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Most capable generalist robotics models today are closed or at best, open weights. But robotics won’t reach its ChatGPT moment without real openness. That GPT moment was built on years of open tools and datasets such as Python, PyTorch, ImageNet and more, that let researchers inspect, reproduce, and build....

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Jiafei Duan

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115,302 görüntüleme • 4 ay önce •via X (Twitter)

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Reasoning is central to purposeful action. Today we introduce MolmoAct — a fully open Action Reasoning Model (ARM) for robotics. Grounded in large-scale pre-training with action reasoning data, every predicted action is interpretable and user-steerable via visual trace. We are open-sourcing everything!
2:46

Reasoning is central to purposeful action. Today we introduce MolmoAct — a fully open Action Reasoning Model (ARM) for robotics. Grounded in large-scale pre-training with action reasoning data, every predicted action is interpretable and user-steerable via visual trace. We are open-sourcing everything!

Jiafei Duan

99,944 görüntüleme • 1 yıl önce

The missing piece for the ChatGPT moment of robotics is finally here Today, we are releasing phosphobot, an open-source app to control robots intuitively and train the most powerful AI models for the cheapest robots possible -> phospho-app/phosphobot 🧵 What we are building:
1:24

The missing piece for the ChatGPT moment of robotics is finally here Today, we are releasing phosphobot, an open-source app to control robots intuitively and train the most powerful AI models for the cheapest robots possible -> phospho-app/phosphobot 🧵 What we are building:

Pierre-Louis Biojout (PLB)

35,860 görüntüleme • 1 yıl önce

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 unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.
0:22

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 unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

Rerun

29,684 görüntüleme • 6 ay önce

Sundar Pichai on the robotics opportunity: ⦿ He's impressed by recent humanoid progress - so much so that he sometimes has to look closely to tell if a robot video is real or fake. ⦿ Google initially moved too early into the application layer, but now sees the combination of AI and robotics as the “sweet spot.” ⦿ He believes we’re 2–3 years away from a “magical moment” in robotics. ⦿ Google is working on playing a role similar to Android in robotics - one of its internal bets is already focused on supporting robotics manufacturers. ⦿ Gemini robotics models will work very well - how to productize them is still an open question at the company.
2:43

Sundar Pichai on the robotics opportunity: ⦿ He's impressed by recent humanoid progress - so much so that he sometimes has to look closely to tell if a robot video is real or fake. ⦿ Google initially moved too early into the application layer, but now sees the combination of AI and robotics as the “sweet spot.” ⦿ He believes we’re 2–3 years away from a “magical moment” in robotics. ⦿ Google is working on playing a role similar to Android in robotics - one of its internal bets is already focused on supporting robotics manufacturers. ⦿ Gemini robotics models will work very well - how to productize them is still an open question at the company.

The Humanoid Hub

87,088 görüntüleme • 1 yıl önce

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:20

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,457 görüntüleme • 3 ay önce

A toolkit for building agents that watch, listen, and understand video. Low latency by design. Open source. Production ready. Vision Agents lets you build real time video AI that works with your models and your edge layer. Supports YOLO, Moondream, Cartesia, Deepgram, ElevenLabs, HeyGen, Gemini, OpenAI, and more. Quick model switching. Easy to use API. Perfect for coaching tools, collaboration apps, avatars, and robotics.
0:43

A toolkit for building agents that watch, listen, and understand video. Low latency by design. Open source. Production ready. Vision Agents lets you build real time video AI that works with your models and your edge layer. Supports YOLO, Moondream, Cartesia, Deepgram, ElevenLabs, HeyGen, Gemini, OpenAI, and more. Quick model switching. Easy to use API. Perfect for coaching tools, collaboration apps, avatars, and robotics.

GitHub Projects Community

30,274 görüntüleme • 8 ay önce

The next frontier of autonomous driving is unlocked by reasoning models. NVIDIA Alpamayo brings together open AI models with reasoning capabilities, closed-loop simulation tools, and massive real-world driving datasets. Alpamayo 1 is a vision–language–action model that explains its own decisions through explicit reasoning traces, enabling trustworthy, humanlike decision-making. Together with NVIDIA’s Physical AI dataset and AlpaSim simulation, Alpamayo provides the tools and scale required to enable level 4 autonomous vehicles. ▶️ Watch now:
0:45

The next frontier of autonomous driving is unlocked by reasoning models. NVIDIA Alpamayo brings together open AI models with reasoning capabilities, closed-loop simulation tools, and massive real-world driving datasets. Alpamayo 1 is a vision–language–action model that explains its own decisions through explicit reasoning traces, enabling trustworthy, humanlike decision-making. Together with NVIDIA’s Physical AI dataset and AlpaSim simulation, Alpamayo provides the tools and scale required to enable level 4 autonomous vehicles. ▶️ Watch now:

NVIDIA DRIVE

35,324 görüntüleme • 8 ay önce

Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:16

Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

21,046 görüntüleme • 9 ay önce

🚨 Breaking News: Solo Tech won at CES 2026. 🦾🏆 In a 10×10 booth, we showcased the highest density of working robots on the floor proving that the future of AI is physical, performant, and deployable today. Up close with real systems. No slides. No simulations. Just Physical AI in action. Alongside leaders shaping what’s next NVIDIA Robotics , Sharpa , and Boston Dynamics the message was clear: This is the ChatGPT moment for robotics. And it won’t happen behind screens. It will happen in Robotics Gyms spaces around the world where machines train, models adapt, and builders learn by doing. The era of AI that acts has begun. #BreakingNews #CES2026 #SoloTech #PhysicalAI #RoboticsGym #EdgeAI
2:09

🚨 Breaking News: Solo Tech won at CES 2026. 🦾🏆 In a 10×10 booth, we showcased the highest density of working robots on the floor proving that the future of AI is physical, performant, and deployable today. Up close with real systems. No slides. No simulations. Just Physical AI in action. Alongside leaders shaping what’s next NVIDIA Robotics , Sharpa , and Boston Dynamics the message was clear: This is the ChatGPT moment for robotics. And it won’t happen behind screens. It will happen in Robotics Gyms spaces around the world where machines train, models adapt, and builders learn by doing. The era of AI that acts has begun. #BreakingNews #CES2026 #SoloTech #PhysicalAI #RoboticsGym #EdgeAI

Dhruv Diddi

14,821 görüntüleme • 7 ay önce

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:
1:25

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

Boyi Li

67,529 görüntüleme • 8 ay önce

This is what open robotics actually looks like! 🔓 I was in Bordeaux, sitting inside Pollen Robotics, and had a chance to play around with their humanoid robot Reachy 2. It’s expressive, modular, and can even bring you coffee, and of course runs Python or Rust. I was talking with Reachy in 3 different languages, tele-operating it and manipulating kitchen stuff, and make it even mimic my gestures. The best part is that there are no locked APIs, or hidden stuff, just an open platform that you can build upon. The connection with Hugging Face makes it super easy to start, as the open source approach allows everyone to contribute, experiment and add their own things. I already ordered his little brother Reachy Mini, let's see what a robotics rookie can do with that robot! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:36

This is what open robotics actually looks like! 🔓 I was in Bordeaux, sitting inside Pollen Robotics, and had a chance to play around with their humanoid robot Reachy 2. It’s expressive, modular, and can even bring you coffee, and of course runs Python or Rust. I was talking with Reachy in 3 different languages, tele-operating it and manipulating kitchen stuff, and make it even mimic my gestures. The best part is that there are no locked APIs, or hidden stuff, just an open platform that you can build upon. The connection with Hugging Face makes it super easy to start, as the open source approach allows everyone to contribute, experiment and add their own things. I already ordered his little brother Reachy Mini, let's see what a robotics rookie can do with that robot! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

19,433 görüntüleme • 7 ay önce

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.
4:31

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

CyberRobo

17,092 görüntüleme • 3 ay önce

Robotics is the next frontier for AI, surpassing $150B in the next 2 years. OpenMind works alongside major players like Circle, NVIDIA, and Unitree to build important software that powers the AI brains in robots. Therefore, Fabric Foundation was established to build a path for open robotics across the world and to hasten the development of onchain payments, identity, and governance infrastructure. The decentralized robot economy begins today, powered by $ROBO.
0:36

Robotics is the next frontier for AI, surpassing $150B in the next 2 years. OpenMind works alongside major players like Circle, NVIDIA, and Unitree to build important software that powers the AI brains in robots. Therefore, Fabric Foundation was established to build a path for open robotics across the world and to hasten the development of onchain payments, identity, and governance infrastructure. The decentralized robot economy begins today, powered by $ROBO.

Fabric Foundation

105,733 görüntüleme • 6 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

The Physical AI data space is exploding, but most vendors still share samples through Google Drive or Hugging Face links that are slow and painful to evaluate. We built the Human Archive Catalog so researchers can quickly browse and filter our off-the-shelf datasets by environment, hardware configuration, modality, annotations, and more. If you’re a robotics or world model researcher, request access below:
0:51

The Physical AI data space is exploding, but most vendors still share samples through Google Drive or Hugging Face links that are slow and painful to evaluate. We built the Human Archive Catalog so researchers can quickly browse and filter our off-the-shelf datasets by environment, hardware configuration, modality, annotations, and more. If you’re a robotics or world model researcher, request access below:

Raj Patel

12,484 görüntüleme • 1 ay önce

Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming
49:26

Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming

Y Combinator

117,359 görüntüleme • 4 ay önce

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .
2:20

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .

Jeff Boudier 🤗

21,947 görüntüleme • 1 yıl önce

Introducing LTX-2.5, the world model the world builds on. One of the biggest upgrades yet to the model already powering film, robotics, and real-time workflows. Higher pixel fidelity, multishot scenes that hold together across cuts, and a pretrained foundation built to be finetuned across domains. Plus: Diffusion Fidelity Rendering, a new rendering approach built for pixel quality that holds up frame by frame, even on a cinema screen. This isn't a tool you rent. It's a foundation you build on, open and yours.
2:03

Introducing LTX-2.5, the world model the world builds on. One of the biggest upgrades yet to the model already powering film, robotics, and real-time workflows. Higher pixel fidelity, multishot scenes that hold together across cuts, and a pretrained foundation built to be finetuned across domains. Plus: Diffusion Fidelity Rendering, a new rendering approach built for pixel quality that holds up frame by frame, even on a cinema screen. This isn't a tool you rent. It's a foundation you build on, open and yours.

LTX.io

13,724,110 görüntüleme • 23 gün önce