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# Data for Agents

**[Hugging Face](https://daily.dev/sources/huggingface)** · 6 min read · 0 upvotes · 0 comments

## Summary

NVIDIA argues that building capable AI agents requires more than open model weights — it demands open, inspectable training data. The post covers NVIDIA's Nemotron open data ecosystem, including over 10 trillion pre-training tokens and millions of post-training samples. Key releases include the Nemotron Post-Training v3 Prompt Atlas (an interactive visual map of prompt data), Nemotron-Personas (synthetically generated demographic personas now covering 2.4B people across 10 countries), and datasets like Nemotron-CC, Nemotron-MATH, and Nemotron-CLIMB. The core argument is that synthetic data enables organizations to share useful signals without exposing proprietary workflows, and that open data methods are essential for reproducibility, agent behavior inspection, and building trust across organizations.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://huggingface.co/blog/nvidia/open-data-for-agents>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#nvidia](https://daily.dev/tags/nvidia)

[View this post on daily.dev](https://daily.dev/posts/data-for-agents-aa1acepyk)

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