AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a...

AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a significant, often overlooked, attack surface by running tools from the command line with the same permissions and entitlements as the user, making them computer use agents, with all the risks those entail. The primary threat to these tools is…

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What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands?...

What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands? In Part 1 of our series on building a computer use agent, we built a custom Bash computer-use agent using NVIDIA Nemotron in just one hour. In this sequel, we’ll take it further by teaching the same reasoning model with no prior…

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Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep...

Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep up. Throughput is rising, SLAs are shrinking, and fleets of AMRs, conveyors, and sensors expand every year. But beneath that technological surface, most sites still rely on a familiar trio: a Warehouse Management System (WMS)…

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As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with...

As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with AI more frequently, meaning that more tokens need to be generated. To serve these tokens at the lowest possible cost, AI platforms need to deliver the best possible token throughput per watt. Through extreme co-design across GPUs, CPUs…

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Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want...

Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want to run conversational AI agents, multimodal perception, and high-level planning directly on the vehicle or robot – where latency, reliability, and the ability to operate offline matter most. While many existing LLM and vision language…

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AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions of tokens and models scale toward...

AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions of tokens and models scale toward trillions of parameters. These systems currently rely on agentic long‑term memory for context that persists across turns, tools, and sessions so agents can build on prior reasoning instead of starting from scratch on every request.

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AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI...

AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI factories that continuously convert power, silicon, and data into intelligence at scale. These factories now underpin applications that generate business plans, analyze markets, conduct deep research, and reason across vast bodies of…

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The AI boom reshaping the computing landscape is poised to scale even faster in 2026. As breakthroughs in model capability and computing power drive rapid...

The AI boom reshaping the computing landscape is poised to scale even faster in 2026. As breakthroughs in model capability and computing power drive rapid growth, enterprise data centers are being pushed beyond the limits of conventional server and rack architectures. This is creating new pressures on power budgets, thermal envelopes, and facility space. NVIDIA MGX modular reference…

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Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large...

Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large contexts and long time spans. These systems demand models that deliver fast throughput, strong reasoning accuracy, and persistent coherence over large inputs. They also require a level of openness that allows developers to customize, extend…

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Today’s best AI agents rely on retrieval-augmented generation (RAG) to enable more accurate results. A RAG system facilitates the use of a knowledge base to...

Today’s best AI agents rely on retrieval-augmented generation (RAG) to enable more accurate results. A RAG system facilitates the use of a knowledge base to augment context to large language models (LLMs). A typical design pattern includes a RAG server that accepts prompt queries, consults a vector database for nearest context vectors, and then redirects the query with the appended context to an…

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Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety...

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing. But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind…

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The new Mistral 3 open model family delivers industry-leading accuracy, efficiency, and customization capabilities for developers and enterprises. Optimized...

The new Mistral 3 open model family delivers industry-leading accuracy, efficiency, and customization capabilities for developers and enterprises. Optimized from NVIDIA GB200 NVL72 to edge platforms, Mistral 3 includes: All the models were trained on NVIDIA Hopper GPUs and are now available through Mistral AI on Hugging Face. Developers can choose from a variety of options for deploying…

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Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...

Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets for training can be expensive, time-intensive, and dangerous. NVIDIA Cosmos open world foundation models (WFMs) address these challenges by enabling scalable, high-fidelity synthetic data generation for physical AI and the augmentation of…

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At Microsoft Ignite 2025, the vision for an AI-ready enterprise database becomes a reality with the announcement of Microsoft SQL Server 2025, giving developers...

At Microsoft Ignite 2025, the vision for an AI-ready enterprise database becomes a reality with the announcement of Microsoft SQL Server 2025, giving developers powerful new tools like built-in vector search and SQL native APIs to call external AI models. NVIDIA has partnered with Microsoft to seamlessly connect SQL Server 2025 with the NVIDIA Nemotron RAG collection of open models.

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AI is reshaping scientific research and innovation. Scientists can leverage AI to generate, summarize, combine, and analyze scientific data. AI models can find...

AI is reshaping scientific research and innovation. Scientists can leverage AI to generate, summarize, combine, and analyze scientific data. AI models can find patterns in data that human scientists have overlooked, find connections between seemingly unrelated fields and phenomena, and even propose new hypotheses to be tested. An AI co-scientist is a collaborative, multi-agent AI system…

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