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Genesis AI just unveiled Eno. It's humanoid robot that challenges everything the industry assumed about what robots should look like. Forbes just called it 'the iPhone moment for humanoid robots'. No head. No face. No exposed motors or cables. 22 degrees of freedom per hand with different finger lengths...

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Lukas Ziegler

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29,453 görüntüleme • 2 ay önce •via X (Twitter)

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🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

16,062 görüntüleme • 2 ay önce

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
0:10

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 görüntüleme • 8 ay önce

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.
0:17

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

Rohan Paul

3,802,543 görüntüleme • 9 ay önce

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
0:16

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 görüntüleme • 1 yıl önce

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.
0:10

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,841,161 görüntüleme • 4 ay önce

Grok is evolving into the operating system for the modern world Most AI systems are heavily filtered and built to “play it safe" and designed to avoid uncomfortable truths rather than confront them But Grok is different. It's being trusted with real responsibility in some of the most sensitive environments on the planet. Because when there's a crisis, you don't need a censored assistant. You need the truth On 𝕏, Grok tackles even the most controversial questions head-on It doesn't bend the knee to ideological narratives - it challenges them And now, Grok is being deployed where it matters most: In education: El Salvador is using it to tutor over 1 million students across 5,000+ schools. An entire generation is growing up with xAI In defense: Grok powers for 3 million military and civilian personnel. Real-time intelligence changes everything In government: every U.S. federal agency can access it through the GSA for just 42 cents per agency In health: Official U.S. government sites like use it to cut through corporate messaging and provide raw nutrition facts. When you ask a question, it redirects straight to Grok In research: Lawrence Livermore National Laboratory is applying Grok to frontier science and breakthrough work In Tesla: Grok is becoming the voice and brain of millions of electric vehicles worldwide In Optimus: It serves as the reasoning engine powering the next generation of humanoid robots In space: SpaceX acquired xAI in February 2026, and together they're building orbital data centers Grok is going off-planet The world is choosing the "Truth Shield" over the "Safe Space" Truth always wins - and Grok is the relentless pursuit of truth
0:18

Grok is evolving into the operating system for the modern world Most AI systems are heavily filtered and built to “play it safe" and designed to avoid uncomfortable truths rather than confront them But Grok is different. It's being trusted with real responsibility in some of the most sensitive environments on the planet. Because when there's a crisis, you don't need a censored assistant. You need the truth On 𝕏, Grok tackles even the most controversial questions head-on It doesn't bend the knee to ideological narratives - it challenges them And now, Grok is being deployed where it matters most: In education: El Salvador is using it to tutor over 1 million students across 5,000+ schools. An entire generation is growing up with xAI In defense: Grok powers for 3 million military and civilian personnel. Real-time intelligence changes everything In government: every U.S. federal agency can access it through the GSA for just 42 cents per agency In health: Official U.S. government sites like use it to cut through corporate messaging and provide raw nutrition facts. When you ask a question, it redirects straight to Grok In research: Lawrence Livermore National Laboratory is applying Grok to frontier science and breakthrough work In Tesla: Grok is becoming the voice and brain of millions of electric vehicles worldwide In Optimus: It serves as the reasoning engine powering the next generation of humanoid robots In space: SpaceX acquired xAI in February 2026, and together they're building orbital data centers Grok is going off-planet The world is choosing the "Truth Shield" over the "Safe Space" Truth always wins - and Grok is the relentless pursuit of truth

X Freeze

13,489 görüntüleme • 6 ay önce

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.
0:17

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 görüntüleme • 1 ay önce

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.
0:10

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 görüntüleme • 3 ay önce

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.
0:13

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.

Shade

20,190 görüntüleme • 3 ay önce

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,494 görüntüleme • 5 ay önce

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution
0:16

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution

Zayro.ETH

27,929 görüntüleme • 2 ay önce

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.
0:10

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

Shanaka Anslem Perera ⚡

54,458 görüntüleme • 2 ay önce

NEW ROBOT: SOLAR PANEL DEPLOYER 🌞 San Francisco Gritt (a.k.a. Gritt AI) is probably one of the hottest robotics startups you have never heard of. Founded in 2023 by two CMU roboticists, it came out of stealth on July 21, 2026 with a $26M Series A ,led by Obvious Ventures . Gritt does not make robots. It bolts an off-the-shelf Kawasaki robotic arm onto heavy equipment for construction, and runs its own AI to unload utility-scale solar panels. They also carry them, and set them onto metal racking with sub-millimeter precision. A human then has to fasten them. It replaces the manual overhead lifting of ~100-lb glass panels on solar farms. Solar is for now their only market, but they will expand to data centers (of course) and other large infrastructures. Solar was chosen first because it's the most factory-like task on an outdoor site. Gritt is making the bet on buying, while most of its competitors are building the robots. Their own capex is therefore near-zero, and moves the scaling constraint to ops crews and software -> it does not make it necessarily easier! All depends where your strengths lie. Gritt has currently two systems deployed in the field, over signed contracts to install ~2.8–3 GW of solar over 18 months. They plan to reach 48 systems within six months -> a ~24× deployment ramp in half a year! Gritt says the first skills took weeks to train but rebar tying took a single day on the same software pipeline after the solar work. That's a ~10–30× drop in per-skill training cost! And goes against the idea that deployment data is near-worthless, since novelty is the scarce input. Also worth mentioning: Gritt has a Chinese competitors, Trinabot. Trinabot is vertically integrated: tied to Trina, a giant Chinese solar-panel maker. Make the panel and the robot that installs it, while Gritt's hardware-agnostic. Interesting to see another example where China integrates manufacturing plus robot, and the US startup goes capex-light software on rented equipment.
0:16

NEW ROBOT: SOLAR PANEL DEPLOYER 🌞 San Francisco Gritt (a.k.a. Gritt AI) is probably one of the hottest robotics startups you have never heard of. Founded in 2023 by two CMU roboticists, it came out of stealth on July 21, 2026 with a $26M Series A ,led by Obvious Ventures . Gritt does not make robots. It bolts an off-the-shelf Kawasaki robotic arm onto heavy equipment for construction, and runs its own AI to unload utility-scale solar panels. They also carry them, and set them onto metal racking with sub-millimeter precision. A human then has to fasten them. It replaces the manual overhead lifting of ~100-lb glass panels on solar farms. Solar is for now their only market, but they will expand to data centers (of course) and other large infrastructures. Solar was chosen first because it's the most factory-like task on an outdoor site. Gritt is making the bet on buying, while most of its competitors are building the robots. Their own capex is therefore near-zero, and moves the scaling constraint to ops crews and software -> it does not make it necessarily easier! All depends where your strengths lie. Gritt has currently two systems deployed in the field, over signed contracts to install ~2.8–3 GW of solar over 18 months. They plan to reach 48 systems within six months -> a ~24× deployment ramp in half a year! Gritt says the first skills took weeks to train but rebar tying took a single day on the same software pipeline after the solar work. That's a ~10–30× drop in per-skill training cost! And goes against the idea that deployment data is near-worthless, since novelty is the scarce input. Also worth mentioning: Gritt has a Chinese competitors, Trinabot. Trinabot is vertically integrated: tied to Trina, a giant Chinese solar-panel maker. Make the panel and the robot that installs it, while Gritt's hardware-agnostic. Interesting to see another example where China integrates manufacturing plus robot, and the US startup goes capex-light software on rented equipment.

Léo

45,464 görüntüleme • 1 ay önce

Claude Code + Google Stitch 2.0 is f*cking cracked 🤯 Google just dropped a free AI design agent that solves Claude Code's biggest weakness: frontend design. One screenshot of a high-converting landing page → a production-ready site for your brand in minutes. All inside Google Stitch + Claude Code. Perfect for DTC brands and agencies who are building advertorial pages and product launch pages for Meta but burning days on designer back-and-forth. If you're running Meta ads and need 5-10 different landing pages testing different hooks, angles, and offers — each one targeting a different audience and pain point — you know the bottleneck isn't the ads. It's the pages. Briefing designers, waiting for revisions, paying $2-5K per page. Stitch eliminates the design bottleneck: → Find a high-converting advertorial that's scaling on Meta → Screenshot it and drop it into Stitch (powered by Gemini 3.1) → Stitch redesigns it with your brand's colors, fonts, and imagery using Nano Banana 2 → Edit sections visually — headlines, CTAs, layouts — without touching code → Export the code and paste it into Claude Code → Claude builds the full production site and deploys to Vercel or Netlify in 60 seconds No designer. No $3K per landing page. No Claude Code frontend that looks like a template from 2019. What you get: → Designer-quality landing pages and advertorials built in minutes, not weeks → Visual editing so you actually see the design before you code it → Nano Banana 2 generating on-brand product imagery and hero shots → A repeatable system — new angle, new page, same pipeline Built 100% with Google Stitch 2.0 + Claude Code. I put together a full playbook showing the exact workflow: how to find winning pages, redesign them in Stitch, and deploy with Claude Code. Want it for free? > Like this post > Comment "STITCH" And I'll send it over (must be following so I can DM)
0:14

Claude Code + Google Stitch 2.0 is f*cking cracked 🤯 Google just dropped a free AI design agent that solves Claude Code's biggest weakness: frontend design. One screenshot of a high-converting landing page → a production-ready site for your brand in minutes. All inside Google Stitch + Claude Code. Perfect for DTC brands and agencies who are building advertorial pages and product launch pages for Meta but burning days on designer back-and-forth. If you're running Meta ads and need 5-10 different landing pages testing different hooks, angles, and offers — each one targeting a different audience and pain point — you know the bottleneck isn't the ads. It's the pages. Briefing designers, waiting for revisions, paying $2-5K per page. Stitch eliminates the design bottleneck: → Find a high-converting advertorial that's scaling on Meta → Screenshot it and drop it into Stitch (powered by Gemini 3.1) → Stitch redesigns it with your brand's colors, fonts, and imagery using Nano Banana 2 → Edit sections visually — headlines, CTAs, layouts — without touching code → Export the code and paste it into Claude Code → Claude builds the full production site and deploys to Vercel or Netlify in 60 seconds No designer. No $3K per landing page. No Claude Code frontend that looks like a template from 2019. What you get: → Designer-quality landing pages and advertorials built in minutes, not weeks → Visual editing so you actually see the design before you code it → Nano Banana 2 generating on-brand product imagery and hero shots → A repeatable system — new angle, new page, same pipeline Built 100% with Google Stitch 2.0 + Claude Code. I put together a full playbook showing the exact workflow: how to find winning pages, redesign them in Stitch, and deploy with Claude Code. Want it for free? > Like this post > Comment "STITCH" And I'll send it over (must be following so I can DM)

Mike Futia

126,193 görüntüleme • 5 ay önce

MUZZLE FLASH AND RECOIL IS WHERE AI VIDEO USUALLY DIES Outdoor range, bright afternoon. Girl in a blue bikini, safety glasses, ear protection. Instructor next to her adjusts her strap so it won't catch on the rifle stock, tells her to go. She shoulders the rifle, holds it clean, fires a burst. None of it exists. There is no range, no instructor, no rifle, no ammunition. - Weapons have been the quiet wall for AI video Everything else about a firearm clip is manageable - the girl, the setting, the lighting. The moment the trigger pulls is where the models fall apart. Muzzle flash has to appear at the right point on the barrel, the right shape, the right frame duration. Recoil has to propagate through the shoulder, arm, head - not just the gun. Brass has to eject. Smoke has to trail. The next shot has to happen with the shooter's stance reset in a physically plausible way. Get any one of those wrong and it reads as bad CGI in an otherwise clean clip. This one holds the whole sequence. - The strap adjustment is the setup, not the reveal The instructor moving the bikini strap off her shoulder before the shot is a specific narrative beat - it grabs attention, sets the tone as light not tactical, and gives the model a small two-body interaction to warm up on before the harder gunfire sequence. It's the same construction technique a live director would use. The operator wrote the prompt with a director's understanding of pacing. - The genre is 'girls with guns' and it's massive This is one of the largest niche formats on Instagram and TikTok - bikini range days, tactical-aesthetic content, gun influencer culture. Millions of views a week, well-monetized by ammunition brands, firearms manufacturers, tactical gear companies. Until now the format required an actual live shoot: range day fees ($150-300), an experienced shooter, live rounds, a rifle, insurance, a permit for filming, and a location that would allow it. That whole cost stack just went to zero. - What it costs to build One locked character reference, one for the instructor. Prompt structured with the strap adjustment as the pre-shot beat and the burst fire as the main sequence. Roughly $4-6 per 5-second clip because of the physics load. Realistically 30-50 rerolls to land a clean burst where muzzle flash, recoil and stance all read correct. Under $200 in compute. One evening. - What this actually opens Every weapons-adjacent content niche just became fully synthetic-viable. Gun influencers that don't own guns. Ammunition brand ads that don't need range access. Tactical gear campaigns without stunt coordinators. And the harder implication: the same pipeline generates fake footage of specific weapons in specific hands - a category that until now required actual footage of actual events. The trust cost on any weapons-related clip circulating on social media just went up permanently.
0:13

MUZZLE FLASH AND RECOIL IS WHERE AI VIDEO USUALLY DIES Outdoor range, bright afternoon. Girl in a blue bikini, safety glasses, ear protection. Instructor next to her adjusts her strap so it won't catch on the rifle stock, tells her to go. She shoulders the rifle, holds it clean, fires a burst. None of it exists. There is no range, no instructor, no rifle, no ammunition. - Weapons have been the quiet wall for AI video Everything else about a firearm clip is manageable - the girl, the setting, the lighting. The moment the trigger pulls is where the models fall apart. Muzzle flash has to appear at the right point on the barrel, the right shape, the right frame duration. Recoil has to propagate through the shoulder, arm, head - not just the gun. Brass has to eject. Smoke has to trail. The next shot has to happen with the shooter's stance reset in a physically plausible way. Get any one of those wrong and it reads as bad CGI in an otherwise clean clip. This one holds the whole sequence. - The strap adjustment is the setup, not the reveal The instructor moving the bikini strap off her shoulder before the shot is a specific narrative beat - it grabs attention, sets the tone as light not tactical, and gives the model a small two-body interaction to warm up on before the harder gunfire sequence. It's the same construction technique a live director would use. The operator wrote the prompt with a director's understanding of pacing. - The genre is 'girls with guns' and it's massive This is one of the largest niche formats on Instagram and TikTok - bikini range days, tactical-aesthetic content, gun influencer culture. Millions of views a week, well-monetized by ammunition brands, firearms manufacturers, tactical gear companies. Until now the format required an actual live shoot: range day fees ($150-300), an experienced shooter, live rounds, a rifle, insurance, a permit for filming, and a location that would allow it. That whole cost stack just went to zero. - What it costs to build One locked character reference, one for the instructor. Prompt structured with the strap adjustment as the pre-shot beat and the burst fire as the main sequence. Roughly $4-6 per 5-second clip because of the physics load. Realistically 30-50 rerolls to land a clean burst where muzzle flash, recoil and stance all read correct. Under $200 in compute. One evening. - What this actually opens Every weapons-adjacent content niche just became fully synthetic-viable. Gun influencers that don't own guns. Ammunition brand ads that don't need range access. Tactical gear campaigns without stunt coordinators. And the harder implication: the same pipeline generates fake footage of specific weapons in specific hands - a category that until now required actual footage of actual events. The trust cost on any weapons-related clip circulating on social media just went up permanently.

capONE 💎

2,167,218 görüntüleme • 11 gün önce

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.
0:10

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

Shade

537,174 görüntüleme • 3 ay önce

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."
0:14

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

Rohan Paul

12,761 görüntüleme • 7 ay önce

This guy cracked the code on AI girlfriend monetization using real-time technology and now pulls $76,000 per month from one Instagram profile without ever showing his real face or hiring an actual model. He got tired of watching creators split 80 percent of revenue with agencies while their competitors ran 24/7 chat operations with zero burnout, so he built a system that generates hyperrealistic AI influencer content using motion capture and synthetic face generation instead of photographers, makeup artists, or Miami beach rentals. His monthly profit hit $76,455 last month from just 90.4K followers and organic short-form traffic, while traditional creators cap out at $15K after paying 40 percent platform fees and $2,000 monthly for content production teams. Here is the exact breakdown: → Real-time face swap technology becomes the only tool you need, but most people butcher the setup by skipping gesture synchronization in the first 10 seconds → Character design comes first, and if you mess this up nothing saves it. Stick to approachable features (freckles, natural makeup, warm smile) because that is where parasocial engagement lives → Profile building is not random. You craft one consistent AI persona that repeats across all content so your audience recognizes the girl → You are picking who your subscriber projects onto, not who looks unattainable. That is your retention baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys engagement in 3 seconds → You mirror your own gestures through webcam: confused shrug, hand raise, lean-in shock, peace sign wave. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 91 percent skip: same room setup, multiple emotion sequences, one recording session. → The system generates 7 to 10 TikToks before dinner, while traditional creators test 3 per week and wonder why their conversion rates are stuck at 0.4 percent The economics are stupid: each video costs him $0 in talent fees, pulls 2 million views organically, converts at 2 percent into 1,800 clicks to private platforms at $10 to $15 subscription with $40 to $60 backend PPV per fan. That is $76,455 profit per month, while real creators pay $5,000 for production and net $22,000 after platform cuts. The key move nobody talks about: you cannot skip the natural gesture library. If you generate the AI face without mirroring your own spontaneous reactions first, the avatar moves like a CGI render. The eye contact breaks. The smile timing lags. The whole thing screams and your retention dies at 2.1 seconds. His system records him doing the exact confusion-to-delight emotional arc first, so the AI mask inherits human timing, natural eyebrow raises, and spontaneous energy that reads as a real girl reacting to comments, not a scripted advertisement. One Instagram profile generated 12 variants of the same "how I afford this lifestyle" hook in 40 minutes with different outfits, different lighting setups, different trending audios, and found the winner in 96 hours without spending $8,000 on influencer collaborations. They were previously paying $1,200 per UGC creator and burning $6,400 per week on content that plateaued at 60K views. Now they spend $0 for 12 variants and their cost per subscriber dropped from $48 to $11. Agencies now panic because their entire margin was built on model exclusivity, and this removes the human dependency. The outfit changes between clips like a wardrobe swap filter. The lighting matches bedroom authenticity. The hand gestures sync with emotional beats. No casting call. No model contract. No location scouting. Just a webcamera, a real-time face swap AI, and the discipline to batch-test emotional hooks before you commit traffic spend to one persona.
0:10

This guy cracked the code on AI girlfriend monetization using real-time technology and now pulls $76,000 per month from one Instagram profile without ever showing his real face or hiring an actual model. He got tired of watching creators split 80 percent of revenue with agencies while their competitors ran 24/7 chat operations with zero burnout, so he built a system that generates hyperrealistic AI influencer content using motion capture and synthetic face generation instead of photographers, makeup artists, or Miami beach rentals. His monthly profit hit $76,455 last month from just 90.4K followers and organic short-form traffic, while traditional creators cap out at $15K after paying 40 percent platform fees and $2,000 monthly for content production teams. Here is the exact breakdown: → Real-time face swap technology becomes the only tool you need, but most people butcher the setup by skipping gesture synchronization in the first 10 seconds → Character design comes first, and if you mess this up nothing saves it. Stick to approachable features (freckles, natural makeup, warm smile) because that is where parasocial engagement lives → Profile building is not random. You craft one consistent AI persona that repeats across all content so your audience recognizes the girl → You are picking who your subscriber projects onto, not who looks unattainable. That is your retention baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys engagement in 3 seconds → You mirror your own gestures through webcam: confused shrug, hand raise, lean-in shock, peace sign wave. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 91 percent skip: same room setup, multiple emotion sequences, one recording session. → The system generates 7 to 10 TikToks before dinner, while traditional creators test 3 per week and wonder why their conversion rates are stuck at 0.4 percent The economics are stupid: each video costs him $0 in talent fees, pulls 2 million views organically, converts at 2 percent into 1,800 clicks to private platforms at $10 to $15 subscription with $40 to $60 backend PPV per fan. That is $76,455 profit per month, while real creators pay $5,000 for production and net $22,000 after platform cuts. The key move nobody talks about: you cannot skip the natural gesture library. If you generate the AI face without mirroring your own spontaneous reactions first, the avatar moves like a CGI render. The eye contact breaks. The smile timing lags. The whole thing screams and your retention dies at 2.1 seconds. His system records him doing the exact confusion-to-delight emotional arc first, so the AI mask inherits human timing, natural eyebrow raises, and spontaneous energy that reads as a real girl reacting to comments, not a scripted advertisement. One Instagram profile generated 12 variants of the same "how I afford this lifestyle" hook in 40 minutes with different outfits, different lighting setups, different trending audios, and found the winner in 96 hours without spending $8,000 on influencer collaborations. They were previously paying $1,200 per UGC creator and burning $6,400 per week on content that plateaued at 60K views. Now they spend $0 for 12 variants and their cost per subscriber dropped from $48 to $11. Agencies now panic because their entire margin was built on model exclusivity, and this removes the human dependency. The outfit changes between clips like a wardrobe swap filter. The lighting matches bedroom authenticity. The hand gestures sync with emotional beats. No casting call. No model contract. No location scouting. Just a webcamera, a real-time face swap AI, and the discipline to batch-test emotional hooks before you commit traffic spend to one persona.

Shade

21,137 görüntüleme • 3 ay önce

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.
0:11

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.

Mufasa

13,066 görüntüleme • 13 gün önce

They did not take cursive from the schools because children no longer needed it. They took it because of what it was quietly building in them. Consider what the exercise actually is. A child, six years old, is handed a pen and asked to draw a single unbroken line that becomes a word. The wrist must float. The fingers must hold a living pressure, never quite the same twice, always correcting. The eye must follow the ink forward and trust the hand to finish what it has begun. There is no lifting, no stopping, no starting over mid-word. The loop must close. The ascender must rise and return. The sentence must travel from one margin to the other as a single continuous gesture, and at the end of it the hand must still be steady. Twelve years of this. Every day. Ten thousand small acts of sustained, self-correcting attention, carried out below the level of conscious thought, until the motion belongs to the body and the body belongs to the motion. This is not penmanship. It is the slow construction of an interior form. The hand that has learned to carry a line without breaking it is the hand of a mind that has learned to carry a thought without breaking it. The two are not metaphors for one another. They are the same faculty, trained in the same child, by the same daily discipline. Continuity of the stroke becomes continuity of the reasoning. The patience of the loop becomes the patience of the argument. The commitment to finish a word one has started becomes the commitment to finish a sentence, a paragraph, a life's idea, without reaching for the nearest distraction halfway through. Print is a different creature entirely. Print lifts. Print stops. Print assembles a word out of separate, stamped, interchangeable pieces, each one beginning and ending in isolation. A mind raised only on print learns to think the way print is made, in discrete tokens, in replaceable units, in fragments that can be recombined by any outside hand without the owner noticing the substitution. It is precisely the shape of thought a language model produces. It is precisely the shape of thought a language model can steer. Cursive is kata. This is the whole of it. A form repeated daily, for years, not for the sake of the form but for what the repetition lays down in the practitioner beneath the form. The swordsman does not train kata so that one day he may fight in kata. He trains it so that when the moment comes and there is no time to think, the movement is already inside him, older and deeper than thought, and it rises on its own. Cursive was the kata of the literate mind, the daily quiet drilling of continuity, of patience, of a line held steady under the long pressure of its own length. And the signature it produced at the end, that small flourished mark unique to a single human being on earth, was only the outward proof of an inward form no machine and no other hand could ever reproduce. Take the kata away and the practitioner is left with vocabulary in place of faculty. He can recognise a whole thought when he encounters one. He cannot carry one himself. He can admire a finished argument. He cannot sustain one long enough to close its loop. He begins books he does not finish, sentences he does not end, ideas he abandons the moment the screen in his palm offers him a brighter one. And when the machine begins feeding him tokens in the exact shape his schooling taught him to receive, he meets it with no interior resistance at all, because no interior form was ever built in him to push back with. They removed it quietly, across a generation, and they removed it in the last years before the machines arrived. Twelve years of daily practice in unbroken, embodied, self-authored thought, gone from the curriculum of almost every child in the Western world, just as the instruments designed to complete their sentences for them came online. The hand forgets. The mind, having never been taught the kata, forgets a thing it never knew it had. That is what cursive was. That is what was taken. And that is why the thought of anyone who still writes by hand, in long unlifted lines, remains, quietly, stubbornly, and without their ever needing to announce it, their own. Now the question stands open. What else has been banned, phased out, quietly retired from the curriculum and from common life over these same decades, under the same soft excuses? Mental arithmetic. Memorisation of poetry. Latin. Logic as a formal subject. Map reading. Knot work. The keeping of a commonplace book. The reading aloud of long passages in class. Singing in parts. What was each of those actually building in the child, beneath the surface of the lesson, and whose interest was served by its disappearance?
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They did not take cursive from the schools because children no longer needed it. They took it because of what it was quietly building in them. Consider what the exercise actually is. A child, six years old, is handed a pen and asked to draw a single unbroken line that becomes a word. The wrist must float. The fingers must hold a living pressure, never quite the same twice, always correcting. The eye must follow the ink forward and trust the hand to finish what it has begun. There is no lifting, no stopping, no starting over mid-word. The loop must close. The ascender must rise and return. The sentence must travel from one margin to the other as a single continuous gesture, and at the end of it the hand must still be steady. Twelve years of this. Every day. Ten thousand small acts of sustained, self-correcting attention, carried out below the level of conscious thought, until the motion belongs to the body and the body belongs to the motion. This is not penmanship. It is the slow construction of an interior form. The hand that has learned to carry a line without breaking it is the hand of a mind that has learned to carry a thought without breaking it. The two are not metaphors for one another. They are the same faculty, trained in the same child, by the same daily discipline. Continuity of the stroke becomes continuity of the reasoning. The patience of the loop becomes the patience of the argument. The commitment to finish a word one has started becomes the commitment to finish a sentence, a paragraph, a life's idea, without reaching for the nearest distraction halfway through. Print is a different creature entirely. Print lifts. Print stops. Print assembles a word out of separate, stamped, interchangeable pieces, each one beginning and ending in isolation. A mind raised only on print learns to think the way print is made, in discrete tokens, in replaceable units, in fragments that can be recombined by any outside hand without the owner noticing the substitution. It is precisely the shape of thought a language model produces. It is precisely the shape of thought a language model can steer. Cursive is kata. This is the whole of it. A form repeated daily, for years, not for the sake of the form but for what the repetition lays down in the practitioner beneath the form. The swordsman does not train kata so that one day he may fight in kata. He trains it so that when the moment comes and there is no time to think, the movement is already inside him, older and deeper than thought, and it rises on its own. Cursive was the kata of the literate mind, the daily quiet drilling of continuity, of patience, of a line held steady under the long pressure of its own length. And the signature it produced at the end, that small flourished mark unique to a single human being on earth, was only the outward proof of an inward form no machine and no other hand could ever reproduce. Take the kata away and the practitioner is left with vocabulary in place of faculty. He can recognise a whole thought when he encounters one. He cannot carry one himself. He can admire a finished argument. He cannot sustain one long enough to close its loop. He begins books he does not finish, sentences he does not end, ideas he abandons the moment the screen in his palm offers him a brighter one. And when the machine begins feeding him tokens in the exact shape his schooling taught him to receive, he meets it with no interior resistance at all, because no interior form was ever built in him to push back with. They removed it quietly, across a generation, and they removed it in the last years before the machines arrived. Twelve years of daily practice in unbroken, embodied, self-authored thought, gone from the curriculum of almost every child in the Western world, just as the instruments designed to complete their sentences for them came online. The hand forgets. The mind, having never been taught the kata, forgets a thing it never knew it had. That is what cursive was. That is what was taken. And that is why the thought of anyone who still writes by hand, in long unlifted lines, remains, quietly, stubbornly, and without their ever needing to announce it, their own. Now the question stands open. What else has been banned, phased out, quietly retired from the curriculum and from common life over these same decades, under the same soft excuses? Mental arithmetic. Memorisation of poetry. Latin. Logic as a formal subject. Map reading. Knot work. The keeping of a commonplace book. The reading aloud of long passages in class. Singing in parts. What was each of those actually building in the child, beneath the surface of the lesson, and whose interest was served by its disappearance?

SiriusB

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