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    <title>AI News Hub</title>
    <link>https://ai-news.helloworldfirm.com</link>
    <description>Primary-source AI news, releases and research from major labs and model providers.</description>
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    <lastBuildDate>Sat, 20 Jun 2026 12:26:57 GMT</lastBuildDate>
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    <item>
      <title>Introducing Web Search on Amazon Bedrock AgentCore</title>
      <link>https://aws.amazon.com/blogs/machine-learning/introducing-web-search-on-amazon-bedrock-agentcore/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 19 Jun 2026 14:15:24 GMT</pubDate>
      <description>Web Search on Amazon Bedrock AgentCore is now generally available. In this post, we walk through what makes Web Search on Amazon Bedrock AgentCore different, why it matters, and how to wire it in with a few lines of code.</description>
    </item>
    <item>
      <title>Accelerate campaign workflow with insights from Adobe Marketing Agent for Amazon Quick</title>
      <link>https://aws.amazon.com/blogs/machine-learning/accelerate-campaign-workflow-with-insights-from-adobe-marketing-agent-for-amazon-quick/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 19 Jun 2026 14:05:04 GMT</pubDate>
      <description>This post shows how to enable Adobe Marketing Agent for Amazon Quick using a Model Context Protocol (MCP). We walk you through how to configure the integration, authenticate using your Adobe credentials, and get the latest insights in Amazon Quick. The sample workflow returns audience rankings, loyalty segment summaries, journey usage, and conflict recommendations.</description>
    </item>
    <item>
      <title>Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch</title>
      <link>https://aws.amazon.com/blogs/machine-learning/monitor-and-debug-generative-ai-inference-with-sagemaker-detailed-metrics-and-insights-dashboard-on-cloudwatch/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 23:31:58 GMT</pubDate>
      <description>Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.</description>
    </item>
    <item>
      <title>How FERC’s Large-Load Interconnection Actions Help Address Grid Stress, Improve Affordability</title>
      <link>https://blogs.nvidia.com/blog/ferc-large-load-interconnection/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 20:00:27 GMT</pubDate>
      <description>In a consequential grid infrastructure decision, the Federal Energy Regulatory Commission (FERC) today issued a major milestone on large-load interconnection impacting how those building AI factories, semiconductor fabrication support systems and advanced manufacturing facilities can connect to the grid. In the era of AI, which NVIDIA founder and CEO Jensen Huang has described as a […]</description>
    </item>
    <item>
      <title>MosaicLeaks: Can your research agent keep a secret?</title>
      <link>https://huggingface.co/blog/ServiceNow/mosaicleaks</link>
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      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Thu, 18 Jun 2026 18:13:13 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>Amazon Bedrock AgentCore harness is now generally available: Go from idea to production-grade agent in minutes</title>
      <link>https://aws.amazon.com/blogs/machine-learning/amazon-bedrock-agentcore-harness-is-now-generally-available-go-from-idea-to-production-grade-agent-in-minutes/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 17:32:22 GMT</pubDate>
      <description>Today, Amazon Bedrock AgentCore harness is generally available. Two API calls (CreateHarness to define an agent, and InvokeHarness to run it), and you have an agent running in seconds. The agent runs in its own isolated environment with a filesystem and shell, so it can read files, run commands, and write code safely. It remembers users and conversations across sessions, picks up skills you point it at (including the AWS-curated catalog), browses the web, calls your tools through gateway or MCP,</description>
    </item>
    <item>
      <title>New usage analytics and updated spend controls for enterprises</title>
      <link>https://openai.com/index/chatgpt-enterprise-spend-controls</link>
      <guid isPermaLink="false">provider-a8a48c3b3b70188786</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 17:00:00 GMT</pubDate>
      <description>OpenAI introduces new spend controls and usage analytics for ChatGPT Enterprise, helping organizations manage costs and scale AI with confidence.</description>
    </item>
    <item>
      <title>At Cannes Lions, NVIDIA Partners Reshape Advertising and Marketing With AI</title>
      <link>https://blogs.nvidia.com/blog/nvidia-ai-marketing-advertising-cannes-lions/</link>
      <guid isPermaLink="false">provider-86c43061612c7b50f5</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 13:00:43 GMT</pubDate>
      <description>The digital era gave the advertising and marketing industry speed; the AI era is giving it autonomous operations. For companies building next-generation technologies for advertising and marketing, the question is no longer whether to adopt AI but whether their infrastructure can support it at the speed and scale the industry demands. At Cannes Lions, running […]</description>
    </item>
    <item>
      <title>Sync and Stream: GeForce NOW Connects to Members’ Game Libraries Across Devices</title>
      <link>https://blogs.nvidia.com/blog/geforce-now-thursday-game-stores/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 13:00:32 GMT</pubDate>
      <description>Play favorite titles from popular game libraries, keep progress synced and jump back into gaming sessions on virtually any device. That’s the power of GeForce NOW cloud gaming. From providing access to members’ favorite game libraries to offering some of the season’s best membership pricing, GeForce NOW is making it easier than ever to get […]</description>
    </item>
    <item>
      <title>Improving health intelligence in ChatGPT</title>
      <link>https://openai.com/index/improving-health-intelligence-in-chatgpt</link>
      <guid isPermaLink="false">provider-f7e92e0abe0068751e</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 11:00:00 GMT</pubDate>
      <description>Learn how GPT-5.5 Instant improves ChatGPT’s health and wellness responses with stronger reasoning, better context, clearer communication, and physician-informed evaluations.</description>
    </item>
    <item>
      <title>Using AI to help physicians diagnose rare genetic diseases affecting children</title>
      <link>https://openai.com/index/diagnose-rare-childhood-diseases</link>
      <guid isPermaLink="false">provider-cdae118f5e2b1b0dde</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 08:00:00 GMT</pubDate>
      <description>Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.</description>
    </item>
    <item>
      <title>France Advances Europe’s AI Future With NVIDIA Technologies</title>
      <link>https://blogs.nvidia.com/blog/france-advances-europes-ai-future/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Thu, 18 Jun 2026 06:00:59 GMT</pubDate>
      <description>A year ago at NVIDIA GTC Paris at VivaTech, France laid out plans to advance local AI — from new AI factories and national compute capacity to open frontier models and industrial platforms. Now, that AI infrastructure is coming online. AI agents are running in production, startups are deploying applications and the French AI ecosystem […]</description>
    </item>
    <item>
      <title>Allstate explores quantum computing for insurance portfolios</title>
      <link>https://research.ibm.com/blog/allstate-quantum-insurance-portfolio?utm_medium=rss&amp;utm_source=rss</link>
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      <dc:creator>IBM Research</dc:creator>
      <pubDate>Thu, 18 Jun 2026 04:00:00 GMT</pubDate>
      <description>Insurance involves hard problems with complex, correlated risks. Allstate and IBM are showing how quantum could help solve them.</description>
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    <item>
      <title>Beyond LoRA: Can you beat the most popular fine-tuning technique?</title>
      <link>https://huggingface.co/blog/peft-beyond-lora</link>
      <guid isPermaLink="false">provider-4c3abb262078de7a84</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Thu, 18 Jun 2026 00:00:00 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>Is it agentic enough? Benchmarking open models on your own tooling</title>
      <link>https://huggingface.co/blog/is-it-agentic-enough</link>
      <guid isPermaLink="false">provider-43325f0432eefad4ef</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Thu, 18 Jun 2026 00:00:00 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>Amazon SageMaker AI Async Inference now supports inline request payloads</title>
      <link>https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-ai-async-inference-now-supports-inline-request-payloads/</link>
      <guid isPermaLink="false">provider-6974e0b50fe27d773f</guid>
      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 20:56:36 GMT</pubDate>
      <description>Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.</description>
    </item>
    <item>
      <title>Get back hours every day with autonomous agents in Amazon Quick</title>
      <link>https://aws.amazon.com/blogs/machine-learning/get-back-hours-every-day-with-autonomous-agents-in-amazon-quick/</link>
      <guid isPermaLink="false">provider-a349aaa9cfec2a9a2d</guid>
      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 20:35:39 GMT</pubDate>
      <description>Today, Quick gets even more powerful: new autonomous agents that work continuously&amp;nbsp;on your behalf,&amp;nbsp;an activity feed that helps you prioritize your most important work, and the ability to&amp;nbsp;find insights&amp;nbsp;across every data source your business runs on from a single&amp;nbsp;question.</description>
    </item>
    <item>
      <title>Context intelligence for your data and AI agents at scale</title>
      <link>https://aws.amazon.com/blogs/machine-learning/context-intelligence-for-your-data-and-ai-agents-at-scale/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 17:17:37 GMT</pubDate>
      <description>Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in institutional knowledge that has never been written down. You want to trust the decisions made by your AI agents, but that can&apos;t happen until agents have context. Imagine what becomes possible when we give agents a safe way to access the context they need to deliver trusted decisions. This is why at the AWS Summit New </description>
    </item>
    <item>
      <title>New in Amazon Bedrock AgentCore: Build agents with broader knowledge and continuous learning</title>
      <link>https://aws.amazon.com/blogs/machine-learning/new-in-amazon-bedrock-agentcore-build-agents-with-broader-knowledge-and-continuous-learning/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 15:29:36 GMT</pubDate>
      <description>Today we&apos;re introducing new capabilities on Amazon Bedrock AgentCore, the platform to build, connect, and optimize agents. In this post, we cover how these capabilities close each gap: connecting agents to organizational, web, and paid knowledge; helping teams find and fix what&apos;s going wrong in production; and enforcing controls that scale as agents grow more capable. Together, they help you build more capable agents faster, govern them with controls that scale, and improve them continuously.</description>
    </item>
    <item>
      <title>MolmoMotion: Language-guided 3D motion forecasting</title>
      <link>https://huggingface.co/blog/allenai/molmomotion</link>
      <guid isPermaLink="false">provider-dedb6c008f63482a2b</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Wed, 17 Jun 2026 15:26:44 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot</title>
      <link>https://huggingface.co/blog/amazon/strands-lerobot-hub-to-hardware</link>
      <guid isPermaLink="false">provider-4b7334650a4c81586b</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Wed, 17 Jun 2026 10:18:05 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry</title>
      <link>https://openai.com/index/ai-chemist-improves-reaction</link>
      <guid isPermaLink="false">provider-367e584ff6628f098a</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 10:00:00 GMT</pubDate>
      <description>OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research.</description>
    </item>
    <item>
      <title>GLM-5.2: Built for Long-Horizon Tasks</title>
      <link>https://huggingface.co/blog/zai-org/glm-52-blog</link>
      <guid isPermaLink="false">provider-fb1eba26290ed6e538</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Wed, 17 Jun 2026 09:01:25 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>Introducing LifeSciBench</title>
      <link>https://openai.com/index/introducing-life-sci-bench</link>
      <guid isPermaLink="false">provider-c0db56626898f83c32</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <description>Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.</description>
    </item>
    <item>
      <title>Agentic Resource Discovery: Let agents search</title>
      <link>https://huggingface.co/blog/agentic-resource-discovery-launch</link>
      <guid isPermaLink="false">provider-20021aa0f37d518fdd</guid>
      <dc:creator>Hugging Face</dc:creator>
      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <description></description>
    </item>
    <item>
      <title>Safeguard your agentic AI applications with the Amazon Bedrock Guardrails InvokeGuardrailChecks API</title>
      <link>https://aws.amazon.com/blogs/machine-learning/safeguard-your-agentic-ai-applications-with-the-amazon-bedrock-guardrails-invokeguardrailchecks-api/</link>
      <guid isPermaLink="false">provider-9672be267246ad9fb4</guid>
      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 22:46:46 GMT</pubDate>
      <description>Today, we’re announcing a new API with Amazon Bedrock Guardrails. With this API, you can apply individual safeguards, also referred to as safety checks, at any point in your agentic AI applications without creating guardrail resources. In this post, we walk through how the InvokeGuardrailChecks API works and how to use it to build safe, multi-turn agentic AI applications.</description>
    </item>
    <item>
      <title>Hands Free, AIs Forward: NVIDIA XR AI Brings Agents to AR Glasses</title>
      <link>https://blogs.nvidia.com/blog/nvidia-xr-ai/</link>
      <guid isPermaLink="false">provider-7cc881b3f3865bc979</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 22:30:41 GMT</pubDate>
      <description>NVIDIA XR AI is now available in public beta, giving developers a framework for building multimodal AI agents for AR glasses and XR devices.</description>
    </item>
    <item>
      <title>Building AI Agents for AR Glasses and XR Devices with NVIDIA XR AI</title>
      <link>https://developer.nvidia.com/blog/building-ai-agents-for-ar-glasses-and-xr-devices-with-nvidia-xr-ai/</link>
      <guid isPermaLink="false">provider-c79dd36cbac7dc4965</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 22:30:00 GMT</pubDate>
      <description>Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live...</description>
    </item>
    <item>
      <title>Coherent Breaks Ground on Expanded Texas Facility, Scaling AI’s Optical Backbone</title>
      <link>https://blogs.nvidia.com/blog/coherent-texas-ai-optical/</link>
      <guid isPermaLink="false">provider-488e41ae501cef2b10</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 22:10:56 GMT</pubDate>
      <description>AI runs at the speed of light. More and more, that light is made in Texas. Coherent broke ground today on an expanded manufacturing building in Sherman, Texas. The company makes the lasers, optical components and compound semiconductors that wire AI systems together — and runs what it calls the world’s first 6-inch indium phosphide […]</description>
    </item>
    <item>
      <title>Unlocking UK house-building with AI-accelerated planning</title>
      <link>https://deepmind.google/blog/unlocking-uk-house-building-with-ai-accelerated-planning/</link>
      <guid isPermaLink="false">provider-a20ffd8a4170aa7284</guid>
      <dc:creator>Google DeepMind</dc:creator>
      <pubDate>Tue, 16 Jun 2026 21:29:50 GMT</pubDate>
      <description>UK government partners with Google DeepMind to build a new AI-powered prototype aimed at faster housing decisions.</description>
    </item>
    <item>
      <title>Introducing container caching in Amazon SageMaker AI for faster model scaling</title>
      <link>https://aws.amazon.com/blogs/machine-learning/introducing-container-caching-in-amazon-sagemaker-ai-for-faster-model-scaling/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 20:16:02 GMT</pubDate>
      <description>Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up end-to-end latency by up to 2x for generative AI models during scale-out events.</description>
    </item>
    <item>
      <title>Parallelize speculative decoding with P-EAGLE on Amazon SageMaker AI</title>
      <link>https://aws.amazon.com/blogs/machine-learning/parallelize-speculative-decoding-with-p-eagle-on-amazon-sagemaker-ai/</link>
      <guid isPermaLink="false">provider-43ecb7ebe03aa00850</guid>
      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 17:47:09 GMT</pubDate>
      <description>This post walks you through how to use P-EAGLE directly within Amazon SageMaker AI. It will demonstrate how to select a compatible model from the SageMaker JumpStart catalog, configure the parallel drafting specifications, and deploy a highly optimized real-time SageMaker AI endpoint to accelerate your generative AI applications.</description>
    </item>
    <item>
      <title>HPE AI Factory With NVIDIA Expands for the Era of Agents</title>
      <link>https://blogs.nvidia.com/blog/hpe-ai-factory-agentic-enterprise/</link>
      <guid isPermaLink="false">provider-cc735625f874e15456</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 16:30:27 GMT</pubDate>
      <description>Enterprises are moving agentic AI from proof of concept to production — and the next generation of AI factories are built for the era of agents. At HPE Discover Las Vegas, running through Thursday, June 18, NVIDIA and HPE are expanding the HPE AI Factory with NVIDIA, including NVIDIA Vera CPU and NVIDIA Agent Toolkit […]</description>
    </item>
    <item>
      <title>Securing the future of AI agents</title>
      <link>https://deepmind.google/blog/securing-the-future-of-ai-agents/</link>
      <guid isPermaLink="false">provider-eb1b9a2ae81f9316f0</guid>
      <dc:creator>Google DeepMind</dc:creator>
      <pubDate>Tue, 16 Jun 2026 15:46:31 GMT</pubDate>
      <description>Securing internal systems with an AI Control Roadmap, combining traditional safeguards and real-time monitoring.</description>
    </item>
    <item>
      <title>NVIDIA Blackwell Tops MLPerf Training 6.0 with Industry-Leading Scale and Performance</title>
      <link>https://developer.nvidia.com/blog/nvidia-blackwell-tops-mlperf-training-6-0-with-industry-leading-scale-and-performance/</link>
      <guid isPermaLink="false">provider-ce5a393b9c7d3e5ed2</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 15:11:13 GMT</pubDate>
      <description>NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium....</description>
    </item>
    <item>
      <title>Fastest, Largest, Strongest: NVIDIA Blackwell Sweeps MLPerf Training 6.0</title>
      <link>https://blogs.nvidia.com/blog/blackwell-mlperf-training-6-0/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 15:00:36 GMT</pubDate>
      <description>Every breakthrough AI model starts the same way: with a training run. The infrastructure running those training jobs shapes everything: how fast teams can iterate, what scale of model they can build and whether those jobs complete reliably. As models grow in size, complexity and intelligence, the demands on training infrastructure are also rising. In […]</description>
    </item>
    <item>
      <title>Predicting model behavior before release by simulating deployment</title>
      <link>https://openai.com/index/deployment-simulation</link>
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      <dc:creator>OpenAI</dc:creator>
      <pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate>
      <description>OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.</description>
    </item>
    <item>
      <title>Introducing Gemma 4 models on Amazon Bedrock</title>
      <link>https://aws.amazon.com/blogs/machine-learning/introducing-gemma-4-models-on-amazon-bedrock/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Mon, 15 Jun 2026 20:24:15 GMT</pubDate>
      <description>Today, we are announcing the availability of the Gemma 4 family on Amazon Bedrock. Built by Google DeepMind and released under the Apache 2.0 license, Gemma 4 is a family of open-weight models designed with a focus on intelligence-per-parameter across a broad range of deployment scenarios. The family includes three instruction-tuned variants: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B. These cover dense and mixture-of-experts (MoE) architectures, where only a fraction of the model’s parameter</description>
    </item>
    <item>
      <title>AI Agent Failure Detection and Root Cause Analysis with Strands Evals</title>
      <link>https://aws.amazon.com/blogs/machine-learning/ai-agent-failure-detection-and-root-cause-analysis-with-strands-evals/</link>
      <guid isPermaLink="false">provider-2007a416f1913af104</guid>
      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Mon, 15 Jun 2026 18:07:59 GMT</pubDate>
      <description>In this post, we walk you through calling the detector functions to diagnose real agent failures. You learn how to interpret their structured output: categorized failures with confidence scores, causal chains linking root causes to downstream symptoms, and fix recommendations specifying whether a change belongs in your system prompt or tool definitions. You also learn how to integrate detection into your evaluation pipeline for automated diagnosis on every test run.</description>
    </item>
    <item>
      <title>Boosting MoE Training Throughput with Advanced Fusion Kernels</title>
      <link>https://developer.nvidia.com/blog/boosting-moe-training-throughput-with-advanced-fusion-kernels/</link>
      <guid isPermaLink="false">provider-48efc678c6b6f2b73e</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Mon, 15 Jun 2026 16:45:41 GMT</pubDate>
      <description>Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable...</description>
    </item>
    <item>
      <title>Build context-rich research agents with Deep Agents and Bedrock AgentCore</title>
      <link>https://aws.amazon.com/blogs/machine-learning/build-context-rich-research-agents-with-deep-agents-and-bedrock-agentcore/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Mon, 15 Jun 2026 13:56:33 GMT</pubDate>
      <description>In this post, you&apos;ll build a competitive research agent that demonstrates this pattern end to end. This walkthrough targets developers building multi-step AI workflows who need isolated execution environments for their agents. In Part 2 of the notebook, you can deploy this same agent to Bedrock AgentCore Runtime using the AgentCore CLI, so it runs as a managed, session-isolated service.</description>
    </item>
    <item>
      <title>Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models</title>
      <link>https://developer.nvidia.com/blog/pretrained-to-imagine-fine-tuned-to-act-the-rise-of-world-action-models/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Mon, 15 Jun 2026 12:00:00 GMT</pubDate>
      <description>Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it...</description>
    </item>
    <item>
      <title>Introducing the OpenAI Partner Network</title>
      <link>https://openai.com/index/introducing-openai-partner-network</link>
      <guid isPermaLink="false">provider-705de8fc126536abaf</guid>
      <dc:creator>OpenAI</dc:creator>
      <pubDate>Sun, 14 Jun 2026 17:00:00 GMT</pubDate>
      <description>OpenAI launches the Partner Network, investing $150M to help global partners accelerate enterprise AI adoption, deployment, and transformation.</description>
    </item>
    <item>
      <title>NVIDIA Achieves Leading Agentic Coding Performance on First Agentic AI Benchmark</title>
      <link>https://developer.nvidia.com/blog/nvidia-achieves-leading-agentic-coding-performance-on-first-agentic-ai-benchmark/</link>
      <guid isPermaLink="false">provider-e99bf52aea0ecb0da1</guid>
      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 21:12:40 GMT</pubDate>
      <description>AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how...</description>
    </item>
    <item>
      <title>NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark</title>
      <link>https://blogs.nvidia.com/blog/nvidia-blackwell-agentperf-artificial-analysis/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 21:00:08 GMT</pubDate>
      <description>AgentPerf from Artificial Analysis, the industry’s first agentic AI benchmark, gives developers, enterprises and infrastructure providers a clear way to compare systems for agentic AI. In the first round of published results, the NVIDIA Blackwell Ultra NVL72 platform delivers leading performance across the agentic AI workloads tested, running 20x more agents per megawatt than NVIDIA […]</description>
    </item>
    <item>
      <title>Building Supercharger: How Rocket Close optimized title operations with agentic AI</title>
      <link>https://aws.amazon.com/blogs/machine-learning/building-supercharger-how-rocket-close-optimized-title-operations-with-agentic-ai/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 20:43:56 GMT</pubDate>
      <description>In this post, we explore how Rocket Close built a solution using Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools. We cover solution features, the rationale for the technology stack, lessons learned, and the business impact at Rocket Close.</description>
    </item>
    <item>
      <title>Build a meeting prep and follow-up assistant with Amazon Quick and Cisco Webex MCP servers</title>
      <link>https://aws.amazon.com/blogs/machine-learning/build-a-meeting-prep-and-follow-up-assistant-with-amazon-quick-and-cisco-webex-mcp-servers/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 14:49:40 GMT</pubDate>
      <description>This post shows how to build a custom meeting prep and follow-up assistant using Amazon Quick and Cisco Webex MCP servers. From a single prompt, the agent finds an upcoming Webex meeting, reviews prior meeting summaries and transcripts, and pulls related Vidcast highlights and transcript context. It then searches Webex message threads for unresolved follow-ups and creates a concise prep brief. After the meeting, the same assistant can summarize the discussion and identify action items. It can al</description>
    </item>
    <item>
      <title>Deploy Long-Context Reasoning and Agentic Workflows with MiniMax M3 on NVIDIA Accelerated Infrastructure</title>
      <link>https://developer.nvidia.com/blog/deploy-long-context-reasoning-and-agentic-workflows-with-minimax-m3-on-nvidia-accelerated-infrastructure/</link>
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      <dc:creator>NVIDIA AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 14:43:17 GMT</pubDate>
      <description>As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision, and...</description>
    </item>
    <item>
      <title>From PDFs to insights: Architecting an intelligent document processing pipeline with AWS generative AI services</title>
      <link>https://aws.amazon.com/blogs/machine-learning/from-pdfs-to-insights-architecting-an-intelligent-document-processing-pipeline-with-aws-generative-ai-services/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 14:43:11 GMT</pubDate>
      <description>This post outlines the development of a cost-effective and scalable intelligent document processing pipeline on AWS, powered by Amazon Bedrock and its features. BDA is a managed service within Amazon Bedrock that automates the extraction of insights from documents. We demonstrate how BDA extracts and analyzes document content, while Strands Agent hosted on Amazon Bedrock AgentCore Runtime coordinate specialized processing tasks, and Amazon Bedrock Knowledge Base enable contextual understanding a</description>
    </item>
    <item>
      <title>Built from the inside out: How AWS Professional Services became a frontier team first</title>
      <link>https://aws.amazon.com/blogs/machine-learning/built-from-the-inside-out-how-aws-professional-services-became-a-frontier-team-first/</link>
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      <dc:creator>Amazon AI</dc:creator>
      <pubDate>Fri, 12 Jun 2026 13:00:10 GMT</pubDate>
      <description>AWS Professional Services (AWS ProServe) compressed engagement timelines from months to days, not by adding artificial intelligence (AI) tools to an existing process, but by fundamentally rebuilding how we deliver from the inside out. In this post, we share how AWS ProServe became a frontier team, the practices that enabled it, and what your engineering organization can take from our experience.</description>
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