Building @MidcenturyAI | prev @openai @harvard
New York
Joined June 2019
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“Almost nobody has stereo off the shelf.”
At CoRL this week - we’ve built one of the largest OTS stereo datasets and benchmark it obsessively.
Would love to talk stereo, data quality, and post-training results. DMs are open.
We built Sortex because “2M+ hours” tells you almost nothing by itself. What matters is what’s in there: whether each clip actually shows the work clearly, which tasks and environments the corpus covers, and what every new recording adds. Our goal is to make quality measurable and inspectable at both levels, from a single frame up to the whole dataset. Read more below!
Sortex: our data quality platform built to search and filter the world's largest egocentric dataset.
Across 50+ environments and 20k+ tasks, it uses an in-house model and evaluation harness to score each clip's usability, selecting for specific training objectives by:
→ Narrating tasks and steps
→ Checking hand and action visibility
→ Comparing against the rest of the corpus
We also tested Sortex on popular open datasets and found they differ widely in quality and coverage.
We're out of stealth!! Data is the bottleneck for robotics, and the bet we're making is that human action data is how you break it. Not raw video, but millions of hours of real work enriched frame by frame with the pose, depth, and tactile signal that makes it something a robot can actually learn from. That's the machine I get to help run at Midcentury. The next era of physical AI gets built on top of it. Let's go 🚀
Introducing Midcentury.
We’re building the data and simulation infra for physical AI.
Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos.
We’re already supporting frontier labs with:
→ The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks
→ Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale