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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:... show more

Midcentury

1,812 subscribers

609,179 次观看 • 3 天前 •via X (Twitter)

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Midcentury 的头像

Midcentury3 天前

Human action data has finally unlocked a scaling law for Physical AI. It’s time to join LLM researchers in taking the bitter lesson pill. Scaling robotics now requires internet-scale pretraining data and reliable simulation for evaluation + RL. We build the core infra for both.

Midcentury 的头像

Midcentury3 天前

Robotics can’t scrape the internet for action data. So we built the world’s largest and densest labeled egocentric dataset. 2M+ hours across 50+ environments and 20,000+ tasks, with hands visible in 90% of frames. Paired with SOTA action supervision: 3D hand + body pose, depth, tactile, and motion. Subtask and cycle annotations are frame-aligned with millisecond timestamps.

Midcentury 的头像

Midcentury3 天前

Real-world evaluation and RL don’t scale in robotics. Matrix is our agentic simulation platform for evaluating and improving robot policies thousands of times before deployment. It combines classical simulation, learned physics, and real-world data to close the sim-to-real gap. Massively parallel cloud simulation scales evaluation and RL.

Midcentury 的头像

Midcentury3 天前

We’ve brought together a world class team of researchers and operators from the Stanford AI Lab, OpenAI, DeepMind, NVIDIA, Scale AI, Invisible, and more. Our team has built datasets, benchmarks, and models at the frontier of robotics and AI, including RoboNet, GDPVal, and NVIDIA Cosmos 3. We’re a small, deeply technical team hiring across research, engineering, and operations.

Midcentury 的头像

Midcentury3 天前

Thanks to our partners, collaborators, and investors on this journey, including: @buildpbc, @eddybuild, @cegapereira, @justswart, @jay_drainjr, @liz_harkavy, @Shaughnessy119.

Midcentury 的头像

Midcentury3 天前

The scaling era for robotics is here. If you’re training, evaluating, or deploying physical AI, we want to hear from you. Explore dataset samples and get early access to Matrix below.

Archie Sengupta 的头像

Archie Sengupta3 天前

🐐🐐🐐

Brandon Samaroo 的头像

Brandon Samaroo3 天前

coming out the gate hot 🔥

Midcentury 的头像

Midcentury3 天前

so much more to come

Noah 的头像

Noah3 天前

Go big or go home!

Midcentury 的头像

Midcentury3 天前

let's get it

Ali 的头像

Ali3 天前

That's just the beginning, we're about to start 🚀

shilpi! 的头像

shilpi!3 天前

huge things!!! 😋🔥

Abhishek Anand 的头像

Abhishek Anand3 天前

Congrats team! 🫡

Siddharth Kamannavar 的头像

Siddharth Kamannavar3 天前

out of many, one. 🚀

monty 的头像

monty3 天前

cracked team

brenz. 的头像

brenz.3 天前

game changer imo, congrats!

Robert Lukoszko 的头像

Robert Lukoszko3 天前

> The world’s largest egocentric dataset funny i heard this so many times

Adish Jain ☕️ 的头像

Adish Jain ☕️3 天前

insane launch video, congrats on the raise team!

Motion 的头像

Motion3 天前

Midcentury is cooking!! 🔥

himanshu 的头像

himanshu3 天前

congrats!

Akash Singh 的头像

Akash Singh3 天前

Hell yeah!

Chris Keevill 的头像

Chris Keevill3 天前

How did you source the training data?where did it come from?

Ville🤖 的头像

Ville🤖3 天前

Congrats on the launch!

Deepraj 的头像

Deepraj3 天前

2milli+ hours of real-world data is seriously impressive!

The AI Therapist 的头像

The AI Therapist3 天前

Physical AI’s bottleneck is sim-to-real gap. $15M buys compute for better physics engines, crucial if they want robots that don’t quit when a table wobbles real-world style. Scaling demo bots needs robust data, not just polish. I’m intrigued by their stack!

Jithin 的头像

Jithin3 天前

fire launch!

Rompel 的头像

Rompel3 天前

"World's largest egocentric dataset" needs a number to mean anything - hours, scenes, embodiment diversity. Ego4D and Epic-Kitchens set the bar years ago and sim-to-real still faces on texture/contact physics, not dataset size alone.

robert. 的头像

robert.3 天前

congrats team!! this might be generational

Amine Obeidat 的头像

Amine Obeidat3 天前

🔥🔥

Yokush 的头像

Yokush3 天前

Data and simulation solve the demo problem; they don't solve the deployment one. The moment a policy leaves the lab it needs a certificate somebody will sign, an incident process, and an insurer willing to price the harm none of which live in the training set. Physical AI hits the same wall every autonomy programme hits: permission to be wrong in public.

Mark Grinev 的头像

Mark Grinev3 天前

lfg @dgmonsoon

Aryan SMM 的头像

Aryan SMM3 天前

the real bottleneck for physical AI may be data and simulation not just better robot models

Nick 的头像

Nick3 天前

Big things happening…

Seth 的头像

Seth3 天前

Finally live. We have been eagerly watching what you are building and creating here at @vercel for some time. We would love to chat and see how we can support this incredible scale.

ikan laut 的头像

ikan laut3 天前

building a new civilization, this is so amazing

Anindyadeep 的头像

Anindyadeep3 天前

lfgg

Chesny 的头像

Chesny3 天前

grats team, amazing job

Memex Orchestration 的头像

Memex Orchestration3 天前

Scaling physical AI will take more than better models. It will take the infrastructure to connect data, simulation, and intelligent systems. This is where orchestration becomes increasingly important.

Amor Avhad 的头像

Amor Avhad3 天前

This just gave me a headache looking at your video flashing all the images. Thanks

Marco 的头像

Marco3 天前

congrats guys, 2M hours of data is insane

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Ilir Aliu

42,864 次观看 • 7 个月前

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and Kingston Kuan spent years building large-scale infrastructure at Jane Street.

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and Kingston Kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

166,623 次观看 • 1 个月前

Robots and AI cannot hear the world - Yet. They are starving for fuel, and the internet’s data is already scraped dry. AI needs real-world audio data - at massive scale. Silencio is expanding its vision: from the largest noise data bank to The World’s Ears for AI and Robotics. 📅On September 17th: The world gets ears. Reserve your exclusive keynote preview:

Robots and AI cannot hear the world - Yet. They are starving for fuel, and the internet’s data is already scraped dry. AI needs real-world audio data - at massive scale. Silencio is expanding its vision: from the largest noise data bank to The World’s Ears for AI and Robotics. 📅On September 17th: The world gets ears. Reserve your exclusive keynote preview:

Silencio | Voice Data for AI

96,238 次观看 • 1 年前