ This post walks you through understanding audio embeddings, implementing Amazon Nova Multimodal Embeddings, and building a practical search system for your audio content. You&#039;ll learn how embeddings represent audio as vectors, explore the technical capabilities of Amazon Nova, and see hands-on code examples for indexing and querying your audio libraries. By the end, you&#039;ll have the knowledge to deploy production-ready audio search capabilities. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, audio, search, with, Amazon, Nova, Embeddings:, deep, dive, into, semantic, audio, understanding</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock: Best practices</title>
<link>https://news.jatlink.uk/8376</link>
<guid>https://news.jatlink.uk/8376</guid>
<description><![CDATA[ In this post, we explore where RFT is most effective, using the GSM8K mathematical reasoning dataset as a concrete example. We then walk through best practices for dataset preparation and reward function design, show how to monitor training progress using Amazon Bedrock metrics, and conclude with practical hyperparameter tuning guidelines informed by experiments across multiple models and use cases. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock:, Best, practices</media:keywords>
</item>

<item>
<title>Customize Amazon Nova models with Amazon Bedrock fine&amp;tuning</title>
<link>https://news.jatlink.uk/8373</link>
<guid>https://news.jatlink.uk/8373</guid>
<description><![CDATA[ In this post, we&#039;ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you&#039;ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We&#039;ll show you how to evaluate your results using training metrics and loss curves. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, Amazon, Nova, models, with, Amazon, Bedrock, fine-tuning</media:keywords>
</item>

<item>
<title>Introducing the Child Safety Blueprint</title>
<link>https://news.jatlink.uk/8339</link>
<guid>https://news.jatlink.uk/8339</guid>
<description><![CDATA[ Discover OpenAI’s Child Safety Blueprint—a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online. ]]></description>
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<pubDate>Wed, 08 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Child, Safety, Blueprint</media:keywords>
</item>

<item>
<title>Manage AI costs with Amazon Bedrock Projects</title>
<link>https://news.jatlink.uk/8297</link>
<guid>https://news.jatlink.uk/8297</guid>
<description><![CDATA[ With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs. ]]></description>
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<pubDate>Wed, 08 Apr 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, costs, with, Amazon, Bedrock, Projects</media:keywords>
</item>

<item>
<title>Building real&amp;time conversational podcasts with Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/8267</link>
<guid>https://news.jatlink.uk/8267</guid>
<description><![CDATA[ This post walks through building an automated podcast generator that creates engaging conversations between two AI hosts on any topic, demonstrating the streaming capabilities of Nova Sonic, stage-aware content filtering, and real-time audio generation. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, conversational, podcasts, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Text&amp;to&amp;SQL solution powered by Amazon Bedrock</title>
<link>https://news.jatlink.uk/8268</link>
<guid>https://news.jatlink.uk/8268</guid>
<description><![CDATA[ In this post, we show you how to build a natural text-to-SQL solution using Amazon Bedrock that transforms business questions into database queries and returns actionable answers. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Text-to-SQL, solution, powered, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From isolated alerts to contextual intelligence: Agentic maritime anomaly analysis with generative AI</title>
<link>https://news.jatlink.uk/8202</link>
<guid>https://news.jatlink.uk/8202</guid>
<description><![CDATA[ This blog post demonstrates how Windward helps enhance and accelerate alert investigation processes by combining geospatial intelligence with generative AI, enabling analysts to focus on decision-making rather than data collection. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, isolated, alerts, contextual, intelligence:, Agentic, maritime, anomaly, analysis, with, generative</media:keywords>
</item>

<item>
<title>Build AI&amp;powered employee onboarding agents with Amazon Quick</title>
<link>https://news.jatlink.uk/8199</link>
<guid>https://news.jatlink.uk/8199</guid>
<description><![CDATA[ In this post, we walk through building a custom HR onboarding agent with Quick. We show how to configure an agent that understands your organization’s processes, connects to your HR systems, and automates common tasks, such as answering new-hire questions and tracking document completion. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, employee, onboarding, agents, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/8200</link>
<guid>https://news.jatlink.uk/8200</guid>
<description><![CDATA[ In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, tool, calling, with, serverless, model, customization, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions</title>
<link>https://news.jatlink.uk/8201</link>
<guid>https://news.jatlink.uk/8201</guid>
<description><![CDATA[ In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents and Amazon OpenSearch. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Intelligent, Search, with, Amazon, Bedrock, and, Amazon, OpenSearch, for, hybrid, RAG, solutions</media:keywords>
</item>

<item>
<title>Announcing the OpenAI Safety Fellowship</title>
<link>https://news.jatlink.uk/8198</link>
<guid>https://news.jatlink.uk/8198</guid>
<description><![CDATA[ A pilot program to support independent safety and alignment research and develop the next generation of talent ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 06 Apr 2026 21:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, the, OpenAI, Safety, Fellowship</media:keywords>
</item>

<item>
<title>Connecting MCP servers to Amazon Bedrock AgentCore Gateway using Authorization Code flow</title>
<link>https://news.jatlink.uk/8185</link>
<guid>https://news.jatlink.uk/8185</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Gateway provides a centralized layer for managing how AI agents connect to tools and MCP servers across your organization. In this post, we walk through how to configure AgentCore Gateway to connect to an OAuth-protected MCP server using the Authorization Code flow. ]]></description>
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<pubDate>Mon, 06 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connecting, MCP, servers, Amazon, Bedrock, AgentCore, Gateway, using, Authorization, Code, flow</media:keywords>
</item>

<item>
<title>Industrial policy for the Intelligence Age</title>
<link>https://news.jatlink.uk/8174</link>
<guid>https://news.jatlink.uk/8174</guid>
<description><![CDATA[ Explore our ambitious, people-first industrial policy ideas for the AI era—focused on expanding opportunity, sharing prosperity, and building resilient institutions as advanced intelligence evolves. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 06 Apr 2026 13:00:34 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Industrial, policy, for, the, Intelligence, Age</media:keywords>
</item>

<item>
<title>National Robotics Week — Latest Physical AI Research, Breakthroughs and Resources</title>
<link>https://news.jatlink.uk/8073</link>
<guid>https://news.jatlink.uk/8073</guid>
<description><![CDATA[ This National Robotics Week, NVIDIA is highlighting the breakthroughs that are bringing AI into the physical world — as well as the growing wave of robots transforming industries, from agricultural and manufacturing to energy and beyond. Advancements in robot learning, simulation and foundation models are accelerating development, enabling robots to move from training in virtual […] ]]></description>
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<pubDate>Sat, 04 Apr 2026 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>National, Robotics, Week, —, Latest, Physical, Research, Breakthroughs, and, Resources</media:keywords>
</item>

<item>
<title>Simulate realistic users to evaluate multi&amp;turn AI agents in Strands Evals</title>
<link>https://news.jatlink.uk/7929</link>
<guid>https://news.jatlink.uk/7929</guid>
<description><![CDATA[ In this post, we explore how ActorSimulator in Strands Evaluations SDK addresses the challenge with structured user simulation that integrates into your evaluation pipeline. ]]></description>
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<pubDate>Thu, 02 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simulate, realistic, users, evaluate, multi-turn, agents, Strands, Evals</media:keywords>
</item>

<item>
<title>OpenAI acquires TBPN</title>
<link>https://news.jatlink.uk/7927</link>
<guid>https://news.jatlink.uk/7927</guid>
<description><![CDATA[ OpenAI acquires TBPN to accelerate global conversations around AI and support independent media, expanding dialogue with builders, businesses, and the broader tech community. ]]></description>
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<pubDate>Thu, 02 Apr 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, acquires, TBPN</media:keywords>
</item>

<item>
<title>Codex now offers more flexible pricing for teams</title>
<link>https://news.jatlink.uk/7928</link>
<guid>https://news.jatlink.uk/7928</guid>
<description><![CDATA[ Codex now includes pay-as-you-go pricing for ChatGPT Business and Enterprise, providing teams a more flexible option to start and scale adoption. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 02 Apr 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Codex, now, offers, more, flexible, pricing, for, teams</media:keywords>
</item>

<item>
<title>From RTX to Spark: NVIDIA Accelerates Gemma 4 for Local Agentic AI</title>
<link>https://news.jatlink.uk/7910</link>
<guid>https://news.jatlink.uk/7910</guid>
<description><![CDATA[ Open models are driving a new wave of on-device AI, extending innovation beyond the cloud to everyday devices. As these models advance, their value increasingly depends on access to local, real-time context that can turn meaningful insights into action.  Designed for this shift, Google’s latest additions to the Gemma 4 family introduce a class of small, fast and omni-capable models built for efficient local execution across a wide range […] ]]></description>
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<pubDate>Thu, 02 Apr 2026 19:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, RTX, Spark:, NVIDIA, Accelerates, Gemma, for, Local, Agentic</media:keywords>
</item>

<item>
<title>Control which domains your AI agents can access</title>
<link>https://news.jatlink.uk/7909</link>
<guid>https://news.jatlink.uk/7909</guid>
<description><![CDATA[ In this post, we show you how to configure AWS Network Firewall to restrict AgentCore resources to an allowlist of approved internet domains. This post focuses on domain-level filtering using SNI inspection — the first layer of a defense-in-depth approach. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Control, which, domains, your, agents, can, access</media:keywords>
</item>

<item>
<title>Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows</title>
<link>https://news.jatlink.uk/7908</link>
<guid>https://news.jatlink.uk/7908</guid>
<description><![CDATA[ This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, seismic, foundation, models, AWS:, Distributed, training, with, Amazon, SageMaker, HyperPod, and, expanding, context, windows</media:keywords>
</item>

<item>
<title>Press Start on April: GeForce NOW Brings 10 Games to the Cloud</title>
<link>https://news.jatlink.uk/7892</link>
<guid>https://news.jatlink.uk/7892</guid>
<description><![CDATA[ No joke — GFN Thursday is skipping the tricks and heading straight into the games. April kicks off with ten new titles, bringing fresh adventures to GeForce NOW, including the launch of Capcom’s highly anticipated PRAGMATA. A dozen new games are available to stream this week, including Arknights: Endfield, which expands the acclaimed series into a full […] ]]></description>
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<pubDate>Thu, 02 Apr 2026 15:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Press, Start, April:, GeForce, NOW, Brings, Games, the, Cloud</media:keywords>
</item>

<item>
<title>Persist session state with filesystem configuration and execute shell commands</title>
<link>https://news.jatlink.uk/7890</link>
<guid>https://news.jatlink.uk/7890</guid>
<description><![CDATA[ In this post, we go through how to use managed session storage to persist your agent&#039;s filesystem state and how to execute shell commands directly in your agent&#039;s environment. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Persist, session, state, with, filesystem, configuration, and, execute, shell, commands</media:keywords>
</item>

<item>
<title>Rocket Close transforms mortgage document processing with Amazon Bedrock and Amazon Textract</title>
<link>https://news.jatlink.uk/7889</link>
<guid>https://news.jatlink.uk/7889</guid>
<description><![CDATA[ Through a strategic partnership with the AWS Generative AI Innovation Center (GenAIIC), Rocket Close developed an intelligent document processing solution that has significantly reduced processing time, making the process 15 times faster. The solution, which uses Amazon Textract for OCR processing and Amazon Bedrock for foundation models (FMs), achieves a strong 90% overall accuracy in document segmentation, classification, and field extraction. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rocket, Close, transforms, mortgage, document, processing, with, Amazon, Bedrock, and, Amazon, Textract</media:keywords>
</item>

<item>
<title>Automating competitive price intelligence with Amazon Nova Act</title>
<link>https://news.jatlink.uk/7825</link>
<guid>https://news.jatlink.uk/7825</guid>
<description><![CDATA[ This post demonstrates how to build an automated competitive price intelligence system that streamlines manual workflows, supporting teams to make data-driven pricing decisions with real-time market insights. ]]></description>
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<pubDate>Wed, 01 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automating, competitive, price, intelligence, with, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Gradient Labs gives every bank customer an AI account manager</title>
<link>https://news.jatlink.uk/7788</link>
<guid>https://news.jatlink.uk/7788</guid>
<description><![CDATA[ Gradient Labs uses GPT-4.1 and GPT-5.4 mini and nano to power AI agents that automate banking support workflows with low latency and high reliability. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 01 Apr 2026 13:00:33 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Gradient, Labs, gives, every, bank, customer, account, manager</media:keywords>
</item>

<item>
<title>Build reliable AI agents with Amazon Bedrock AgentCore Evaluations</title>
<link>https://news.jatlink.uk/7744</link>
<guid>https://news.jatlink.uk/7744</guid>
<description><![CDATA[ In this post, we introduce Amazon Bedrock AgentCore Evaluations, a fully managed service for assessing AI agent performance across the development lifecycle. We walk through how the service measures agent accuracy across multiple quality dimensions. We explain the two evaluation approaches for development and production and share practical guidance for building agents you can deploy with confidence. ]]></description>
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<pubDate>Wed, 01 Apr 2026 02:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, agents, with, Amazon, Bedrock, AgentCore, Evaluations</media:keywords>
</item>

<item>
<title>Game On: Five New Titles Now Streaming on GeForce NOW</title>
<link>https://news.jatlink.uk/7728</link>
<guid>https://news.jatlink.uk/7728</guid>
<description><![CDATA[ That gaming backlog won’t clear itself — GeForce NOW is here to help. Stream the latest titles straight from the cloud across a variety of devices. This week, five new titles are ready to play instantly in the cloud gaming platform’s library. Screamer drifts onto the scene with retro‑racing attitude and pixel‑perfect speed. Plus, Honkai: Star Rail Version […] ]]></description>
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<pubDate>Tue, 31 Mar 2026 23:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Game, On:, Five, New, Titles, Now, Streaming, GeForce, NOW</media:keywords>
</item>

<item>
<title>Building an AI powered system for compliance evidence collection</title>
<link>https://news.jatlink.uk/7726</link>
<guid>https://news.jatlink.uk/7726</guid>
<description><![CDATA[ In this post, we show you how to build a similar system for your organization. You will learn the architecture decisions, implementation details, and deployment process that can help you automate your own compliance workflows. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, powered, system, for, compliance, evidence, collection</media:keywords>
</item>

<item>
<title>Build a FinOps agent using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/7725</link>
<guid>https://news.jatlink.uk/7725</guid>
<description><![CDATA[ In this post, you learn how to build a FinOps agent using Amazon Bedrock AgentCore that helps your finance team manage AWS costs across multiple accounts. This conversational agent consolidates data from AWS Cost Explorer, AWS Budgets, and AWS Compute Optimizer into a single interface, so your team can ask questions like &quot;What are my top cost drivers this month?&quot; and receive immediate answers. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, FinOps, agent, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Accelerating software delivery with agentic QA automation using Amazon Nova Act</title>
<link>https://news.jatlink.uk/7727</link>
<guid>https://news.jatlink.uk/7727</guid>
<description><![CDATA[ In this post, we demonstrate how to implement agentic QA automation through QA Studio, a reference solution built with Amazon Nova Act. You will see how to define tests in natural language that adapt automatically to UI changes, explore the serverless architecture that executes tests reliably at scale, and get step-by-step deployment guidance for your AWS environment. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, software, delivery, with, agentic, automation, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Accelerating the next phase of AI</title>
<link>https://news.jatlink.uk/7724</link>
<guid>https://news.jatlink.uk/7724</guid>
<description><![CDATA[ OpenAI raises $122 billion in new funding to expand frontier AI globally, invest in next-generation compute, and meet growing demand for ChatGPT, Codex, and enterprise AI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 31 Mar 2026 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, the, next, phase</media:keywords>
</item>

<item>
<title>Efficiency at Scale: NVIDIA, Energy Leaders Accelerating Power‑Flexible AI Factories to Fortify the Grid</title>
<link>https://news.jatlink.uk/7696</link>
<guid>https://news.jatlink.uk/7696</guid>
<description><![CDATA[ CERAWeek — dubbed the Davos of energy — is where policymakers, producers, technologists and financiers gather to discuss how the world powers itself next.  NVIDIA and Emerald AI unveiled at the conference last week a new way forward — treating AI factories not as static power loads but as flexible, intelligent grid assets. This collaboration […] ]]></description>
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<pubDate>Tue, 31 Mar 2026 19:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Efficiency, Scale:, NVIDIA, Energy, Leaders, Accelerating, Power‑Flexible, Factories, Fortify, the, Grid</media:keywords>
</item>

<item>
<title>Can your governance keep pace with your AI ambitions? AI risk intelligence in the agentic era</title>
<link>https://news.jatlink.uk/7695</link>
<guid>https://news.jatlink.uk/7695</guid>
<description><![CDATA[ Traditional frameworks designed for static deployments cannot address the dynamic interactions that define agentic workloads. AI Risk Intelligence (AIRI), from AWS Generative AI Innovation Center, provides the automated rigor required to govern agents at enterprise scale—a fundamental reimagining of how security, operations, and governance work together systemically. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Can, your, governance, keep, pace, with, your, ambitions, risk, intelligence, the, agentic, era</media:keywords>
</item>

<item>
<title>AWS launches frontier agents for security testing and cloud operations</title>
<link>https://news.jatlink.uk/7694</link>
<guid>https://news.jatlink.uk/7694</guid>
<description><![CDATA[ I&#039;m excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, launches, frontier, agents, for, security, testing, and, cloud, operations</media:keywords>
</item>

<item>
<title>NVIDIA AI Ecosystem Expands as Marvell Joins Forces Through NVLink Fusion</title>
<link>https://news.jatlink.uk/7674</link>
<guid>https://news.jatlink.uk/7674</guid>
<description><![CDATA[ NVIDIA and Marvell Technology, Inc. (NASDAQ: MRVL) today announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion™, offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. ]]></description>
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<pubDate>Tue, 31 Mar 2026 15:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Ecosystem, Expands, Marvell, Joins, Forces, Through, NVLink, Fusion</media:keywords>
</item>

<item>
<title>Deliver hyper&amp;personalized viewer experiences with an agentic AI movie assistant using Amazon Bedrock AgentCore and Amazon Nova Sonic 2.0</title>
<link>https://news.jatlink.uk/7597</link>
<guid>https://news.jatlink.uk/7597</guid>
<description><![CDATA[ In this post, we walk through two use cases that help enhance the user viewing experience using agentic AI tools and frameworks including Strands Agents SDK, Amazon Bedrock AgentCore, and Amazon Nova Sonic 2.0. This agentic AI system uses a Model Context Protocol (MCP) to deliver a personal entertainment concierge that understands user preferences through natural dialogue. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:28 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deliver, hyper-personalized, viewer, experiences, with, agentic, movie, assistant, using, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic, 2.0</media:keywords>
</item>

<item>
<title>How Ring scales global customer support with Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/7594</link>
<guid>https://news.jatlink.uk/7594</guid>
<description><![CDATA[ In this post, you&#039;ll learn how Ring implemented metadata-driven filtering for Region-specific content, separated content management into ingestion, evaluation and promotion workflows, and achieved cost savings while scaling up. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ring, scales, global, customer, support, with, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data</title>
<link>https://news.jatlink.uk/7596</link>
<guid>https://news.jatlink.uk/7596</guid>
<description><![CDATA[ In this post, we show you how to use Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency&#039;s STIX instrument. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, solar, flare, detection, system, SageMaker, LSTM, networks, and, ESA, STIX, data</media:keywords>
</item>

<item>
<title>Reimagine marketing at Volkswagen Group with generative AI</title>
<link>https://news.jatlink.uk/7595</link>
<guid>https://news.jatlink.uk/7595</guid>
<description><![CDATA[ In this post, we explore the challenges that Volkswagen Group faced in producing brand-compliant marketing assets at scale. We walk through how we built a generative AI solution that generates photorealistic vehicle images, validates technical accuracy at the component level, and helps enforce brand guideline compliance alignment across the ten brands. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reimagine, marketing, Volkswagen, Group, with, generative</media:keywords>
</item>

<item>
<title>Helping disaster response teams turn AI into action across Asia</title>
<link>https://news.jatlink.uk/7560</link>
<guid>https://news.jatlink.uk/7560</guid>
<description><![CDATA[ AI for Disaster Response in Asia: OpenAI Workshop with Gates Foundation ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 30 Mar 2026 09:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Helping, disaster, response, teams, turn, into, action, across, Asia</media:keywords>
</item>

<item>
<title>STADLER reshapes knowledge work at a 230&amp;year&amp;old company</title>
<link>https://news.jatlink.uk/7362</link>
<guid>https://news.jatlink.uk/7362</guid>
<description><![CDATA[ Learn how STADLER uses ChatGPT to transform knowledge work, saving time and accelerating productivity across 650 employees. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 27 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>STADLER, reshapes, knowledge, work, 230-year-old, company</media:keywords>
</item>

<item>
<title>Run Generative AI inference with Amazon Bedrock in Asia Pacific (New Zealand)</title>
<link>https://news.jatlink.uk/7274</link>
<guid>https://news.jatlink.uk/7274</guid>
<description><![CDATA[ Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, Opus 4.6, Sonnet 4.5, Sonnet 4.6, and Haiku 4.5) and Amazon (Nova 2 Lite) models directly in the Auckland Region with cross region inference. In this post, we explore how cross-Region inference works from the New Zealand Region, the models available through geographic and global routing, and how to get started with your first API call. We ]]></description>
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<pubDate>Fri, 27 Mar 2026 01:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, Generative, inference, with, Amazon, Bedrock, Asia, Pacific, New, Zealand</media:keywords>
</item>

<item>
<title>Accelerating LLM fine&amp;tuning with unstructured data using SageMaker Unified Studio and S3</title>
<link>https://news.jatlink.uk/7258</link>
<guid>https://news.jatlink.uk/7258</guid>
<description><![CDATA[ Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, fine-tuning, with, unstructured, data, using, SageMaker, Unified, Studio, and</media:keywords>
</item>

<item>
<title>Introducing Amazon Polly Bidirectional Streaming: Real&amp;time speech synthesis for conversational AI</title>
<link>https://news.jatlink.uk/7259</link>
<guid>https://news.jatlink.uk/7259</guid>
<description><![CDATA[ Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Polly, Bidirectional, Streaming:, Real-time, speech, synthesis, for, conversational</media:keywords>
</item>

<item>
<title>Building age&amp;responsive, context&amp;aware AI with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/7257</link>
<guid>https://news.jatlink.uk/7257</guid>
<description><![CDATA[ In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, age-responsive, context-aware, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Into the Omniverse: NVIDIA GTC Showcases Virtual Worlds Powering the Physical AI Era</title>
<link>https://news.jatlink.uk/7229</link>
<guid>https://news.jatlink.uk/7229</guid>
<description><![CDATA[ Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners, and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. NVIDIA GTC last week showcased a turning point in physical AI: Robots, vehicles and factories are scaling from single use cases and […] ]]></description>
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<pubDate>Thu, 26 Mar 2026 18:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Into, the, Omniverse:, NVIDIA, GTC, Showcases, Virtual, Worlds, Powering, the, Physical, Era</media:keywords>
</item>

<item>
<title>Game On: Five New Titles Now Streaming on GeForce NOW</title>
<link>https://news.jatlink.uk/7213</link>
<guid>https://news.jatlink.uk/7213</guid>
<description><![CDATA[ That gaming backlog won’t clear itself — GeForce NOW is here to help. Stream the latest titles straight from the cloud across a variety of devices. This week, five new titles are ready to play instantly in the cloud gaming platform’s library. Screamer drifts onto the scene with retro‑racing attitude and pixel‑perfect speed. Plus, Honkai: Star Rail Version […] ]]></description>
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<pubDate>Thu, 26 Mar 2026 14:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Game On: Five New Titles Now Streaming on GeForce NOW</media:keywords>
</item>

<item>
<title>The Future of AI Is Open and Proprietary</title>
<link>https://news.jatlink.uk/7152</link>
<guid>https://news.jatlink.uk/7152</guid>
<description><![CDATA[ AI is the defining technology of our time, quickly becoming core business infrastructure. It’s fueled by a diverse ecosystem of models: large and small, open and proprietary, generalist and specialist.  This variety is essential for a future where every application will be powered by AI, every country will build it and every company will use […] ]]></description>
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<pubDate>Wed, 25 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Future, Open, and, Proprietary</media:keywords>
</item>

<item>
<title>Unlocking video insights at scale with Amazon Bedrock multimodal models</title>
<link>https://news.jatlink.uk/7149</link>
<guid>https://news.jatlink.uk/7149</guid>
<description><![CDATA[ In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each approach is designed for different use cases and cost-performance trade-offs. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, insights, scale, with, Amazon, Bedrock, multimodal, models</media:keywords>
</item>

<item>
<title>Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1</title>
<link>https://news.jatlink.uk/7150</link>
<guid>https://news.jatlink.uk/7150</guid>
<description><![CDATA[ In this series of posts, you will learn how streaming architectures help address these challenges using Pipecat voice agents on Amazon Bedrock AgentCore Runtime. In Part 1, you will learn how to deploy Pipecat voice agents on AgentCore Runtime using different network transport approaches including WebSockets, WebRTC and telephony integration, with practical deployment guidance and code samples. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, voice, agents, with, Pipecat, and, Amazon, Bedrock, AgentCore, Runtime, –, Part</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock with OpenAI&amp;Compatible APIs: a technical walkthrough</title>
<link>https://news.jatlink.uk/7151</link>
<guid>https://news.jatlink.uk/7151</guid>
<description><![CDATA[ In this post, we walk through the end-to-end workflow of using RFT on Amazon Bedrock with OpenAI-compatible APIs: from setting up authentication, to deploying a Lambda-based reward function, to kicking off a training job and running on-demand inference on your fine-tuned model. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock, with, OpenAI-Compatible, APIs:, technical, walkthrough</media:keywords>
</item>

<item>
<title>Inside our approach to the Model Spec</title>
<link>https://news.jatlink.uk/7147</link>
<guid>https://news.jatlink.uk/7147</guid>
<description><![CDATA[ Learn how OpenAI’s Model Spec serves as a public framework for model behavior, balancing safety, user freedom, and accountability as AI systems advance. ]]></description>
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<pubDate>Wed, 25 Mar 2026 20:00:34 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Inside, our, approach, the, Model, Spec</media:keywords>
</item>

<item>
<title>Introducing the OpenAI Safety Bug Bounty program</title>
<link>https://news.jatlink.uk/7148</link>
<guid>https://news.jatlink.uk/7148</guid>
<description><![CDATA[ OpenAI launches a Safety Bug Bounty program to identify AI abuse and safety risks, including agentic vulnerabilities, prompt injection, and data exfiltration. ]]></description>
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<pubDate>Wed, 25 Mar 2026 20:00:34 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, OpenAI, Safety, Bug, Bounty, program</media:keywords>
</item>

<item>
<title>Blowing Off Steam: How Power&amp;Flexible AI Factories Can Stabilize the Global Energy Grid</title>
<link>https://news.jatlink.uk/7101</link>
<guid>https://news.jatlink.uk/7101</guid>
<description><![CDATA[ At the half-time whistle of the UEFA EURO 2020 round of 16 football match between England and Germany, millions of viewers stepped away from their screens in the U.K. to do the same thing at the same time — turn on their kettles. National Grid, which provides electricity for England and Wales, saw a demand […] ]]></description>
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<pubDate>Wed, 25 Mar 2026 14:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Blowing, Off, Steam:, How, Power-Flexible, Factories, Can, Stabilize, the, Global, Energy, Grid</media:keywords>
</item>

<item>
<title>Accelerating custom entity recognition with Claude tool use in Amazon Bedrock</title>
<link>https://news.jatlink.uk/7046</link>
<guid>https://news.jatlink.uk/7046</guid>
<description><![CDATA[ This post introduces Claude Tool use in Amazon Bedrock which uses the power of large language models (LLMs) to perform dynamic, adaptable entity recognition without extensive setup or training. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, custom, entity, recognition, with, Claude, tool, use, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Deploy SageMaker AI inference endpoints with set GPU capacity using training plans</title>
<link>https://news.jatlink.uk/7045</link>
<guid>https://news.jatlink.uk/7045</guid>
<description><![CDATA[ In this post, we walk through how to search for available p-family GPU capacity, create a training plan reservation for inference, and deploy a SageMaker AI inference endpoint on that reserved capacity. We follow a data scientist&#039;s journey as they reserve capacity for model evaluation and manage the endpoint throughout the reservation lifecycle. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, SageMaker, inference, endpoints, with, set, GPU, capacity, using, training, plans</media:keywords>
</item>

<item>
<title>Helping developers build safer AI experiences for teens</title>
<link>https://news.jatlink.uk/7044</link>
<guid>https://news.jatlink.uk/7044</guid>
<description><![CDATA[ OpenAI releases prompt-based teen safety policies for developers using gpt-oss-safeguard, helping moderate age-specific risks in AI systems. ]]></description>
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<pubDate>Tue, 24 Mar 2026 20:00:19 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Helping, developers, build, safer, experiences, for, teens</media:keywords>
</item>

<item>
<title>Update on the OpenAI Foundation</title>
<link>https://news.jatlink.uk/7017</link>
<guid>https://news.jatlink.uk/7017</guid>
<description><![CDATA[ The OpenAI Foundation announces plans to invest at least $1 billion in curing diseases, economic opportunity, AI resilience, and community programs. ]]></description>
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<pubDate>Tue, 24 Mar 2026 16:00:39 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Update, the, OpenAI, Foundation</media:keywords>
</item>

<item>
<title>Powering product discovery in ChatGPT</title>
<link>https://news.jatlink.uk/7018</link>
<guid>https://news.jatlink.uk/7018</guid>
<description><![CDATA[ ChatGPT introduces richer, visually immersive shopping powered by the Agentic Commerce Protocol, enabling product discovery, side-by-side comparisons, and merchant integration. ]]></description>
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<pubDate>Tue, 24 Mar 2026 16:00:39 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Powering, product, discovery, ChatGPT</media:keywords>
</item>

<item>
<title>Advancing Open Source AI, NVIDIA Donates Dynamic Resource Allocation Driver for GPUs to Kubernetes Community</title>
<link>https://news.jatlink.uk/6982</link>
<guid>https://news.jatlink.uk/6982</guid>
<description><![CDATA[ Artificial intelligence has rapidly emerged as one of the most critical workloads in modern computing. For the vast majority of enterprises, this workload runs on Kubernetes, an open source platform that automates the deployment, scaling and management of containerized applications. To help the global developer community manage high-performance AI infrastructure with greater transparency and efficiency, […] ]]></description>
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<pubDate>Tue, 24 Mar 2026 10:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, Open, Source, AI, NVIDIA, Donates, Dynamic, Resource, Allocation, Driver, for, GPUs, Kubernetes, Community</media:keywords>
</item>

<item>
<title>How Autonomous AI Agents Become Secure by Design With NVIDIA OpenShell</title>
<link>https://news.jatlink.uk/6916</link>
<guid>https://news.jatlink.uk/6916</guid>
<description><![CDATA[ Autonomous agents mark a new inflection point in AI. Systems are no longer limited to generating responses or reasoning through tasks. They can take action: Agents can read files, use tools, write and run code, and execute workflows across enterprise systems, all while expanding their own capabilities.  Application-layer risk grows exponentially when agents continuously improve […] ]]></description>
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<pubDate>Mon, 23 Mar 2026 18:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Autonomous, Agents, Become, Secure, Design, With, NVIDIA, OpenShell</media:keywords>
</item>

<item>
<title>Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova</title>
<link>https://news.jatlink.uk/6915</link>
<guid>https://news.jatlink.uk/6915</guid>
<description><![CDATA[ In this post, we’re excited to showcase how AWS ISV Partner Artificial Genius is using Amazon SageMaker AI and Amazon Nova to deliver a solution that is probabilistic on input but deterministic on output, helping to enable safe, enterprise-grade adoption. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Overcoming, LLM, hallucinations, regulated, industries:, Artificial, Genius’s, deterministic, models, Amazon, Nova</media:keywords>
</item>

<item>
<title>How Reco transforms security alerts using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6913</link>
<guid>https://news.jatlink.uk/6913</guid>
<description><![CDATA[ In this blog post, we show you how Reco implemented Amazon Bedrock to help transform security alerts and achieve significant improvements in incident response times. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Reco, transforms, security, alerts, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Integrating Amazon Bedrock AgentCore with Slack</title>
<link>https://news.jatlink.uk/6914</link>
<guid>https://news.jatlink.uk/6914</guid>
<description><![CDATA[ In this post, we demonstrate how to build a Slack integration using AWS Cloud Development Kit (AWS CDK). You will learn how to deploy the infrastructure with three specialized AWS Lambda functions, configure event subscriptions properly to handle Slack&#039;s security requirements, and implement conversation management patterns that work for many agent use cases. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrating, Amazon, Bedrock, AgentCore, with, Slack</media:keywords>
</item>

<item>
<title>Creating with Sora Safely</title>
<link>https://news.jatlink.uk/6912</link>
<guid>https://news.jatlink.uk/6912</guid>
<description><![CDATA[ To address the novel safety challenges posed by a state-of-the-art video model as well as a new social creation platform, we’ve built Sora 2 and the Sora app with safety at the foundation. Our approach is anchored in concrete protections. ]]></description>
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<pubDate>Mon, 23 Mar 2026 16:00:33 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Creating, with, Sora, Safely</media:keywords>
</item>

<item>
<title>NVIDIA and Emerald AI Join Leading Energy Companies to Pioneer Flexible AI Factories as Grid Assets</title>
<link>https://news.jatlink.uk/6895</link>
<guid>https://news.jatlink.uk/6895</guid>
<description><![CDATA[ Collaboration Combines AI Factory Design, Energy Resources and Flexibility to Speed Time to Power and Support Grid ReliabilityHOUSTON, March 23, 2026 (GLOBE NEWSWIRE) -- CERAWeek 2026 -- ... ]]></description>
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<pubDate>Mon, 23 Mar 2026 14:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Emerald, Join, Leading, Energy, Companies, Pioneer, Flexible, Factories, Grid, Assets</media:keywords>
</item>

<item>
<title>Enforce data residency with Amazon Quick extensions for Microsoft Teams</title>
<link>https://news.jatlink.uk/6596</link>
<guid>https://news.jatlink.uk/6596</guid>
<description><![CDATA[ In this post, we will show you how to enforce data residency when deploying Amazon Quick Microsoft Teams extensions across multiple AWS Regions. You will learn how to configure multi-Region Amazon Quick extensions that automatically route users to AWS Region-appropriate resources, helping keep compliance with GDPR and other data sovereignty requirements. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enforce, data, residency, with, Amazon, Quick, extensions, for, Microsoft, Teams</media:keywords>
</item>

<item>
<title>Introducing V&amp;RAG: revolutionizing AI&amp;powered video production with Retrieval Augmented Generation</title>
<link>https://news.jatlink.uk/6594</link>
<guid>https://news.jatlink.uk/6594</guid>
<description><![CDATA[ This post introduces Video Retrieval-Augmented Generation (V-RAG), an approach to help improve video content creation. By combining retrieval augmented generation with advanced video AI models, V-RAG offers an efficient, and reliable solution for generating AI videos. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, V-RAG:, revolutionizing, AI-powered, video, production, with, Retrieval, Augmented, Generation</media:keywords>
</item>

<item>
<title>Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance</title>
<link>https://news.jatlink.uk/6595</link>
<guid>https://news.jatlink.uk/6595</guid>
<description><![CDATA[ SageMaker AI endpoints now support enhanced metrics with configurable publishing frequency. This launch provides the granular visibility needed to monitor, troubleshoot, and improve your production endpoints. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhanced, metrics, for, Amazon, SageMaker, endpoints:, deeper, visibility, for, better, performance</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Super on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6592</link>
<guid>https://news.jatlink.uk/6592</guid>
<description><![CDATA[ This post explores the technical characteristics of the Nemotron 3 Super model and discusses potential application use cases. It also provides technical guidance to get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Super, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel</title>
<link>https://news.jatlink.uk/6593</link>
<guid>https://news.jatlink.uk/6593</guid>
<description><![CDATA[ In this post, we explore our approach to video generation through VRAG, transforming natural language text prompts and images into grounded, high-quality videos. Through this fully automated solution, you can generate realistic, AI-powered video sequences from structured text and image inputs, streamlining the video creation process. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, RAG, for, video, generation, using, Amazon, Bedrock, and, Amazon, Nova, Reel</media:keywords>
</item>

<item>
<title>How we monitor internal coding agents for misalignment</title>
<link>https://news.jatlink.uk/6591</link>
<guid>https://news.jatlink.uk/6591</guid>
<description><![CDATA[ How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 19 Mar 2026 20:00:19 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, monitor, internal, coding, agents, for, misalignment</media:keywords>
</item>

<item>
<title>OpenAI to acquire Astral</title>
<link>https://news.jatlink.uk/6561</link>
<guid>https://news.jatlink.uk/6561</guid>
<description><![CDATA[ Accelerates Codex growth to power the next generation of Python developer tools ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 19 Mar 2026 16:00:27 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, acquire, Astral</media:keywords>
</item>

<item>
<title>Smooth Moves: 90 Frames&amp;Per&amp;Second Virtual Reality Arrives on GeForce NOW</title>
<link>https://news.jatlink.uk/6543</link>
<guid>https://news.jatlink.uk/6543</guid>
<description><![CDATA[ It’s a double feature on GFN Thursday. This week, GeForce NOW offers smoother sights in virtual reality (VR) and a sprawling new land to conquer. Streaming at 90 frames per second (fps) comes to supported VR headsets. And Crimson Desert, which recently surpassed 3 million wishlist additions on Steam, debuts in the cloud with GeForce […] ]]></description>
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<pubDate>Thu, 19 Mar 2026 14:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Smooth, Moves:, Frames-Per-Second, Virtual, Reality, Arrives, GeForce, NOW</media:keywords>
</item>

<item>
<title>OpenAI Japan announces Japan Teen Safety Blueprint to put teen safety first</title>
<link>https://news.jatlink.uk/6493</link>
<guid>https://news.jatlink.uk/6493</guid>
<description><![CDATA[ OpenAI Japan announces the Japan Teen Safety Blueprint, introducing stronger age protections, parental controls, and well-being safeguards for teens using generative AI. ]]></description>
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<pubDate>Wed, 18 Mar 2026 20:00:21 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, Japan, announces, Japan, Teen, Safety, Blueprint, put, teen, safety, first</media:keywords>
</item>

<item>
<title>How Bark.com and AWS collaborated to build a scalable video generation solution</title>
<link>https://news.jatlink.uk/6465</link>
<guid>https://news.jatlink.uk/6465</guid>
<description><![CDATA[ Working with the AWS Generative AI Innovation Center, Bark developed an AI-powered content generation solution that demonstrated a substantial reduction in production time in experimental trials while improving content quality scores. In this post, we walk you through the technical architecture we built, the key design decisions that contributed to success, and the measurable results achieved, giving you a blueprint for implementing similar solutions. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Bark.com, and, AWS, collaborated, build, scalable, video, generation, solution</media:keywords>
</item>

<item>
<title>Migrate from Amazon Nova 1 to Amazon Nova 2 on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6466</link>
<guid>https://news.jatlink.uk/6466</guid>
<description><![CDATA[ In this post, you will learn how to migrate from Nova 1 to Nova 2 on Amazon Bedrock. We cover model mapping, API changes, code examples using the Converse API, guidance on configuring new capabilities, and a summary of use cases. We conclude with a migration checklist to help you plan and execute your transition. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, from, Amazon, Nova, Amazon, Nova, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Evaluating AI agents for production: A practical guide to Strands Evals</title>
<link>https://news.jatlink.uk/6463</link>
<guid>https://news.jatlink.uk/6463</guid>
<description><![CDATA[ In this post, we show how to evaluate AI agents systematically using Strands Evals. We walk through the core concepts, built-in evaluators, multi-turn simulation capabilities and practical approaches and patterns for integration. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents, for, production:, practical, guide, Strands, Evals</media:keywords>
</item>

<item>
<title>Introducing Nova Forge SDK, a seamless way to customize Nova models for enterprise AI</title>
<link>https://news.jatlink.uk/6462</link>
<guid>https://news.jatlink.uk/6462</guid>
<description><![CDATA[ Today, we are launching Nova Forge SDK that makes LLM customization accessible, empowering teams to harness the full potential of language models without the challenges of dependency management, image selection, and recipe configuration and eventually lowering the barrier of entry. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Nova, Forge, SDK, seamless, way, customize, Nova, models, for, enterprise</media:keywords>
</item>

<item>
<title>Build an AI&amp;Powered A/B testing engine using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6464</link>
<guid>https://news.jatlink.uk/6464</guid>
<description><![CDATA[ This post shows you how to build an AI-powered A/B testing engine using Amazon Bedrock, Amazon Elastic Container Service, Amazon DynamoDB, and the Model Context Protocol (MCP). The system improves traditional A/B testing by analyzing user context  to make smarter variant assignment decisions during the experiment. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-Powered, AB, testing, engine, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Kick off Nova customization experiments using Nova Forge SDK</title>
<link>https://news.jatlink.uk/6461</link>
<guid>https://news.jatlink.uk/6461</guid>
<description><![CDATA[ In this post, we walk you through the process of using the Nova Forge SDK to train an Amazon Nova model using Amazon SageMaker AI Training Jobs. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Kick, off, Nova, customization, experiments, using, Nova, Forge, SDK</media:keywords>
</item>

<item>
<title>From Simulation to Production: How to Build Robots With AI</title>
<link>https://news.jatlink.uk/6439</link>
<guid>https://news.jatlink.uk/6439</guid>
<description><![CDATA[ The latest open models and frameworks from NVIDIA bring together simulation, robot learning and embedded compute to accelerate cloud-to-robot workflows. ]]></description>
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<pubDate>Wed, 18 Mar 2026 14:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Simulation, Production:, How, Build, Robots, With</media:keywords>
</item>

<item>
<title>Equipping workers with insights about compensation</title>
<link>https://news.jatlink.uk/6372</link>
<guid>https://news.jatlink.uk/6372</guid>
<description><![CDATA[ New research shows Americans send nearly 3 million daily messages to ChatGPT asking about compensation and earnings, helping close the wage information gap. ]]></description>
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<pubDate>Tue, 17 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Equipping, workers, with, insights, about, compensation</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.4 mini and nano</title>
<link>https://news.jatlink.uk/6371</link>
<guid>https://news.jatlink.uk/6371</guid>
<description><![CDATA[ GPT-5.4 mini and nano are smaller, faster versions of GPT-5.4 optimized for coding, tool use, multimodal reasoning, and high-volume API and sub-agent workloads. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 17 Mar 2026 20:00:28 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.4, mini, and, nano</media:keywords>
</item>

<item>
<title>More Than Meets the Eye: NVIDIA RTX&amp;Accelerated Computers Now Connect Directly to Apple Vision Pro</title>
<link>https://news.jatlink.uk/6348</link>
<guid>https://news.jatlink.uk/6348</guid>
<description><![CDATA[ NVIDIA and Apple’s collaboration brings native integration of NVIDIA CloudXR 6.0 to visionOS, securely delivering NVIDIA RTX-powered simulators and professional 3D graphics applications — like Immersive for Autodesk VRED on Innoactive’s XR streaming solutions — to Apple Vision Pro. ]]></description>
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<pubDate>Tue, 17 Mar 2026 18:00:17 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>More, Than, Meets, the, Eye:, NVIDIA, RTX-Accelerated, Computers, Now, Connect, Directly, Apple, Vision, Pro</media:keywords>
</item>

<item>
<title>NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks</title>
<link>https://news.jatlink.uk/6349</link>
<guid>https://news.jatlink.uk/6349</guid>
<description><![CDATA[ As AI‑native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI.  At NVIDIA GTC 2026, leading operators in the U.S. and Asia showed that this shift is underway, announcing AI grids — geographically distributed and interconnected AI infrastructure — using their network footprint to power […] ]]></description>
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<pubDate>Tue, 17 Mar 2026 18:00:17 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Telecom, Leaders, Build, Grids, Optimize, Inference, Distributed, Networks</media:keywords>
</item>

<item>
<title>AWS AI League: Atos fine&amp;tunes approach to AI education</title>
<link>https://news.jatlink.uk/6347</link>
<guid>https://news.jatlink.uk/6347</guid>
<description><![CDATA[ In this post, we’ll explore how Atos used the AWS AI League to help accelerate AI education across 400+ participants, highlight the tangible benefits of gamified, experiential learning, and share actionable insights you can apply to your own AI enablement programs. ]]></description>
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<pubDate>Tue, 17 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Atos, fine-tunes, approach, education</media:keywords>
</item>

<item>
<title>GTC Spotlights NVIDIA RTX PCs and DGX Sparks Running Latest Open Models and AI Agents Locally</title>
<link>https://news.jatlink.uk/6331</link>
<guid>https://news.jatlink.uk/6331</guid>
<description><![CDATA[ The paradigm of consumer computing has revolved around the concept of a personal device — from PCs to smartphones and tablets. Now, generative AI — particularly OpenClaw — has introduced a new category: agent computers. These devices, like the NVIDIA DGX Spark desktop AI supercomputer or dedicated NVIDIA RTX PCs, are ideal for running personal […] ]]></description>
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<pubDate>Tue, 17 Mar 2026 14:00:19 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GTC, Spotlights, NVIDIA, RTX, PCs, and, DGX, Sparks, Running, Latest, Open, Models, and, Agents, Locally</media:keywords>
</item>

<item>
<title>Snap Decisions: How Open Libraries for Accelerated Data Processing Boost A/B Testing for Snapchat</title>
<link>https://news.jatlink.uk/6332</link>
<guid>https://news.jatlink.uk/6332</guid>
<description><![CDATA[ The features on social media apps like Snapchat evolve nearly as fast as what’s trending. To keep pace, its parent company Snap has adopted open data processing libraries from NVIDIA on Google Cloud services to boost development.  Every new feature rolled out to Snapchat’s more than 940 million monthly active users goes through a set […] ]]></description>
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<pubDate>Tue, 17 Mar 2026 14:00:19 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Snap, Decisions:, How, Open, Libraries, for, Accelerated, Data, Processing, Boost, AB, Testing, for, Snapchat</media:keywords>
</item>

<item>
<title>NVIDIA and Global Robotics Leaders Take Physical AI to the Real World</title>
<link>https://news.jatlink.uk/6260</link>
<guid>https://news.jatlink.uk/6260</guid>
<description><![CDATA[ NVIDIA is partnering with the global robotics ecosystem — including leading robot brain developers, industrial robot giants and humanoid pioneers — to power production-scale physical AI. NVIDIA also unveiled new NVIDIA Isaac™ simulation frameworks and new NVIDIA Cosmos™ and NVIDIA Isaac GR00T open models for the industry to develop, train and deploy the next generation of intelligent robots. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Global, Robotics, Leaders, Take, Physical, the, Real, World</media:keywords>
</item>

<item>
<title>NVIDIA and Global Industrial Software Giants Bring Design, Engineering and Manufacturing Into the AI Era</title>
<link>https://news.jatlink.uk/6261</link>
<guid>https://news.jatlink.uk/6261</guid>
<description><![CDATA[ NVIDIA today announced it is working with global industrial software leaders Cadence, Dassault Systèmes, PTC, Siemens and Synopsys to bring NVIDIA CUDA-X™, NVIDIA Omniverse™ and GPU-accelerated industrial software and tools to FANUC, HD Hyundai, Honda, JLR, KION, Mercedes-Benz, MediaTek, PepsiCo, Samsung, SK hynix and TSMC to accelerate design, engineering and manufacturing. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Global, Industrial, Software, Giants, Bring, Design, Engineering, and, Manufacturing, Into, the, Era</media:keywords>
</item>

<item>
<title>Hyundai Motor, Kia and NVIDIA Expand Strategic Partnership for Next&amp;Generation Autonomous Driving Technology</title>
<link>https://news.jatlink.uk/6262</link>
<guid>https://news.jatlink.uk/6262</guid>
<description><![CDATA[ NVIDIA today announced an expanded collaboration with Hyundai Motor Company and Kia Corporation to advance next-generation autonomous driving technologies built on the NVIDIA DRIVE Hyperion™ autonomous vehicle development platform. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Hyundai, Motor, Kia, and, NVIDIA, Expand, Strategic, Partnership, for, Next-Generation, Autonomous, Driving, Technology</media:keywords>
</item>

<item>
<title>NVIDIA Announces Open Physical AI Data Factory Blueprint to Accelerate Robotics, Vision AI Agents and Autonomous Vehicle Development</title>
<link>https://news.jatlink.uk/6263</link>
<guid>https://news.jatlink.uk/6263</guid>
<description><![CDATA[ NVIDIA today announced the NVIDIA Physical AI Data Factory Blueprint, an open reference architecture that unifies and automates how training data is generated, augmented and evaluated, reducing the costs, time and complexity of training physical AI systems at scale. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Open, Physical, Data, Factory, Blueprint, Accelerate, Robotics, Vision, Agents, and, Autonomous, Vehicle, Development</media:keywords>
</item>

<item>
<title>NVIDIA Enters Production With Dynamo, the Broadly Adopted Inference Operating System for AI Factories</title>
<link>https://news.jatlink.uk/6264</link>
<guid>https://news.jatlink.uk/6264</guid>
<description><![CDATA[ NVIDIA today announced NVIDIA Dynamo 1.0, open source software for generative and agentic inference at scale, with widespread global adoption. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Enters, Production, With, Dynamo, the, Broadly, Adopted, Inference, Operating, System, for, Factories</media:keywords>
</item>

<item>
<title>Roche Scales NVIDIA AI Factories Globally to Accelerate Drug Discovery, Diagnostic Solutions and Manufacturing Breakthroughs</title>
<link>https://news.jatlink.uk/6265</link>
<guid>https://news.jatlink.uk/6265</guid>
<description><![CDATA[ Roche&#039;s new deployment spans more than 3,500 NVIDIA Blackwell GPUs across its worldwide operations and embedded across the entire value chain, massively scaling R&amp;D productivity, next-generation diagnostics and manufacturing efficiencies. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Roche, Scales, NVIDIA, Factories, Globally, Accelerate, Drug, Discovery, Diagnostic, Solutions, and, Manufacturing, Breakthroughs</media:keywords>
</item>

<item>
<title>Adobe and NVIDIA Announce Strategic Partnership to Deliver the Next Generation of Firefly Models and Creative, Marketing and Agentic Workflows</title>
<link>https://news.jatlink.uk/6266</link>
<guid>https://news.jatlink.uk/6266</guid>
<description><![CDATA[ Adobe and NVIDIA today announced a strategic partnership to accelerate AI-powered creation, production and personalization, including delivering the next generation of foundational Adobe Firefly models and agentic workflows. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Adobe, and, NVIDIA, Announce, Strategic, Partnership, Deliver, the, Next, Generation, Firefly, Models, and, Creative, Marketing, and, Agentic, Workflows</media:keywords>
</item>

<item>
<title>NVIDIA, T&amp;Mobile and Partners Integrate Physical AI Applications on AI&amp;RAN&amp;Ready Infrastructure</title>
<link>https://news.jatlink.uk/6267</link>
<guid>https://news.jatlink.uk/6267</guid>
<description><![CDATA[ NVIDIA and T-Mobile today announced they are working with Nokia and a growing ecosystem of developers to bring physical AI applications over distributed edge AI networks. ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, T-Mobile, and, Partners, Integrate, Physical, Applications, AI-RAN-Ready, Infrastructure</media:keywords>
</item>

<item>
<title>BYD, Geely, Isuzu and Nissan Adopt NVIDIA DRIVE Hyperion for Level 4 Vehicles</title>
<link>https://news.jatlink.uk/6268</link>
<guid>https://news.jatlink.uk/6268</guid>
<description><![CDATA[ News Summary:


	BYD, Geely, Isuzu and Nissan are building level 4-ready vehicles on the NVIDIA DRIVE Hyperion platform.
	​​NVIDIA full-stack robotaxis to launch with Uber across 28 markets by... ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>BYD, Geely, Isuzu, and, Nissan, Adopt, NVIDIA, DRIVE, Hyperion, for, Level, Vehicles</media:keywords>
</item>

<item>
<title>NVIDIA Ignites the Next Industrial Revolution in Knowledge Work With Open Agent Development Platform</title>
<link>https://news.jatlink.uk/6269</link>
<guid>https://news.jatlink.uk/6269</guid>
<description><![CDATA[ NVIDIA Agent Toolkit Equips Enterprises to Build and Run AI AgentsNews Summary: NVIDIA Agent Toolkit includes NVIDIA OpenShell open source runtime for building self-evolving agents and claws ... ]]></description>
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<pubDate>Mon, 16 Mar 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Ignites, the, Next, Industrial, Revolution, Knowledge, Work, With, Open, Agent, Development, Platform</media:keywords>
</item>

<item>
<title>AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production</title>
<link>https://news.jatlink.uk/6259</link>
<guid>https://news.jatlink.uk/6259</guid>
<description><![CDATA[ Today at NVIDIA GTC 2026, AWS and NVIDIA announced an expanded collaboration with new technology integrations to support growing AI compute demand and help you build and run AI solutions that are production-ready. ]]></description>
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<pubDate>Mon, 16 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, and, NVIDIA, deepen, strategic, collaboration, accelerate, from, pilot, production</media:keywords>
</item>

<item>
<title>Agentic AI in the Enterprise Part 2: Guidance by Persona</title>
<link>https://news.jatlink.uk/6257</link>
<guid>https://news.jatlink.uk/6257</guid>
<description><![CDATA[ This is Part II of a two-part series from the AWS Generative AI Innovation Center. In Part II, we speak directly to the leaders who must turn that shared foundation into action. Each role carries a distinct set of responsibilities, risks, and leverage points. Whether you own a P&amp;L, run enterprise architecture, lead security, govern data, or manage compliance, this section is written in the language of your job—because that&#039;s where agentic AI either succeeds or quietly dies. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, the, Enterprise, Part, Guidance, Persona</media:keywords>
</item>

<item>
<title>Introducing Disaggregated Inference on AWS powered by llm&amp;d</title>
<link>https://news.jatlink.uk/6258</link>
<guid>https://news.jatlink.uk/6258</guid>
<description><![CDATA[ In this blog post, we introduce the concepts behind next-generation inference capabilities, including disaggregated serving, intelligent request scheduling, and expert parallelism. We discuss their benefits and walk through how you can implement them on Amazon SageMaker HyperPod EKS to achieve significant improvements in inference performance, resource utilization, and operational efficiency. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Disaggregated, Inference, AWS powered, llm-d</media:keywords>
</item>

<item>
<title>Why Codex Security Doesn’t Include a SAST Report</title>
<link>https://news.jatlink.uk/6256</link>
<guid>https://news.jatlink.uk/6256</guid>
<description><![CDATA[ A deep dive into why Codex Security doesn’t rely on traditional SAST, instead using AI-driven constraint reasoning and validation to find real vulnerabilities with fewer false positives. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Why, Codex, Security, Doesn’t, Include, SAST, Report</media:keywords>
</item>

<item>
<title>How Workhuman built multi&amp;tenant self&amp;service reporting using Amazon Quick Sight embedded dashboards</title>
<link>https://news.jatlink.uk/6238</link>
<guid>https://news.jatlink.uk/6238</guid>
<description><![CDATA[ This post explores how Workhuman transformed their analytics delivery model and the key lessons learned from their implementation. We go through their architecture approach, implementation strategy, and the business outcomes they achieved—providing you with a practical blueprint for adding embedded analytics to your own software as a service (SaaS) applications. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Workhuman, built, multi-tenant, self-service, reporting, using, Amazon, Quick, Sight, embedded, dashboards</media:keywords>
</item>

<item>
<title>Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog</title>
<link>https://news.jatlink.uk/6239</link>
<guid>https://news.jatlink.uk/6239</guid>
<description><![CDATA[ This blog post provides step-by-step guidance on implementing an offline feature store using SageMaker Catalog within a SageMaker Unified Studio domain. By adopting a publish-subscribe pattern, data producers can use this solution to publish curated, versioned feature tables—while data consumers can securely discover, subscribe to, and reuse them for model development. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, offline, feature, store, using, Amazon, SageMaker, Unified, Studio, and, SageMaker, Catalog</media:keywords>
</item>

<item>
<title>P&amp;EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM</title>
<link>https://news.jatlink.uk/6078</link>
<guid>https://news.jatlink.uk/6078</guid>
<description><![CDATA[ In this post, we explain how P-EAGLE works, how we integrated it into vLLM starting from v0.16.0 (PR#32887), and how to serve it with our pre-trained checkpoints. ]]></description>
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<pubDate>Fri, 13 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>P-EAGLE:, Faster, LLM, inference, with, Parallel, Speculative, Decoding, vLLM</media:keywords>
</item>

<item>
<title>Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption</title>
<link>https://news.jatlink.uk/6015</link>
<guid>https://news.jatlink.uk/6015</guid>
<description><![CDATA[ Today, we’re announcing two new Amazon CloudWatch metrics for Amazon Bedrock, TimeToFirstToken and EstimatedTPMQuotaUsage. In this post, we cover how these work and how to set alarms, establish baselines, and proactively manage capacity using them. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improve, operational, visibility, for, inference, workloads, Amazon, Bedrock, with, new, CloudWatch, metrics, for, TTFT, and, Estimated, Quota, Consumption</media:keywords>
</item>

<item>
<title>Secure AI agents with Policy in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/6016</link>
<guid>https://news.jatlink.uk/6016</guid>
<description><![CDATA[ In this post, you will understand how Policy in Amazon Bedrock AgentCore creates a deterministic enforcement layer that operates independently of the agent&#039;s own reasoning. You will learn how to turn natural language descriptions of your business rules into Cedar policies, then use those policies to enforce fine-grained, identity-aware controls so that agents only access the tools and data that their users are authorized to use. You will also see how to apply Policy through AgentCore Gateway, intercepting and evaluating every agent-to-tool request at runtime. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, agents, with, Policy, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Into the Omniverse: How Industrial AI and Digital Twins Accelerate Design, Engineering and Manufacturing Across Industries</title>
<link>https://news.jatlink.uk/5983</link>
<guid>https://news.jatlink.uk/5983</guid>
<description><![CDATA[ Industrial AI, digital twins, AI physics and accelerated AI infrastructure are empowering companies across industries to accelerate and scale the design, simulation and optimization of products, processes and facilities before building in the real world. ]]></description>
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<pubDate>Thu, 12 Mar 2026 18:01:17 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Into, the, Omniverse:, How, Industrial, and, Digital, Twins, Accelerate, Design, Engineering, and, Manufacturing, Across, Industries</media:keywords>
</item>

<item>
<title>Fine&amp;tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation</title>
<link>https://news.jatlink.uk/5982</link>
<guid>https://news.jatlink.uk/5982</guid>
<description><![CDATA[ In this post, we explore how to fine-tune a leaderboard-topping, NVIDIA Nemotron Speech Automatic Speech Recognition (ASR) model; Parakeet TDT 0.6B V2. Using synthetic speech data to achieve superior transcription results for specialised applications, we&#039;ll walk through an end-to-end workflow that combines AWS infrastructure with the following popular open-source frameworks. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tuning, NVIDIA, Nemotron, Speech, ASR, Amazon, EC2, for, domain, adaptation</media:keywords>
</item>

<item>
<title>Multimodal embeddings at scale: AI data lake for media and entertainment workloads</title>
<link>https://news.jatlink.uk/5981</link>
<guid>https://news.jatlink.uk/5981</guid>
<description><![CDATA[ This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multimodal, embeddings, scale:, data, lake, for, media, and, entertainment, workloads</media:keywords>
</item>

<item>
<title>GeForce NOW Raises the Game at the Game Developers Conference</title>
<link>https://news.jatlink.uk/5967</link>
<guid>https://news.jatlink.uk/5967</guid>
<description><![CDATA[ GeForce NOW is bringing the game to the Game Developers Conference (GDC), running this week in San Francisco. While developers build the future of gaming, GeForce NOW is delivering it to gamers. The latest updates bring smoother performance, easier game discovery and a fresh lineup of blockbuster titles to the cloud. Game discoverability gets a […] ]]></description>
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<pubDate>Thu, 12 Mar 2026 14:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Raises, the, Game, the, Game, Developers, Conference</media:keywords>
</item>

<item>
<title>Operationalizing Agentic AI Part 1: A Stakeholder’s Guide</title>
<link>https://news.jatlink.uk/5928</link>
<guid>https://news.jatlink.uk/5928</guid>
<description><![CDATA[ The AWS Generative AI Innovation Center has helped 1,000+ customers move AI into production, delivering millions in documented productivity gains. In this post, we share guidance for leaders across the C-suite: CTOs, CISOs, CDOs, and Chief Data Science/AI officers, as well as business owners and compliance leads. ]]></description>
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<pubDate>Wed, 11 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Operationalizing, Agentic, Part, Stakeholder’s, Guide</media:keywords>
</item>

<item>
<title>Rakuten fixes issues twice as fast with Codex</title>
<link>https://news.jatlink.uk/5927</link>
<guid>https://news.jatlink.uk/5927</guid>
<description><![CDATA[ Rakuten uses Codex, the coding agent from OpenAI, to ship software faster and safer, reducing MTTR 50%, automating CI/CD reviews, and delivering full-stack builds in weeks. ]]></description>
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<pubDate>Wed, 11 Mar 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rakuten, fixes, issues, twice, fast, with, Codex</media:keywords>
</item>

<item>
<title>Designing AI agents to resist prompt injection</title>
<link>https://news.jatlink.uk/5924</link>
<guid>https://news.jatlink.uk/5924</guid>
<description><![CDATA[ How ChatGPT defends against prompt injection and social engineering by constraining risky actions and protecting sensitive data in agent workflows. ]]></description>
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<pubDate>Wed, 11 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Designing, agents, resist, prompt, injection</media:keywords>
</item>

<item>
<title>Wayfair boosts catalog accuracy and support speed with OpenAI</title>
<link>https://news.jatlink.uk/5925</link>
<guid>https://news.jatlink.uk/5925</guid>
<description><![CDATA[ Wayfair uses OpenAI models to improve ecommerce support and product catalog accuracy, automating ticket triage and enhancing millions of product attributes at scale. ]]></description>
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<pubDate>Wed, 11 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Wayfair, boosts, catalog, accuracy, and, support, speed, with, OpenAI</media:keywords>
</item>

<item>
<title>From model to agent: Equipping the Responses API with a computer environment</title>
<link>https://news.jatlink.uk/5926</link>
<guid>https://news.jatlink.uk/5926</guid>
<description><![CDATA[ How OpenAI built an agent runtime using the Responses API, shell tool, and hosted containers to run secure, scalable agents with files, tools, and state. ]]></description>
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<pubDate>Wed, 11 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, model, agent:, Equipping, the, Responses, API, with, computer, environment</media:keywords>
</item>

<item>
<title>NVIDIA GTC 2026: Live Updates on What’s Next in AI</title>
<link>https://news.jatlink.uk/5904</link>
<guid>https://news.jatlink.uk/5904</guid>
<description><![CDATA[ Rolling coverage from San Jose, including NVIDIA CEO Jensen Huang’s keynote, news highlights, live demos and on‑the‑ground color through March 20. ]]></description>
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<pubDate>Wed, 11 Mar 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, GTC, 2026:, Live, Updates, What’s, Next</media:keywords>
</item>

<item>
<title>New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI</title>
<link>https://news.jatlink.uk/5903</link>
<guid>https://news.jatlink.uk/5903</guid>
<description><![CDATA[ Launched today, NVIDIA Nemotron 3 Super is a 120‑billion‑parameter open model with 12 billion active parameters designed to run complex agentic AI systems at scale.  Available now, the model combines advanced reasoning capabilities to efficiently complete tasks with high accuracy for autonomous agents. AI-Native Companies: Perplexity offers its users access to Nemotron 3 Super for […] ]]></description>
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<pubDate>Wed, 11 Mar 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, NVIDIA, Nemotron, Super, Delivers, Higher, Throughput, for, Agentic</media:keywords>
</item>

<item>
<title>NVIDIA and Nebius Partner to Scale Full&amp;Stack AI Cloud</title>
<link>https://news.jatlink.uk/5890</link>
<guid>https://news.jatlink.uk/5890</guid>
<description><![CDATA[ SANTA CLARA, Calif. and AMSTERDAM -- NVIDIA and Nebius Group N.V. (NASDAQ: NBIS) today announced a strategic partnership to develop and deploy the next generation of hyperscale cloud for the ... ]]></description>
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<pubDate>Wed, 11 Mar 2026 14:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Nebius, Partner, Scale, Full-Stack, Cloud</media:keywords>
</item>

<item>
<title>New ways to learn math and science in ChatGPT</title>
<link>https://news.jatlink.uk/5850</link>
<guid>https://news.jatlink.uk/5850</guid>
<description><![CDATA[ ChatGPT introduces interactive visual explanations for math and science, helping students explore formulas, variables, and concepts in real time. ]]></description>
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<pubDate>Tue, 10 Mar 2026 20:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, ways, learn, math, and, science, ChatGPT</media:keywords>
</item>

<item>
<title>Improving instruction hierarchy in frontier LLMs</title>
<link>https://news.jatlink.uk/5849</link>
<guid>https://news.jatlink.uk/5849</guid>
<description><![CDATA[ IH-Challenge trains models to prioritize trusted instructions, improving instruction hierarchy, safety steerability, and resistance to prompt injection attacks. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 10 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improving, instruction, hierarchy, frontier, LLMs</media:keywords>
</item>

<item>
<title>As Open Models Spark AI Boom, NVIDIA Jetson Brings It to Life at the Edge</title>
<link>https://news.jatlink.uk/5828</link>
<guid>https://news.jatlink.uk/5828</guid>
<description><![CDATA[ The Cat 306 CR mini-excavator weighs just under eight tons and fits inside a standard shipping container. It’s the machine a contractor rents when the job site is tight: a utility trench near a foundation, a basement dig in a dense neighborhood. The cab is roughly the size of a phone booth. The operator sits […] ]]></description>
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<pubDate>Tue, 10 Mar 2026 18:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Open, Models, Spark, Boom, NVIDIA, Jetson, Brings, Life, the, Edge</media:keywords>
</item>

<item>
<title>NVIDIA Virtualizes Game Development With RTX PRO Server</title>
<link>https://news.jatlink.uk/5829</link>
<guid>https://news.jatlink.uk/5829</guid>
<description><![CDATA[ Game development teams are working across larger worlds, more complex pipelines and more distributed teams than ever. At the same time, many studios still rely on fixed, desk-bound GPU hardware for critical production work. At the Game Developers Conference (GDC) this week in San Francisco, NVIDIA is showcasing a new approach to bring together disparate […] ]]></description>
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<pubDate>Tue, 10 Mar 2026 18:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Virtualizes, Game, Development, With, RTX, PRO, Server</media:keywords>
</item>

<item>
<title>NVIDIA and ComfyUI Streamline Local AI Video Generation for Game Developers and Creators at GDC</title>
<link>https://news.jatlink.uk/5830</link>
<guid>https://news.jatlink.uk/5830</guid>
<description><![CDATA[ Game developers and artists are building cinematic worlds and iconic characters — raising the bar for immersive experiences on NVIDIA RTX AI PCs.  At the Game Developers Conference (GDC) in San Francisco this week, NVIDIA announced a suite of updates that streamline AI video generation for concept development and storyboarding on RTX GPUs and the […] ]]></description>
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<pubDate>Tue, 10 Mar 2026 18:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, ComfyUI, Streamline, Local, Video, Generation, for, Game, Developers, and, Creators, GDC</media:keywords>
</item>

<item>
<title>Accelerate custom LLM deployment: Fine&amp;tune with Oumi and deploy to Amazon Bedrock</title>
<link>https://news.jatlink.uk/5826</link>
<guid>https://news.jatlink.uk/5826</guid>
<description><![CDATA[ In this post, we show how to fine-tune a Llama model using Oumi on Amazon EC2 (with the option to create synthetic data using Oumi), store artifacts in Amazon S3, and deploy to Amazon Bedrock using Custom Model Import for managed inference. ]]></description>
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<pubDate>Tue, 10 Mar 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, custom, LLM, deployment:, Fine-tune, with, Oumi, and, deploy, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>AI Is a 5&amp;Layer Cake</title>
<link>https://news.jatlink.uk/5811</link>
<guid>https://news.jatlink.uk/5811</guid>
<description><![CDATA[ AI is one of the most powerful forces shaping the world today. It is not a clever app or a single model; it is essential infrastructure, like electricity and the internet. ]]></description>
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<pubDate>Tue, 10 Mar 2026 14:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>5-Layer, Cake</media:keywords>
</item>

<item>
<title>NVIDIA and Thinking Machines Lab Announce Long&amp;Term Gigawatt&amp;Scale Strategic Partnership</title>
<link>https://news.jatlink.uk/5810</link>
<guid>https://news.jatlink.uk/5810</guid>
<description><![CDATA[ NVIDIA and Thinking Machines Lab announced today a multiyear strategic partnership to deploy at least one gigawatt of next-generation NVIDIA Vera Rubin systems to support Thinking Machines’ frontier model training and platforms delivering customizable AI at scale. Deployment on the NVIDIA Vera Rubin platform is targeted for early next year. The partnership also includes an […] ]]></description>
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<pubDate>Tue, 10 Mar 2026 14:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Thinking, Machines, Lab, Announce, Long-Term, Gigawatt-Scale, Strategic, Partnership</media:keywords>
</item>

<item>
<title>Access Anthropic Claude models in India on Amazon Bedrock with Global cross&amp;Region inference</title>
<link>https://news.jatlink.uk/5765</link>
<guid>https://news.jatlink.uk/5765</guid>
<description><![CDATA[ In this post, you will discover how to use Amazon Bedrock&#039;s Global cross-Region Inference for Claude models in India. We will guide you through the capabilities of each Claude model variant and how to get started with a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Access, Anthropic, Claude, models, India, Amazon, Bedrock, with, Global, cross-Region, inference</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock</title>
<link>https://news.jatlink.uk/5764</link>
<guid>https://news.jatlink.uk/5764</guid>
<description><![CDATA[ We are excited to announce that NVIDIA’s Nemotron 3 Nano is now available as a fully managed and serverless model in Amazon Bedrock. This follows our earlier announcement at AWS re:Invent supporting NVIDIA Nemotron 2 Nano 9B and NVIDIA Nemotron 2 Nano VL 12B models. This post explores the technical characteristics of the NVIDIA Nemotron 3 Nano model and discusses potential application use cases. Additionally, it provides technical guidance to help you get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Nano, fully, managed, serverless, model, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>OpenAI to acquire Promptfoo</title>
<link>https://news.jatlink.uk/5763</link>
<guid>https://news.jatlink.uk/5763</guid>
<description><![CDATA[ OpenAI is acquiring Promptfoo, an AI security platform that helps enterprises identify and remediate vulnerabilities in AI systems during development. ]]></description>
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<pubDate>Mon, 09 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, acquire, Promptfoo</media:keywords>
</item>

<item>
<title>ABB Robotics Taps NVIDIA Omniverse to Deliver Industrial‑Grade Physical AI at Scale</title>
<link>https://news.jatlink.uk/5748</link>
<guid>https://news.jatlink.uk/5748</guid>
<description><![CDATA[ ABB Robotics and NVIDIA today announced a breakthrough partnership that brings industrial‑grade physical AI to the factory floor.  By integrating NVIDIA Omniverse libraries directly into its RobotStudio programming and simulation suite, ABB Robotics will now deliver physically accurate simulation capabilities in its platform, dramatically cutting engineering time, reducing deployment costs by up to 40% and […] ]]></description>
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<pubDate>Mon, 09 Mar 2026 18:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ABB, Robotics, Taps, NVIDIA, Omniverse, Deliver, Industrial‑Grade, Physical, Scale</media:keywords>
</item>

<item>
<title>How AI Is Driving Revenue, Cutting Costs and Boosting Productivity for Every Industry in 2026</title>
<link>https://news.jatlink.uk/5749</link>
<guid>https://news.jatlink.uk/5749</guid>
<description><![CDATA[ AI is everywhere and accelerating everything — becoming essential infrastructure to create the intelligence that will advance every industry. That’s why companies are increasingly focusing on the technology’s return on investment (ROI), as well as how to best apply AI to their own use cases. NVIDIA’s annual “State of AI” reports show how AI is […] ]]></description>
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<pubDate>Mon, 09 Mar 2026 18:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Driving, Revenue, Cutting, Costs, and, Boosting, Productivity, for, Every, Industry, 2026</media:keywords>
</item>

<item>
<title>How Descript enables multilingual video dubbing at scale</title>
<link>https://news.jatlink.uk/5599</link>
<guid>https://news.jatlink.uk/5599</guid>
<description><![CDATA[ Descript uses OpenAI models to scale multilingual video dubbing, optimizing translations for both meaning and timing so dubbed speech sounds natural across languages. ]]></description>
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<pubDate>Fri, 06 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Descript, enables, multilingual, video, dubbing, scale</media:keywords>
</item>

<item>
<title>Codex Security: now in research preview</title>
<link>https://news.jatlink.uk/5600</link>
<guid>https://news.jatlink.uk/5600</guid>
<description><![CDATA[ Codex Security is an AI application security agent that analyzes project context to detect, validate, and patch complex vulnerabilities with higher confidence and less noise. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Codex, Security:, now, research, preview</media:keywords>
</item>

<item>
<title>How Balyasny Asset Management built an AI research engine for investing</title>
<link>https://news.jatlink.uk/5576</link>
<guid>https://news.jatlink.uk/5576</guid>
<description><![CDATA[ See how Balyasny built an AI research system with GPT-5.4, rigorous model evaluation, and agent workflows to transform investment analysis at scale. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Mar 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Balyasny, Asset, Management, built, research, engine, for, investing</media:keywords>
</item>

<item>
<title>The five AI value models driving business reinvention</title>
<link>https://news.jatlink.uk/5528</link>
<guid>https://news.jatlink.uk/5528</guid>
<description><![CDATA[ Five AI value models show how leaders can sequence AI from workforce fluency to process reinvention and build durable business advantage. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, five, value, models, driving, business, reinvention</media:keywords>
</item>

<item>
<title>Introducing the Adoption news channel</title>
<link>https://news.jatlink.uk/5529</link>
<guid>https://news.jatlink.uk/5529</guid>
<description><![CDATA[ Practical insights and frameworks to turn AI progress into business advantage ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Adoption, news, channel</media:keywords>
</item>

<item>
<title>Introducing ChatGPT for Excel and new financial data integrations</title>
<link>https://news.jatlink.uk/5530</link>
<guid>https://news.jatlink.uk/5530</guid>
<description><![CDATA[ OpenAI introduces ChatGPT for Excel and new financial app integrations, powered by GPT-5.4 to accelerate modeling, research, and analysis in regulated environments. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, for, Excel, and, new, financial, data, integrations</media:keywords>
</item>

<item>
<title>Reasoning models struggle to control their chains of thought, and that’s good</title>
<link>https://news.jatlink.uk/5512</link>
<guid>https://news.jatlink.uk/5512</guid>
<description><![CDATA[ OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reasoning, models, struggle, control, their, chains, thought, and, that’s, good</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.4</title>
<link>https://news.jatlink.uk/5513</link>
<guid>https://news.jatlink.uk/5513</guid>
<description><![CDATA[ Introducing GPT-5.4, OpenAI’s most most capable and efficient frontier model for professional work, with state-of-the-art coding, computer use, tool search, and 1M-token context. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.4</media:keywords>
</item>

<item>
<title>Ensuring AI use in education leads to opportunity</title>
<link>https://news.jatlink.uk/5514</link>
<guid>https://news.jatlink.uk/5514</guid>
<description><![CDATA[ OpenAI shares new tools, certifications, and measurement resources to help schools and universities close AI capability gaps and expand opportunity. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Ensuring, use, education, leads, opportunity</media:keywords>
</item>

<item>
<title>GPT&amp;5.4 Thinking System Card</title>
<link>https://news.jatlink.uk/5511</link>
<guid>https://news.jatlink.uk/5511</guid>
<description><![CDATA[  ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.4, Thinking, System, Card</media:keywords>
</item>

<item>
<title>March Into the Cloud With 15 New Games Coming to GeForce NOW</title>
<link>https://news.jatlink.uk/5493</link>
<guid>https://news.jatlink.uk/5493</guid>
<description><![CDATA[ March is in full bloom, and that means a fresh wave of games heading to the cloud. 15 new titles are joining the GeForce NOW library this month. Leading the March lineup is Pearl Abyss’ Crimson Desert, an open‑world action‑adventure set in a war‑torn fantasy land, alongside plenty of other games to explore. Whether looking […] ]]></description>
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<pubDate>Thu, 05 Mar 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>March, Into, the, Cloud, With, New, Games, Coming, GeForce, NOW</media:keywords>
</item>

<item>
<title>Drive organizational growth with Amazon Lex multi&amp;developer CI/CD pipeline</title>
<link>https://news.jatlink.uk/5491</link>
<guid>https://news.jatlink.uk/5491</guid>
<description><![CDATA[ In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Drive, organizational, growth, with, Amazon, Lex, multi-developer, CICD, pipeline</media:keywords>
</item>

<item>
<title>Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints</title>
<link>https://news.jatlink.uk/5492</link>
<guid>https://news.jatlink.uk/5492</guid>
<description><![CDATA[ This post demonstrates how to build custom model parsers for Strands agents when working with LLMs hosted on SageMaker that don&#039;t natively support the Bedrock Messages API format. We&#039;ll walk through deploying Llama 3.1 with SGLang on SageMaker using awslabs/ml-container-creator, then implementing a custom parser to integrate it with Strands agents. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, custom, model, provider, for, Strands, Agents, with, LLMs, hosted, SageMaker, endpoints</media:keywords>
</item>

<item>
<title>Embed Amazon Quick Suite chat agents in enterprise applications</title>
<link>https://news.jatlink.uk/5447</link>
<guid>https://news.jatlink.uk/5447</guid>
<description><![CDATA[ Organizations find it challenging to implement a secure embedded chat in their applications and can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. In this post, we show you how to solve this with a one-click deployment solution to embed the chat agents using the Quick Suite Embedding SDK in enterprise portals. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, Amazon, Quick, Suite, chat, agents, enterprise, applications</media:keywords>
</item>

<item>
<title>Unlock powerful call center analytics with Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/5448</link>
<guid>https://news.jatlink.uk/5448</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova demonstrates capabilities in conversational analytics, call classification, and other use cases often relevant to contact center solutions. We examine these capabilities for both single-call and multi-call analytics use cases. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlock, powerful, call, center, analytics, with, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>How Ricoh built a scalable intelligent document processing solution on AWS</title>
<link>https://news.jatlink.uk/5432</link>
<guid>https://news.jatlink.uk/5432</guid>
<description><![CDATA[ This post explores how Ricoh built a standardized, multi-tenant solution for automated document classification and extraction using the AWS GenAI IDP Accelerator as a foundation, transforming their document processing from a custom-engineering bottleneck into a scalable, repeatable service. ]]></description>
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<pubDate>Wed, 04 Mar 2026 21:00:12 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ricoh, built, scalable, intelligent, document, processing, solution, AWS</media:keywords>
</item>

<item>
<title>How Axios uses AI to help deliver high&amp;impact local journalism</title>
<link>https://news.jatlink.uk/5430</link>
<guid>https://news.jatlink.uk/5430</guid>
<description><![CDATA[ Axios COO Allison Murphy explains how the company uses AI to support local reporters, streamline newsroom workflows, and deliver high-impact local journalism at scale. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 04 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Axios, uses, help, deliver, high-impact, local, journalism</media:keywords>
</item>

<item>
<title>Understanding AI and learning outcomes</title>
<link>https://news.jatlink.uk/5431</link>
<guid>https://news.jatlink.uk/5431</guid>
<description><![CDATA[ OpenAI introduces the Learning Outcomes Measurement Suite to assess AI’s impact on student learning across diverse educational environments over time. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 04 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Understanding, and, learning, outcomes</media:keywords>
</item>

<item>
<title>Extending single&amp;minus amplitudes to gravitons</title>
<link>https://news.jatlink.uk/5429</link>
<guid>https://news.jatlink.uk/5429</guid>
<description><![CDATA[ A new preprint extends single-minus amplitudes to gravitons, with GPT-5.2 Pro helping derive and verify nonzero graviton tree amplitudes in quantum gravity. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 04 Mar 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Extending, single-minus, amplitudes, gravitons</media:keywords>
</item>

<item>
<title>GPT&amp;5.3 Instant: Smoother, more useful everyday conversations</title>
<link>https://news.jatlink.uk/5360</link>
<guid>https://news.jatlink.uk/5360</guid>
<description><![CDATA[  ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 03 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.3, Instant:, Smoother, more, useful, everyday, conversations</media:keywords>
</item>

<item>
<title>GPT&amp;5.3 Instant System Card</title>
<link>https://news.jatlink.uk/5361</link>
<guid>https://news.jatlink.uk/5361</guid>
<description><![CDATA[  ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 03 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.3, Instant, System, Card</media:keywords>
</item>

<item>
<title>NVIDIA CEO Jensen Huang and Global Technology Leaders to Showcase Age of AI at GTC 2026</title>
<link>https://news.jatlink.uk/5342</link>
<guid>https://news.jatlink.uk/5342</guid>
<description><![CDATA[ NVIDIA today announced that GTC, the world’s premier conference on AI and accelerated computing, will take place March 16-19 this year in San Jose, California. ]]></description>
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<pubDate>Tue, 03 Mar 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, CEO, Jensen, Huang, and, Global, Technology, Leaders, Showcase, Age, GTC, 2026</media:keywords>
</item>

<item>
<title>Building a scalable virtual try&amp;on solution using Amazon Nova on AWS: part 1</title>
<link>https://news.jatlink.uk/5339</link>
<guid>https://news.jatlink.uk/5339</guid>
<description><![CDATA[ In this post, we explore the virtual try-on capability now available in Amazon Nova Canvas, including sample code to get started quickly and tips to help get the best outputs. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, scalable, virtual, try-on, solution, using, Amazon, Nova, AWS:, part</media:keywords>
</item>

<item>
<title>How Lendi revamped the refinance journey for its customers using agentic AI in 16 weeks using Amazon Bedrock</title>
<link>https://news.jatlink.uk/5340</link>
<guid>https://news.jatlink.uk/5340</guid>
<description><![CDATA[ This post details how Lendi Group built their AI-powered Home Loan Guardian using Amazon Bedrock, the challenges they faced, the architecture they implemented, and the significant business outcomes they’ve achieved. Their journey offers valuable insights for organizations that want to use generative AI to transform customer experiences while maintaining the human touch that builds trust and loyalty. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Lendi, revamped, the, refinance, journey, for, its, customers, using, agentic, weeks, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Tines enhances security analysis with Amazon Quick Suite</title>
<link>https://news.jatlink.uk/5341</link>
<guid>https://news.jatlink.uk/5341</guid>
<description><![CDATA[ In this post, we show you how to connect Quick Suite with Tines to securely retrieve, analyze, and visualize enterprise data from any security or IT system. We walk through an example that uses a MCP server in Tines to retrieve data from various tools, such as AWS CloudTrail, Okta, and VirusTotal, to remediate security events using Quick Suite. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tines, enhances, security, analysis, with, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>Build safe generative AI applications like a Pro: Best Practices with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/5284</link>
<guid>https://news.jatlink.uk/5284</guid>
<description><![CDATA[ In this post, we will show you how to configure Amazon Bedrock Guardrails for efficient performance, implement best practices to protect your applications, and monitor your deployment effectively to maintain the right balance between safety and user experience. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, safe, generative, applications, like, Pro:, Best, Practices, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Building specialized AI without sacrificing intelligence: Nova Forge data mixing in action</title>
<link>https://news.jatlink.uk/5282</link>
<guid>https://news.jatlink.uk/5282</guid>
<description><![CDATA[ In this post, we share results from the AWS China Applied Science team&#039;s comprehensive evaluation of Nova Forge using a challenging Voice of Customer (VOC) classification task, benchmarked against open-source models. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, specialized, without, sacrificing, intelligence: Nova, Forge, data, mixing, action</media:keywords>
</item>

<item>
<title>Build a serverless conversational AI agent using Claude with LangGraph and managed MLflow on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/5283</link>
<guid>https://news.jatlink.uk/5283</guid>
<description><![CDATA[ This post explores how to build an intelligent conversational agent using Amazon Bedrock, LangGraph, and managed MLflow on Amazon SageMaker AI. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, conversational, agent, using, Claude, with, LangGraph, and, managed, MLflow, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>NVIDIA and Coherent Announce Strategic Partnership to Develop Optics Technology to Scale Next&amp;Generation Data Center Architecture</title>
<link>https://news.jatlink.uk/5253</link>
<guid>https://news.jatlink.uk/5253</guid>
<description><![CDATA[ NVIDIA to Invest $2 Billion in Coherent to Expand Supply, Deepen R&amp;D and Advance US-Based Manufacturing ]]></description>
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<pubDate>Mon, 02 Mar 2026 14:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Coherent, Announce, Strategic, Partnership, Develop, Optics, Technology, Scale, Next-Generation, Data, Center, Architecture</media:keywords>
</item>

<item>
<title>NVIDIA Announces Strategic Partnership With Lumentum to Develop State&amp;of&amp;the&amp;Art Optics Technology</title>
<link>https://news.jatlink.uk/5254</link>
<guid>https://news.jatlink.uk/5254</guid>
<description><![CDATA[ NVIDIA to Invest $2 Billion in Lumentum to Grow Capacity, Advance US-Based Manufacturing and Deepen R&amp;D Collaboration in Data Center Optics ]]></description>
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<pubDate>Mon, 02 Mar 2026 14:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Strategic, Partnership, With, Lumentum, Develop, State-of-the-Art, Optics, Technology</media:keywords>
</item>

<item>
<title>NVIDIA Advances Autonomous Networks With Agentic AI Blueprints and Telco Reasoning Models</title>
<link>https://news.jatlink.uk/5187</link>
<guid>https://news.jatlink.uk/5187</guid>
<description><![CDATA[ Autonomous networks — intelligent, self-managing telecommunications operations — are moving from a future vision to a current priority for telecom operators. In the latest NVIDIA State of AI in Telecommunications report, network automation emerged as the top AI use case for investment and return on investment. Automation is different from autonomy. Beyond executing predefined workflows,	
		Read Article ]]></description>
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<pubDate>Sun, 01 Mar 2026 10:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Advances, Autonomous, Networks, With, Agentic, Blueprints, and, Telco, Reasoning, Models</media:keywords>
</item>

<item>
<title>NVIDIA and Partners Show That Software&amp;Defined AI&amp;RAN Is the Next Wireless Generation</title>
<link>https://news.jatlink.uk/5188</link>
<guid>https://news.jatlink.uk/5188</guid>
<description><![CDATA[ AI-RAN is moving from lab to field, showing that a software-defined approach is the only viable way to build future AI-native wireless networks. Ahead of Mobile World Congress (MWC), running March 2-5 in Barcelona, NVIDIA and Nokia announced new AI-RAN collaborations with top telecom operators across Europe, Asia and North America, powered by NVIDIA AI-RAN	
		Read Article ]]></description>
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<pubDate>Sun, 01 Mar 2026 10:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Partners, Show, That, Software-Defined, AI-RAN, the, Next, Wireless, Generation</media:keywords>
</item>

<item>
<title>NVIDIA and Global Telecom Leaders Commit to Build 6G on Open and Secure AI&amp;Native Platforms</title>
<link>https://news.jatlink.uk/5189</link>
<guid>https://news.jatlink.uk/5189</guid>
<description><![CDATA[ NVIDIA today announced a commitment — together with Booz Allen, BT Group, Cisco, Deutsche Telekom, Ericsson, MITRE, Nokia, OCUDU Ecosystem Foundation, ODC, SK Telecom, SoftBank Corp. and T-Mobile — to build the world’s next generation of wireless networks on AI-native, open, secure and trustworthy platforms. ]]></description>
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<pubDate>Sun, 01 Mar 2026 10:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Global, Telecom, Leaders, Commit, Build, Open, and, Secure, AI-Native, Platforms</media:keywords>
</item>

<item>
<title>Our agreement with the Department of War</title>
<link>https://news.jatlink.uk/5157</link>
<guid>https://news.jatlink.uk/5157</guid>
<description><![CDATA[ Details on OpenAI’s contract with the Department of War, outlining safety redlines, legal protections, and how AI systems will be deployed in classified environments. ]]></description>
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<pubDate>Sat, 28 Feb 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, agreement, with, the, Department, War</media:keywords>
</item>

<item>
<title>An update on our mental health&amp;related work</title>
<link>https://news.jatlink.uk/5117</link>
<guid>https://news.jatlink.uk/5117</guid>
<description><![CDATA[ OpenAI shares updates on its mental health safety work, including parental controls, trusted contacts, improved distress detection, and recent litigation developments. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 28 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>update, our, mental, health-related, work</media:keywords>
</item>

<item>
<title>The Nightmare Returns in the Cloud: GeForce NOW Unleashes Capcom’s ‘Resident Evil Requiem’</title>
<link>https://news.jatlink.uk/5102</link>
<guid>https://news.jatlink.uk/5102</guid>
<description><![CDATA[ GeForce NOW’s anniversary celebration reaches a chilling crescendo as Capcom’s Resident Evil: Requiem creeps into the cloud — and the horrors look better than ever on a GeForce NOW Ultimate membership. To mark the occasion, a special launch bundle rises from the shadows, pairing the game with a yearlong Ultimate membership for a limited time.	
		Read Article ]]></description>
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<pubDate>Fri, 27 Feb 2026 22:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, Nightmare, Returns, the, Cloud:, GeForce, NOW, Unleashes, Capcom’s, ‘Resident, Evil, Requiem’</media:keywords>
</item>

<item>
<title>Scaling AI for everyone</title>
<link>https://news.jatlink.uk/5079</link>
<guid>https://news.jatlink.uk/5079</guid>
<description><![CDATA[ Today we’re announcing $110B in new investment at a $730B pre money valuation. This includes $30B from SoftBank, $30B from NVIDIA, and $50B from Amazon. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 27 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, for, everyone</media:keywords>
</item>

<item>
<title>Joint Statement from OpenAI and Microsoft</title>
<link>https://news.jatlink.uk/5080</link>
<guid>https://news.jatlink.uk/5080</guid>
<description><![CDATA[ Microsoft and OpenAI continue to work closely across research, engineering, and product development, building on years of deep collaboration and shared success. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 27 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Joint, Statement, from, OpenAI, and, Microsoft</media:keywords>
</item>

<item>
<title>Introducing the Stateful Runtime Environment for Agents in Amazon Bedrock</title>
<link>https://news.jatlink.uk/5081</link>
<guid>https://news.jatlink.uk/5081</guid>
<description><![CDATA[ Stateful Runtime for Agents in Amazon Bedrock brings persistent orchestration, memory, and secure execution to multi-step AI workflows powered by OpenAI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 27 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Stateful, Runtime, Environment, for, Agents, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>OpenAI and Amazon announce strategic partnership</title>
<link>https://news.jatlink.uk/5082</link>
<guid>https://news.jatlink.uk/5082</guid>
<description><![CDATA[ OpenAI and Amazon announce a strategic partnership bringing OpenAI’s Frontier platform to AWS, expanding AI infrastructure, custom models, and enterprise AI agents. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 27 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, and, Amazon, announce, strategic, partnership</media:keywords>
</item>

<item>
<title>Now Live: The World’s Most Powerful AI Factory for Pharmaceutical Discovery and Development</title>
<link>https://news.jatlink.uk/5025</link>
<guid>https://news.jatlink.uk/5025</guid>
<description><![CDATA[ Lilly this week launched the most powerful AI factory wholly owned and operated by a pharmaceutical company to help its teams make meaningful medical advancements faster, more accurately and at unprecedented scale. Dubbed LillyPod, it’s the world’s first NVIDIA DGX SuperPOD with DGX B300 systems.  ]]></description>
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<pubDate>Thu, 26 Feb 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Now, Live:, The, World’s, Most, Powerful, Factory, for, Pharmaceutical, Discovery, and, Development</media:keywords>
</item>

<item>
<title>Learnings from COBOL modernization in the real world</title>
<link>https://news.jatlink.uk/5022</link>
<guid>https://news.jatlink.uk/5022</guid>
<description><![CDATA[ Delivering successful COBOL modernization requires a solution that can reverse engineer deterministically, produce validated and traceable specs, and help those specs flow into any AI-powered coding assistant for the forward engineering. A successful modernization requires both reverse engineering and forward engineering. Learn more about COBOL in this post. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Learnings, from, COBOL, modernization, the, real, world</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning for Amazon Nova: Teaching AI through feedback</title>
<link>https://news.jatlink.uk/5023</link>
<guid>https://news.jatlink.uk/5023</guid>
<description><![CDATA[ In this post, we explore reinforcement fine-tuning (RFT) for Amazon Nova models, which can be a powerful customization technique that learns through evaluation rather than imitation. We&#039;ll cover how RFT works, when to use it versus supervised fine-tuning, real-world applications from code generation to customer service, and implementation options ranging from fully managed Amazon Bedrock to multi-turn agentic workflows with Nova Forge. You&#039;ll also learn practical guidance on data preparation, reward function design, and best practices for achieving optimal results. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, for, Amazon, Nova:, Teaching, through, feedback</media:keywords>
</item>

<item>
<title>Large model inference container – latest capabilities and performance enhancements</title>
<link>https://news.jatlink.uk/5024</link>
<guid>https://news.jatlink.uk/5024</guid>
<description><![CDATA[ AWS recently released significant updates to the Large Model Inference (LMI) container, delivering comprehensive performance improvements, expanded model support, and streamlined deployment capabilities for customers hosting LLMs on AWS. These releases focus on reducing operational complexity while delivering measurable performance gains across popular model architectures. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Large, model, inference, container, –, latest, capabilities, and, performance, enhancements</media:keywords>
</item>

<item>
<title>Pacific Northwest National Laboratory and OpenAI partner to accelerate federal permitting</title>
<link>https://news.jatlink.uk/5021</link>
<guid>https://news.jatlink.uk/5021</guid>
<description><![CDATA[ OpenAI and Pacific Northwest National Laboratory introduce DraftNEPABench, a new benchmark evaluating how AI coding agents can accelerate federal permitting—showing potential to reduce NEPA drafting time by up to 15% and modernize infrastructure reviews. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 26 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Pacific, Northwest, National, Laboratory, and, OpenAI, partner, accelerate, federal, permitting</media:keywords>
</item>

<item>
<title>Horror Awakens in the Cloud: GeForce NOW Unleashes Capcom’s ‘Resident Evil: Requiem’</title>
<link>https://news.jatlink.uk/4999</link>
<guid>https://news.jatlink.uk/4999</guid>
<description><![CDATA[ GeForce NOW’s anniversary celebration reaches a chilling crescendo as Capcom’s Resident Evil: Requiem creeps into the cloud — and the horrors look better than ever on a GeForce NOW Ultimate membership. To mark the occasion, a special launch bundle rises from the shadows, pairing the game with a yearlong Ultimate membership for a limited time.	
		Read Article ]]></description>
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<pubDate>Thu, 26 Feb 2026 18:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Horror, Awakens, the, Cloud:, GeForce, NOW, Unleashes, Capcom’s, ‘Resident, Evil:, Requiem’</media:keywords>
</item>

<item>
<title>OpenAI Codex and Figma launch seamless code&amp;to&amp;design experience</title>
<link>https://news.jatlink.uk/4998</link>
<guid>https://news.jatlink.uk/4998</guid>
<description><![CDATA[ OpenAI and Figma launch a new Codex integration that connects code and design, enabling teams to move between implementation and the Figma canvas to iterate and ship faster. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 26 Feb 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, Codex, and, Figma, launch, seamless, code-to-design, experience</media:keywords>
</item>

<item>
<title>NVIDIA Announces Financial Results for Fourth Quarter and Fiscal 2026</title>
<link>https://news.jatlink.uk/4943</link>
<guid>https://news.jatlink.uk/4943</guid>
<description><![CDATA[ NVIDIA (NASDAQ: NVDA) today reported record revenue for the fourth quarter ended January 25, 2026, of $68.1 billion, up 20% from the previous quarter and up 73% from a year ago. For fiscal ... ]]></description>
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<pubDate>Wed, 25 Feb 2026 22:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Announces, Financial, Results, for, Fourth, Quarter, and, Fiscal, 2026</media:keywords>
</item>

<item>
<title>Efficiently serve dozens of fine&amp;tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4942</link>
<guid>https://news.jatlink.uk/4942</guid>
<description><![CDATA[ In this post, we explain how we implemented multi-LoRA inference for Mixture of Experts (MoE) models in vLLM, describe the kernel-level optimizations we performed, and show you how you can benefit from this work. We use GPT-OSS 20B as our primary example throughout this post. ]]></description>
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<pubDate>Wed, 25 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Efficiently, serve, dozens, fine-tuned, models, with, vLLM, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/4941</link>
<guid>https://news.jatlink.uk/4941</guid>
<description><![CDATA[ This post demonstrates how to quickly deploy a production-ready event assistant using the components of Amazon Bedrock AgentCore. We&#039;ll build an intelligent companion that remembers attendee preferences and builds personalized experiences over time, while Amazon Bedrock AgentCore handles the heavy lifting of production deployment: Amazon Bedrock AgentCore Memory for maintaining both conversation context and long-term preferences without custom storage solutions, Amazon Bedrock AgentCore Identity for secure multi-IDP authentication, and Amazon Bedrock AgentCore Runtime for serverless scaling and session isolation. We will also use Amazon Bedrock Knowledge Bases for managed RAG and event data retrieval. ]]></description>
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<pubDate>Wed, 25 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, event, agents, using, Amazon, Bedrock, AgentCore, and, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Disrupting malicious uses of AI | February 2026</title>
<link>https://news.jatlink.uk/4921</link>
<guid>https://news.jatlink.uk/4921</guid>
<description><![CDATA[ Our latest threat report examines how malicious actors combine AI models with websites and social platforms—and what it means for detection and defense. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 25 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Disrupting, malicious, uses, February, 2026</media:keywords>
</item>

<item>
<title>Arvind KC appointed Chief People Officer</title>
<link>https://news.jatlink.uk/4880</link>
<guid>https://news.jatlink.uk/4880</guid>
<description><![CDATA[ OpenAI appoints Arvind KC as Chief People Officer to help scale the company, strengthen its culture, and lead how work evolves in the age of AI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 25 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Arvind, appointed, Chief, People, Officer</media:keywords>
</item>

<item>
<title>Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4864</link>
<guid>https://news.jatlink.uk/4864</guid>
<description><![CDATA[ In this post, we show you how to build a comprehensive photo search system using the AWS Cloud Development Kit (AWS CDK) that integrates Amazon Rekognition for face and object detection, Amazon Neptune for relationship mapping, and Amazon Bedrock for AI-powered captioning. ]]></description>
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<pubDate>Tue, 24 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, photo, search, using, Amazon, Rekognition, Amazon, Neptune, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From Radiology to Drug Discovery, Survey Reveals AI Is Delivering Clear Return on Investment in Healthcare</title>
<link>https://news.jatlink.uk/4843</link>
<guid>https://news.jatlink.uk/4843</guid>
<description><![CDATA[ AI is accelerating every aspect of healthcare — from radiology and drug discovery to medical device manufacturing and new treatment methods enabled by digital twins of the human body. NVIDIA’s second annual “State of AI in Healthcare and Life Sciences” survey report reveals how the industry is moving from AI experimentation to execution, reaping return	
		Read Article ]]></description>
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<pubDate>Tue, 24 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, Radiology, Drug, Discovery, Survey, Reveals, Delivering, Clear, Return, Investment, Healthcare</media:keywords>
</item>

<item>
<title>Introducing Amazon Bedrock global cross&amp;Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)</title>
<link>https://news.jatlink.uk/4842</link>
<guid>https://news.jatlink.uk/4842</guid>
<description><![CDATA[ We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. In this post, we guide you through the capabilities of each Anthropic Claude model variant, the key advantages of global cross-Region inference including improved resilience, real-world use cases you can implement, and a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Bedrock, global, cross-Region, inference, for, Anthropic’s, Claude, models, the, Middle, East, Regions, UAE, and, Bahrain</media:keywords>
</item>

<item>
<title>Generate structured output from LLMs with Dottxt Outlines in AWS</title>
<link>https://news.jatlink.uk/4840</link>
<guid>https://news.jatlink.uk/4840</guid>
<description><![CDATA[ This post explores the implementation of Dottxt’s Outlines framework as a practical approach to implementing structured outputs using AWS Marketplace in Amazon SageMaker. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, structured, output, from, LLMs, with, Dottxt, Outlines, AWS</media:keywords>
</item>

<item>
<title>Train CodeFu&amp;7B with veRL and Ray on Amazon SageMaker Training jobs</title>
<link>https://news.jatlink.uk/4839</link>
<guid>https://news.jatlink.uk/4839</guid>
<description><![CDATA[ In this post, we demonstrate how to train CodeFu-7B, a specialized 7-billion parameter model for competitive programming, using Group Relative Policy Optimization (GRPO) with veRL, a flexible and efficient training library for large language models (LLMs) that enables straightforward extension of diverse RL algorithms and seamless integration with existing LLM infrastructure, within a distributed Ray cluster managed by SageMaker training jobs. We walk through the complete implementation, covering data preparation, distributed training setup, and comprehensive observability, showcasing how this unified approach delivers both computational scale and developer experience for sophisticated RL training workloads. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Train, CodeFu-7B, with, veRL, and, Ray, Amazon, SageMaker, Training, jobs</media:keywords>
</item>

<item>
<title>Global cross&amp;Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan</title>
<link>https://news.jatlink.uk/4841</link>
<guid>https://news.jatlink.uk/4841</guid>
<description><![CDATA[ In this post, we are exciting to announce availability of Global CRIS for customers in Thailand, Malaysia, Singapore, Indonesia, and Taiwan and give a walkthrough of technical implementation steps, and cover quota management best practices to maximize the value of your AI Inference deployments. We also provide guidance on best practices for production deployments. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Global, cross-Region, inference, for, latest, Anthropic, Claude, Opus, Sonnet, and, Haiku, models, Amazon, Bedrock, Thailand, Malaysia, Singapore, Indonesia, and, Taiwan</media:keywords>
</item>

<item>
<title>Scaling data annotation using vision&amp;language models to power physical AI systems</title>
<link>https://news.jatlink.uk/4803</link>
<guid>https://news.jatlink.uk/4803</guid>
<description><![CDATA[ In this post, we examine how Bedrock Robotics tackles this challenge. By joining the AWS Physical AI Fellowship, the startup partnered with the AWS Generative AI Innovation Center to apply vision-language models that analyze construction video footage, extract operational details, and generate labeled training datasets at scale, to improve data preparation for autonomous construction equipment. ]]></description>
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<pubDate>Tue, 24 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, data, annotation, using, vision-language, models, power, physical, systems</media:keywords>
</item>

<item>
<title>How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials</title>
<link>https://news.jatlink.uk/4788</link>
<guid>https://news.jatlink.uk/4788</guid>
<description><![CDATA[ In this post, we explore how Sonrai, a life sciences AI company, partnered with AWS to build a robust MLOps framework using Amazon SageMaker AI that addresses these challenges while maintaining the traceability and reproducibility required in regulated environments. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Sonrai, uses, Amazon, SageMaker, accelerate, precision, medicine, trials</media:keywords>
</item>

<item>
<title>Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/4789</link>
<guid>https://news.jatlink.uk/4789</guid>
<description><![CDATA[ In this blog post, we demonstrate how Hexagon collaborated with Amazon Web Services to scale their AI model production by pretraining state-of-the-art segmentation models, using the model training infrastructure of Amazon SageMaker HyperPod. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, model, production, Hexagon, with, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Why we no longer evaluate SWE&amp;bench Verified</title>
<link>https://news.jatlink.uk/4787</link>
<guid>https://news.jatlink.uk/4787</guid>
<description><![CDATA[ SWE-bench Verified is increasingly contaminated and mismeasures frontier coding progress. Our analysis shows flawed tests and training leakage. We recommend SWE-bench Pro. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 23 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Why, longer, evaluate, SWE-bench, Verified</media:keywords>
</item>

<item>
<title>NVIDIA Brings AI&amp;Powered Cybersecurity to World’s Critical Infrastructure</title>
<link>https://news.jatlink.uk/4764</link>
<guid>https://news.jatlink.uk/4764</guid>
<description><![CDATA[ As technologies and systems become more digitalized and connected across the world, operational technology (OT) environments and industrial control systems (ICS) — from energy and manufacturing to transportation and utilities — are increasingly depending on enterprise networks and the cloud. This expands OT and ICS capabilities — but also their exposure to cyber threats. Unlike	
		Read Article ]]></description>
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<pubDate>Mon, 23 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Brings, AI-Powered, Cybersecurity, World’s, Critical, Infrastructure</media:keywords>
</item>

<item>
<title>Agentic AI with multi&amp;model framework using Hugging Face smolagents on AWS</title>
<link>https://news.jatlink.uk/4763</link>
<guid>https://news.jatlink.uk/4763</guid>
<description><![CDATA[ Hugging Face smolagents is an open source Python library designed to make it straightforward to build and run agents using a few lines of code. We will show you how to build an agentic AI solution by integrating Hugging Face smolagents with Amazon Web Services (AWS) managed services. You&#039;ll learn how to deploy a healthcare AI agent that demonstrates multi-model deployment options, vector-enhanced knowledge retrieval, and clinical decision support capabilities. ]]></description>
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<pubDate>Mon, 23 Feb 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, with, multi-model, framework, using, Hugging, Face, smolagents, AWS</media:keywords>
</item>

<item>
<title>OpenAI announces Frontier Alliance Partners</title>
<link>https://news.jatlink.uk/4762</link>
<guid>https://news.jatlink.uk/4762</guid>
<description><![CDATA[ OpenAI announces Frontier Alliance Partners to help enterprises move from AI pilots to production with secure, scalable agent deployments. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 23 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, announces, Frontier, Alliance, Partners</media:keywords>
</item>

<item>
<title>Introducing EVMbench</title>
<link>https://news.jatlink.uk/4737</link>
<guid>https://news.jatlink.uk/4737</guid>
<description><![CDATA[ OpenAI and Paradigm introduce EVMbench, a benchmark evaluating AI agents’ ability to detect, patch, and exploit high-severity smart contract vulnerabilities. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 23 Feb 2026 01:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, EVMbench</media:keywords>
</item>

<item>
<title>Our First Proof submissions</title>
<link>https://news.jatlink.uk/4633</link>
<guid>https://news.jatlink.uk/4633</guid>
<description><![CDATA[ We share our AI model’s proof attempts for the First Proof math challenge, testing research-grade reasoning on expert-level problems. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sat, 21 Feb 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, First, Proof, submissions</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting</title>
<link>https://news.jatlink.uk/4621</link>
<guid>https://news.jatlink.uk/4621</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate a whole new class of customer use cases to be hosted on SageMaker AI. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Improved, observability, and, enhanced, features, for, SageMaker, model, customization, and, hosting</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads</title>
<link>https://news.jatlink.uk/4620</link>
<guid>https://news.jatlink.uk/4620</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance for inference workloads. In Part 2, we discuss enhancements made to observability, model customization, and model hosting. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Flexible, Training, Plans, and, improvements, price, performance, for, inference, workloads</media:keywords>
</item>

<item>
<title>Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP)</title>
<link>https://news.jatlink.uk/4598</link>
<guid>https://news.jatlink.uk/4598</guid>
<description><![CDATA[ In this post, you’ll use a six-step checklist to build a new MCP server or validate and adjust an existing MCP server for Amazon Quick integration. The Amazon Quick User Guide describes the MCP client behavior and constraints. This is a “How to” guide for detailed implementation required by 3P partners to integrate with Amazon Quick with MCP. ]]></description>
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<pubDate>Fri, 20 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrate, external, tools, with, Amazon, Quick, Agents, using, Model, Context, Protocol, MCP</media:keywords>
</item>

<item>
<title>Amazon Quick now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4560</link>
<guid>https://news.jatlink.uk/4560</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Fri, 20 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Advancing independent research on AI alignment</title>
<link>https://news.jatlink.uk/4548</link>
<guid>https://news.jatlink.uk/4548</guid>
<description><![CDATA[ OpenAI commits $7.5M to The Alignment Project to fund independent AI alignment research, strengthening global efforts to address AGI safety and security risks. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 19 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, independent, research, alignment</media:keywords>
</item>

<item>
<title>Survey Reveals AI Advances in Telecom: Networks and Automation in Driver’s Seat as Return on Investment Climbs</title>
<link>https://news.jatlink.uk/4527</link>
<guid>https://news.jatlink.uk/4527</guid>
<description><![CDATA[ AI is accelerating the telecommunications industry’s transformation, becoming the backbone of autonomous networks and AI-native wireless infrastructure. At the same time, the technology is unlocking new business and revenue opportunities, as telecom operators accelerate AI adoption across consumers, enterprises and nations. NVIDIA’s fourth annual “State of AI in Telecommunications” survey report unpacks these trends, underscoring	
		Read Article ]]></description>
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<pubDate>Thu, 19 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Survey, Reveals, Advances, Telecom:, Networks, and, Automation, Driver’s, Seat, Return, Investment, Climbs</media:keywords>
</item>

<item>
<title>All About the Games: Play Over 4,500 Titles With GeForce NOW</title>
<link>https://news.jatlink.uk/4528</link>
<guid>https://news.jatlink.uk/4528</guid>
<description><![CDATA[ The GeForce NOW anniversary celebration keeps on rolling, and this week is all about the games that make it possible. With more than 4,500 titles supported in the cloud — plus 12 new games this week — there’s always something new to stream, share and discover. The #6YearsofGFN fun continues with a community giveaway hosted	
		Read Article ]]></description>
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<pubDate>Thu, 19 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>All, About, the, Games:, Play, Over, 4, 500, Titles, With, GeForce, NOW</media:keywords>
</item>

<item>
<title>Amazon Quick Suite now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4526</link>
<guid>https://news.jatlink.uk/4526</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, Suite, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Build AI workflows on Amazon EKS with Union.ai and Flyte</title>
<link>https://news.jatlink.uk/4525</link>
<guid>https://news.jatlink.uk/4525</guid>
<description><![CDATA[ In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, workflows, Amazon, EKS, with, Union.ai, and, Flyte</media:keywords>
</item>

<item>
<title>Introducing OpenAI for India</title>
<link>https://news.jatlink.uk/4491</link>
<guid>https://news.jatlink.uk/4491</guid>
<description><![CDATA[ OpenAI for India expands AI access across the country—building local infrastructure, powering enterprises, and advancing workforce skills. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 19 Feb 2026 04:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, OpenAI, for, India</media:keywords>
</item>

<item>
<title>Build unified intelligence with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/4479</link>
<guid>https://news.jatlink.uk/4479</guid>
<description><![CDATA[ In this post, we demonstrate how to build unified intelligence systems using Amazon Bedrock AgentCore through our real-world implementation of the Customer Agent and Knowledge Engine (CAKE). ]]></description>
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<pubDate>Thu, 19 Feb 2026 00:00:15 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, unified, intelligence, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Evaluating AI agents: Real&amp;world lessons from building agentic systems at Amazon</title>
<link>https://news.jatlink.uk/4463</link>
<guid>https://news.jatlink.uk/4463</guid>
<description><![CDATA[ In this post, we present a comprehensive evaluation framework for Amazon agentic AI systems that addresses the complexity of agentic AI applications at Amazon through two core components: a generic evaluation workflow that standardizes assessment procedures across diverse agent implementations, and an agent evaluation library that provides systematic measurements and metrics in Amazon Bedrock AgentCore Evaluations, along with Amazon use case-specific evaluation approaches and metrics.  ]]></description>
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<pubDate>Wed, 18 Feb 2026 20:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents:, Real-world, lessons, from, building, agentic, systems, Amazon</media:keywords>
</item>

<item>
<title>India Fuels Its AI Mission With NVIDIA</title>
<link>https://news.jatlink.uk/4408</link>
<guid>https://news.jatlink.uk/4408</guid>
<description><![CDATA[ From AI infrastructure leaders to frontier model developers, India is teaming with NVIDIA to drive AI transformation across the nation. ]]></description>
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<pubDate>Wed, 18 Feb 2026 02:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>India, Fuels, Its, Mission, With, NVIDIA</media:keywords>
</item>

<item>
<title>India’s Global Systems Integrators Build Next Wave of Enterprise Agents With NVIDIA AI, Transforming Back Office and Customer Support</title>
<link>https://news.jatlink.uk/4409</link>
<guid>https://news.jatlink.uk/4409</guid>
<description><![CDATA[ Agentic AI is reshaping India’s tech industry, delivering leaps in services worldwide. Tapping into NVIDIA AI Enterprise software and NVIDIA Nemotron models, India’s technology leaders are accelerating productivity and efficiency across industries — from call centers to telecommunications and healthcare. Infosys, Persistent, Tech Mahindra and Wipro are leading the way for business transformation, improving back-office	
		Read Article ]]></description>
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<pubDate>Wed, 18 Feb 2026 02:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>India’s, Global, Systems, Integrators, Build, Next, Wave, Enterprise, Agents, With, NVIDIA, AI, Transforming, Back, Office, and, Customer, Support</media:keywords>
</item>

<item>
<title>NVIDIA and Global Industrial Software Leaders Partner With India’s Largest Manufacturers to Drive AI Boom</title>
<link>https://news.jatlink.uk/4410</link>
<guid>https://news.jatlink.uk/4410</guid>
<description><![CDATA[ India is entering a new age of industrialization, as AI transforms how the world designs, builds and runs physical products and systems. The country is investing $134 billion dollars in new manufacturing capacity across construction, automotive, renewable energy and robotics, creating both a massive challenge and opportunity to build software-defined factories from day one. At	
		Read Article ]]></description>
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<pubDate>Wed, 18 Feb 2026 02:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, Global, Industrial, Software, Leaders, Partner, With, India’s, Largest, Manufacturers, Drive, Boom</media:keywords>
</item>

<item>
<title>Meta Builds AI Infrastructure With NVIDIA</title>
<link>https://news.jatlink.uk/4394</link>
<guid>https://news.jatlink.uk/4394</guid>
<description><![CDATA[ NVIDIA today announced a multiyear, multigenerational strategic partnership with Meta spanning on-premises, cloud and AI infrastructure. ]]></description>
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<pubDate>Tue, 17 Feb 2026 22:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Meta, Builds, Infrastructure, With, NVIDIA</media:keywords>
</item>

<item>
<title>New Data Shows NVIDIA Blackwell Ultra Delivers up to 50x Better Performance and 35x Lower Costs for Agentic AI</title>
<link>https://news.jatlink.uk/4309</link>
<guid>https://news.jatlink.uk/4309</guid>
<description><![CDATA[ The NVIDIA Blackwell platform has been widely adopted by leading inference providers such as Baseten, DeepInfra, Fireworks AI and Together AI to reduce cost per token by up to 10x. Now, the NVIDIA Blackwell Ultra platform is taking this momentum further for agentic AI. AI agents and coding assistants are driving explosive growth in software-programming-related	
		Read Article ]]></description>
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<pubDate>Mon, 16 Feb 2026 18:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, Data, Shows, NVIDIA, Blackwell, Ultra, Delivers, 50x, Better, Performance, and, 35x, Lower, Costs, for, Agentic</media:keywords>
</item>

<item>
<title>Customize AI agent browsing with proxies, profiles, and extensions in Amazon Bedrock AgentCore Browser</title>
<link>https://news.jatlink.uk/4172</link>
<guid>https://news.jatlink.uk/4172</guid>
<description><![CDATA[ Today, we are announcing three new capabilities that address these requirements: proxy configuration, browser profiles, and browser extensions. Together, these features give you fine-grained control over how your AI agents interact with the web. This post will walk through each capability with configuration examples and practical use cases to help you get started. ]]></description>
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<pubDate>Sat, 14 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, agent, browsing, with, proxies, profiles, and, extensions, Amazon, Bedrock, AgentCore, Browser</media:keywords>
</item>

<item>
<title>Scaling social science research</title>
<link>https://news.jatlink.uk/4161</link>
<guid>https://news.jatlink.uk/4161</guid>
<description><![CDATA[ GABRIEL is a new open-source toolkit from OpenAI that uses GPT to turn qualitative text and images into quantitative data, helping social scientists analyze research at scale. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 13 Feb 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, social, science, research</media:keywords>
</item>

<item>
<title>GPT&amp;5.2 derives a new result in theoretical physics</title>
<link>https://news.jatlink.uk/4158</link>
<guid>https://news.jatlink.uk/4158</guid>
<description><![CDATA[ A new preprint shows GPT-5.2 proposing a new formula for a gluon amplitude, later formally proved and verified by OpenAI and academic collaborators. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 13 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.2, derives, new, result, theoretical, physics</media:keywords>
</item>

<item>
<title>Introducing Lockdown Mode and Elevated Risk labels in ChatGPT</title>
<link>https://news.jatlink.uk/4159</link>
<guid>https://news.jatlink.uk/4159</guid>
<description><![CDATA[ Introducing Lockdown Mode and Elevated Risk labels in ChatGPT to help organizations defend against prompt injection and AI-driven data exfiltration. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 13 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Lockdown, Mode, and, Elevated, Risk, labels, ChatGPT</media:keywords>
</item>

<item>
<title>Beyond rate limits: scaling access to Codex and Sora</title>
<link>https://news.jatlink.uk/4160</link>
<guid>https://news.jatlink.uk/4160</guid>
<description><![CDATA[ How OpenAI built a real-time access system combining rate limits, usage tracking, and credits to power continuous access to Sora and Codex. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 13 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, rate, limits:, scaling, access, Codex, and, Sora</media:keywords>
</item>

<item>
<title>Code, Compute and Connection: Inside the Inaugural NVIDIA AI Day São Paulo</title>
<link>https://news.jatlink.uk/4094</link>
<guid>https://news.jatlink.uk/4094</guid>
<description><![CDATA[ The worldwide tour of NVIDIA AI Days — bringing together AI enthusiasts, developers, researchers and startups — made its latest stop in São Paulo, Brazil. ]]></description>
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<pubDate>Fri, 13 Feb 2026 02:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Code, Compute, and, Connection:, Inside, the, Inaugural, NVIDIA, Day, São, Paulo</media:keywords>
</item>

<item>
<title>AI meets HR: Transforming talent acquisition with Amazon Bedrock</title>
<link>https://news.jatlink.uk/4078</link>
<guid>https://news.jatlink.uk/4078</guid>
<description><![CDATA[ In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>meets, HR:, Transforming, talent, acquisition, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build long&amp;running MCP servers on Amazon Bedrock AgentCore with Strands Agents integration</title>
<link>https://news.jatlink.uk/4079</link>
<guid>https://news.jatlink.uk/4079</guid>
<description><![CDATA[ In this post, we provide you with a comprehensive approach to achieve this. First, we introduce a context message strategy that maintains continuous communication between servers and clients during extended operations. Next, we develop an asynchronous task management framework that allows your AI agents to initiate long-running processes without blocking other operations. Finally, we demonstrate how to bring these strategies together with Amazon Bedrock AgentCore and Strands Agents to build production-ready AI agents that can handle complex, time-intensive operations reliably. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, long-running, MCP, servers, Amazon, Bedrock, AgentCore, with, Strands, Agents, integration</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.3&amp;Codex&amp;Spark</title>
<link>https://news.jatlink.uk/4077</link>
<guid>https://news.jatlink.uk/4077</guid>
<description><![CDATA[ Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 12 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.3-Codex-Spark</media:keywords>
</item>

<item>
<title>Leading Inference Providers Cut AI Costs by up to 10x With Open Source Models on NVIDIA Blackwell</title>
<link>https://news.jatlink.uk/4054</link>
<guid>https://news.jatlink.uk/4054</guid>
<description><![CDATA[ A diagnostic insight in healthcare. A character’s dialogue in an interactive game. An autonomous resolution from a customer service agent. Each of these AI-powered interactions is built on the same unit of intelligence: a token. Scaling these AI interactions requires businesses to consider whether they can afford more tokens. The answer lies in better tokenomics	
		Read Article ]]></description>
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<pubDate>Thu, 12 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Leading, Inference, Providers, Cut, Costs, 10x, With, Open, Source, Models, NVIDIA, Blackwell</media:keywords>
</item>

<item>
<title>NVIDIA DGX Spark Powers Big Projects in Higher Education</title>
<link>https://news.jatlink.uk/4055</link>
<guid>https://news.jatlink.uk/4055</guid>
<description><![CDATA[ At leading institutions across the globe, the NVIDIA DGX Spark desktop supercomputer is bringing data‑center‑class AI to lab benches, faculty offices and students’ systems. There’s even a DGX Spark hard at work in the South Pole, at the IceCube Neutrino Observatory run by the University of Wisconsin-Madison. The compact supercomputer’s petaflop‑class performance enables local deployment	
		Read Article ]]></description>
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<pubDate>Thu, 12 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, DGX, Spark, Powers, Big, Projects, Higher, Education</media:keywords>
</item>

<item>
<title>GeForce NOW Turns Screens Into a Gaming Machine</title>
<link>https://news.jatlink.uk/4056</link>
<guid>https://news.jatlink.uk/4056</guid>
<description><![CDATA[ The GeForce NOW sixth-anniversary festivities roll on this February, continuing a monthlong celebration of NVIDIA’s cloud gaming service. This week brings even more reasons to join the party, as GeForce NOW launches on a new platform with support for Amazon Fire TV devices, and eight new games to keep the streaming going strong. The new	
		Read Article ]]></description>
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<pubDate>Thu, 12 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Turns, Screens, Into, Gaming, Machine</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart</title>
<link>https://news.jatlink.uk/4004</link>
<guid>https://news.jatlink.uk/4004</guid>
<description><![CDATA[ Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart. ]]></description>
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<pubDate>Wed, 11 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, Nano, 30B, MoE, model, now, available, Amazon, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Harness engineering: leveraging Codex in an agent&amp;first world</title>
<link>https://news.jatlink.uk/3984</link>
<guid>https://news.jatlink.uk/3984</guid>
<description><![CDATA[ By Ryan Lopopolo, Member of the Technical Staff ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 11 Feb 2026 18:39:43 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Harness, engineering:, leveraging, Codex, agent-first, world</media:keywords>
</item>

<item>
<title>Mastering Amazon Bedrock throttling and service availability: A comprehensive guide</title>
<link>https://news.jatlink.uk/3981</link>
<guid>https://news.jatlink.uk/3981</guid>
<description><![CDATA[ This post shows you how to implement robust error handling strategies that can help improve application reliability and user experience when using Amazon Bedrock. We&#039;ll dive deep into strategies for optimizing performances for the application with these errors. Whether this is for a fairly new application or matured AI application, in this post you will be able to find the practical guidelines to operate with on these errors. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Mastering, Amazon, Bedrock, throttling, and, service, availability:, comprehensive, guide</media:keywords>
</item>

<item>
<title>Swann provides Generative AI to millions of IoT Devices using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3982</link>
<guid>https://news.jatlink.uk/3982</guid>
<description><![CDATA[ This post shows you how to implement intelligent notification filtering using Amazon Bedrock and its gen-AI capabilities. You&#039;ll learn model selection strategies, cost optimization techniques, and architectural patterns for deploying gen-AI at IoT scale, based on Swann Communications deployment across millions of devices. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Swann, provides, Generative, millions, IoT, Devices, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How LinqAlpha assesses investment theses using Devil’s Advocate on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3983</link>
<guid>https://news.jatlink.uk/3983</guid>
<description><![CDATA[ LinqAlpha is a Boston-based multi-agent AI system built specifically for institutional investors. The system supports and streamlines agentic workflows across company screening, primer generation, stock price catalyst mapping, and now, pressure-testing investment ideas through a new AI agent called Devil’s Advocate. In this post, we share how LinqAlpha uses Amazon Bedrock to build and scale Devil’s Advocate. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, LinqAlpha, assesses, investment, theses, using, Devil’s, Advocate, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Amazon uses Amazon Nova models to automate operational readiness testing for new fulfillment centers</title>
<link>https://news.jatlink.uk/3930</link>
<guid>https://news.jatlink.uk/3930</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, uses, Amazon, Nova, models, automate, operational, readiness, testing, for, new, fulfillment, centers</media:keywords>
</item>

<item>
<title>Iberdrola enhances IT operations using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3931</link>
<guid>https://news.jatlink.uk/3931</guid>
<description><![CDATA[ Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. Through its partnership with AWS, Iberdrola implemented different agentic architectures using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Iberdrola, enhances, operations, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Building real&amp;time voice assistants with Amazon Nova Sonic compared to cascading architectures</title>
<link>https://news.jatlink.uk/3932</link>
<guid>https://news.jatlink.uk/3932</guid>
<description><![CDATA[ Amazon Nova Sonic delivers real-time, human-like voice conversations through the bidirectional streaming interface. In this post, you learn how Amazon Nova Sonic can solve some of the challenges faced by cascaded approaches, simplify building voice AI agents, and provide natural conversational capabilities. We also provide guidance on when to choose each approach to help you make informed decisions for your voice AI projects. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, voice, assistants, with, Amazon, Nova, Sonic, compared, cascading, architectures</media:keywords>
</item>

<item>
<title>Bringing ChatGPT to GenAI.mil</title>
<link>https://news.jatlink.uk/3872</link>
<guid>https://news.jatlink.uk/3872</guid>
<description><![CDATA[ OpenAI for Government announces the deployment of a custom ChatGPT on GenAI.mil, bringing secure, safety-forward AI to U.S. defense teams. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 10 Feb 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bringing, ChatGPT, GenAI.mil</media:keywords>
</item>

<item>
<title>Automated Reasoning checks rewriting chatbot reference implementation</title>
<link>https://news.jatlink.uk/3858</link>
<guid>https://news.jatlink.uk/3858</guid>
<description><![CDATA[ This blog post dives deeper into the implementation architecture for the Automated Reasoning checks rewriting chatbot. ]]></description>
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<pubDate>Mon, 09 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, Reasoning, checks, rewriting, chatbot, reference, implementation</media:keywords>
</item>

<item>
<title>Testing ads in ChatGPT</title>
<link>https://news.jatlink.uk/3857</link>
<guid>https://news.jatlink.uk/3857</guid>
<description><![CDATA[ OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 09 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Testing, ads, ChatGPT</media:keywords>
</item>

<item>
<title>Agent&amp;to&amp;agent collaboration: Using Amazon Nova 2 Lite and Amazon Nova Act for multi&amp;agent systems</title>
<link>https://news.jatlink.uk/3836</link>
<guid>https://news.jatlink.uk/3836</guid>
<description><![CDATA[ This post walks through how agent-to-agent collaboration on Amazon Bedrock works in practice, using Amazon Nova 2 Lite for planning and Amazon Nova Act for browser interaction, to turn a fragile single-agent setup into a predictable multi-agent system. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent-to-agent, collaboration:, Using, Amazon, Nova, Lite, and, Amazon, Nova, Act, for, multi-agent, systems</media:keywords>
</item>

<item>
<title>Scale LLM fine&amp;tuning with Hugging Face and Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3833</link>
<guid>https://news.jatlink.uk/3833</guid>
<description><![CDATA[ In this post, we show how this integrated approach transforms enterprise LLM fine-tuning from a complex, resource-intensive challenge into a streamlined, scalable solution for achieving better model performance in domain-specific applications. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, LLM, fine-tuning, with, Hugging, Face, and, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>New Relic transforms productivity with generative AI on AWS</title>
<link>https://news.jatlink.uk/3834</link>
<guid>https://news.jatlink.uk/3834</guid>
<description><![CDATA[ Working with the Generative AI Innovation Center, New Relic NOVA (New Relic Omnipresence Virtual Assistant) evolved from a knowledge assistant into a comprehensive productivity engine. We explore the technical architecture, development journey, and key lessons learned in building an enterprise-grade AI solution that delivers measurable productivity gains at scale. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, Relic, transforms, productivity, with, generative, AWS</media:keywords>
</item>

<item>
<title>Accelerate agentic application development with a full&amp;stack starter template for Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3835</link>
<guid>https://news.jatlink.uk/3835</guid>
<description><![CDATA[ In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, application, development, with, full-stack, starter, template, for, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Structured outputs on Amazon Bedrock: Schema&amp;compliant AI responses</title>
<link>https://news.jatlink.uk/3664</link>
<guid>https://news.jatlink.uk/3664</guid>
<description><![CDATA[ Today, we&#039;re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples. ]]></description>
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<pubDate>Fri, 06 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Structured, outputs, Amazon, Bedrock:, Schema-compliant, responses</media:keywords>
</item>

<item>
<title>Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK</title>
<link>https://news.jatlink.uk/3663</link>
<guid>https://news.jatlink.uk/3663</guid>
<description><![CDATA[ In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices. ]]></description>
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<pubDate>Fri, 06 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, Amazon, SageMaker, HyperPod, clusters, using, the, HyperPod, CLI, and, SDK</media:keywords>
</item>

<item>
<title>Making AI work for everyone, everywhere: our approach to localization</title>
<link>https://news.jatlink.uk/3661</link>
<guid>https://news.jatlink.uk/3661</guid>
<description><![CDATA[ OpenAI shares its approach to AI localization, showing how globally shared frontier models can be adapted to local languages, laws, and cultures without compromising safety. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Making, work, for, everyone, everywhere:, our, approach, localization</media:keywords>
</item>

<item>
<title>Korea privacy policy</title>
<link>https://news.jatlink.uk/3662</link>
<guid>https://news.jatlink.uk/3662</guid>
<description><![CDATA[ Korea privacy policy ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 06 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Korea, privacy, policy</media:keywords>
</item>

<item>
<title>Evaluate generative AI models with an Amazon Nova rubric&amp;based LLM judge on Amazon SageMaker AI (Part 2)</title>
<link>https://news.jatlink.uk/3641</link>
<guid>https://news.jatlink.uk/3641</guid>
<description><![CDATA[ In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs. ]]></description>
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<pubDate>Fri, 06 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, generative, models, with, Amazon, Nova, rubric-based, LLM, judge, Amazon, SageMaker, Part</media:keywords>
</item>

<item>
<title>A practical guide to Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3583</link>
<guid>https://news.jatlink.uk/3583</guid>
<description><![CDATA[ In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>practical, guide, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3582</link>
<guid>https://news.jatlink.uk/3582</guid>
<description><![CDATA[ Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost savings while maintaining operational excellence. The document classification system, developed using the Generative AI Intelligent Document Processing (GenAI IDP) Accelerator, is designed to integrate seamlessly into existing workflows. It revolutionizes how employees interact with document management systems by reducing the time spent on manual classification tasks. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Associa, transforms, document, classification, with, the, GenAI, IDP, Accelerator, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>GPT&amp;5 lowers the cost of cell&amp;free protein synthesis</title>
<link>https://news.jatlink.uk/3578</link>
<guid>https://news.jatlink.uk/3578</guid>
<description><![CDATA[ An autonomous lab combining OpenAI’s GPT-5 with Ginkgo Bioworks’ cloud automation cut cell-free protein synthesis costs by 40% through closed-loop experimentation. ]]></description>
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<pubDate>Thu, 05 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5, lowers, the, cost, cell-free, protein, synthesis</media:keywords>
</item>

<item>
<title>Introducing Trusted Access for Cyber</title>
<link>https://news.jatlink.uk/3579</link>
<guid>https://news.jatlink.uk/3579</guid>
<description><![CDATA[ OpenAI introduces Trusted Access for Cyber, a trust-based framework that expands access to frontier cyber capabilities while strengthening safeguards against misuse. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Trusted, Access, for, Cyber</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.3&amp;Codex</title>
<link>https://news.jatlink.uk/3580</link>
<guid>https://news.jatlink.uk/3580</guid>
<description><![CDATA[ GPT-5.3-Codex is a Codex-native agent that pairs frontier coding performance with general reasoning to support long-horizon, real-world technical work. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.3-Codex</media:keywords>
</item>

<item>
<title>GPT&amp;5.3&amp;Codex System Card</title>
<link>https://news.jatlink.uk/3581</link>
<guid>https://news.jatlink.uk/3581</guid>
<description><![CDATA[ GPT‑5.3-Codex is the most capable agentic coding model to date, combining the frontier coding performance of GPT‑5.2-Codex with the reasoning and professional knowledge capabilities of GPT‑5.2. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GPT-5.3-Codex, System, Card</media:keywords>
</item>

<item>
<title>GeForce NOW Celebrates Six Years of Streaming With 24 Games in February</title>
<link>https://news.jatlink.uk/3562</link>
<guid>https://news.jatlink.uk/3562</guid>
<description><![CDATA[ Break out the cake and green sprinkles — GeForce NOW is turning six. Since launch, members have streamed over 1 billion hours, and the party’s just getting started. Throughout February, members can look forward to new games, fresh ways to play across more devices and even more ways to bring RTX power to every screen	
		Read Article ]]></description>
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<pubDate>Thu, 05 Feb 2026 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Celebrates, Six, Years, Streaming, With, Games, February</media:keywords>
</item>

<item>
<title>Introducing OpenAI Frontier</title>
<link>https://news.jatlink.uk/3561</link>
<guid>https://news.jatlink.uk/3561</guid>
<description><![CDATA[ OpenAI Frontier is an enterprise platform for building, deploying, and managing AI agents with shared context, onboarding, permissions, and governance. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, OpenAI, Frontier</media:keywords>
</item>

<item>
<title>Navigating health questions with ChatGPT</title>
<link>https://news.jatlink.uk/3517</link>
<guid>https://news.jatlink.uk/3517</guid>
<description><![CDATA[ A family shares how ChatGPT helped them prepare for critical cancer treatment decisions for their son alongside expert guidance from his doctors. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Navigating, health, questions, with, ChatGPT</media:keywords>
</item>

<item>
<title>Unlocking the Codex harness: how we built the App Server</title>
<link>https://news.jatlink.uk/3516</link>
<guid>https://news.jatlink.uk/3516</guid>
<description><![CDATA[ Learn how to embed the Codex agent using the Codex App Server, a bidirectional JSON-RPC API powering streaming progress, tool use, approvals, and diffs. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 05 Feb 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, the, Codex, harness:, how, built, the, App, Server</media:keywords>
</item>

<item>
<title>Nemotron Labs: How AI Agents Are Turning Documents Into Real&amp;Time Business Intelligence</title>
<link>https://news.jatlink.uk/3440</link>
<guid>https://news.jatlink.uk/3440</guid>
<description><![CDATA[ Businesses today face the challenge of uncovering valuable insights buried within a wide variety of documents — including reports, presentations, PDFs, web pages and spreadsheets.  ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Nemotron, Labs:, How, Agents, Are, Turning, Documents, Into, Real-Time, Business, Intelligence</media:keywords>
</item>

<item>
<title>Everything Will Be Represented in a Virtual Twin, NVIDIA CEO Jensen Huang Says at 3DEXPERIENCE World</title>
<link>https://news.jatlink.uk/3441</link>
<guid>https://news.jatlink.uk/3441</guid>
<description><![CDATA[ NVIDIA founder and CEO Jensen Huang and Dassault Systèmes CEO Pascal Daloz announced a partnership to build a shared industrial AI architecture, merging virtual twins with physics-based AI to redefine the future of design, engineering and manufacturing. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Everything, Will, Represented, Virtual, Twin, NVIDIA, CEO, Jensen, Huang, Says, 3DEXPERIENCE, World</media:keywords>
</item>

<item>
<title>Dassault Systèmes and NVIDIA Partner to Build Industrial AI Platform Powering Virtual Twins</title>
<link>https://news.jatlink.uk/3442</link>
<guid>https://news.jatlink.uk/3442</guid>
<description><![CDATA[ Dassault Systèmes (Euronext Paris: FR0014003TT8, DSY.PA) and NVIDIA today announced a long-term strategic partnership to establish a shared industrial architecture for mission-critical artificial intelligence across industries. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Dassault, Systèmes, and, NVIDIA, Partner, Build, Industrial, Platform, Powering, Virtual, Twins</media:keywords>
</item>

<item>
<title>Mercedes&amp;Benz Unveils New S&amp;Class Built on NVIDIA DRIVE AV, Which Enables an L4&amp;Ready Architecture</title>
<link>https://news.jatlink.uk/3443</link>
<guid>https://news.jatlink.uk/3443</guid>
<description><![CDATA[ Mercedes-Benz is marking 140 years of automotive innovation with a new S-Class built for the AI era, bringing together automotive safety and NVIDIA’s advanced autonomous driving platform to enable a level 4-ready architecture designed for trust. The new S-Class with MB.OS, which will be equipped with the NVIDIA DRIVE Hyperion architecture and full-stack NVIDIA DRIVE	
		Read Article ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Mercedes-Benz, Unveils, New, S-Class, Built, NVIDIA, DRIVE, AV, Which, Enables, L4-Ready, Architecture</media:keywords>
</item>

<item>
<title>Into the Omniverse: Physical AI Open Models and Frameworks Advance Robots and Autonomous Systems</title>
<link>https://news.jatlink.uk/3444</link>
<guid>https://news.jatlink.uk/3444</guid>
<description><![CDATA[ Open source has become essential for driving innovation in robotics and autonomy. By providing access to critical infrastructure — from simulation frameworks to AI models — NVIDIA is enabling collaborative development that accelerates the path to safer, more capable autonomous systems.  ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Into, the, Omniverse:, Physical, Open, Models, and, Frameworks, Advance, Robots, and, Autonomous, Systems</media:keywords>
</item>

<item>
<title>GeForce NOW Brings GeForce RTX Gaming to Linux PCs</title>
<link>https://news.jatlink.uk/3445</link>
<guid>https://news.jatlink.uk/3445</guid>
<description><![CDATA[ Get ready to game — the native GeForce NOW app for Linux PCs is now available in beta, letting Linux desktops tap directly into GeForce RTX performance from the cloud. Alongside the expansion comes ten new games, including The Bard’s Tale IV: Director’s Cut and The Bard’s Tale Trilogy for a leveled-up gaming weekend. And	
		Read Article ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>GeForce, NOW, Brings, GeForce, RTX, Gaming, Linux, PCs</media:keywords>
</item>

<item>
<title>NVIDIA Sets Conference Call for Fourth&amp;Quarter Financial Results</title>
<link>https://news.jatlink.uk/3446</link>
<guid>https://news.jatlink.uk/3446</guid>
<description><![CDATA[ NVIDIA will host a conference call on Wednesday, February 25, at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the fourth quarter and fiscal year 2026, which ended January 25, 2026. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Sets, Conference, Call, for, Fourth-Quarter, Financial, Results</media:keywords>
</item>

<item>
<title>Accelerating Science: A Blueprint for a Renewed National Quantum Initiative</title>
<link>https://news.jatlink.uk/3447</link>
<guid>https://news.jatlink.uk/3447</guid>
<description><![CDATA[ Quantum technologies are rapidly emerging as foundational capabilities for economic competitiveness, national security and scientific leadership in the 21st century. Sustained U.S. leadership in quantum information science is critical to ensuring that breakthroughs in computing, sensing, networking and materials translate into secure technologies and industries, a skilled domestic workforce and long-term strategic advantage. To secure	
		Read Article ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, Science:, Blueprint, for, Renewed, National, Quantum, Initiative</media:keywords>
</item>

<item>
<title>NVIDIA Launches Earth&amp;2 Family of Open Models — the World’s First Fully Open, Accelerated Set of Models and Tools for AI Weather</title>
<link>https://news.jatlink.uk/3448</link>
<guid>https://news.jatlink.uk/3448</guid>
<description><![CDATA[ At the American Meteorological Society’s Annual Meeting, NVIDIA today unveiled a new NVIDIA Earth-2 family of open models, libraries and frameworks for weather and climate AI, offering the world’s first fully open, production-ready weather AI software stack. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Launches, Earth-2, Family, Open, Models, —, the, World’s, First, Fully, Open, Accelerated, Set, Models, and, Tools, for, Weather</media:keywords>
</item>

<item>
<title>NVIDIA and CoreWeave Strengthen Collaboration to Accelerate Buildout of AI Factories</title>
<link>https://news.jatlink.uk/3449</link>
<guid>https://news.jatlink.uk/3449</guid>
<description><![CDATA[ NVIDIA (Nasdaq: NVDA) and CoreWeave, Inc. (Nasdaq: CRWV) today announced an expansion of their long-standing complementary relationship to enable CoreWeave to accelerate the buildout of more than 5 gigawatts of AI factories by 2030 to advance AI adoption at global scale. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:33:48 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, and, CoreWeave, Strengthen, Collaboration, Accelerate, Buildout, Factories</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references</title>
<link>https://news.jatlink.uk/3428</link>
<guid>https://news.jatlink.uk/3428</guid>
<description><![CDATA[ Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand guidelines, deliver consistent content, and enhance the effectiveness and efficiency of new campaign creation. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, Generate, custom, marketing, images, from, historical, references</media:keywords>
</item>

<item>
<title>VfL Wolfsburg turns ChatGPT into a club&amp;wide capability</title>
<link>https://news.jatlink.uk/3427</link>
<guid>https://news.jatlink.uk/3427</guid>
<description><![CDATA[ By focusing on people, not pilots, the Bundesliga club is scaling efficiency, creativity, and knowledge—without losing its football identity. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 04 Feb 2026 10:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>VfL, Wolfsburg, turns, ChatGPT, into, club-wide, capability</media:keywords>
</item>

<item>
<title>Democratizing business intelligence: BGL’s journey with Claude Agent SDK and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3426</link>
<guid>https://news.jatlink.uk/3426</guid>
<description><![CDATA[ BGL is a leading provider of self-managed superannuation fund (SMSF) administration solutions that help individuals manage the complex compliance and reporting of their own or a client’s retirement savings, serving over 12,700 businesses across 15 countries. In this blog post, we explore how BGL built its production-ready AI agent using Claude Agent SDK and Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Tue, 03 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Democratizing, business, intelligence:, BGL’s, journey, with, Claude, Agent, SDK, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Use Amazon Quick Suite custom action connectors to upload text files to Google Drive using OpenAPI specification</title>
<link>https://news.jatlink.uk/3425</link>
<guid>https://news.jatlink.uk/3425</guid>
<description><![CDATA[ In this post, we demonstrate how to build a secure file upload solution by integrating Google Drive with Amazon Quick Suite custom connectors using Amazon API Gateway and AWS Lambda. ]]></description>
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<pubDate>Tue, 03 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, Amazon, Quick, Suite, custom, action, connectors, upload, text, files, Google, Drive, using, OpenAPI, specification</media:keywords>
</item>

<item>
<title>AI agents in enterprises: Best practices with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3424</link>
<guid>https://news.jatlink.uk/3424</guid>
<description><![CDATA[ This post explores nine essential best practices for building enterprise AI agents using Amazon Bedrock AgentCore. Amazon Bedrock AgentCore is an agentic platform that provides the services you need to create, deploy, and manage AI agents at scale. In this post, we cover everything from initial scoping to organizational scaling, with practical guidance that you can apply immediately. ]]></description>
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<pubDate>Tue, 03 Feb 2026 19:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agents, enterprises:, Best, practices, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Agentic AI for healthcare data analysis with Amazon SageMaker Data Agent</title>
<link>https://news.jatlink.uk/3423</link>
<guid>https://news.jatlink.uk/3423</guid>
<description><![CDATA[ On November 21, 2025, Amazon SageMaker introduced a built-in data agent within Amazon SageMaker Unified Studio that transforms large-scale data analysis. In this post, we demonstrate, through a detailed case study of an epidemiologist conducting clinical cohort analysis, how SageMaker Data Agent can help reduce weeks of data preparation into days, and days of analysis development into hours—ultimately accelerating the path from clinical questions to research conclusions. ]]></description>
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<pubDate>Tue, 03 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, for, healthcare, data, analysis, with, Amazon, SageMaker, Data, Agent</media:keywords>
</item>

<item>
<title>Introducing the Codex app</title>
<link>https://news.jatlink.uk/3398</link>
<guid>https://news.jatlink.uk/3398</guid>
<description><![CDATA[ Introducing the Codex app for macOS—a command center for AI coding and software development with multiple agents, parallel workflows, and long-running tasks. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 02 Feb 2026 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, Codex, app</media:keywords>
</item>

<item>
<title>How Clarus Care uses Amazon Bedrock to deliver conversational contact center interactions</title>
<link>https://news.jatlink.uk/3397</link>
<guid>https://news.jatlink.uk/3397</guid>
<description><![CDATA[ In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities--when requested or for urgent cases--and an analytics pipeline for performance insights. ]]></description>
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<pubDate>Mon, 02 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Clarus, Care, uses, Amazon, Bedrock, deliver, conversational, contact, center, interactions</media:keywords>
</item>

<item>
<title>Snowflake and OpenAI partner to bring frontier intelligence to enterprise data</title>
<link>https://news.jatlink.uk/3396</link>
<guid>https://news.jatlink.uk/3396</guid>
<description><![CDATA[ OpenAI and Snowflake partner in a $200M agreement to bring frontier intelligence into enterprise data, enabling AI agents and insights directly in Snowflake. ]]></description>
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<pubDate>Mon, 02 Feb 2026 15:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Snowflake, and, OpenAI, partner, bring, frontier, intelligence, enterprise, data</media:keywords>
</item>

<item>
<title>Evaluating generative AI models with Amazon Nova LLM&amp;as&amp;a&amp;Judge on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3395</link>
<guid>https://news.jatlink.uk/3395</guid>
<description><![CDATA[ Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […] ]]></description>
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<pubDate>Fri, 30 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, generative, models, with, Amazon, Nova, LLM-as-a-Judge, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Simplify ModelOps with Amazon SageMaker AI Projects using Amazon S3&amp;based templates</title>
<link>https://news.jatlink.uk/3393</link>
<guid>https://news.jatlink.uk/3393</guid>
<description><![CDATA[ This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simplify, ModelOps, with, Amazon, SageMaker, Projects, using, Amazon, S3-based, templates</media:keywords>
</item>

<item>
<title>Scale AI in South Africa using Amazon Bedrock global cross&amp;Region inference with Anthropic Claude 4.5 models</title>
<link>https://news.jatlink.uk/3394</link>
<guid>https://news.jatlink.uk/3394</guid>
<description><![CDATA[ In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you&#039;ll have a working implementation of global cross-Region inference in af-south-1. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, South, Africa, using, Amazon, Bedrock, global, cross-Region, inference, with, Anthropic Claude 4.5, models</media:keywords>
</item>

<item>
<title>Taisei Corporation shapes the next generation of talent with ChatGPT</title>
<link>https://news.jatlink.uk/3391</link>
<guid>https://news.jatlink.uk/3391</guid>
<description><![CDATA[ Taisei Corporation uses ChatGPT Enterprise to support HR-led talent development and scale generative AI across its global construction business. ]]></description>
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<pubDate>Fri, 30 Jan 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Taisei, Corporation, shapes, the, next, generation, talent, with, ChatGPT</media:keywords>
</item>

<item>
<title>Scaling content review operations with multi&amp;agent workflow</title>
<link>https://news.jatlink.uk/3392</link>
<guid>https://news.jatlink.uk/3392</guid>
<description><![CDATA[ The agent-based approach we present is applicable to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To demonstrate these concepts in action, we walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to various content review needs by adjusting the agent configurations, tools, and verification sources. ]]></description>
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<pubDate>Fri, 30 Jan 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, content, review, operations, with, multi-agent, workflow</media:keywords>
</item>

<item>
<title>Retiring GPT&amp;4o, GPT&amp;4.1, GPT&amp;4.1 mini, and OpenAI o4&amp;mini in ChatGPT</title>
<link>https://news.jatlink.uk/3390</link>
<guid>https://news.jatlink.uk/3390</guid>
<description><![CDATA[ On February 13, 2026, alongside the previously announced retirement⁠ of GPT‑5 (Instant, Thinking, and Pro), we will retire GPT‑4o, GPT‑4.1, GPT‑4.1 mini, and OpenAI o4-mini from ChatGPT. In the API, there are no changes at this time. ]]></description>
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<pubDate>Thu, 29 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Retiring, GPT-4o, GPT-4.1, GPT-4.1, mini, and, OpenAI, o4-mini, ChatGPT</media:keywords>
</item>

<item>
<title>Inside OpenAI’s in&amp;house data agent</title>
<link>https://news.jatlink.uk/3389</link>
<guid>https://news.jatlink.uk/3389</guid>
<description><![CDATA[ How OpenAI built an in-house AI data agent that uses GPT-5, Codex, and memory to reason over massive datasets and deliver reliable insights in minutes. ]]></description>
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<pubDate>Thu, 29 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Inside, OpenAI’s, in-house, data, agent</media:keywords>
</item>

<item>
<title>Keeping your data safe when an AI agent clicks a link</title>
<link>https://news.jatlink.uk/3388</link>
<guid>https://news.jatlink.uk/3388</guid>
<description><![CDATA[ Learn how OpenAI protects user data when AI agents open links, preventing URL-based data exfiltration and prompt injection with built-in safeguards. ]]></description>
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<pubDate>Wed, 28 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Keeping, your, data, safe, when, agent, clicks, link</media:keywords>
</item>

<item>
<title>The next chapter for AI in the EU</title>
<link>https://news.jatlink.uk/3386</link>
<guid>https://news.jatlink.uk/3386</guid>
<description><![CDATA[ OpenAI launches the EU Economic Blueprint 2.0 with new data, partnerships, and initiatives to accelerate AI adoption, skills, and growth across Europe. ]]></description>
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<pubDate>Wed, 28 Jan 2026 09:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, next, chapter, for, the</media:keywords>
</item>

<item>
<title>EMEA Youth &amp;amp; Wellbeing Grant</title>
<link>https://news.jatlink.uk/3387</link>
<guid>https://news.jatlink.uk/3387</guid>
<description><![CDATA[ Apply for the EMEA Youth &amp; Wellbeing Grant, a €500,000 program funding NGOs and researchers advancing youth safety and wellbeing in the age of AI. ]]></description>
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<pubDate>Wed, 28 Jan 2026 09:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>EMEA, Youth, Wellbeing, Grant</media:keywords>
</item>

<item>
<title>Powering tax donations with AI powered personalized recommendations</title>
<link>https://news.jatlink.uk/3385</link>
<guid>https://news.jatlink.uk/3385</guid>
<description><![CDATA[ TRUSTBANK partnered with Recursive to build Choice AI using OpenAI models, delivering personalized, conversational recommendations that simplify Furusato Nozei gift discovery. A multi-agent system helps donors navigate thousands of options and find gifts that match their preferences. ]]></description>
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<pubDate>Wed, 28 Jan 2026 04:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Powering, tax, donations, with, powered, personalized, recommendations</media:keywords>
</item>

<item>
<title>Build reliable Agentic AI solution with Amazon Bedrock: Learn from Pushpay’s journey on GenAI evaluation</title>
<link>https://news.jatlink.uk/3384</link>
<guid>https://news.jatlink.uk/3384</guid>
<description><![CDATA[ In this post, we walk you through Pushpay&#039;s journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS. ]]></description>
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<pubDate>Tue, 27 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, Agentic, solution, with, Amazon, Bedrock:, Learn, from, Pushpay’s, journey, GenAI, evaluation</media:keywords>
</item>

<item>
<title>Introducing Prism</title>
<link>https://news.jatlink.uk/3383</link>
<guid>https://news.jatlink.uk/3383</guid>
<description><![CDATA[ Prism is a free LaTeX-native workspace with GPT-5.2 built in, helping researchers write, collaborate, and reason in one place. ]]></description>
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<pubDate>Tue, 27 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Prism</media:keywords>
</item>

<item>
<title>Build an intelligent contract management solution with Amazon Quick Suite and Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3382</link>
<guid>https://news.jatlink.uk/3382</guid>
<description><![CDATA[ This blog post demonstrates how to build an intelligent contract management solution using Amazon Quick Suite as your primary contract management solution, augmented with Amazon Bedrock AgentCore for advanced multi-agent capabilities. ]]></description>
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<pubDate>Tue, 27 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, contract, management, solution, with, Amazon, Quick, Suite, and, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>PVH reimagines the future of fashion with OpenAI</title>
<link>https://news.jatlink.uk/3381</link>
<guid>https://news.jatlink.uk/3381</guid>
<description><![CDATA[ PVH Corp., parent company of Calvin Klein and Tommy Hilfiger, is adopting ChatGPT Enterprise to bring AI into fashion design, supply chain, and consumer engagement. ]]></description>
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<pubDate>Tue, 27 Jan 2026 14:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>PVH, reimagines, the, future, fashion, with, OpenAI</media:keywords>
</item>

<item>
<title>How Indeed uses AI to help evolve the job search</title>
<link>https://news.jatlink.uk/3380</link>
<guid>https://news.jatlink.uk/3380</guid>
<description><![CDATA[ Indeed’s CRO Maggie Hulce shares how AI is transforming job search, recruiting, and talent acquisition for employers and job seekers. ]]></description>
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<pubDate>Tue, 27 Jan 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Indeed, uses, help, evolve, the, job, search</media:keywords>
</item>

<item>
<title>Build a serverless AI Gateway architecture with AWS AppSync Events</title>
<link>https://news.jatlink.uk/3379</link>
<guid>https://news.jatlink.uk/3379</guid>
<description><![CDATA[ In this post, we discuss how to use AppSync Events as the foundation of a capable, serverless, AI gateway architecture. We explore how it integrates with AWS services for comprehensive coverage of the capabilities offered in AI gateway architectures. Finally, we get you started on your journey with sample code you can launch in your account and begin building. ]]></description>
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<pubDate>Mon, 26 Jan 2026 18:00:39 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, Gateway, architecture, with, AWS, AppSync, Events</media:keywords>
</item>

<item>
<title>How Totogi automated change request processing with Totogi BSS Magic and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3378</link>
<guid>https://news.jatlink.uk/3378</guid>
<description><![CDATA[ This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock. ]]></description>
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<pubDate>Mon, 26 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Totogi, automated, change, request, processing, with, Totogi, BSS, Magic, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Inside GPT&amp;5 for Work: How Businesses Use GPT&amp;5</title>
<link>https://news.jatlink.uk/3377</link>
<guid>https://news.jatlink.uk/3377</guid>
<description><![CDATA[ A data-driven report on how workers across industries use ChatGPT—covering adoption trends, top tasks, departmental patterns, and the future of AI at work. ]]></description>
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<pubDate>Sat, 24 Jan 2026 01:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Inside, GPT-5, for, Work:, How, Businesses, Use, GPT-5</media:keywords>
</item>

<item>
<title>Unrolling the Codex agent loop</title>
<link>https://news.jatlink.uk/3376</link>
<guid>https://news.jatlink.uk/3376</guid>
<description><![CDATA[ A technical deep dive into the Codex agent loop, explaining how Codex CLI orchestrates models, tools, prompts, and performance using the Responses API. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 23 Jan 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unrolling, the, Codex, agent, loop</media:keywords>
</item>

<item>
<title>How the Amazon.com Catalog Team built self&amp;learning generative AI at scale with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3375</link>
<guid>https://news.jatlink.uk/3375</guid>
<description><![CDATA[ In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon.com, Catalog, Team, built, self-learning, generative, scale, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build AI agents with Amazon Bedrock AgentCore using AWS CloudFormation</title>
<link>https://news.jatlink.uk/3374</link>
<guid>https://news.jatlink.uk/3374</guid>
<description><![CDATA[ Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, with, Amazon, Bedrock, AgentCore, using, AWS, CloudFormation</media:keywords>
</item>

<item>
<title>Scaling PostgreSQL to power 800 million ChatGPT users</title>
<link>https://news.jatlink.uk/3373</link>
<guid>https://news.jatlink.uk/3373</guid>
<description><![CDATA[ An inside look at how OpenAI scaled PostgreSQL to millions of queries per second using replicas, caching, rate limiting, and workload isolation. ]]></description>
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<pubDate>Fri, 23 Jan 2026 10:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, PostgreSQL, power, 800, million, ChatGPT, users</media:keywords>
</item>

<item>
<title>How CLICKFORCE accelerates data&amp;driven advertising with Amazon Bedrock Agents</title>
<link>https://news.jatlink.uk/3372</link>
<guid>https://news.jatlink.uk/3372</guid>
<description><![CDATA[ In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, CLICKFORCE, accelerates, data-driven, advertising, with, Amazon, Bedrock, Agents</media:keywords>
</item>

<item>
<title>How PDI built an enterprise&amp;grade RAG system for AI applications with AWS</title>
<link>https://news.jatlink.uk/3371</link>
<guid>https://news.jatlink.uk/3371</guid>
<description><![CDATA[ PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. In this post, we walk through the PDI Intelligence Query (PDIQ) process flow and architecture, focusing on the implementation details and the business outcomes it has helped PDI achieve. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, PDI, built, enterprise-grade, RAG, system, for, applications, with, AWS</media:keywords>
</item>

<item>
<title>Inside Praktika&amp;apos;s conversational approach to language learning</title>
<link>https://news.jatlink.uk/3370</link>
<guid>https://news.jatlink.uk/3370</guid>
<description><![CDATA[ How Praktika uses GPT-4.1 and GPT-5.2 to build adaptive AI tutors that personalize lessons, track progress, and help learners achieve real-world language fluency ]]></description>
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<pubDate>Thu, 22 Jan 2026 14:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Inside, Praktikas, conversational, approach, language, learning</media:keywords>
</item>

<item>
<title>How Thomson Reuters built an Agentic Platform Engineering Hub with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3369</link>
<guid>https://news.jatlink.uk/3369</guid>
<description><![CDATA[ This blog post explains how TR&#039;s Platform Engineering team, a geographically distributed unit overseeing TR&#039;s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Wed, 21 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Thomson, Reuters, built, Agentic, Platform, Engineering, Hub, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build agents to learn from experiences using Amazon Bedrock AgentCore episodic memory</title>
<link>https://news.jatlink.uk/3368</link>
<guid>https://news.jatlink.uk/3368</guid>
<description><![CDATA[ In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates. ]]></description>
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<pubDate>Wed, 21 Jan 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, learn, from, experiences, using, Amazon, Bedrock, AgentCore, episodic, memory</media:keywords>
</item>

<item>
<title>How Higgsfield turns simple ideas into cinematic social videos</title>
<link>https://news.jatlink.uk/3367</link>
<guid>https://news.jatlink.uk/3367</guid>
<description><![CDATA[ Discover how Higgsfield gives creators cinematic, social-first video output from simple inputs using OpenAI GPT-4.1, GPT-5, and Sora 2. ]]></description>
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<pubDate>Wed, 21 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Higgsfield, turns, simple, ideas, into, cinematic, social, videos</media:keywords>
</item>

<item>
<title>How bunq handles 97% of support with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3365</link>
<guid>https://news.jatlink.uk/3365</guid>
<description><![CDATA[ In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, bunq, handles, 97, support, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Using Strands Agents to create a multi&amp;agent solution with Meta’s Llama 4 and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3366</link>
<guid>https://news.jatlink.uk/3366</guid>
<description><![CDATA[ In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta&#039;s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, Strands, Agents, create, multi-agent, solution, with, Meta’s, Llama, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing Edu for Countries</title>
<link>https://news.jatlink.uk/3363</link>
<guid>https://news.jatlink.uk/3363</guid>
<description><![CDATA[ Edu for Countries is a new OpenAI initiative helping governments use AI to modernize education systems and build future-ready workforces. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 21 Jan 2026 10:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Edu, for, Countries</media:keywords>
</item>

<item>
<title>How countries can end the capability overhang</title>
<link>https://news.jatlink.uk/3364</link>
<guid>https://news.jatlink.uk/3364</guid>
<description><![CDATA[ Our latest report reveals stark differences in advanced AI adoption across countries and outlines new initiatives to help nations capture productivity gains from AI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 21 Jan 2026 10:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, countries, can, end, the, capability, overhang</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/3361</link>
<guid>https://news.jatlink.uk/3361</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>Wed, 21 Jan 2026 07:00:10 +0000</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/3362</link>
<guid>https://news.jatlink.uk/3362</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>Wed, 21 Jan 2026 07:00:10 +0000</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/3359</link>
<guid>https://news.jatlink.uk/3359</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>Wed, 21 Jan 2026 07:00:09 +0000</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/3360</link>
<guid>https://news.jatlink.uk/3360</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>Wed, 21 Jan 2026 07:00:09 +0000</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/3357</link>
<guid>https://news.jatlink.uk/3357</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>Wed, 21 Jan 2026 07:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ScreenAI:, visual, language, model, for, and, visually-situated, language, understanding</media:keywords>
</item>

<item>
<title>SCIN: A new resource for representative dermatology images</title>
<link>https://news.jatlink.uk/3358</link>
<guid>https://news.jatlink.uk/3358</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>Wed, 21 Jan 2026 07:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>SCIN:, new, resource, for, representative, dermatology, images</media:keywords>
</item>

<item>
<title>AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks</title>
<link>https://news.jatlink.uk/3354</link>
<guid>https://news.jatlink.uk/3354</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>Wed, 21 Jan 2026 07:00:06 +0000</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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<item>
<title>Computer&amp;aided diagnosis for lung cancer screening</title>
<link>https://news.jatlink.uk/3355</link>
<guid>https://news.jatlink.uk/3355</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>Wed, 21 Jan 2026 07:00:06 +0000</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/3356</link>
<guid>https://news.jatlink.uk/3356</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>Wed, 21 Jan 2026 07:00:06 +0000</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/3353</link>
<guid>https://news.jatlink.uk/3353</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>Wed, 21 Jan 2026 07:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generative, quantify, uncertainty, weather, forecasting</media:keywords>
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<title>Horizon 1000: Advancing AI for primary healthcare</title>
<link>https://news.jatlink.uk/3352</link>
<guid>https://news.jatlink.uk/3352</guid>
<description><![CDATA[ OpenAI and the Gates Foundation launch Horizon 1000, a $50M pilot advancing AI capabilities for healthcare in Africa. The initiative aims to reach 1,000 clinics by 2028. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 21 Jan 2026 06:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Horizon, 1000:, Advancing, for, primary, healthcare</media:keywords>
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<item>
<title>Stargate Community</title>
<link>https://news.jatlink.uk/3324</link>
<guid>https://news.jatlink.uk/3324</guid>
<description><![CDATA[ Stargate Community plans detail a community-first approach to AI infrastructure, using locally tailored plans shaped by community input, energy needs, and workforce priorities. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 21 Jan 2026 04:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Stargate, Community</media:keywords>
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<title>Cisco and OpenAI redefine enterprise engineering with AI agents</title>
<link>https://news.jatlink.uk/3323</link>
<guid>https://news.jatlink.uk/3323</guid>
<description><![CDATA[ Cisco and OpenAI redefine enterprise engineering with Codex, an AI software agent embedded in workflows to speed builds, automate defect fixes, and enable AI-native development. ]]></description>
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<pubDate>Tue, 20 Jan 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cisco, and, OpenAI, redefine, enterprise, engineering, with, agents</media:keywords>
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<title>Introducing multimodal retrieval for Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/3322</link>
<guid>https://news.jatlink.uk/3322</guid>
<description><![CDATA[ In this post, we&#039;ll guide you through building multimodal RAG applications. You&#039;ll learn how multimodal knowledge bases work, how to choose the right processing strategy based on your content type, and how to configure and implement multimodal retrieval using both the console and code examples. ]]></description>
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<pubDate>Tue, 20 Jan 2026 19:00:11 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, multimodal, retrieval, for, Amazon, Bedrock, Knowledge, Bases</media:keywords>
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<title>Our approach to age prediction</title>
<link>https://news.jatlink.uk/3321</link>
<guid>https://news.jatlink.uk/3321</guid>
<description><![CDATA[ ChatGPT is rolling out age prediction to estimate if accounts are under or over 18, applying safeguards for teens and refining accuracy over time. ]]></description>
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<pubDate>Tue, 20 Jan 2026 19:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, approach, age, prediction</media:keywords>
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<title>ServiceNow powers actionable enterprise AI with OpenAI</title>
<link>https://news.jatlink.uk/3320</link>
<guid>https://news.jatlink.uk/3320</guid>
<description><![CDATA[ ServiceNow expands access to OpenAI frontier models to power AI-driven enterprise workflows, summarization, search, and voice across the ServiceNow Platform. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Tue, 20 Jan 2026 14:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ServiceNow, powers, actionable, enterprise, with, OpenAI</media:keywords>
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<title>AI for self empowerment</title>
<link>https://news.jatlink.uk/3319</link>
<guid>https://news.jatlink.uk/3319</guid>
<description><![CDATA[ How AI can expand human agency by closing the capability overhang—helping people, businesses, and countries unlock real productivity, growth, and opportunity. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Sun, 18 Jan 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>for, self, empowerment</media:keywords>
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<title>A business that scales with the value of intelligence</title>
<link>https://news.jatlink.uk/3318</link>
<guid>https://news.jatlink.uk/3318</guid>
<description><![CDATA[ OpenAI’s business model scales with intelligence—spanning subscriptions, API, ads, commerce, and compute—driven by deepening ChatGPT adoption. ]]></description>
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<pubDate>Sun, 18 Jan 2026 19:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>business, that, scales, with, the, value, intelligence</media:keywords>
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<title>The truth left out from Elon Musk’s recent court filing</title>
<link>https://news.jatlink.uk/3317</link>
<guid>https://news.jatlink.uk/3317</guid>
<description><![CDATA[ The truth left out from Elon Musk’s recent court filing. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 16 Jan 2026 22:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, truth, left, out, from, Elon, Musk’s, recent, court, filing</media:keywords>
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<title>Our approach to advertising and expanding access to ChatGPT</title>
<link>https://news.jatlink.uk/3315</link>
<guid>https://news.jatlink.uk/3315</guid>
<description><![CDATA[ OpenAI plans to test advertising in the U.S. for ChatGPT’s free and Go tiers to expand affordable access to AI worldwide, while protecting privacy, trust, and answer quality. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 16 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Our, approach, advertising, and, expanding, access, ChatGPT</media:keywords>
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<title>Introducing ChatGPT Go, now available worldwide</title>
<link>https://news.jatlink.uk/3316</link>
<guid>https://news.jatlink.uk/3316</guid>
<description><![CDATA[ ChatGPT Go is now available worldwide, offering expanded access to GPT-5.2 Instant, higher usage limits, and longer memory—making advanced AI more affordable globally. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Fri, 16 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, Go, now, available, worldwide</media:keywords>
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<title>How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3312</link>
<guid>https://news.jatlink.uk/3312</guid>
<description><![CDATA[ Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Palo, Alto, Networks, enhanced, device, security, infra, log, analysis, with, Amazon, Bedrock</media:keywords>
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<title>From beginner to champion: A student’s journey through the AWS AI League ASEAN finals</title>
<link>https://news.jatlink.uk/3313</link>
<guid>https://news.jatlink.uk/3313</guid>
<description><![CDATA[ The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, beginner, champion:, student’s, journey, through, the, AWS, League, ASEAN, finals</media:keywords>
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<title>Deploy AI agents on Amazon Bedrock AgentCore using GitHub Actions</title>
<link>https://news.jatlink.uk/3314</link>
<guid>https://news.jatlink.uk/3314</guid>
<description><![CDATA[ In this post, we demonstrate how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, agents, Amazon, Bedrock, AgentCore, using, GitHub, Actions</media:keywords>
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<title>Advanced fine&amp;tuning techniques for multi&amp;agent orchestration: Patterns from Amazon at scale</title>
<link>https://news.jatlink.uk/3311</link>
<guid>https://news.jatlink.uk/3311</guid>
<description><![CDATA[ In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). This post details the techniques behind these outcomes: from foundational methods like Supervised Fine-Tuning (SFT) (instruction tuning), and Proximal Policy Optimization (PPO), to Direct Preference Optimization (DPO) for human alignment, to cutting-edge reasoning optimizations such as Grouped-based Reinforcement Learning from Policy Optimization (GRPO), Direct Advantage Policy Optimization (DAPO), and Group Sequence Policy Optimization (GSPO) purpose-built for agentic systems. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advanced, fine-tuning, techniques, for, multi-agent, orchestration:, Patterns, from, Amazon, scale</media:keywords>
</item>

<item>
<title>Strengthening the U.S. AI supply chain through domestic manufacturing</title>
<link>https://news.jatlink.uk/3310</link>
<guid>https://news.jatlink.uk/3310</guid>
<description><![CDATA[ OpenAI launches a new RFP to strengthen the U.S. AI supply chain by accelerating domestic manufacturing, creating jobs, and scaling AI infrastructure. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 15 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Strengthening, the, U.S., supply, chain, through, domestic, manufacturing</media:keywords>
</item>

<item>
<title>Safeguard generative AI applications with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/3308</link>
<guid>https://news.jatlink.uk/3308</guid>
<description><![CDATA[ In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Safeguard, generative, applications, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Scale creative asset discovery with Amazon Nova Multimodal Embeddings unified vector search</title>
<link>https://news.jatlink.uk/3309</link>
<guid>https://news.jatlink.uk/3309</guid>
<description><![CDATA[ In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, creative, asset, discovery, with, Amazon, Nova, Multimodal, Embeddings, unified, vector, search</media:keywords>
</item>

<item>
<title>Investing in Merge Labs</title>
<link>https://news.jatlink.uk/3305</link>
<guid>https://news.jatlink.uk/3305</guid>
<description><![CDATA[ OpenAI is investing in Merge Labs to support new brain computer interfaces that bridge biological and artificial intelligence to maximize human ability, agency, and experience. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Investing, Merge, Labs</media:keywords>
</item>

<item>
<title>How the Amazon AMET Payments team accelerates test case generation with Strands Agents</title>
<link>https://news.jatlink.uk/3306</link>
<guid>https://news.jatlink.uk/3306</guid>
<description><![CDATA[ In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon, AMET, Payments, team, accelerates, test, case, generation, with, Strands, Agents</media:keywords>
</item>

<item>
<title>Build a generative AI&amp;powered business reporting solution with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3307</link>
<guid>https://news.jatlink.uk/3307</guid>
<description><![CDATA[ This post introduces generative AI guided business reporting—with a focus on writing achievements &amp; challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, generative, AI-powered, business, reporting, solution, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Transform AI development with new Amazon SageMaker AI model customization and large&amp;scale training capabilities</title>
<link>https://news.jatlink.uk/3304</link>
<guid>https://news.jatlink.uk/3304</guid>
<description><![CDATA[ This post explores how new serverless model customization capabilities, elastic training, checkpointless training, and serverless MLflow work together to accelerate your AI development from months to days. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, development, with, new, Amazon, SageMaker, model, customization, and, large-scale, training, capabilities</media:keywords>
</item>

<item>
<title>How AutoScout24 built a Bot Factory to standardize AI agent development with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3303</link>
<guid>https://news.jatlink.uk/3303</guid>
<description><![CDATA[ In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AutoScout24, built, Bot, Factory, standardize, agent, development, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>OpenAI partners with Cerebras </title>
<link>https://news.jatlink.uk/3302</link>
<guid>https://news.jatlink.uk/3302</guid>
<description><![CDATA[ OpenAI partners with Cerebras to add 750MW of high-speed AI compute, reducing inference latency and making ChatGPT faster for real-time AI workloads. ]]></description>
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<pubDate>Wed, 14 Jan 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, partners, with, Cerebras </media:keywords>
</item>

<item>
<title>Zenken boosts a lean sales team with ChatGPT Enterprise</title>
<link>https://news.jatlink.uk/3301</link>
<guid>https://news.jatlink.uk/3301</guid>
<description><![CDATA[ By rolling out ChatGPT Enterprise company-wide, Zenken has boosted sales performance, cut preparation time, and increased proposal success rates. AI-supported workflows are helping a lean team deliver more personalized, effective customer engagement. ]]></description>
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<pubDate>Wed, 14 Jan 2026 01:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Zenken, boosts, lean, sales, team, with, ChatGPT, Enterprise</media:keywords>
</item>

<item>
<title>Securing Amazon Bedrock cross&amp;Region inference: Geographic and global</title>
<link>https://news.jatlink.uk/3300</link>
<guid>https://news.jatlink.uk/3300</guid>
<description><![CDATA[ In this post, we explore the security considerations and best practices for implementing Amazon Bedrock cross-Region inference profiles. Whether you&#039;re building a generative AI application or need to meet specific regional compliance requirements, this guide will help you understand the secure architecture of Amazon Bedrock CRIS and how to properly configure your implementation. ]]></description>
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<pubDate>Wed, 14 Jan 2026 00:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, Amazon, Bedrock, cross-Region, inference:, Geographic, and, global</media:keywords>
</item>

<item>
<title>How Beekeeper by LumApps optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3299</link>
<guid>https://news.jatlink.uk/3299</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Mon, 12 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, LumApps, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Omada Health scaled patient care by fine&amp;tuning Llama models on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3298</link>
<guid>https://news.jatlink.uk/3298</guid>
<description><![CDATA[ This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health, a longtime innovator in virtual healthcare delivery, launched a new nutrition experience in 2025, featuring OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education. It was built on AWS. OmadaSpark was designed […] ]]></description>
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<pubDate>Mon, 12 Jan 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Omada, Health, scaled, patient, care, fine-tuning, Llama, models, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Crossmodal search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3297</link>
<guid>https://news.jatlink.uk/3297</guid>
<description><![CDATA[ In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications. ]]></description>
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<pubDate>Sat, 10 Jan 2026 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Crossmodal, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>OpenAI and SoftBank Group partner with SB Energy</title>
<link>https://news.jatlink.uk/3296</link>
<guid>https://news.jatlink.uk/3296</guid>
<description><![CDATA[ OpenAI and SoftBank Group partner with SB Energy to develop multi-gigawatt AI data center campuses, including a 1.2 GW Texas facility supporting the Stargate initiative. ]]></description>
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<pubDate>Fri, 09 Jan 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, and, SoftBank, Group, partner, with, Energy</media:keywords>
</item>

<item>
<title>Accelerating LLM inference with post&amp;training weight and activation using AWQ and GPTQ on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3295</link>
<guid>https://news.jatlink.uk/3295</guid>
<description><![CDATA[ Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker. ]]></description>
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<pubDate>Fri, 09 Jan 2026 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, inference, with, post-training, weight, and, activation, using, AWQ, and, GPTQ, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions</title>
<link>https://news.jatlink.uk/3293</link>
<guid>https://news.jatlink.uk/3293</guid>
<description><![CDATA[ This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&amp;D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sentiment, Analysis, with, Text, and, Audio, Using, AWS, Generative, Services:, Approaches, Challenges, and, Solutions</media:keywords>
</item>

<item>
<title>Architecting TrueLook’s AI&amp;powered construction safety system on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3294</link>
<guid>https://news.jatlink.uk/3294</guid>
<description><![CDATA[ This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Architecting, TrueLook’s, AI-powered, construction, safety, system, Amazon, SageMaker AI</media:keywords>
</item>

<item>
<title>How Beekeeper optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3292</link>
<guid>https://news.jatlink.uk/3292</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Datadog uses Codex for system&amp;level code review</title>
<link>https://news.jatlink.uk/3291</link>
<guid>https://news.jatlink.uk/3291</guid>
<description><![CDATA[ OpenAI and Datadog brand graphic with the OpenAI wordmark on the left, the Datadog logo on the right, and a central abstract brown fur-like texture panel on a white background. ]]></description>
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<pubDate>Fri, 09 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Datadog, uses, Codex, for, system-level, code, review</media:keywords>
</item>

<item>
<title>Netomi’s lessons for scaling agentic systems into the enterprise</title>
<link>https://news.jatlink.uk/3290</link>
<guid>https://news.jatlink.uk/3290</guid>
<description><![CDATA[ How Netomi scales enterprise AI agents using GPT-4.1 and GPT-5.2—combining concurrency, governance, and multi-step reasoning for reliable production workflows. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 08 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Netomi’s, lessons, for, scaling, agentic, systems, into, the, enterprise</media:keywords>
</item>

<item>
<title>OpenAI for Healthcare</title>
<link>https://news.jatlink.uk/3289</link>
<guid>https://news.jatlink.uk/3289</guid>
<description><![CDATA[ OpenAI for Healthcare enables secure, enterprise-grade AI that supports HIPAA compliance—reducing administrative burden and supporting clinical workflows. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 08 Jan 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, for, Healthcare</media:keywords>
</item>

<item>
<title>Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)</title>
<link>https://news.jatlink.uk/3288</link>
<guid>https://news.jatlink.uk/3288</guid>
<description><![CDATA[ This two-part series explores Flo Health&#039;s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale. ]]></description>
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<pubDate>Thu, 08 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, medical, content, review, Flo, Health, using, Amazon, Bedrock, Part</media:keywords>
</item>

<item>
<title>Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails</title>
<link>https://news.jatlink.uk/3286</link>
<guid>https://news.jatlink.uk/3286</guid>
<description><![CDATA[ This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Detect, and, redact, personally, identifiable, information, using, Amazon, Bedrock, Data, Automation, and, Guardrails</media:keywords>
</item>

<item>
<title>Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool</title>
<link>https://news.jatlink.uk/3287</link>
<guid>https://news.jatlink.uk/3287</guid>
<description><![CDATA[ Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Speed, meets, scale:, Load, testing, SageMakerAI, endpoints, with, Observe.AI’s, testing, tool</media:keywords>
</item>

<item>
<title>Introducing ChatGPT Health</title>
<link>https://news.jatlink.uk/3285</link>
<guid>https://news.jatlink.uk/3285</guid>
<description><![CDATA[ ChatGPT Health is a dedicated experience that securely connects your health data and apps, with privacy protections and a physician-informed design. ]]></description>
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<pubDate>Wed, 07 Jan 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, ChatGPT, Health</media:keywords>
</item>

<item>
<title>How Tolan builds voice&amp;first AI with GPT&amp;5.1</title>
<link>https://news.jatlink.uk/3284</link>
<guid>https://news.jatlink.uk/3284</guid>
<description><![CDATA[ Tolan built a voice-first AI companion with GPT-5.1, combining low-latency responses, real-time context reconstruction, and memory-driven personalities for natural conversations. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Wed, 07 Jan 2026 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tolan, builds, voice-first, with, GPT-5.1</media:keywords>
</item>

<item>
<title>Announcing OpenAI Grove Cohort 2</title>
<link>https://news.jatlink.uk/3283</link>
<guid>https://news.jatlink.uk/3283</guid>
<description><![CDATA[ Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team. ]]></description>
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<pubDate>Fri, 02 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, OpenAI, Grove, Cohort</media:keywords>
</item>

<item>
<title>Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow</title>
<link>https://news.jatlink.uk/3282</link>
<guid>https://news.jatlink.uk/3282</guid>
<description><![CDATA[ This post shows you how to migrate your self-managed MLflow tracking server to a MLflow App – a serverless tracking server on SageMaker AI that automatically scales resources based on demand while removing server patching and storage management tasks at no cost. Learn how to use the MLflow Export Import tool to transfer your experiments, runs, models, and other MLflow resources, including instructions to validate your migration&#039;s success. ]]></description>
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<pubDate>Mon, 29 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, MLflow, tracking, servers, Amazon, SageMaker, with, serverless, MLflow</media:keywords>
</item>

<item>
<title>Build an AI&amp;powered website assistant with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3281</link>
<guid>https://news.jatlink.uk/3281</guid>
<description><![CDATA[ This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases. ]]></description>
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<pubDate>Mon, 29 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, website, assistant, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM&amp; Optimizer</title>
<link>https://news.jatlink.uk/3280</link>
<guid>https://news.jatlink.uk/3280</guid>
<description><![CDATA[ In this post, we demonstrate how to optimize large language model (LLM) inference on Amazon SageMaker AI using BentoML&#039;s LLM-Optimizer to systematically identify the best serving configurations for your workload. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, LLM, inference, Amazon, SageMaker, with, BentoML’s, LLM-, Optimizer</media:keywords>
</item>

<item>
<title>AI agent&amp;driven browser automation for enterprise workflow management</title>
<link>https://news.jatlink.uk/3278</link>
<guid>https://news.jatlink.uk/3278</guid>
<description><![CDATA[ Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks […] ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agent-driven, browser, automation, for, enterprise, workflow, management</media:keywords>
</item>

<item>
<title>Agentic QA automation using Amazon Bedrock AgentCore Browser and Amazon Nova Act</title>
<link>https://news.jatlink.uk/3279</link>
<guid>https://news.jatlink.uk/3279</guid>
<description><![CDATA[ In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, automation, using, Amazon, Bedrock, AgentCore, Browser, and, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Programmatically creating an IDP solution with Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/3277</link>
<guid>https://news.jatlink.uk/3277</guid>
<description><![CDATA[ In this post, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users to upload multi-modal business documents and extract insights using BDA as a parser to retrieve relevant chunks and augment a prompt to a foundational model (FM). ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Programmatically, creating, IDP, solution, with, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Exploring the zero operator access design of Mantle</title>
<link>https://news.jatlink.uk/3276</link>
<guid>https://news.jatlink.uk/3276</guid>
<description><![CDATA[ In this post, we explore how Mantle, Amazon&#039;s next-generation inference engine for Amazon Bedrock, implements a zero operator access (ZOA) design that eliminates any technical means for AWS operators to access customer data. ]]></description>
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<pubDate>Tue, 23 Dec 2025 23:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Exploring, the, zero, operator, access, design, Mantle</media:keywords>
</item>

<item>
<title>How dLocal automated compliance reviews using Amazon Quick Automate</title>
<link>https://news.jatlink.uk/3273</link>
<guid>https://news.jatlink.uk/3273</guid>
<description><![CDATA[ In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, dLocal, automated, compliance, reviews, using, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Advancing ADHD diagnosis: How Qbtech built a mobile AI assessment Model Using Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3274</link>
<guid>https://news.jatlink.uk/3274</guid>
<description><![CDATA[ In this post, we explore how Qbtech streamlined their machine learning (ML) workflow using Amazon SageMaker AI, a fully managed service to build, train and deploy ML models, and AWS Glue, a serverless service that makes data integration simpler, faster, and more cost effective. This new solution reduced their feature engineering time from weeks to hours, while maintaining the high clinical standards required by healthcare providers. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, ADHD, diagnosis:, How, Qbtech, built, mobile, assessment, Model, Using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 1: From idea to generation with the Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/3275</link>
<guid>https://news.jatlink.uk/3275</guid>
<description><![CDATA[ In this post, the first of a series of three, we focus on how you can use Amazon Nova to streamline, simplify, and accelerate marketing campaign creation through generative AI. We show how Bancolombia, one of Colombia’s largest banks, is experimenting with the Amazon Nova models to generate visuals for their marketing campaigns. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, From, idea, generation, with, the, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>AWS AI League: Model customization and agentic showdown</title>
<link>https://news.jatlink.uk/3271</link>
<guid>https://news.jatlink.uk/3271</guid>
<description><![CDATA[ In this post, we explore the new AWS AI League challenges and how they are transforming how organizations approach AI development. The grand finale at AWS re:Invent 2025 was an exciting showcase of their ingenuity and skills. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Model, customization, and, agentic, showdown</media:keywords>
</item>

<item>
<title>Accelerate Enterprise AI Development using Weights &amp;amp; Biases and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3272</link>
<guid>https://news.jatlink.uk/3272</guid>
<description><![CDATA[ In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&amp;B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Enterprise, Development, using, Weights, Biases, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing Visa Intelligent Commerce on AWS: Enabling agentic commerce with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3270</link>
<guid>https://news.jatlink.uk/3270</guid>
<description><![CDATA[ In this post, we explore how AWS and Visa are partnering to enable agentic commerce through Visa Intelligent Commerce using Amazon Bedrock AgentCore. We demonstrate how autonomous AI agents can transform fragmented shopping and travel experiences into seamless, end-to-end workflows—from discovery and comparison to secure payment authorization—all driven by natural language. ]]></description>
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<pubDate>Tue, 23 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Visa, Intelligent, Commerce, AWS:, Enabling, agentic, commerce, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Deploy Mistral AI’s Voxtral on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3267</link>
<guid>https://news.jatlink.uk/3267</guid>
<description><![CDATA[ In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach. vLLM is a high-performance library for serving large language models (LLMs) that features paged attention for improved memory management and tensor parallelism for distributing models across multiple GPUs. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, Mistral, AI’s, Voxtral, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Enhance document analytics with Strands AI Agents for the GenAI IDP Accelerator</title>
<link>https://news.jatlink.uk/3268</link>
<guid>https://news.jatlink.uk/3268</guid>
<description><![CDATA[ To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhance, document, analytics, with, Strands, Agents, for, the, GenAI, IDP, Accelerator</media:keywords>
</item>

<item>
<title>Build a multimodal generative AI assistant for root cause diagnosis in predictive maintenance using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3269</link>
<guid>https://news.jatlink.uk/3269</guid>
<description><![CDATA[ In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon&#039;s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multimodal, generative, assistant, for, root, cause, diagnosis, predictive, maintenance, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Continuously hardening ChatGPT Atlas against prompt injection</title>
<link>https://news.jatlink.uk/3265</link>
<guid>https://news.jatlink.uk/3265</guid>
<description><![CDATA[ OpenAI is strengthening ChatGPT Atlas against prompt injection attacks using automated red teaming trained with reinforcement learning. This proactive discover-and-patch loop helps identify novel exploits early and harden the browser agent’s defenses as AI becomes more agentic. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 22 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Continuously, hardening, ChatGPT, Atlas, against, prompt, injection</media:keywords>
</item>

<item>
<title>Move Beyond Chain&amp;of&amp;Thought with Chain&amp;of&amp;Draft on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3266</link>
<guid>https://news.jatlink.uk/3266</guid>
<description><![CDATA[ This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Move, Beyond, Chain-of-Thought, with, Chain-of-Draft, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>One in a million: celebrating the customers shaping AI’s future</title>
<link>https://news.jatlink.uk/3264</link>
<guid>https://news.jatlink.uk/3264</guid>
<description><![CDATA[ More than one million customers around the world now use OpenAI to empower their teams and unlock new opportunities. This post highlights how companies like PayPal, Virgin Atlantic, BBVA, Cisco, Moderna, and Canva are transforming the way work gets done with AI. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Mon, 22 Dec 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>One, million:, celebrating, the, customers, shaping, AI’s, future</media:keywords>
</item>

<item>
<title>Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads</title>
<link>https://news.jatlink.uk/3263</link>
<guid>https://news.jatlink.uk/3263</guid>
<description><![CDATA[ Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. ]]></description>
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<pubDate>Fri, 19 Dec 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, SOCI, indexing, for, Amazon, SageMaker, Studio:, Faster, container, startup, times, for, AIML, workloads</media:keywords>
</item>

<item>
<title>Evaluating chain&amp;of&amp;thought monitorability</title>
<link>https://news.jatlink.uk/3262</link>
<guid>https://news.jatlink.uk/3262</guid>
<description><![CDATA[ OpenAI introduces a new framework and evaluation suite for chain-of-thought monitorability, covering 13 evaluations across 24 environments. Our findings show that monitoring a model’s internal reasoning is far more effective than monitoring outputs alone, offering a promising path toward scalable control as AI systems grow more capable. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 18 Dec 2025 23:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, chain-of-thought, monitorability</media:keywords>
</item>

<item>
<title>Updating our Model Spec with teen protections</title>
<link>https://news.jatlink.uk/3260</link>
<guid>https://news.jatlink.uk/3260</guid>
<description><![CDATA[ OpenAI is updating its Model Spec with new Under-18 Principles that define how ChatGPT should support teens with safe, age-appropriate guidance grounded in developmental science. The update strengthens guardrails, clarifies expected model behavior in higher-risk situations, and builds on our broader work to improve teen safety across ChatGPT. ]]></description>
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<pubDate>Thu, 18 Dec 2025 20:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Updating, our, Model, Spec, with, teen, protections</media:keywords>
</item>

<item>
<title>Deepening our collaboration with the U.S. Department of Energy</title>
<link>https://news.jatlink.uk/3261</link>
<guid>https://news.jatlink.uk/3261</guid>
<description><![CDATA[ OpenAI and the U.S. Department of Energy have signed a memorandum of understanding to deepen collaboration on AI and advanced computing in support of scientific discovery. The agreement builds on ongoing work with national laboratories and helps establish a framework for applying AI to high-impact research across the DOE ecosystem. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 18 Dec 2025 20:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deepening, our, collaboration, with, the, U.S., Department, Energy</media:keywords>
</item>

<item>
<title>Introducing GPT&amp;5.2&amp;Codex</title>
<link>https://news.jatlink.uk/3258</link>
<guid>https://news.jatlink.uk/3258</guid>
<description><![CDATA[ GPT-5.2-Codex is OpenAI’s most advanced coding model, offering long-horizon reasoning, large-scale code transformations, and enhanced cybersecurity capabilities. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 18 Dec 2025 19:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, GPT-5.2-Codex</media:keywords>
</item>

<item>
<title>AI literacy resources for teens and parents</title>
<link>https://news.jatlink.uk/3257</link>
<guid>https://news.jatlink.uk/3257</guid>
<description><![CDATA[ OpenAI shares new AI literacy resources to help teens and parents use ChatGPT thoughtfully, safely, and with confidence. The guides include expert-vetted tips for responsible use, critical thinking, healthy boundaries, and supporting teens through emotional or sensitive topics. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 18 Dec 2025 19:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>literacy, resources, for, teens, and, parents</media:keywords>
</item>

<item>
<title>Addendum to GPT&amp;5.2 System Card: GPT&amp;5.2&amp;Codex</title>
<link>https://news.jatlink.uk/3259</link>
<guid>https://news.jatlink.uk/3259</guid>
<description><![CDATA[ This system card outlines the comprehensive safety measures implemented for GPT‑5.2-Codex. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access. ]]></description>
<enclosure url="http://news.jatlink.uk" length="4096" type="image/jpeg"/>
<pubDate>Thu, 18 Dec 2025 19:00:03 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Addendum, GPT-5.2, System, Card:, GPT-5.2-Codex</media:keywords>
</item>

<item>
<title>Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents</title>
<link>https://news.jatlink.uk/3255</link>
<guid>https://news.jatlink.uk/3255</guid>
<description><![CDATA[ This post demonstrates how to use the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, and, deploy, scalable, agents, with, NVIDIA, NeMo, Amazon, Bedrock, AgentCore, and, Strands, Agents</media:keywords>
</item>

<item>
<title>Bi&amp;directional streaming for real&amp;time agent interactions now available in Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/3256</link>
<guid>https://news.jatlink.uk/3256</guid>
<description><![CDATA[ In this post, you will learn about bi-directional streaming on AgentCore Runtime and the prerequisites to create a WebSocket implementation. You will also learn how to use Strands Agents to implement a bi-directional streaming solution for voice agents. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bi-directional, streaming, for, real-time, agent, interactions, now, available, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Developers can now submit apps to ChatGPT</title>
<link>https://news.jatlink.uk/3254</link>
<guid>https://news.jatlink.uk/3254</guid>
<description><![CDATA[ Developers can now submit apps for review and publication in ChatGPT, with approved apps appearing in a new in-product directory for easy discovery. Updated tools, guidelines, and the Apps SDK help developers build powerful chat-native experiences that bring real-world actions into ChatGPT. ]]></description>
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<pubDate>Wed, 17 Dec 2025 23:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Developers, can, now, submit, apps, ChatGPT</media:keywords>
</item>

<item>
<title>Tracking and managing assets used in AI development with Amazon SageMaker AI </title>
<link>https://news.jatlink.uk/3252</link>
<guid>https://news.jatlink.uk/3252</guid>
<description><![CDATA[ In this post, we&#039;ll explore the new capabilities and core concepts that help organizations track and manage models development and deployment lifecycles. We will show you how the features are configured to train models with automatic end-to-end lineage, from dataset upload and versioning to model fine-tuning, evaluation, and seamless endpoint deployment. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tracking, and, managing, assets, used, development, with Amazon, SageMaker, AI </media:keywords>
</item>

<item>
<title>Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration</title>
<link>https://news.jatlink.uk/3253</link>
<guid>https://news.jatlink.uk/3253</guid>
<description><![CDATA[ In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Track, machine, learning, experiments, with, MLflow, Amazon, SageMaker, using, Snowflake, integration</media:keywords>
</item>

<item>
<title>Introducing OpenAI Academy for News Organizations</title>
<link>https://news.jatlink.uk/3251</link>
<guid>https://news.jatlink.uk/3251</guid>
<description><![CDATA[ OpenAI is launching the OpenAI Academy for News Organizations, a new learning hub built with the American Journalism Project and The Lenfest Institute to help newsrooms use AI effectively. The Academy offers training, practical use cases, and responsible-use guidance to support journalists, editors, and publishers as they adopt AI in their reporting and operations. ]]></description>
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<pubDate>Wed, 17 Dec 2025 15:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, OpenAI, Academy, for, News, Organizations</media:keywords>
</item>

<item>
<title>Governance by design: The essential guide for successful AI scaling</title>
<link>https://news.jatlink.uk/3250</link>
<guid>https://news.jatlink.uk/3250</guid>
<description><![CDATA[ Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain control as AI applications multiply? ]]></description>
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<pubDate>Tue, 16 Dec 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Governance, design:, The, essential, guide, for, successful, scaling</media:keywords>
</item>

<item>
<title>How Tata Power CoE built a scalable AI&amp;powered solar panel inspection solution with Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3248</link>
<guid>https://news.jatlink.uk/3248</guid>
<description><![CDATA[ In this post, we explore how Tata Power CoE and Oneture Technologies use AWS services to automate the inspection process end-to-end. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tata, Power, CoE, built, scalable, AI-powered, solar, panel, inspection, solution, with, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Unlocking video understanding with TwelveLabs Marengo on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3249</link>
<guid>https://news.jatlink.uk/3249</guid>
<description><![CDATA[ In this post, we&#039;ll show how the TwelveLabs Marengo embedding model, available on Amazon Bedrock, enhances video understanding through multimodal AI. We&#039;ll build a video semantic search and analysis solution using embeddings from the Marengo model with Amazon OpenSearch Serverless as the vector database, for semantic search capabilities that go beyond simple metadata matching to deliver intelligent content discovery. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, understanding, with, TwelveLabs, Marengo, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>The new ChatGPT Images is here</title>
<link>https://news.jatlink.uk/3247</link>
<guid>https://news.jatlink.uk/3247</guid>
<description><![CDATA[ The new ChatGPT Images is powered by our flagship image generation model, delivering more precise edits, consistent details, and image generation up to 4× faster. The upgraded model is rolling out to all ChatGPT users today and is also available in the API as GPT-Image-1.5. ]]></description>
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<pubDate>Tue, 16 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, new, ChatGPT, Images, here</media:keywords>
</item>

<item>
<title>Evaluating AI’s ability to perform scientific research tasks</title>
<link>https://news.jatlink.uk/3246</link>
<guid>https://news.jatlink.uk/3246</guid>
<description><![CDATA[ OpenAI introduces FrontierScience, a benchmark testing AI reasoning in physics, chemistry, and biology to measure progress toward real scientific research. ]]></description>
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<pubDate>Tue, 16 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, AI’s, ability, perform, scientific, research, tasks</media:keywords>
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