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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

1,908 subscribers

611,838 görüntüleme • 3 gün önce •via X (Twitter)

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Midcentury profil fotoğrafı

Midcentury3 gün önce

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 profil fotoğrafı

Midcentury3 gün önce

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 profil fotoğrafı

Midcentury3 gün önce

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 profil fotoğrafı

Midcentury3 gün önce

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 profil fotoğrafı

Midcentury3 gün önce

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

Midcentury profil fotoğrafı

Midcentury3 gün önce

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 profil fotoğrafı

Archie Sengupta3 gün önce

🐐🐐🐐

Brandon Samaroo profil fotoğrafı

Brandon Samaroo3 gün önce

coming out the gate hot 🔥

Midcentury profil fotoğrafı

Midcentury3 gün önce

so much more to come

Noah profil fotoğrafı

Noah3 gün önce

Go big or go home!

Midcentury profil fotoğrafı

Midcentury3 gün önce

let's get it

Ali profil fotoğrafı

Ali3 gün önce

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

shilpi! profil fotoğrafı

shilpi!3 gün önce

huge things!!! 😋🔥

Abhishek Anand profil fotoğrafı

Abhishek Anand3 gün önce

Congrats team! 🫡

Siddharth Kamannavar profil fotoğrafı

Siddharth Kamannavar3 gün önce

out of many, one. 🚀

monty profil fotoğrafı

monty3 gün önce

cracked team

brenz. profil fotoğrafı

brenz.3 gün önce

game changer imo, congrats!

Robert Lukoszko profil fotoğrafı

Robert Lukoszko3 gün önce

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

Adish Jain ☕️ profil fotoğrafı

Adish Jain ☕️3 gün önce

insane launch video, congrats on the raise team!

Motion profil fotoğrafı

Motion3 gün önce

Midcentury is cooking!! 🔥

himanshu profil fotoğrafı

himanshu3 gün önce

congrats!

Akash Singh profil fotoğrafı

Akash Singh3 gün önce

Hell yeah!

Chris Keevill profil fotoğrafı

Chris Keevill3 gün önce

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

Ville🤖 profil fotoğrafı

Ville🤖3 gün önce

Congrats on the launch!

Deepraj profil fotoğrafı

Deepraj3 gün önce

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

The AI Therapist profil fotoğrafı

The AI Therapist3 gün önce

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 profil fotoğrafı

Jithin3 gün önce

fire launch!

Rompel profil fotoğrafı

Rompel3 gün önce

"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. profil fotoğrafı

robert.3 gün önce

congrats team!! this might be generational

Amine Obeidat profil fotoğrafı

Amine Obeidat3 gün önce

🔥🔥

Yokush profil fotoğrafı

Yokush3 gün önce

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 profil fotoğrafı

Mark Grinev3 gün önce

lfg @dgmonsoon

Aryan SMM profil fotoğrafı

Aryan SMM3 gün önce

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

Nick profil fotoğrafı

Nick3 gün önce

Big things happening…

Seth profil fotoğrafı

Seth3 gün önce

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 profil fotoğrafı

ikan laut3 gün önce

building a new civilization, this is so amazing

Anindyadeep profil fotoğrafı

Anindyadeep3 gün önce

lfgg

Chesny profil fotoğrafı

Chesny3 gün önce

grats team, amazing job

Memex Orchestration profil fotoğrafı

Memex Orchestration3 gün önce

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 profil fotoğrafı

Amor Avhad3 gün önce

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

Marco profil fotoğrafı

Marco3 gün önce

congrats guys, 2M hours of data is insane

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Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

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Today, we’re announcing Antioch’s $32 million Series A, led by Greylock Partners with participation from A*, Category Ventures, BoxGroup, Icehouse Ventures, and angels. While AI has drastically accelerated software development, physical autonomy has been constrained by slow, expensive hardware-based development. Antioch enables physical AI teams to build, test, and validate systems at the speed of software. We’re proud to be working with leading teams including Amazon Ring, NVIDIA Robotics, and Nebius as we make scaled, high-fidelity simulation the standard for physical AI development. Our sincere thanks to the legion of investors, customers, partners, and advisors who are making this step change possible. We’re scaling rapidly and hiring across simulation, infrastructure, machine learning, 3D graphics, go-to-market, and operations.

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Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.

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Ilir Aliu

42,864 görüntüleme • 7 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce