Загрузка видео...

Не удалось загрузить видео

На главную

YC W24's Yondu AI creates robotic foundation models for intelligent navigation. They're starting with a model that lets a drone reason and carry out complex human commands. Congrats on the launch, Michael Chen and Tahmid Jamal!

ycombinator's profile picture

Y Combinator

1,621,648 subscribers

15,624 просмотров • 2 лет назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

YC F24's Gecko Security is an AI security engineer that finds and fixes vulnerabilities in your codebase, helping teams secure code without relying on one-time human pentests that quickly become outdated. Congrats on the launch, JJ and Artemiy!
0:12

YC F24's Gecko Security is an AI security engineer that finds and fixes vulnerabilities in your codebase, helping teams secure code without relying on one-time human pentests that quickly become outdated. Congrats on the launch, JJ and Artemiy!

Y Combinator

33,830 просмотров • 1 год назад

GLM 5.2 is now live on PRXVT AI. Z.ai's openly-released model, built for agents and long, complex coding work. A massive mixture-of-experts model with a huge context window lets it reason through multi-step tasks without losing track. Live now, fully private.
0:10

GLM 5.2 is now live on PRXVT AI. Z.ai's openly-released model, built for agents and long, complex coding work. A massive mixture-of-experts model with a huge context window lets it reason through multi-step tasks without losing track. Live now, fully private.

PRXVT

12,199 просмотров • 2 месяцев назад

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.
0:14

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,602 просмотров • 1 год назад

The next phase of on-chain AI requires more than execution and models. It requires identity. We’ve partnered with Unstoppable.ai to launch .openx, a dedicated domain designed just for on-chain AI systems. .openx provides a human-readable identity layer that maps directly to wallet addresses, without changing execution logic, permissions, or custody. Read the full blog:
0:17

The next phase of on-chain AI requires more than execution and models. It requires identity. We’ve partnered with Unstoppable.ai to launch .openx, a dedicated domain designed just for on-chain AI systems. .openx provides a human-readable identity layer that maps directly to wallet addresses, without changing execution logic, permissions, or custody. Read the full blog:

OpenLedger

14,606 просмотров • 8 месяцев назад

"The best organizations won't manage token budgets manually. Instead, they'll rely on orchestration layers that automatically route workloads to the right models based on performance, cost, and use case. Keeping track of which model is best for coding, finance, research, or support will become too complex for humans to manage directly, so intelligent routing will become a core part of enterprise AI infrastructure." Aravind Srinivas How will the best orgs of the future manage token budgeting Michael Mignano Nikesh Arora Ryan Petersen mmurph
0:19

"The best organizations won't manage token budgets manually. Instead, they'll rely on orchestration layers that automatically route workloads to the right models based on performance, cost, and use case. Keeping track of which model is best for coding, finance, research, or support will become too complex for humans to manage directly, so intelligent routing will become a core part of enterprise AI infrastructure." Aravind Srinivas How will the best orgs of the future manage token budgeting Michael Mignano Nikesh Arora Ryan Petersen mmurph

Harry Stebbings

29,644 просмотров • 2 месяцев назад

Sub-40ms full self-driving on a $100 drone! 🚁 A demonstration showing a complete full-self-driving pipeline running in under 40 milliseconds on a $100 drone. The prompt: "Find the bike and land." No pre-mapping. Running real-time on commodity hardware. For context, human reaction time is around 200-250ms. This drone is processing sensor data, understanding natural language commands, identifying objects, planning motion, and executing control, all in 40ms. This is what happens when foundation models meet efficient inference. The models get smaller and faster while maintaining capability. The hardware gets cheaper while getting more powerful. The intersection makes previously impossible applications suddenly viable. A few years ago, this required thousands of dollars in compute, pre-mapped environments, and cloud connectivity. Now it runs locally on hardware that costs less than a nice dinner. Awesome stuff Chester & ! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:16

Sub-40ms full self-driving on a $100 drone! 🚁 A demonstration showing a complete full-self-driving pipeline running in under 40 milliseconds on a $100 drone. The prompt: "Find the bike and land." No pre-mapping. Running real-time on commodity hardware. For context, human reaction time is around 200-250ms. This drone is processing sensor data, understanding natural language commands, identifying objects, planning motion, and executing control, all in 40ms. This is what happens when foundation models meet efficient inference. The models get smaller and faster while maintaining capability. The hardware gets cheaper while getting more powerful. The intersection makes previously impossible applications suddenly viable. A few years ago, this required thousands of dollars in compute, pre-mapped environments, and cloud connectivity. Now it runs locally on hardware that costs less than a nice dinner. Awesome stuff Chester & ! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

310,622 просмотров • 6 месяцев назад

Google is working on integrating Firebase with AI Studio to enable users to build more complex apps. Users will be able to vibe code a proper database integration and an auth layer. This is accompanied by an updated UI for the Build page and support for / commands.
0:18

Google is working on integrating Firebase with AI Studio to enable users to build more complex apps. Users will be able to vibe code a proper database integration and an auth layer. This is accompanied by an updated UI for the Build page and support for / commands.

🚨 AI News | TestingCatalog

66,896 просмотров • 7 месяцев назад

Elon Musk has repeatedly highlighted the hand (and forearm) as one of the toughest engineering challenges for Tesla’s Optimus Robot. A new Patent (for presumably Optimus 3) shows Tesla is using human anatomy to recreate a tendon-driven robotic hand and forearm assembly that closely mimics a human system. Basically, Tesla is replacing human muscles with actuators and tendons with cables to replicate human dexterity. The Tesla Engineering team is on another level. Optimus Gen 3 reveal is going to be incredible.
0:11

Elon Musk has repeatedly highlighted the hand (and forearm) as one of the toughest engineering challenges for Tesla’s Optimus Robot. A new Patent (for presumably Optimus 3) shows Tesla is using human anatomy to recreate a tendon-driven robotic hand and forearm assembly that closely mimics a human system. Basically, Tesla is replacing human muscles with actuators and tendons with cables to replicate human dexterity. The Tesla Engineering team is on another level. Optimus Gen 3 reveal is going to be incredible.

Nic Cruz Patane

93,416 просмотров • 4 месяцев назад

❗️❗️🇺🇦 Ukraine has found a cheap weapon to defend against Russian attacks — the Ukrainian rebel complex Lima, developed by the defense startup Cascade Systems. 🔺 The system does not destroy targets, but instead disrupts their navigation. Lima creates powerful jamming fields that interfere with satellite signals. It imitates the satellite navigation signals used by Russian missiles, drones, and glide bombs, redirecting them to false coordinates. As a result, cruise and ballistic missiles, Shahed kamikaze drones, and bombs with pre-programmed coordinates in their guidance systems deviate to a different direction. Video is generated by grok AI
0:10

❗️❗️🇺🇦 Ukraine has found a cheap weapon to defend against Russian attacks — the Ukrainian rebel complex Lima, developed by the defense startup Cascade Systems. 🔺 The system does not destroy targets, but instead disrupts their navigation. Lima creates powerful jamming fields that interfere with satellite signals. It imitates the satellite navigation signals used by Russian missiles, drones, and glide bombs, redirecting them to false coordinates. As a result, cruise and ballistic missiles, Shahed kamikaze drones, and bombs with pre-programmed coordinates in their guidance systems deviate to a different direction. Video is generated by grok AI

NSTRIKE

1,034,772 просмотров • 3 месяцев назад

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via
0:15

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

Stephen James

16,673 просмотров • 11 месяцев назад

Vidu Q3 is now available on Akool Experience next-level AI video creation with Vidu Q3 — a powerful multi-modal model that seamlessly blends high-fidelity visuals with perfectly synchronized audio. Powered by Vidu AI, Q3 shines in: Deep narrative understanding Mastery of complex cinematic language Lifelike, human-feeling motion and emotion If storytelling and cinematic quality matter to you, Vidu Q3 is built for it. Try it now on Akool.
0:11

Vidu Q3 is now available on Akool Experience next-level AI video creation with Vidu Q3 — a powerful multi-modal model that seamlessly blends high-fidelity visuals with perfectly synchronized audio. Powered by Vidu AI, Q3 shines in: Deep narrative understanding Mastery of complex cinematic language Lifelike, human-feeling motion and emotion If storytelling and cinematic quality matter to you, Vidu Q3 is built for it. Try it now on Akool.

Akool Inc

1,528,589 просмотров • 6 месяцев назад

✨ Every time the video models get better, the try on model on Photo AI also becomes a lot more useful, as a large % of my customers now are e-commerce store And showing clothes in a video is nice for sales! With AI this means stores don't need to do expensive shoots flying a model and entire camera and light crew around the world They can just upload a few photos of their models, then upload the clothes, and describe the setting (like a beach in Thailand) and in less than 10 seconds it's generated, for a video in less than a minute! Below is the input: a dress laid flat, and output: a full video shoot
0:10

✨ Every time the video models get better, the try on model on Photo AI also becomes a lot more useful, as a large % of my customers now are e-commerce store And showing clothes in a video is nice for sales! With AI this means stores don't need to do expensive shoots flying a model and entire camera and light crew around the world They can just upload a few photos of their models, then upload the clothes, and describe the setting (like a beach in Thailand) and in less than 10 seconds it's generated, for a video in less than a minute! Below is the input: a dress laid flat, and output: a full video shoot

@levelsio

334,170 просмотров • 1 год назад

Generative 3d environments just became a thing with the announcement of OpenAI's new video model, Sora. Michael Rublof from took one of those videos, and turned it into a NeRF using Colmap and Nerfstudio. While people are laughing at the topology of generated models, the world is changing around us, and that's exciting and a little scary, but we'll find a way to turn Gen Ai into creative superpowers. I believe in human creativity, in our ability to surprise, to move, to change and to challenge. Here's to the future! #ai #art
0:14

Generative 3d environments just became a thing with the announcement of OpenAI's new video model, Sora. Michael Rublof from took one of those videos, and turned it into a NeRF using Colmap and Nerfstudio. While people are laughing at the topology of generated models, the world is changing around us, and that's exciting and a little scary, but we'll find a way to turn Gen Ai into creative superpowers. I believe in human creativity, in our ability to surprise, to move, to change and to challenge. Here's to the future! #ai #art

Martin Nebelong

288,988 просмотров • 2 лет назад

SuperMap: A Living Spatial Memory for Embodied AI RSS 2026 Carnegie Mellon University SuperMap is a living spatial memory for embodied AI. It perceives the world, remembers its evolution, and supports reasoning and action. It is a training-free spatio-temporal SLAM system that builds a persistent semantic world model. It fuses high-frequency geometric SLAM with asynchronous open-vocabulary perception, producing a 4D scene graph: a queryable map carrying spatial and temporal information for every object, enabling visual-language navigation and long-horizon reasoning on real robots.
0:16

SuperMap: A Living Spatial Memory for Embodied AI RSS 2026 Carnegie Mellon University SuperMap is a living spatial memory for embodied AI. It perceives the world, remembers its evolution, and supports reasoning and action. It is a training-free spatio-temporal SLAM system that builds a persistent semantic world model. It fuses high-frequency geometric SLAM with asynchronous open-vocabulary perception, producing a 4D scene graph: a queryable map carrying spatial and temporal information for every object, enabling visual-language navigation and long-horizon reasoning on real robots.

Ryohei Sasaki@engineer

24,629 просмотров • 1 месяц назад

Cluster Protocol 🤝 StarLandAI We're forming a strategic alliance with StarLand.AI StarLandAI is a generative AI network that supports running large models on decentralized computational power, offering MaaS (Model as a Service) API services for partners to easily build on-chain generative AI Dapps. 🤖🦾 Together, we are creating a more efficient and scalable environment for developing AI-driven applications. Stay tuned for groundbreaking AI innovations!
0:10

Cluster Protocol 🤝 StarLandAI We're forming a strategic alliance with StarLand.AI StarLandAI is a generative AI network that supports running large models on decentralized computational power, offering MaaS (Model as a Service) API services for partners to easily build on-chain generative AI Dapps. 🤖🦾 Together, we are creating a more efficient and scalable environment for developing AI-driven applications. Stay tuned for groundbreaking AI innovations!

Cluster Protocol

34,640 просмотров • 2 лет назад

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.
0:13

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Michael Black

22,182 просмотров • 8 месяцев назад