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We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the...

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29,684 görüntüleme • 6 ay önce •via X (Twitter)

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The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.
1:56

The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.

GEODNET

149,296 görüntüleme • 2 ay önce

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.
1:10

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_victorad

18,903 görüntüleme • 10 gün önce

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.
1:18

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

Lightwheel

72,607 görüntüleme • 14 gün önce

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 1 ay önce

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."
1:57

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

Dwarkesh Patel

325,893 görüntüleme • 2 yıl önce

a16z co-founder Marc Andreessen on why the shift from software AI to physical AI will create the biggest industry in human history: AI today is software. You open ChatGPT on your phone, and there's no manufacturing component, no real generator of middle-class jobs. But Marc Andreessen 🇺🇸 argues that a second, far more consequential turn is coming: the shift to embodied physical AI. "There's another turn on AI that's coming, which is the turn to embodied physical AI, which is robotics." This transition, he points out, has already begun. Drones went from human-piloted to fully autonomous. Cars went from purely physical products to rolling computers that drive themselves. "If you ever have the misfortune of finding yourself in San Francisco, take a Waymo. It's amazing to live in a cyberpunk future reality where you're in literally a state-of-the-art self-driving car that's literally out of the Jetsons." He makes clear: this isn't science fiction anymore. The self-driving car really works, and it's only the beginning. The next frontier is humanoid robots. "The general purpose robotics thing is going to happen. And it's going to happen in the next decade and it's going to happen at giant scale." Andreessen shares Elon Musk's view that robotics could become the single biggest industry in the history of the planet: "There's going to be billions, tens of billions, hundreds of billions of robots of all shapes, sizes, descriptions running around doing all kinds of things." And all of those robots need to be designed and built. Which brings Andreessen to his core argument: the real opportunity isn't in trying to recover what was lost. It's in leaning forward into what's coming. "We don't try to get the old manufacturing jobs back. What we should do is lean hard into the manufacturing jobs of the future."
1:36

a16z co-founder Marc Andreessen on why the shift from software AI to physical AI will create the biggest industry in human history: AI today is software. You open ChatGPT on your phone, and there's no manufacturing component, no real generator of middle-class jobs. But Marc Andreessen 🇺🇸 argues that a second, far more consequential turn is coming: the shift to embodied physical AI. "There's another turn on AI that's coming, which is the turn to embodied physical AI, which is robotics." This transition, he points out, has already begun. Drones went from human-piloted to fully autonomous. Cars went from purely physical products to rolling computers that drive themselves. "If you ever have the misfortune of finding yourself in San Francisco, take a Waymo. It's amazing to live in a cyberpunk future reality where you're in literally a state-of-the-art self-driving car that's literally out of the Jetsons." He makes clear: this isn't science fiction anymore. The self-driving car really works, and it's only the beginning. The next frontier is humanoid robots. "The general purpose robotics thing is going to happen. And it's going to happen in the next decade and it's going to happen at giant scale." Andreessen shares Elon Musk's view that robotics could become the single biggest industry in the history of the planet: "There's going to be billions, tens of billions, hundreds of billions of robots of all shapes, sizes, descriptions running around doing all kinds of things." And all of those robots need to be designed and built. Which brings Andreessen to his core argument: the real opportunity isn't in trying to recover what was lost. It's in leaning forward into what's coming. "We don't try to get the old manufacturing jobs back. What we should do is lean hard into the manufacturing jobs of the future."

Big Brain AI

35,113 görüntüleme • 1 ay önce

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.
1:02

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

Harper Carroll

32,624 görüntüleme • 18 gün önce

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.
1:31:59

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

Robert Scoble

17,484 görüntüleme • 3 ay önce

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.
1:14

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

Runway

35,040 görüntüleme • 8 ay önce

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado
1:49

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

76,473 görüntüleme • 1 ay önce

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.
0:20

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Dhruv Diddi

31,751 görüntüleme • 4 ay önce

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.
1:00

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,796 görüntüleme • 9 ay önce

"SpaceX AI is doing something that Anthropic and Open AI both didn't and couldn't do... what is xAI doing? xAI is building data centers, right now on Earth, that are larger, more productive, and they're built faster and at lower cost than any other data centers in the world... so when you look at that and you actually plot that out, 1.8 trillion may be low." Larry Goldberg
4:33

"SpaceX AI is doing something that Anthropic and Open AI both didn't and couldn't do... what is xAI doing? xAI is building data centers, right now on Earth, that are larger, more productive, and they're built faster and at lower cost than any other data centers in the world... so when you look at that and you actually plot that out, 1.8 trillion may be low." Larry Goldberg

Ellie in Space 🚀💫

58,309 görüntüleme • 3 ay önce

Jack Dorsey says the real threat isn’t open-source AI. it’s five CEOs deciding what the world is allowed to build with AI. “These AI companies are building platforms, and they’re all incentivized around their own models. You have to ask for permission, you hit rate limits, and even what the models return is constrained.” “What technologies let you build without asking a company—or a CEO—for permission? The durable ones. That’s why open protocols matter.” “When a handful of CEOs make the call, it caps the upside of great ideas that could move humanity forward. They might know what’s best for their company—but not what’s best for the world’s creativity.” “Luckily, open-source AI is gaining real momentum. DeepSeek was a key moment—not just showing a different path, but proving it can be competitive, even better than what the big corporations are shipping.” “We shouldn’t be dependent on five companies claiming they know best—and calling open source ‘dangerous.’ We should build in the open and race toward solutions that stay ahead of the risks.” PS. If you found value in this post, like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:
2:07

Jack Dorsey says the real threat isn’t open-source AI. it’s five CEOs deciding what the world is allowed to build with AI. “These AI companies are building platforms, and they’re all incentivized around their own models. You have to ask for permission, you hit rate limits, and even what the models return is constrained.” “What technologies let you build without asking a company—or a CEO—for permission? The durable ones. That’s why open protocols matter.” “When a handful of CEOs make the call, it caps the upside of great ideas that could move humanity forward. They might know what’s best for their company—but not what’s best for the world’s creativity.” “Luckily, open-source AI is gaining real momentum. DeepSeek was a key moment—not just showing a different path, but proving it can be competitive, even better than what the big corporations are shipping.” “We shouldn’t be dependent on five companies claiming they know best—and calling open source ‘dangerous.’ We should build in the open and race toward solutions that stay ahead of the risks.” PS. If you found value in this post, like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

AI Evolution

79,345 görüntüleme • 1 ay önce

You are the habits you create and your habits are built by a way of thinking. What if YOUR private and personal local AI can help you help build the right habits? What would your world look like? “OpenAI” or Google isn’t going to sell this. It will be FREE and open source.
0:44

You are the habits you create and your habits are built by a way of thinking. What if YOUR private and personal local AI can help you help build the right habits? What would your world look like? “OpenAI” or Google isn’t going to sell this. It will be FREE and open source.

Brian Roemmele

124,200 görüntüleme • 1 yıl önce

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!
2:46

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Featherless AI

39,769 görüntüleme • 15 gün önce

Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein
1:15

Matt Bornstein says open source is how AI companies stop being wrappers: "A year ago, a bunch of smaller companies or new application companies were trying to figure out, 'How do I really build an AI without just being a wrapper on top of OpenAI?' The answer to that question turned out to be open source." "This is what Cursor did. This is what Decagon and Harvey are in the process of doing now. A bunch of really strong application-level startups made the determination: we can't build just on closed source." "We need to do our own mid-training, our own post-training, our own inference and deployment tricks. All of that means it must be built on top of open source. The closed-source vendors won't give you the access to do this." "Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. Some of the most innovative products and applications now really depend on this very deeply." Matt Bornstein

a16z

40,207 görüntüleme • 24 gün 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.
2:17

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

165,966 görüntüleme • 26 gün önce