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Over 100 contestants joined our #EmbodiedAIHackathon in Shenzhen! 🤖🔥 From single #robot arm pick & place to dual-arm manipulation, from handling rigid objects to soft textiles, teams pushed boundaries — evolving into long-horizon actions with stronger generalization. Next stop: Mountain View, Oct 25–26! Join the final demo presentation at... show more

Seeed Studio

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11,605 просмотров • 10 месяцев назад •via X (Twitter)

#EmbodiedAIHackathon #robot

Anya Rossi• Live Now

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🦾 From seeing to doing. We're closing the loop between video prediction and real-world action. On the final day of Robbyant Open Source Week, we bring you LingBot-VA—the world's first causal video-action world model for generalist robot control. 🔥 Key Highlights: 🤖 Predicts & Acts: A single model generates both future video and the actions to make it real. 🧠 Remembers the Past: True long-term memory for complex, sequential tasks. ⚡ Learns in a Snap: Masters new skills with just 30-50 real-world demos. The result? 📈 SOTA on RoboTwin (92.9%) and LIBERO (98.5%), +20% over π0.5 on challenging real-world long-horizon & high-precision tasks. More below 👇 #AI #Robotics #EmbodiedAI #WorldModel #OpenSource

🦾 From seeing to doing. We're closing the loop between video prediction and real-world action. On the final day of Robbyant Open Source Week, we bring you LingBot-VA—the world's first causal video-action world model for generalist robot control. 🔥 Key Highlights: 🤖 Predicts & Acts: A single model generates both future video and the actions to make it real. 🧠 Remembers the Past: True long-term memory for complex, sequential tasks. ⚡ Learns in a Snap: Masters new skills with just 30-50 real-world demos. The result? 📈 SOTA on RoboTwin (92.9%) and LIBERO (98.5%), +20% over π0.5 on challenging real-world long-horizon & high-precision tasks. More below 👇 #AI #Robotics #EmbodiedAI #WorldModel #OpenSource

Robbyant

704,157 просмотров • 7 месяцев назад

BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:

BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:

Ilir Aliu

72,419 просмотров • 6 месяцев назад

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 просмотров • 4 месяцев назад

Imagine controlling a real robot from your home… no money, no experience needed. Sounds crazy, right? But it’s already possible. BitRobot 🦾 is building the world’s first open robotics lab powered by crypto incentives. Instead of one company doing everything, it connects people from all over the world to work together on real robotics and AI tasks. The network is made up of specialized subnets, each focused on different missions from collecting real-world data with robots to developing humanoid robots for everyday use. What makes it powerful? It uses crypto rewards to coordinate global resources like compute power, robot fleets, teleoperation time, and even human effort. This allows BitRobot to scale much faster than traditional labs. Now here’s the best part 👇 The easiest way to get involved right now is through TeleArms. You don’t need: – a robot – engineering skills – or any investment – Hardware All you need is a laptop and an internet connection. From your home, you can remotely control a real robotic arm inside BitRobot’s lab using your keyboard or mouse to pick up, move, and place objects. Every action you take helps generate real-world data that trains the next generation of AI to perform useful physical tasks. So you’re not just playing with a robot… You’re actually helping build the future of AI. I’ve been talking about BitRobot for a while, and now TeleArms is live! You can control a real robotic arm from home, but it’s in a private beta with limited access. I’m now an ambassador for BitRobot Network. I’m giving 4 exclusive access codes to my community so they can experience it too. A lot of people want to experience this, but since it’s limited, I decided to do a random giveaway. To participate in this giveaway : 1. Join the BitRobot Network Discord (Link in comments) 2. Come back to this post and comment below, explaining why you want to join TeleArms and how you plan to contribute. Note : Winner will be announced in the last 7 days. Once you do that, you’ll be in the running for one of the codes! Good luck, and I can’t wait to see your ideas!

Imagine controlling a real robot from your home… no money, no experience needed. Sounds crazy, right? But it’s already possible. BitRobot 🦾 is building the world’s first open robotics lab powered by crypto incentives. Instead of one company doing everything, it connects people from all over the world to work together on real robotics and AI tasks. The network is made up of specialized subnets, each focused on different missions from collecting real-world data with robots to developing humanoid robots for everyday use. What makes it powerful? It uses crypto rewards to coordinate global resources like compute power, robot fleets, teleoperation time, and even human effort. This allows BitRobot to scale much faster than traditional labs. Now here’s the best part 👇 The easiest way to get involved right now is through TeleArms. You don’t need: – a robot – engineering skills – or any investment – Hardware All you need is a laptop and an internet connection. From your home, you can remotely control a real robotic arm inside BitRobot’s lab using your keyboard or mouse to pick up, move, and place objects. Every action you take helps generate real-world data that trains the next generation of AI to perform useful physical tasks. So you’re not just playing with a robot… You’re actually helping build the future of AI. I’ve been talking about BitRobot for a while, and now TeleArms is live! You can control a real robotic arm from home, but it’s in a private beta with limited access. I’m now an ambassador for BitRobot Network. I’m giving 4 exclusive access codes to my community so they can experience it too. A lot of people want to experience this, but since it’s limited, I decided to do a random giveaway. To participate in this giveaway : 1. Join the BitRobot Network Discord (Link in comments) 2. Come back to this post and comment below, explaining why you want to join TeleArms and how you plan to contribute. Note : Winner will be announced in the last 7 days. Once you do that, you’ll be in the running for one of the codes! Good luck, and I can’t wait to see your ideas!

Apurba.Eth

36,318 просмотров • 5 месяцев назад

The next evolution: VLA+ models Just yesterday Microsoft Research released Rho-alpha (ρα) – their first robotics model, built on the Phi family. While most Vision-Language-Action (VLA) models stop at vision and language, Rho-alpha adds: ▪️ Tactile sensing to feel objects during manipulation ▪️ Online learning that lets it improve from human corrections (via teleoperation, 3D mouse or other tools) in real-time even after deployment. Both these sides make adaptability central rather than incidental. Microsoft calls it a VLA+ model, positioning it as an extension beyond what current VLA systems support. ➡️ Today Rho-alpha can control dual-arm robot setups to perform tasks such as: • Manipulating the BusyBox following natural-language instructions • Plug insertion • Toolbox packing and object arrangement with bimanual coordination But to understand why this "plus" matters, we need to understand what came before. Here, we'll take you through the entire landscape of VLA models – Gemini Robotics, π0, SmolVLA, Helix, ACoT-VLA and others:

The next evolution: VLA+ models Just yesterday Microsoft Research released Rho-alpha (ρα) – their first robotics model, built on the Phi family. While most Vision-Language-Action (VLA) models stop at vision and language, Rho-alpha adds: ▪️ Tactile sensing to feel objects during manipulation ▪️ Online learning that lets it improve from human corrections (via teleoperation, 3D mouse or other tools) in real-time even after deployment. Both these sides make adaptability central rather than incidental. Microsoft calls it a VLA+ model, positioning it as an extension beyond what current VLA systems support. ➡️ Today Rho-alpha can control dual-arm robot setups to perform tasks such as: • Manipulating the BusyBox following natural-language instructions • Plug insertion • Toolbox packing and object arrangement with bimanual coordination But to understand why this "plus" matters, we need to understand what came before. Here, we'll take you through the entire landscape of VLA models – Gemini Robotics, π0, SmolVLA, Helix, ACoT-VLA and others:

Turing Post

62,494 просмотров • 7 месяцев назад

First time ever we combined the Seeed Studio ReBot with the Unitree Robotics Go2! 🤖🐕 The future of Physical AI isn’t just about moving; it’s about interaction. By mounting the ReBot arm onto the Unitree Go2, we’ve created a mobile manipulator that moves beyond simple locomotion into true household utility. What can this combo do? 1. Utility: Grab a drink from the fridge and bring it to you. 2. Maintenance: Identify and pick up trash around the house autonomously. 3. Care: Assist the elderly and children in navigating home environments safely. We are moving past "cool robot dog demos" and into the era of functional, embodied intelligence that solves real-world problems. Join the Movement This build is officially part of our Solo Seven challenge, our global mission to activate the world’s most talented builders across seven cities. We’re kicking things off by bringing this hardware to the BETA Berkeley Hackathon. Come get hands-on access, see the "fridge-run" in person, and help us define the next chapter of SoloTech 📍 Where: Frontier Tower 🗓️ When: This Sunday, April 26th Luma: See you there! 🚀 #SoloSeven #PhysicalAI #Robotics #UnitreeGo2 #SeeedStudio #SoloTech #BETA #Innovation

First time ever we combined the Seeed Studio ReBot with the Unitree Robotics Go2! 🤖🐕 The future of Physical AI isn’t just about moving; it’s about interaction. By mounting the ReBot arm onto the Unitree Go2, we’ve created a mobile manipulator that moves beyond simple locomotion into true household utility. What can this combo do? 1. Utility: Grab a drink from the fridge and bring it to you. 2. Maintenance: Identify and pick up trash around the house autonomously. 3. Care: Assist the elderly and children in navigating home environments safely. We are moving past "cool robot dog demos" and into the era of functional, embodied intelligence that solves real-world problems. Join the Movement This build is officially part of our Solo Seven challenge, our global mission to activate the world’s most talented builders across seven cities. We’re kicking things off by bringing this hardware to the BETA Berkeley Hackathon. Come get hands-on access, see the "fridge-run" in person, and help us define the next chapter of SoloTech 📍 Where: Frontier Tower 🗓️ When: This Sunday, April 26th Luma: See you there! 🚀 #SoloSeven #PhysicalAI #Robotics #UnitreeGo2 #SeeedStudio #SoloTech #BETA #Innovation

Dhruv Diddi

20,709 просмотров • 4 месяцев назад

$Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:$

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 просмотров • 10 месяцев назад

This guy connected a computer vision model to dual robotic manipulators on his desk and the system now folds shirts in 47 seconds per garment without any human intervention after loading Automated laundry folding is one of those problems that sounds trivial until you realize fabric has no rigid structure and every wrinkle changes the optimal fold path You need the robot to detect garment boundaries through visual segmentation, identify sleeve edges and collar positions on randomly oriented fabric, generate dynamic reference coordinates that shift with garment size, synchronize two independent robotic arms to pull opposing fabric edges without tearing, and execute all of this without a conveyor belt or fixed staging area Most people assume you need a commercial folding machine or at least a rigid frame to hold clothes in place This guy just bolted two robot arms to a workbench, ran a Flask server with a Laundrobot vision library, and built a preset selection interface that handles nine garment types The setup was minimal: a Python backend processing camera frames, a segmentation model running inference locally, two manipulators with soft grippers, and a heads-up display showing red and blue anchor points overlaid on live fabric The system scans the garment, the vision pipeline outputs coordinates like 284.262 and 965.262, the dashboard waits for a RUN command, and the arms fold the item in two geometric steps The robot picks up shirts, pants, towels, and socks from any position on the desk with zero calibration and zero pre-staging It is the same principle robotic pick-and-place systems use in factories but instead of metal parts it is handling deformable textiles that compress and slide unpredictably The arms have no concept of what clean laundry means to a human They think they are executing waypoint trajectories but the output is getting transformed into neatly stacked garments that take zero cognitive load from the operator If a household generates 14 loads of laundry per month and folding takes eleven minutes per load this is how you reclaim 154 minutes without outsourcing or spending four figures on hardware This is the cleanest domestic automation I have seen: one desk, two arms, one camera, and between them a folding operation that runs while you do anything else

This guy connected a computer vision model to dual robotic manipulators on his desk and the system now folds shirts in 47 seconds per garment without any human intervention after loading Automated laundry folding is one of those problems that sounds trivial until you realize fabric has no rigid structure and every wrinkle changes the optimal fold path You need the robot to detect garment boundaries through visual segmentation, identify sleeve edges and collar positions on randomly oriented fabric, generate dynamic reference coordinates that shift with garment size, synchronize two independent robotic arms to pull opposing fabric edges without tearing, and execute all of this without a conveyor belt or fixed staging area Most people assume you need a commercial folding machine or at least a rigid frame to hold clothes in place This guy just bolted two robot arms to a workbench, ran a Flask server with a Laundrobot vision library, and built a preset selection interface that handles nine garment types The setup was minimal: a Python backend processing camera frames, a segmentation model running inference locally, two manipulators with soft grippers, and a heads-up display showing red and blue anchor points overlaid on live fabric The system scans the garment, the vision pipeline outputs coordinates like 284.262 and 965.262, the dashboard waits for a RUN command, and the arms fold the item in two geometric steps The robot picks up shirts, pants, towels, and socks from any position on the desk with zero calibration and zero pre-staging It is the same principle robotic pick-and-place systems use in factories but instead of metal parts it is handling deformable textiles that compress and slide unpredictably The arms have no concept of what clean laundry means to a human They think they are executing waypoint trajectories but the output is getting transformed into neatly stacked garments that take zero cognitive load from the operator If a household generates 14 loads of laundry per month and folding takes eleven minutes per load this is how you reclaim 154 minutes without outsourcing or spending four figures on hardware This is the cleanest domestic automation I have seen: one desk, two arms, one camera, and between them a folding operation that runs while you do anything else

Blaze

24,799 просмотров • 3 месяцев назад

In Crimson Desert you CAN.. (Hold your breath) 1. Catapult using tree & other objects 2. Tame a Horse 3. Fishing and Hunting 4. Base building, build your own home 5. Mining and Crafting gear 6. Alchemy and Cooking 7. Horse Races 8. Fight Clubs 9. Shooting competitions 10. Arm Wrestling 11. Gambling 12. Help townsfolk & vendors 13. Tackle and rob people 14. Hijack carriages and horses 15. Steal items 16. Pickpocket NPCs 17. Threaten NPCs to give over items 18. You can become a wanted criminal 19. Guards will come after you 20. Get taken to Prison if caught 21. Walk or use a horse like RDR2 22. Climb like in BOTW 23. Fly an umbrella 24. Mount a DRAGON 25. Pilot a MECH 26. Swing like SPIDER-MAN with a grapple hook 27. Fly with magic wings 28. Use wrestling moves 29. Perform RKO 30. Pet the dog 31. Pick up a cat and hold it close 32. Tame rats with a flute 33. Carry a pig on your back 34. Pick from 1/3 character at later stage

In Crimson Desert you CAN.. (Hold your breath) 1. Catapult using tree & other objects 2. Tame a Horse 3. Fishing and Hunting 4. Base building, build your own home 5. Mining and Crafting gear 6. Alchemy and Cooking 7. Horse Races 8. Fight Clubs 9. Shooting competitions 10. Arm Wrestling 11. Gambling 12. Help townsfolk & vendors 13. Tackle and rob people 14. Hijack carriages and horses 15. Steal items 16. Pickpocket NPCs 17. Threaten NPCs to give over items 18. You can become a wanted criminal 19. Guards will come after you 20. Get taken to Prison if caught 21. Walk or use a horse like RDR2 22. Climb like in BOTW 23. Fly an umbrella 24. Mount a DRAGON 25. Pilot a MECH 26. Swing like SPIDER-MAN with a grapple hook 27. Fly with magic wings 28. Use wrestling moves 29. Perform RKO 30. Pet the dog 31. Pick up a cat and hold it close 32. Tame rats with a flute 33. Carry a pig on your back 34. Pick from 1/3 character at later stage

SpawnYaard 🎮

129,539 просмотров • 6 месяцев назад

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning *inside* a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation *inside* a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning inside a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation inside a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

Jim Fan

227,634 просмотров • 6 месяцев назад

I'VE SAID IT A HUNDRED TIMES $BTC local top was $82K And right on cue, here we are Look back at every call: ➮ $126K - the cycle top. Called in Oct '25 ➮ $60K - local bottom. Called in Feb '26 ➮ $63K - quick rally up. Called in Mar '26 ➮ $82K - local top. Called in Apr '26 Four calls. Four hits No paid group. No subscription. All completely for FREE Here's what I'm watching now: $69K - $71K is real support Close above $73K this week and $75K is back on the table Lose $70K and the next stop is $65K - where the same buyer who loaded at $60K in February will start reaching for the bid But here's the thing nobody else is going to tell you: The drop from $82K is slow Just like the climb from $65K was slow That's the signal Any move - up or down - without massive liquidations is just noise And right now BTC is doing exactly that. Pretending the short-term trend is real (from 82k to 71k) That's the move big players use to convince the crowd. Get them comfortable. Then flip the table. Two scenarios from here: 1. The slow drift carries us down into the $50K - $60K zone over a few months. That's where I'd expect a real bottom to form. Maybe lower. Crowd sentiment will decide 2. We see a quiet bounce back up to attempt $82K again. New high, then the trap closes 2026 is the year. You either build serious wealth or you watch it disappear. There's no third option. And sitting on your hands is the most dangerous one. Don't worry though - my system flags the exact moment the market shifts from CAUTION to DANGER. You'll be warned before it hits, like always. Follow me with NOTIFS on Many people made 2 simple clicks and changed their lives forever

I'VE SAID IT A HUNDRED TIMES $BTC local top was $82K And right on cue, here we are Look back at every call: ➮ $126K - the cycle top. Called in Oct '25 ➮ $60K - local bottom. Called in Feb '26 ➮ $63K - quick rally up. Called in Mar '26 ➮ $82K - local top. Called in Apr '26 Four calls. Four hits No paid group. No subscription. All completely for FREE Here's what I'm watching now: $69K - $71K is real support Close above $73K this week and $75K is back on the table Lose $70K and the next stop is $65K - where the same buyer who loaded at $60K in February will start reaching for the bid But here's the thing nobody else is going to tell you: The drop from $82K is slow Just like the climb from $65K was slow That's the signal Any move - up or down - without massive liquidations is just noise And right now BTC is doing exactly that. Pretending the short-term trend is real (from 82k to 71k) That's the move big players use to convince the crowd. Get them comfortable. Then flip the table. Two scenarios from here: 1. The slow drift carries us down into the $50K - $60K zone over a few months. That's where I'd expect a real bottom to form. Maybe lower. Crowd sentiment will decide 2. We see a quiet bounce back up to attempt $82K again. New high, then the trap closes 2026 is the year. You either build serious wealth or you watch it disappear. There's no third option. And sitting on your hands is the most dangerous one. Don't worry though - my system flags the exact moment the market shifts from CAUTION to DANGER. You'll be warned before it hits, like always. Follow me with NOTIFS on Many people made 2 simple clicks and changed their lives forever

Reflection🪩

224,113 просмотров • 3 месяцев назад

PrismaX TeleOps feels like 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐚𝐭𝐜𝐡 𝐚 𝐫𝐨𝐛𝐨𝐭 𝐭𝐨𝐝𝐚𝐲 instead 𝐈 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐨𝐧𝐞 𝐟𝐫𝐨𝐦 𝐦𝐢𝐥𝐞𝐬 𝐚𝐰𝐚𝐲 Using PrismaX’s teleoperation, I remotely operated a real robotic arm in a live environment What felt like simple actions moving objects, adjusting grip, navigating space quickly showed how powerful human in the loop robotics really is Every small movement wasn’t just control It was training data for physical AI Great teleoperation isn’t about speed or flashy moves It’s built through consistency, awareness, and deliberate control over time. Small habits practiced every session quietly compound into real performance gains 1. Show up every time Consistency is the real multiplier Even short sessions add up when you never skip them Set reminders, plan ahead, and treat teleop like a commitment. Being present and focused is progress on its own 2. Control always beats speed Teleoperation rewards precision, not rushing You’re given enough time use it Slow, intentional movements reduce mistakes and lead to far better outcomes than fast, reactive inputs 3. Put yourself inside the robot Stop thinking of the robot as something distant. Operate from its point of view When your brain treats the robot as an extension of your body, coordination improves and movements feel more natural 4. Stay active to stay stable Long idle moments can break flow and stability Even subtle movements help maintain control and keep your focus sharp, making it easier to respond when adjustments are needed 5. Repetition creates fluency At first, the controls may feel unfamiliar and that’s expected With repetition, aligning, gripping, and lifting become automatic Muscle memory takes over, and teleop starts to feel intuitive Quick control guide W / S → move forward & backward A / D → arm left & right Q / E → move up & down Z / X → open & close grip ← / → → rotate grip C / V → slide base Teleop mastery is a long game. Stay consistent, stay intentional, and let small improvements stack into real skill

PrismaX TeleOps feels like 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐚𝐭𝐜𝐡 𝐚 𝐫𝐨𝐛𝐨𝐭 𝐭𝐨𝐝𝐚𝐲 instead 𝐈 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐨𝐧𝐞 𝐟𝐫𝐨𝐦 𝐦𝐢𝐥𝐞𝐬 𝐚𝐰𝐚𝐲 Using PrismaX’s teleoperation, I remotely operated a real robotic arm in a live environment What felt like simple actions moving objects, adjusting grip, navigating space quickly showed how powerful human in the loop robotics really is Every small movement wasn’t just control It was training data for physical AI Great teleoperation isn’t about speed or flashy moves It’s built through consistency, awareness, and deliberate control over time. Small habits practiced every session quietly compound into real performance gains 1. Show up every time Consistency is the real multiplier Even short sessions add up when you never skip them Set reminders, plan ahead, and treat teleop like a commitment. Being present and focused is progress on its own 2. Control always beats speed Teleoperation rewards precision, not rushing You’re given enough time use it Slow, intentional movements reduce mistakes and lead to far better outcomes than fast, reactive inputs 3. Put yourself inside the robot Stop thinking of the robot as something distant. Operate from its point of view When your brain treats the robot as an extension of your body, coordination improves and movements feel more natural 4. Stay active to stay stable Long idle moments can break flow and stability Even subtle movements help maintain control and keep your focus sharp, making it easier to respond when adjustments are needed 5. Repetition creates fluency At first, the controls may feel unfamiliar and that’s expected With repetition, aligning, gripping, and lifting become automatic Muscle memory takes over, and teleop starts to feel intuitive Quick control guide W / S → move forward & backward A / D → arm left & right Q / E → move up & down Z / X → open & close grip ← / → → rotate grip C / V → slide base Teleop mastery is a long game. Stay consistent, stay intentional, and let small improvements stack into real skill

kingopw3

11,435 просмотров • 7 месяцев назад

🇸🇩🇺🇳 Sudan: IOM Warns of Escalation in Kordofan — “We Must Avoid the Next El Fasher” At the UN press briefing in Geneva on Friday, Mohamed Refaat, International Organization for Migration (IOM) Chief of Mission in Sudan, warned that escalating violence in Darfur and Kordofan is erasing fragile gains and pushing Sudan back toward a deepening displacement and humanitarian crisis, even as funding collapses. 1.⁠ ⁠Scale of displacement ➤ Sudan remains the world’s largest displacement crisis, with 9.3 million internally displaced people, down from 11.9 million earlier this year, which Refaat described as a brief “moment of hope.” ➤ About 2.6 million IDPs had opted to return over the past 8–9 months, but that trend is now reversing. ➤ Recent attacks on civilian infrastructure, including power stations, left multiple states without electricity. ➤ Refaat said people are increasingly asking whether return is safe, especially as services remain absent. 2.⁠ ⁠Darfur: El Fasher ➤ Following violence that began on Oct. 25, IOM tracked 109,000+ people fleeing El Fasher and surrounding villages. ➤ Many remain stuck in nearby areas, unable to move further due to insecurity and logistical constraints. 3.⁠ ⁠Kordofan escalation - “Must Avoid the Next El Fasher” ➤ Hostilities intensified in South Kordofan after El Fasher fell on Oct. 25, displacing 50,000+ people from the Kordofan states. ➤ Alarmingly, only women and children are arriving. ➤ IDPs Between Dec. 4–15: —From North Kordofan, IOM recorded 40,000+ newly displaced —From South Kordofan: Almost 10,000 IDPs —From West Kordofan: 250 IDPs ➤ At least 3,300 people crossed into South Sudan ➤ Displacement also continued from the large city of El Obeid, with Refaat warning the city could be “one or two steps away” from becoming the next target, potentially displacing 90,000–100,000 people if fighting spreads. 4.⁠ ⁠Children and protection risks ➤ Roughly half of all new displacements are children, which Refaat described as “alarming” for Sudan’s future. 5.⁠ ⁠Aid system under strain ➤ Sudan’s humanitarian response plan is 36% funded ($1.5B of $4.2B required). ➤ IOM Sudan lost $83 million in 2025, forcing closures of health projects, mobile clinics, NGO grants, and staff layoffs. ➤ “We have to choose which lives we can save and which support we have to stop,” Refaat said. ➤ The shelter response is only 11–15% funded, the most underfunded sector Refaat warned that if fighting continues, displacement will become more protracted and spill beyond Sudan’s borders, with regional consequences.

🇸🇩🇺🇳 Sudan: IOM Warns of Escalation in Kordofan — “We Must Avoid the Next El Fasher” At the UN press briefing in Geneva on Friday, Mohamed Refaat, International Organization for Migration (IOM) Chief of Mission in Sudan, warned that escalating violence in Darfur and Kordofan is erasing fragile gains and pushing Sudan back toward a deepening displacement and humanitarian crisis, even as funding collapses. 1.⁠ ⁠Scale of displacement ➤ Sudan remains the world’s largest displacement crisis, with 9.3 million internally displaced people, down from 11.9 million earlier this year, which Refaat described as a brief “moment of hope.” ➤ About 2.6 million IDPs had opted to return over the past 8–9 months, but that trend is now reversing. ➤ Recent attacks on civilian infrastructure, including power stations, left multiple states without electricity. ➤ Refaat said people are increasingly asking whether return is safe, especially as services remain absent. 2.⁠ ⁠Darfur: El Fasher ➤ Following violence that began on Oct. 25, IOM tracked 109,000+ people fleeing El Fasher and surrounding villages. ➤ Many remain stuck in nearby areas, unable to move further due to insecurity and logistical constraints. 3.⁠ ⁠Kordofan escalation - “Must Avoid the Next El Fasher” ➤ Hostilities intensified in South Kordofan after El Fasher fell on Oct. 25, displacing 50,000+ people from the Kordofan states. ➤ Alarmingly, only women and children are arriving. ➤ IDPs Between Dec. 4–15: —From North Kordofan, IOM recorded 40,000+ newly displaced —From South Kordofan: Almost 10,000 IDPs —From West Kordofan: 250 IDPs ➤ At least 3,300 people crossed into South Sudan ➤ Displacement also continued from the large city of El Obeid, with Refaat warning the city could be “one or two steps away” from becoming the next target, potentially displacing 90,000–100,000 people if fighting spreads. 4.⁠ ⁠Children and protection risks ➤ Roughly half of all new displacements are children, which Refaat described as “alarming” for Sudan’s future. 5.⁠ ⁠Aid system under strain ➤ Sudan’s humanitarian response plan is 36% funded ($1.5B of $4.2B required). ➤ IOM Sudan lost $83 million in 2025, forcing closures of health projects, mobile clinics, NGO grants, and staff layoffs. ➤ “We have to choose which lives we can save and which support we have to stop,” Refaat said. ➤ The shelter response is only 11–15% funded, the most underfunded sector Refaat warned that if fighting continues, displacement will become more protracted and spill beyond Sudan’s borders, with regional consequences.

Drop Site

65,925 просмотров • 8 месяцев назад

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:

Léo

15,662 просмотров • 29 дней назад

Today we’re launching Vybe to the world and announcing our $10M Seed round to make vibe-coding actually work inside companies. This is why, how and our vision: Over the last few decades, every fast-growing company has quietly built the same mess behind the scenes: internal ops glued together with rigid SaaS, fragile spreadsheets or custom-coded tools nobody wants to maintain. Meanwhile, eng teams are stretched thin. Internal tools never make it to the top of the backlog. Vibe-coding is changing the game but it’s mostly been good for prototypes, landing pages, and side projects disconnected to production data. Our belief is simple: in the next few years, most internal software will be vibe-coded by teams working with AI, engineers and business teams together. Vybe is built for that collaboration: 1/ Business teams own the surface area: Business teams (Ops, CX, PMs etc.) can build and iterate on apps themselves: flows, UI, fields, and logic; without waiting weeks for eng to pick up another “internal tools” ticket. 2/ Engineers own the foundation: Integrate production data (Postgres, Salesforce, Jira, and 3,000 integrations), define SQL definitions once, set up SSO auth, access control, and keep everything in Git to help when needed (from their favorite IDE!) 3/ Secure by design: Our security and permissioning layer is not vibe-coded and can’t be modified by AI. Everyone can sleep at night. 4/ Team-ready out of the box: SSO, Auth, environments, deployments, and review flows are built in. Over the last few months, we’ve been in closed waitlist mode and have hand-onboarded teams to pressure-test Vybe on real production workflows: - A YC Founder runs his entire CS operation on Vybe and saves ~2 days per week. - Another company ingested millions of rows from their warehouse to build BI-like internal views that would break typical AI builders. - One team fully replaced Metabase/Looker by plugging Redshift into Vybe and just… prompting their way to MAU, DAU, funnels… Remix apps from world-class operators To make it even easier to get started, we’re launching templates co-created with operators who’ve already solved these problems at scale: - Mathilde Collin (CEO @ Front) – how she runs 1:1s - Lenny Rachitsky (yeah, that Lenny!) - how to manage up, do perf reviews and write PRDs - Sushma Nallapeta (CTO @ 23andMe) - her 7Cs Framework for Build vs. Buy Decisions - and many more from the best Tech leaders Backed by people who’ve lived this pain We’ve raised $10M in Seed funding, led by First Round with participation from Y Combinator and an incredible group of operators and founders, including: The CEO Datadog, CEO Grammarly, CEO Reforge, CTO Intercom, Head of Product at OpenAI, Head of Product Anthropic, and 50 more incredible operators who believed in our vision! Huge thank you to our early customers, team, and investors for believing in us this early. 🙏 We’re now in GA: no more waitlist!

Today we’re launching Vybe to the world and announcing our $10M Seed round to make vibe-coding actually work inside companies. This is why, how and our vision: Over the last few decades, every fast-growing company has quietly built the same mess behind the scenes: internal ops glued together with rigid SaaS, fragile spreadsheets or custom-coded tools nobody wants to maintain. Meanwhile, eng teams are stretched thin. Internal tools never make it to the top of the backlog. Vibe-coding is changing the game but it’s mostly been good for prototypes, landing pages, and side projects disconnected to production data. Our belief is simple: in the next few years, most internal software will be vibe-coded by teams working with AI, engineers and business teams together. Vybe is built for that collaboration: 1/ Business teams own the surface area: Business teams (Ops, CX, PMs etc.) can build and iterate on apps themselves: flows, UI, fields, and logic; without waiting weeks for eng to pick up another “internal tools” ticket. 2/ Engineers own the foundation: Integrate production data (Postgres, Salesforce, Jira, and 3,000 integrations), define SQL definitions once, set up SSO auth, access control, and keep everything in Git to help when needed (from their favorite IDE!) 3/ Secure by design: Our security and permissioning layer is not vibe-coded and can’t be modified by AI. Everyone can sleep at night. 4/ Team-ready out of the box: SSO, Auth, environments, deployments, and review flows are built in. Over the last few months, we’ve been in closed waitlist mode and have hand-onboarded teams to pressure-test Vybe on real production workflows: - A YC Founder runs his entire CS operation on Vybe and saves ~2 days per week. - Another company ingested millions of rows from their warehouse to build BI-like internal views that would break typical AI builders. - One team fully replaced Metabase/Looker by plugging Redshift into Vybe and just… prompting their way to MAU, DAU, funnels… Remix apps from world-class operators To make it even easier to get started, we’re launching templates co-created with operators who’ve already solved these problems at scale: - Mathilde Collin (CEO @ Front) – how she runs 1:1s - Lenny Rachitsky (yeah, that Lenny!) - how to manage up, do perf reviews and write PRDs - Sushma Nallapeta (CTO @ 23andMe) - her 7Cs Framework for Build vs. Buy Decisions - and many more from the best Tech leaders Backed by people who’ve lived this pain We’ve raised $10M in Seed funding, led by First Round with participation from Y Combinator and an incredible group of operators and founders, including: The CEO Datadog, CEO Grammarly, CEO Reforge, CTO Intercom, Head of Product at OpenAI, Head of Product Anthropic, and 50 more incredible operators who believed in our vision! Huge thank you to our early customers, team, and investors for believing in us this early. 🙏 We’re now in GA: no more waitlist!

Quang HOANG

108,672 просмотров • 8 месяцев назад

Elijah Brooks played the last two years for us at HCU and is available in the portal. He’s the real deal: • Two-year starter who led our team in scoring (12.3 PPG), finished second in rebounding (4.5 RPG), and led in steals (1.5 SPG) this season. • Switchable defender who can guard 1-4 effectively. • 6’4” PG (6’3” without shoes, 6’4” with shoes. We measure). • American Conference+ level athlete — plays downhill, explosive vertical athlete with high-level strength and burst. • Extremely high basketball IQ — Elijah initiated our offense from the point, which included seven formations and over 50 sets/variations. • Developed into an elite pick-and-roll player — rarely missed a pocket pass or read. Float game established. • Barkley game (watch the 11/27/24 game vs. NAU for the full Barkley bag including 29 PTS on 13-19 FG). Most teams run a double at him and are forced to play in rotation. • Elite cutter off the ball • Game-changer in transition with his speed and vision • Career 189/381 (.496 FG%) — all at the NCAA Division I level • Broke his hand this season, but it’s fully healed with no restrictions. He’s been working out daily in our gym and is 100% ready to go. There will be no issue with the medical hardship as we have x-rays, documents, and info from doctors, specialists and surgeons. He appeared in 8 game, all in the first semester. Call me with any questions. I’ll tell you everything you need to know. Note: Last season we had two high level players at HCU enter the portal for enhanced revenue opportunities: Julian Mackey (to FIU) and Bryson Dawkins (to YSU). Both were double-figure scorers who absolutely crushed it at their next school. Elijah is cut from the same cloth and will do the same for you. Hit me up!

Elijah Brooks played the last two years for us at HCU and is available in the portal. He’s the real deal: • Two-year starter who led our team in scoring (12.3 PPG), finished second in rebounding (4.5 RPG), and led in steals (1.5 SPG) this season. • Switchable defender who can guard 1-4 effectively. • 6’4” PG (6’3” without shoes, 6’4” with shoes. We measure). • American Conference+ level athlete — plays downhill, explosive vertical athlete with high-level strength and burst. • Extremely high basketball IQ — Elijah initiated our offense from the point, which included seven formations and over 50 sets/variations. • Developed into an elite pick-and-roll player — rarely missed a pocket pass or read. Float game established. • Barkley game (watch the 11/27/24 game vs. NAU for the full Barkley bag including 29 PTS on 13-19 FG). Most teams run a double at him and are forced to play in rotation. • Elite cutter off the ball • Game-changer in transition with his speed and vision • Career 189/381 (.496 FG%) — all at the NCAA Division I level • Broke his hand this season, but it’s fully healed with no restrictions. He’s been working out daily in our gym and is 100% ready to go. There will be no issue with the medical hardship as we have x-rays, documents, and info from doctors, specialists and surgeons. He appeared in 8 game, all in the first semester. Call me with any questions. I’ll tell you everything you need to know. Note: Last season we had two high level players at HCU enter the portal for enhanced revenue opportunities: Julian Mackey (to FIU) and Bryson Dawkins (to YSU). Both were double-figure scorers who absolutely crushed it at their next school. Elijah is cut from the same cloth and will do the same for you. Hit me up!

Craig Doty

36,185 просмотров • 4 месяцев назад