
Rushil Agarwal
239 posts

Rushil Agarwal
@Rush26agarwal
Founder at Human Archive (YC W26) | UC Berkeley MET (dropped out)


The Physical AI data space is exploding, but most vendors still share samples through Google Drive or Hugging Face links that are slow and painful to evaluate. We built the Human Archive Catalog so researchers can quickly browse and filter our off-the-shelf datasets by environment, hardware configuration, modality, annotations, and more. If you’re a robotics or world model researcher, request access below: catalog.humanarchive-qa.ai/request-access


Just 13 days into July, Human Archive (YC W26) is having our best month yet, and we’re doubling down alongside leading AI labs. We deliver diverse, human-reviewed physical AI datasets with granular text labels, high-quality stereo capture, and millimeter-level 3D post-processing at scale. Our young, lean research team in San Francisco is working seven days a week to build our own internal models. We're researchers from NYU GRAIL, UPenn GRASP, Berkeley, Johns Hopkins, Stanford, and national robotics champions. If you’re interested in joining us as an ML Engineer, or you’re at a frontier AI lab looking for high-quality physical AI data (video, robot deployments, audio, tactile, motion capture, and more), my DMs are open, or feel free to reach out at raj@humanarchive.ai.




Today, LLMs are no longer built from human data alone. They rely on other LLMs to generate training data, filter corpora, evaluate outputs, provide rewards, and guide development decisions. So how many models and datasets is a modern LLM built on? • OLMo 3 → 89 model + 183 dataset dependencies • Nemotron 3 → 273 model + 560 dataset dependencies How did we find it out? We built ModSleuth. 🧵













