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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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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Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They...

Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They enable “virtual manufacturing,” allowing engineers to design, build, and test transistors and integrated circuits digitally before committing to the costly physical fabrication process. This approach significantly reduces development time…

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The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling...

The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling massive multi-modal datasets. Scientific AI agents can take on much of that busywork, acting as assistants that review literature, generate hypotheses, plan experiments, submit computational jobs, orchestrate lab operations, analyze results…

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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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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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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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Training massive mixture-of-experts (MoE) models has long been the domain of a few advanced users with deep infrastructure and distributed-systems expertise....

Training massive mixture-of-experts (MoE) models has long been the domain of a few advanced users with deep infrastructure and distributed-systems expertise. For most developers, the challenge wasn’t building smarter models—it was scaling them efficiently across hundreds or even thousands of GPUs without breaking the bank. With NVIDIA NeMo Automodel, an open-source library within NVIDIA NeMo…

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