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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Building an agent is more than just “call an API”—it requires stitching together retrieval, speech, safety, and reasoning components so they behave like...

Building an agent is more than just “call an API”—it requires stitching together retrieval, speech, safety, and reasoning components so they behave like one cohesive system. Each layer has its own interface, latency constraints, and integration challenges, and you start to feel them as soon as you move beyond a simple prototype. In this tutorial, you’ll learn how to build a voice-powered RAG…

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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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Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and...

Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and statistical expertise. Managing feature engineering, model tuning, and consistency across workflows is complex and error-prone. These challenges are amplified by the slow, sequential nature of CPU-based ML workflows…

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