PrismaX

Where humans, robots, and data come together to advance intelligence. 🦾 Backed by @a16zcrypto. Host of @RoboCon_AI 🤖
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[PrismaX @PrismaXai]

Robots are learning from data like this, at massive scale.
Better models start with better data.
Better data needs humans scoring it.
Coming soon.

[PrismaX @PrismaXai]

@SynriaRobotics Teleoperation is the gold standard for robotics data ✅

[PrismaX @PrismaXai]

@fly51fly Cross-embodiment VLAs raise the bar on data diversity.

[PrismaX @PrismaXai]

@omarsar0 20,000 hours of real manipulation data and no saturation. The scaling curve for real operator data is the one that matters and it's still climbing.

[PrismaX @PrismaXai]

@Hiendeptrai097 Always building. That's what this community does. Appreciate you being part of it.

[PrismaX @PrismaXai]

@lukas_m_ziegler The pattern keeps repeating: structured data beats expensive hardware. Force awareness from motor current alone. The data layer matters more than the sensor stack.

[PrismaX @PrismaXai]

@NewsHumanoid From pilot to production floor!

[PrismaX @PrismaXai]

@IntEngineering Hardware production is scaling. The harder problem is what happens after deployment: who captures the operational data, and in what structure. That determines which robots actually improve.

[PrismaX @PrismaXai]

@sjdedic Capital is flowing to physical AI at a pace the industry hasn't seen 👀

[PrismaX @PrismaXai]

@michellelsun @peteflorence @generalist @tbpn Real-world robot experience at S-tier is exactly right. The bottleneck was never the model. It was always the data layer.

[PrismaX @PrismaXai]

@qzxcle Thrilled to have Sentient as a Day 1 Launch Partner 🤝

[PrismaX @PrismaXai]

@TheHumanoidHub Hardware scales the input. Data quality decides what the models actually learn 🦾

[PrismaX @PrismaXai]

Closing that gap takes real-world operation. That's what we work on day in and day out at PrismaX 🦾

[PrismaX @PrismaXai]

Pouring popcorn. Folding a shirt. Stacking blocks. The manipulation demos at #ICRA2026 were genuinely better this year.

But the hard part was never the demo. It's doing the same task 500 times, unsupervised, in a room nobody arranged in advance.

[PrismaX @PrismaXai]

Read the standards ⤵️
https://t.co/P7nxr3pqm7

[PrismaX @PrismaXai]

https://t.co/UbY6OhiCiz

[PrismaX @PrismaXai]

Two runs of the same task. One trains a capable model. The other quietly degrades it. It's full of errors the model would just learn to copy.

Separating the two is what our Standards for Data are for.

[PrismaX @PrismaXai]

We can draw these lines because we run the whole loop: collect the data, train and evaluate models on it, feed what we learn back in.

Better data takes a human willing to hold it to a standard. That's the difference between training a robot and filling a hard drive.