Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

People are using Verdent to build projects that are both interesting and genuinely useful. Some are already using computer vision to empower the sports industry. Take this badminton video analysis demo. The result is not perfect: shuttle tracking jitters, speed is only estimated, and pose detection still needs real... show more

Verdent

2,429 subscribers

13,040 просмотров • 3 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

This is a live demo from the future!!! 🤯🤯🤯 An interactive sitcom? A playable episode? Games and movies are merging into one thing. I genuinely cannot stop playing this. Real-time video generation will change the entertainment industry forever. We are so close. Thanks to fal team . MiniMax Design (H3)

This is a live demo from the future!!! 🤯🤯🤯 An interactive sitcom? A playable episode? Games and movies are merging into one thing. I genuinely cannot stop playing this. Real-time video generation will change the entertainment industry forever. We are so close. Thanks to fal team . MiniMax Design (H3)

Öner S. Biberkökü

547,770 просмотров • 1 месяц назад

Really interesting insight from Wayne Rooney on the type of speed that can sometimes get overlooked in football. You’ll often hear coaches say that top speed is overrated. That football is mostly about being quick over short distances. It’s a fair point - most sprints in football are relatively short. Approximately 2-4 secs, over 10-20m. So acceleration, anticipation and timing are hugely important. But Rooney’s point is equally true - give genuinely fast players 30–40m to open up and top end speed is a massive plus. The counter-attack goal against Arsenal in 09/10 is a brilliant example. Rooney starts from deep, sprints about 50m and has the ability to maintain his speed and get on the end of the pass. In the current Premier League era, that ability is particularly valuable in a game increasingly full of transitions. For both attackers and defenders. Manchester United used to publish their players’ top speeds, and Rooney was regularly right up there at or above 35 km/h. There’s another reason developing top speed matters too: speed reserve. Imagine two players: Player A max speed: 36 km/h Player B max speed: 31 km/h At 25 km/h, Player A is at 69% of their maximum speed. Player B is already at 81%. So the same absolute running speed represents a very different relative speed demand for each player. That’s just one more reason maximal speed matters beyond simply being able to win a foot race. Raise your ceiling and you increase the speed you have in reserve.

Really interesting insight from Wayne Rooney on the type of speed that can sometimes get overlooked in football. You’ll often hear coaches say that top speed is overrated. That football is mostly about being quick over short distances. It’s a fair point - most sprints in football are relatively short. Approximately 2-4 secs, over 10-20m. So acceleration, anticipation and timing are hugely important. But Rooney’s point is equally true - give genuinely fast players 30–40m to open up and top end speed is a massive plus. The counter-attack goal against Arsenal in 09/10 is a brilliant example. Rooney starts from deep, sprints about 50m and has the ability to maintain his speed and get on the end of the pass. In the current Premier League era, that ability is particularly valuable in a game increasingly full of transitions. For both attackers and defenders. Manchester United used to publish their players’ top speeds, and Rooney was regularly right up there at or above 35 km/h. There’s another reason developing top speed matters too: speed reserve. Imagine two players: Player A max speed: 36 km/h Player B max speed: 31 km/h At 25 km/h, Player A is at 69% of their maximum speed. Player B is already at 81%. So the same absolute running speed represents a very different relative speed demand for each player. That’s just one more reason maximal speed matters beyond simply being able to win a foot race. Raise your ceiling and you increase the speed you have in reserve.

STATSports

12,369 просмотров • 7 дней назад

I challenged myself to build a real AI SaaS using just "one prompt". No infrastructure. No backend setup. No deployment. I described what I wanted, and naïve handled the rest—from cloud and database to authentication and payments. The result? A live app in minutes. This is one of the easiest ways I've seen to turn an idea into a working product. What would you build first?

I challenged myself to build a real AI SaaS using just "one prompt". No infrastructure. No backend setup. No deployment. I described what I wanted, and naïve handled the rest—from cloud and database to authentication and payments. The result? A live app in minutes. This is one of the easiest ways I've seen to turn an idea into a working product. What would you build first?

Mimu | AI Tools & News

19,611 просмотров • 2 месяцев назад

Today, I had the privilege of speaking to the HUMAIN team at our CEO Townhall. I often say this, and I mean it deeply: I am living my dream... working in a country where energy goes beyond oil. It’s in the people, the ambition, and the belief in building a better future. Saudi Arabia is unlike anywhere else. The hospitality is second to none. The vision is bold. And the commitment to shaping the future is real. What we are building at HUMAIN is foundational. We are not only participating in the AI era, but redefining it. Shifting the narrative from experimentation to real value creation. But more importantly, we are not afraid to challenge what an organization should look like in an AI-native world. In fact, we are pioneering it. We are actively reshaping how we work: - Moving toward a future where AI agents do the work - Empowering our people to focus on high-value thinking, creativity, and decision-making - Building strong foundations in security, governance, and guardrails - Continuously enhancing our models, systems, and operating frameworks This is not theory. This is happening now. What I saw today from our teams gives me absolute confidence: - Products that are not just innovative, but game-changing - Teams building at a speed and quality that challenge global norms - A culture focused on execution, ownership, and impact At #LEAP2026 this year, we will go beyond vision. We will show: Real demos. Real products. Some of them are a first of their kind in the world. And they are built right here in Saudi Arabia. I could not be more proud of this team. What they have accomplished in such a short time is remarkable. This is just the beginning. The future is not something we wait for. It’s something we build. #HUMAIN #LEAP #TheEndOfLimits #AI

Today, I had the privilege of speaking to the HUMAIN team at our CEO Townhall. I often say this, and I mean it deeply: I am living my dream... working in a country where energy goes beyond oil. It’s in the people, the ambition, and the belief in building a better future. Saudi Arabia is unlike anywhere else. The hospitality is second to none. The vision is bold. And the commitment to shaping the future is real. What we are building at HUMAIN is foundational. We are not only participating in the AI era, but redefining it. Shifting the narrative from experimentation to real value creation. But more importantly, we are not afraid to challenge what an organization should look like in an AI-native world. In fact, we are pioneering it. We are actively reshaping how we work: - Moving toward a future where AI agents do the work - Empowering our people to focus on high-value thinking, creativity, and decision-making - Building strong foundations in security, governance, and guardrails - Continuously enhancing our models, systems, and operating frameworks This is not theory. This is happening now. What I saw today from our teams gives me absolute confidence: - Products that are not just innovative, but game-changing - Teams building at a speed and quality that challenge global norms - A culture focused on execution, ownership, and impact At #LEAP2026 this year, we will go beyond vision. We will show: Real demos. Real products. Some of them are a first of their kind in the world. And they are built right here in Saudi Arabia. I could not be more proud of this team. What they have accomplished in such a short time is remarkable. This is just the beginning. The future is not something we wait for. It’s something we build. #HUMAIN #LEAP #TheEndOfLimits #AI

Tareq Amin

17,856 просмотров • 5 месяцев назад

Most people still aren't using AI. (I know, we're in a bubble on X.) But for the majority that are, they're still in chat mode. And they're only using about 5-10% of what's actually there. In this video, I teach you how to use the other 90%. To be honest, I was hating on chat for a while. Then I took the time to really learn it. And in the process, I found that chat mode has 2 features that are world class and not available in the other modes. I break down what those are, how to use them, and the rest of the features you're probably sleeping on. This video is for you if: → You're a beginner looking to do more with AI → You're already in chat / Cowork / Claude Code and want to learn best practices, token efficiency, and which chat features are genuinely GOATED → You have colleagues who need to be doing more with AI but don't know where to start That last one is the real reason I made this. This is the video you send to your team. I'm doing the same with my network right now. Uncle J, Freddie, Genny, this is coming your way. Hope it helps (if so, show some 💙)

Most people still aren't using AI. (I know, we're in a bubble on X.) But for the majority that are, they're still in chat mode. And they're only using about 5-10% of what's actually there. In this video, I teach you how to use the other 90%. To be honest, I was hating on chat for a while. Then I took the time to really learn it. And in the process, I found that chat mode has 2 features that are world class and not available in the other modes. I break down what those are, how to use them, and the rest of the features you're probably sleeping on. This video is for you if: → You're a beginner looking to do more with AI → You're already in chat / Cowork / Claude Code and want to learn best practices, token efficiency, and which chat features are genuinely GOATED → You have colleagues who need to be doing more with AI but don't know where to start That last one is the real reason I made this. This is the video you send to your team. I'm doing the same with my network right now. Uncle J, Freddie, Genny, this is coming your way. Hope it helps (if so, show some 💙)

JJ Englert

30,447 просмотров • 4 месяцев назад

“This is hard. It’s not an easy business… I’m trying to build a combat sports ecosystem, not just a promotion, and that takes time. Time is not our friend, and we’ve got to ramp up awareness of the PFL, but there are interesting ways to do that.” John Martin on his first 6 months as PFL CEO.

“This is hard. It’s not an easy business… I’m trying to build a combat sports ecosystem, not just a promotion, and that takes time. Time is not our friend, and we’ve got to ramp up awareness of the PFL, but there are interesting ways to do that.” John Martin on his first 6 months as PFL CEO.

Ariel Helwani

17,965 просмотров • 6 месяцев назад

Every solution to conflict that people are using is ChatGPT, friends, therapists, but the issue is that they only hear one side of the conflict, so both people feel ‘validated’ in opposite directions and nothing actually gets resolved I built the fix: - Both people enter a private session - AI sits in the middle in real time - Filters reactivity out before the other person sees it - Emotion check-ins, escalation detection - Agreement builder + resolution summary

Every solution to conflict that people are using is ChatGPT, friends, therapists, but the issue is that they only hear one side of the conflict, so both people feel ‘validated’ in opposite directions and nothing actually gets resolved I built the fix: - Both people enter a private session - AI sits in the middle in real time - Filters reactivity out before the other person sees it - Emotion check-ins, escalation detection - Agreement builder + resolution summary

Mika

32,269 просмотров • 7 месяцев назад

Occupying entire seats like bulls is a serious problem in Indian trains. People lie down and take up the whole seat, using illness as an excuse. If you are that sick, book an ambulance and take them to a hospital. Trains are public transport. A seat meant for 4 people cannot be occupied by just 1 person. These are the real everyday issues that should be discussed. But first, people would have to get free from endless debates about temples and mosques.

Occupying entire seats like bulls is a serious problem in Indian trains. People lie down and take up the whole seat, using illness as an excuse. If you are that sick, book an ambulance and take them to a hospital. Trains are public transport. A seat meant for 4 people cannot be occupied by just 1 person. These are the real everyday issues that should be discussed. But first, people would have to get free from endless debates about temples and mosques.

Oppressor

276,248 просмотров • 6 месяцев назад

i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!

i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!

AA

854,914 просмотров • 21 дней назад

In a $4.5T industry where over 50 new Tech roles open every year some still believe in scarcity. I personally think empowering young Africans to take advantage of the opportunities is not a bad idea. “Job no Dey but people sign multi million contracts everyday” Are some Threatened by competition? What are your thoughts on this video👨🏽‍💻

In a $4.5T industry where over 50 new Tech roles open every year some still believe in scarcity. I personally think empowering young Africans to take advantage of the opportunities is not a bad idea. “Job no Dey but people sign multi million contracts everyday” Are some Threatened by competition? What are your thoughts on this video👨🏽‍💻

Tech_baby

23,078 просмотров • 8 месяцев назад

Moon, reconstructed in the browser with AI. This has been one of the harder Zombies maps I’ve tried to port so far. GPT Astra had to recreate all the weird mechanics that make Moon actually feel like Moon: low gravity, the PES oxygen system, No Man’s Land progression, teleporting to the Moon, and everything around it. All running directly in the browser using the real game assets. As you can see in the video, there’s still a lot left to polish. The teleportation animation needs work, the PES mask animation is missing, the astronaut still needs the classic player name tag above him, and there are plenty of smaller details that need to be added. But the core of Moon is already there and playable in the browser. Repo and playable link soon.

Moon, reconstructed in the browser with AI. This has been one of the harder Zombies maps I’ve tried to port so far. GPT Astra had to recreate all the weird mechanics that make Moon actually feel like Moon: low gravity, the PES oxygen system, No Man’s Land progression, teleporting to the Moon, and everything around it. All running directly in the browser using the real game assets. As you can see in the video, there’s still a lot left to polish. The teleportation animation needs work, the PES mask animation is missing, the astronaut still needs the classic player name tag above him, and there are plenty of smaller details that need to be added. But the core of Moon is already there and playable in the browser. Repo and playable link soon.

Luckey Faraday

13,167 просмотров • 17 дней назад

Saw this video compilation on tiktok about Malaysian players defending style. Especially the dive, the sitting, and the lying down. It may not be the best (defending way) one, but certainly the most entertaining to watch. Also it is painful and dangerous. Makes you think, why would they go all the way to that extend, just return that shuttle. Lee Yong Dae 🗣️: 🇮🇩 is detailed one 🇰🇷 is powerful one 🇨🇳 has the speed 🇲🇾 has the defense “They (Malaysian) scoop the shuttle like a spoon.” “All of them did that.” 📸: Tiktok @/Jahzabby #BadmintonMalaysia

Saw this video compilation on tiktok about Malaysian players defending style. Especially the dive, the sitting, and the lying down. It may not be the best (defending way) one, but certainly the most entertaining to watch. Also it is painful and dangerous. Makes you think, why would they go all the way to that extend, just return that shuttle. Lee Yong Dae 🗣️: 🇮🇩 is detailed one 🇰🇷 is powerful one 🇨🇳 has the speed 🇲🇾 has the defense “They (Malaysian) scoop the shuttle like a spoon.” “All of them did that.” 📸: Tiktok @/Jahzabby #BadmintonMalaysia

Giarc Nibisna 🌺🇲🇾

259,953 просмотров • 3 лет назад

A DEX becomes a lot more interesting to me when the question is not only: “What can I trade here?” But also: “Can I actually contribute liquidity here?” That is one part of RivrDEX I think deserves more attention. RivrDEX is permissionless, so you are not limited to simply using the liquidity that is already available. You can add liquidity to a pool, receive LP tokens, and earn a share of the trading fees. It changes the way I look at a DEX. You are not just a trader using someone else’s liquidity. You can also become part of the liquidity behind the market. For smaller markets especially, having an open way to provide liquidity without going through a centralized platform feels pretty important. Trade with the liquidity that is there. Or help build it. That is a much more interesting idea to me.

A DEX becomes a lot more interesting to me when the question is not only: “What can I trade here?” But also: “Can I actually contribute liquidity here?” That is one part of RivrDEX I think deserves more attention. RivrDEX is permissionless, so you are not limited to simply using the liquidity that is already available. You can add liquidity to a pool, receive LP tokens, and earn a share of the trading fees. It changes the way I look at a DEX. You are not just a trader using someone else’s liquidity. You can also become part of the liquidity behind the market. For smaller markets especially, having an open way to provide liquidity without going through a centralized platform feels pretty important. Trade with the liquidity that is there. Or help build it. That is a much more interesting idea to me.

Rhyne

15,607 просмотров • 28 дней назад

There is no way that ethnic tensions could be as pronounced as they currently are without a lot of deep sin going into the generation of those tensions. But ethnic tensions are not the result of a white person waking up one day and thinking he needs to start despising people who are darker than he is. Neither is it the result of black people suddenly lurching into a policy of violence and mayhem. There are two errors to avoid when it comes to the kind of repentance that is needed in an hour like ours. One is to approach the whole thing in a simplistic spirit, calling for people to repent of all their behavior, but only starting at the precise moment the rioting started. But repentance is also needed for all the decisions and policies that led to the conditions that made the rioting seem like a reasonable way out. It isn’t, but why do people come to believe that? The second mistake is that of starting at the wrong end—this happens when we start by confessing the sins of the other guy. That gets us nowhere, and just perpetuates the conflict. You can confess other people’s sins all day long and your joy will not come back.

There is no way that ethnic tensions could be as pronounced as they currently are without a lot of deep sin going into the generation of those tensions. But ethnic tensions are not the result of a white person waking up one day and thinking he needs to start despising people who are darker than he is. Neither is it the result of black people suddenly lurching into a policy of violence and mayhem. There are two errors to avoid when it comes to the kind of repentance that is needed in an hour like ours. One is to approach the whole thing in a simplistic spirit, calling for people to repent of all their behavior, but only starting at the precise moment the rioting started. But repentance is also needed for all the decisions and policies that led to the conditions that made the rioting seem like a reasonable way out. It isn’t, but why do people come to believe that? The second mistake is that of starting at the wrong end—this happens when we start by confessing the sins of the other guy. That gets us nowhere, and just perpetuates the conflict. You can confess other people’s sins all day long and your joy will not come back.

Douglas Wilson

73,863 просмотров • 1 год назад

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

121,478 просмотров • 5 месяцев назад

"What happened to Campbell’s this week is symptomatic of a much broader problem in the food industry. Some consumers genuinely believed the company was using “bioengineered chicken” and “3D-printed meat”—whatever that’s supposed to mean. With food prices rising, people are increasingly suspicious of how their food is made. The landscape has changed, and some companies are clearly not prepared for this new level of scrutiny."

"What happened to Campbell’s this week is symptomatic of a much broader problem in the food industry. Some consumers genuinely believed the company was using “bioengineered chicken” and “3D-printed meat”—whatever that’s supposed to mean. With food prices rising, people are increasingly suspicious of how their food is made. The landscape has changed, and some companies are clearly not prepared for this new level of scrutiny."

The Food Professor

64,315 просмотров • 10 месяцев назад

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

231,389 просмотров • 5 месяцев назад

THIS LOOKS LIKE A MARATHON. IT IS ACTUALLY A COST PROBLEM. The humanoid approaches a painted spot on the track and stops. Three people in vests close in. One sprays the joints. One slams a fresh pack against the back. One lifts a metal flask and pours coolant while the machine just stands there. Then they step off the lane and the robot starts running again. That 40-second stop is the whole industry in one scene. The legs already work. Balance already works. The public already films it. What still does not work is the part companies actually pay for: runtime. swap time. heat. maintenance. how many humans it takes to keep one body moving. A factory does not buy a cool runner. A factory buys hours without a crew. Right now this machine still needs a pit team. That means the unit price is only half the number. The other half is people kneeling on the track with a flask and a battery. The first company that turns this stop into 20 seconds and one technician does not just win a race. It turns a demo into a product. Until then a humanoid is still a race car. Fast on camera. Expensive the moment it has to keep going.

THIS LOOKS LIKE A MARATHON. IT IS ACTUALLY A COST PROBLEM. The humanoid approaches a painted spot on the track and stops. Three people in vests close in. One sprays the joints. One slams a fresh pack against the back. One lifts a metal flask and pours coolant while the machine just stands there. Then they step off the lane and the robot starts running again. That 40-second stop is the whole industry in one scene. The legs already work. Balance already works. The public already films it. What still does not work is the part companies actually pay for: runtime. swap time. heat. maintenance. how many humans it takes to keep one body moving. A factory does not buy a cool runner. A factory buys hours without a crew. Right now this machine still needs a pit team. That means the unit price is only half the number. The other half is people kneeling on the track with a flask and a battery. The first company that turns this stop into 20 seconds and one technician does not just win a race. It turns a demo into a product. Until then a humanoid is still a race car. Fast on camera. Expensive the moment it has to keep going.

P1eSenb

38,791 просмотров • 9 дней назад

Who's Into Polyamory? Polyamory gets stereotyped a lot, but the people drawn to it are not all one type. The more interesting question is why some people are willing to question the relationship script they were handed. This video isn’t about saying polyamory is better or monogamy is outdated. It’s about looking at why some people choose different structures, what that requires emotionally, and why “normal” is not always the same thing as “right for you.”

Who's Into Polyamory? Polyamory gets stereotyped a lot, but the people drawn to it are not all one type. The more interesting question is why some people are willing to question the relationship script they were handed. This video isn’t about saying polyamory is better or monogamy is outdated. It’s about looking at why some people choose different structures, what that requires emotionally, and why “normal” is not always the same thing as “right for you.”

Kate Shelor

24,373 просмотров • 2 месяцев назад