Today, Human Archive is announcing our $8.2M seed round to model human embodied intelligence. Despite decades of research, we still barely understand ourselves. Our goal is to learn how humans interact with the world, and over the past 6 months, our team’s made enormous progress toward that alongside leading AI labs. learn more @TechCrunch techcrunch.com/2026/05/26/hu…

@EkaRobotics has raised a $150M Series A. Generality v/s Performance is the fundamental tension at the frontier of robotics. Generality without performance is just a prototype. Performance without generality is just a single-purpose tool. Customers shouldn’t have to choose. Eka breaks the tradeoff. Powered by our Vision-Force-Action (VFA) model, we deliver the performance and versatility customers need to put robots to work every day. Eka is built on three convictions: ⚡️Force is the language of manipulation. 🚀 Reinforcement learning is how dexterous manipulation scales. 🎂 System 1—fast, reliable physical execution—is the bottleneck to solve. With this funding, we will scale our model and accelerate customer deployments. Grateful to be building alongside a dream team and investors who share our ambition.

We pre-trained the same VLA on 100 hours of Human Archive’s OTS dataset and 100 hours of randomly sampled data from each of @microagi’s and @MeckaAI’s datasets on EgoVerse. Overall, Human Archive data achieved the strongest results, with the highest overall task success rate. We observed particularly strong generalization to unseen objects. Thread below 🧵👇

Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row.

Here are some of the behind the scenes for the F.02 Decommission To be clear: we actually trained our robots to jump autonomously, shipped them to Finland, and had them leap into a vat of molten steel

We benchmarked only 7 hours of Human Archive data against 4 widely used public datasets totaling 17 hours: HOT3D, H2O, HOI4D, and TACO. The results: Human Archive only: 85% task success | 65.6% task progress 4 public datasets: 57.5% task success | 52.5% task progress Mixture of all 5: 60% task success | 56.3% task progress

startups be like "we don't wear shoes at the office >,< uwu we're so quirky rawr xd"

Human Archive (YC W26) has some exciting research releases planned for tomorrow at 10:00 AM PT. Stay tuned

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

Anyone have a Wuji Hand (v1 or v2) they’re willing to sell?

I have been staying in hotels for the last 4 months for work and now I’m starting hate them. As a kid this would have been a dream lol. Belongingness is so important

Does anyone else still use paper and pencil when they need to actually think rather than just prompting AI?