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Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a...

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Ilir Aliu

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24,743 次观看 • 8 个月前 •via X (Twitter)

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Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!
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Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!

Yunzhu Li

11,026 次观看 • 2 个月前

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA
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Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 次观看 • 1 个月前

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:
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AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,676,451 次观看 • 8 个月前

The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI
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The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI

Techniahqrobot | humanoid robots

11,650 次观看 • 23 天前

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:
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Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 次观看 • 25 天前

We’ve been working on something for 8 months. If you write emails, you know the struggle. You’re digging through your inbox, scrolling screenshots, trying to find inspiration that shouldn’t take this long. So Jimmy Kim and I built a solution. One place to search around 1,000,000 real ecommerce emails. But here’s the part I’m most excited about: You can click any email and get instant AI analysis on what worked, what didn’t, and why it converted. Real examples. Real breakdowns. Real insights you can use today. Free trial is live if you want to try it:
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We’ve been working on something for 8 months. If you write emails, you know the struggle. You’re digging through your inbox, scrolling screenshots, trying to find inspiration that shouldn’t take this long. So Jimmy Kim and I built a solution. One place to search around 1,000,000 real ecommerce emails. But here’s the part I’m most excited about: You can click any email and get instant AI analysis on what worked, what didn’t, and why it converted. Real examples. Real breakdowns. Real insights you can use today. Free trial is live if you want to try it:

Chase Dimond | Email Marketing Nerd 📧

38,831 次观看 • 8 个月前

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?
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Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 次观看 • 5 个月前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
0:05

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 5 个月前

Xabi Alonso’s message to all Madrid fans: “I want Madridistas to be proud of the team they watch. When they watch the team, I want them to say: Yeah, that’s my Madrid.” “This is a very special day for me. I will remember it for life. This is my home.” “I feel the excitement of the Real Madrid fans. This is a big moment for everyone. As soon as I stepped foot in Valdebebas my feeling and love for Real Madrid appeared again, but it never left me.” “I feel like I was born again today. Thank you Florentino, for trusting me with this job. From the first day you sat on the bench of Real Madrid’s U14 in 2018… we knew where you would end up. Right here”. “We have a great team, with fantastic players. We’ll have a very good future. I want to build a great team. With the ambition that we will achieve great things.” “I want to talk about Carlo... He was my coach, a great person, a coach who left his mark on me, and probably without his mastery and the learning I received from him, I wouldn’t be here. I take up his legacy with great honor and pride.” Real Madrid C.F.
0:10

Xabi Alonso’s message to all Madrid fans: “I want Madridistas to be proud of the team they watch. When they watch the team, I want them to say: Yeah, that’s my Madrid.” “This is a very special day for me. I will remember it for life. This is my home.” “I feel the excitement of the Real Madrid fans. This is a big moment for everyone. As soon as I stepped foot in Valdebebas my feeling and love for Real Madrid appeared again, but it never left me.” “I feel like I was born again today. Thank you Florentino, for trusting me with this job. From the first day you sat on the bench of Real Madrid’s U14 in 2018… we knew where you would end up. Right here”. “We have a great team, with fantastic players. We’ll have a very good future. I want to build a great team. With the ambition that we will achieve great things.” “I want to talk about Carlo... He was my coach, a great person, a coach who left his mark on me, and probably without his mastery and the learning I received from him, I wouldn’t be here. I take up his legacy with great honor and pride.” Real Madrid C.F.

433

81,499 次观看 • 1 年前

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.
0:19

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.

Sarah Lezito

43,317 次观看 • 8 个月前

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?
0:10

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?

Brian Roemmele

72,158 次观看 • 3 个月前

This is what happens when on-chain infrastructure meets real-world engineering. PinLink has converted a fleet of air-cooled L9 miners into a fully hydro-cooled system, unlocking a new performance ceiling. Higher sustained uptime, tighter thermal control, and consistently stronger hashrate under continuous load. Hydro-cooling is an edge. It removes the inefficiencies that cap air-cooled systems and optimizes physical infrastructure. This is exactly how PinLink operates: pairing on-chain coordination with physical execution to deliver maximum performance from real assets. Watch the video to see the conversion in action.
0:17

This is what happens when on-chain infrastructure meets real-world engineering. PinLink has converted a fleet of air-cooled L9 miners into a fully hydro-cooled system, unlocking a new performance ceiling. Higher sustained uptime, tighter thermal control, and consistently stronger hashrate under continuous load. Hydro-cooling is an edge. It removes the inefficiencies that cap air-cooled systems and optimizes physical infrastructure. This is exactly how PinLink operates: pairing on-chain coordination with physical execution to deliver maximum performance from real assets. Watch the video to see the conversion in action.

PinLink

11,920 次观看 • 7 个月前

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.
0:11

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Pierre-Alexandre Balland

186,061 次观看 • 7 个月前

This looks like a music video. But the real story is what’s happening behind it. For years, creating visuals like this meant big budgets, big teams, and long timelines. Now it starts with a prompt. Seedance 2.0 on Higgsfield AI 🧩 makes this level of output accessible to anyone who knows what they want to create. That’s the shift. Not replacing creativity but removing the barriers around it. And with up to 70% off + 7 days unlimited, there’s really no reason to just watch this better to start building with it. 👉
0:13

This looks like a music video. But the real story is what’s happening behind it. For years, creating visuals like this meant big budgets, big teams, and long timelines. Now it starts with a prompt. Seedance 2.0 on Higgsfield AI 🧩 makes this level of output accessible to anyone who knows what they want to create. That’s the shift. Not replacing creativity but removing the barriers around it. And with up to 70% off + 7 days unlimited, there’s really no reason to just watch this better to start building with it. 👉

GrowAIHub

32,814 次观看 • 4 个月前

The next phase of AI video needs infrastructure, not more tools.. This isn’t just a product update. It’s a new category. Topview 4.0 is the world’s first AI video collaboration board, built for how creators and teams actually work. One shared workspace to create, review, and iterate together without switching tools. Prompt → Image → Video → Avatar in a single flow. Real-time collaboration. All top image and video models in one place. Creator-friendly pricing, cheaper than official APIs. If AI video is part of your workflow, this is the board it belongs on. TopviewAI
0:15

The next phase of AI video needs infrastructure, not more tools.. This isn’t just a product update. It’s a new category. Topview 4.0 is the world’s first AI video collaboration board, built for how creators and teams actually work. One shared workspace to create, review, and iterate together without switching tools. Prompt → Image → Video → Avatar in a single flow. Real-time collaboration. All top image and video models in one place. Creator-friendly pricing, cheaper than official APIs. If AI video is part of your workflow, this is the board it belongs on. TopviewAI

Kuria | AI

12,848 次观看 • 6 个月前

🚨 RWA START-UP EXPLODES 1,200% AT LAUNCH — A PROUD MOMENT FOR US Our incubatee T-RIZE’s debut on Kraken was nothing short of extraordinary — peaking at a 1200% — and we’re deeply honored to have been a proud partner from the start. The $RIZE token is now live and trading, marking a major milestone in real-world asset tokenization. With a $300M deal for Canada’s 960-unit Project Champfleury and a $2B pipeline in motion, T-RIZE is setting the new standard for RWA platforms. Built on Rizenet — a proprietary blockchain optimized for decentralized machine learning and AI-driven insights — it’s pushing the space forward in real time. We’re proud to have contributed across strategy, marketing, partnerships, and the incredible shows that brought this to life. Watch the official demo + explore the platform: Disclaimer: We do not provide financial or investment advice of any kind. Always do your own research, as cryptocurrency prices can be extremely volatile. This project is part of our incubation program.
0:15

🚨 RWA START-UP EXPLODES 1,200% AT LAUNCH — A PROUD MOMENT FOR US Our incubatee T-RIZE’s debut on Kraken was nothing short of extraordinary — peaking at a 1200% — and we’re deeply honored to have been a proud partner from the start. The $RIZE token is now live and trading, marking a major milestone in real-world asset tokenization. With a $300M deal for Canada’s 960-unit Project Champfleury and a $2B pipeline in motion, T-RIZE is setting the new standard for RWA platforms. Built on Rizenet — a proprietary blockchain optimized for decentralized machine learning and AI-driven insights — it’s pushing the space forward in real time. We’re proud to have contributed across strategy, marketing, partnerships, and the incredible shows that brought this to life. Watch the official demo + explore the platform: Disclaimer: We do not provide financial or investment advice of any kind. Always do your own research, as cryptocurrency prices can be extremely volatile. This project is part of our incubation program.

Mario Nawfal

139,739 次观看 • 1 年前

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.
0:12

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

Rina

45,651 次观看 • 18 天前

Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!
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Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!

Tilted Ventures

19,582 次观看 • 9 个月前

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:
0:11

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

95,270 次观看 • 7 个月前

Shido Network, by the numbers and what’s next for builders. - 1,500+ smart contracts deployed - Dozens of active projects and dApps on mainnet - 24,000,000+ blocks produced with zero downtime This momentum is being channeled into developer-first environments, enabling teams to move from idea to mainnet launch faster: with consistent RPCs, clean analytics, clear documentation, and starter templates that make deploying production-ready dApps straightforward. If you’re building the next wave of DeFi, payment rails, or real-world applications, Shido provides the foundation — a fast, reliable network backed by a growing ecosystem built for scale.
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Shido Network, by the numbers and what’s next for builders. - 1,500+ smart contracts deployed - Dozens of active projects and dApps on mainnet - 24,000,000+ blocks produced with zero downtime This momentum is being channeled into developer-first environments, enabling teams to move from idea to mainnet launch faster: with consistent RPCs, clean analytics, clear documentation, and starter templates that make deploying production-ready dApps straightforward. If you’re building the next wave of DeFi, payment rails, or real-world applications, Shido provides the foundation — a fast, reliable network backed by a growing ecosystem built for scale.

Shido

35,359 次观看 • 9 个月前