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Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins... to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:show more

Ilir Aliu

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38,234 views • 6 months ago •via X (Twitter)

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

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

Ilir Aliu

1,676,995 views • 9 months ago

🚨 SCIENTISTS JUST INVENTED A WAY TO PRINT CIRCUIT BOARDS WITH LIQUID METAL AND IT LOOKS LIKE SOMETHING OUT OF TERMINATOR. A startup called Itera has developed a system that can create working PCB prototypes in minutes instead of weeks. You upload your design, and electric fields force a liquid metal alloy into the exact shape of the traces on a glass substrate. The board is then tested and ready almost instantly. Why this matters: • Traditional PCB prototyping can take days or even weeks this could reduce it to minutes • It uses liquid metal instead of etched copper, making it potentially much faster and more flexible for rapid iteration • Backed by $12 million in funding, the company is already focusing on single-layer boards with surface-mount components • The process looks genuinely futuristic glowing rivers of metal flowing into place on command The deeper implication is enormous: We may be watching the beginning of a completely new era of hardware development. Instead of waiting days for a prototype, engineers and makers could design, test, and iterate multiple versions in a single afternoon. This could dramatically speed up innovation in electronics, robotics, AI hardware, and even consumer devices. What happens when making a new circuit board becomes as fast and easy as printing a document? Follow for more frontier physics and future technology.

🚨 SCIENTISTS JUST INVENTED A WAY TO PRINT CIRCUIT BOARDS WITH LIQUID METAL AND IT LOOKS LIKE SOMETHING OUT OF TERMINATOR. A startup called Itera has developed a system that can create working PCB prototypes in minutes instead of weeks. You upload your design, and electric fields force a liquid metal alloy into the exact shape of the traces on a glass substrate. The board is then tested and ready almost instantly. Why this matters: • Traditional PCB prototyping can take days or even weeks this could reduce it to minutes • It uses liquid metal instead of etched copper, making it potentially much faster and more flexible for rapid iteration • Backed by $12 million in funding, the company is already focusing on single-layer boards with surface-mount components • The process looks genuinely futuristic glowing rivers of metal flowing into place on command The deeper implication is enormous: We may be watching the beginning of a completely new era of hardware development. Instead of waiting days for a prototype, engineers and makers could design, test, and iterate multiple versions in a single afternoon. This could dramatically speed up innovation in electronics, robotics, AI hardware, and even consumer devices. What happens when making a new circuit board becomes as fast and easy as printing a document? Follow for more frontier physics and future technology.

TheNewPhysics

19,878 views • 3 months ago

A machine like this can cost $500,000 to well over $1 million to make parts that may be worth only $10-15 This is the real manufacturing story. This INDEX six-spindle automatic carries 6 motorised spindles, up to 12 CNC tool carriers, operates at 8,000+ rpm, and weighs 7.2 tonnes. Different operations happen simultaneously as the spindle drum indexes each workpiece from station to station. A real scale production example produced a precision component in 11 seconds, versus 38 seconds on a conventional single-spindle lathe. That's roughly 327 parts per hour before downtime. But the machine is only the hardware. Tool geometry, CNC programs, cutting parameters, spindle synchronisation, tooling, bar feeding, chip evacuation, coolant, inspection and collision checked simulation all have to be engineered around the exact part. That is what it takes to integrate this machine into a factory workflow. This accumulated capability is what kept Germany and Japan at the pinnacle of machine-tool manufacturing for decades almost unchallenged. The advantage wasn't just building the hardware, but knowing how to program, tool and integrate these machines for thousands of different manufacturing requirements globally. China has now built much of that ecosystem at extraordinary scale, machines, controls, tooling, software, automation and integration. Its huge domestic manufacturing base has accelerated that learning curve dramatically. Today, Chinese manufacturers can increasingly offer sophisticated CNC and multi-spindle systems at 30-40% lower total costs in most applications, putting serious and relentless price pressure on German and Japanese builders. China produced 37% of the world's machine tools in 2025, compared with 12% for Germany and 10% for Japan. These are the machines that make the machines and ultimately determine how much an economy can manufacture. China is the biggest player as of now and growing faster than anyone else in manufacturing high-end machining tools. Source, Daniel Jansson

A machine like this can cost $500,000 to well over $1 million to make parts that may be worth only $10-15 This is the real manufacturing story. This INDEX six-spindle automatic carries 6 motorised spindles, up to 12 CNC tool carriers, operates at 8,000+ rpm, and weighs 7.2 tonnes. Different operations happen simultaneously as the spindle drum indexes each workpiece from station to station. A real scale production example produced a precision component in 11 seconds, versus 38 seconds on a conventional single-spindle lathe. That's roughly 327 parts per hour before downtime. But the machine is only the hardware. Tool geometry, CNC programs, cutting parameters, spindle synchronisation, tooling, bar feeding, chip evacuation, coolant, inspection and collision checked simulation all have to be engineered around the exact part. That is what it takes to integrate this machine into a factory workflow. This accumulated capability is what kept Germany and Japan at the pinnacle of machine-tool manufacturing for decades almost unchallenged. The advantage wasn't just building the hardware, but knowing how to program, tool and integrate these machines for thousands of different manufacturing requirements globally. China has now built much of that ecosystem at extraordinary scale, machines, controls, tooling, software, automation and integration. Its huge domestic manufacturing base has accelerated that learning curve dramatically. Today, Chinese manufacturers can increasingly offer sophisticated CNC and multi-spindle systems at 30-40% lower total costs in most applications, putting serious and relentless price pressure on German and Japanese builders. China produced 37% of the world's machine tools in 2025, compared with 12% for Germany and 10% for Japan. These are the machines that make the machines and ultimately determine how much an economy can manufacture. China is the biggest player as of now and growing faster than anyone else in manufacturing high-end machining tools. Source, Daniel Jansson

Ammanichanda

38,359 views • 1 month ago

Coding software is a joke compared to building hardware. Because when a software fails, it can be fixed quickly. In hardware, one mistake can set you back for months or end you. In hardware, every decision is cash flow. Every tiny error can cost tens of 1000s of dollars. You don’t get second chances. You get lessons, paid for in time and money. But when you finally get it right… You don’t just ship a product. You build a legacy. Something people touch, use, and trust for years. That’s the beauty and the pain of building in the real world.

Coding software is a joke compared to building hardware. Because when a software fails, it can be fixed quickly. In hardware, one mistake can set you back for months or end you. In hardware, every decision is cash flow. Every tiny error can cost tens of 1000s of dollars. You don’t get second chances. You get lessons, paid for in time and money. But when you finally get it right… You don’t just ship a product. You build a legacy. Something people touch, use, and trust for years. That’s the beauty and the pain of building in the real world.

Prakash Dadlani

12,402 views • 10 months ago

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

Pascal Bornet

47,489 views • 4 months ago

$Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.$

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.

Markus J. Buehler

10,095 views • 4 months ago

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 views • 26 days ago

Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

317,920 views • 9 months ago

TESLA’S NEXT-GEN OPTIMUS V3 HAND IS GETTING CLOSE TO HUMAN-LEVEL Tesla engineers just shared new details on the Optimus V3 hand — and the progress is striking. They’re now saying that as they move into Gen-3 and mass production, the hand is getting very close to human functionality and form factor. One engineer described it clearly: “It won’t even look like a robot. It will look like a human in a superhero suit. It will be something revolutionary.” This level of dexterity and human-like design is a critical milestone. The hand is one of the hardest parts of building a truly useful humanoid robot — and Tesla is iterating extremely fast.

TESLA’S NEXT-GEN OPTIMUS V3 HAND IS GETTING CLOSE TO HUMAN-LEVEL Tesla engineers just shared new details on the Optimus V3 hand — and the progress is striking. They’re now saying that as they move into Gen-3 and mass production, the hand is getting very close to human functionality and form factor. One engineer described it clearly: “It won’t even look like a robot. It will look like a human in a superhero suit. It will be something revolutionary.” This level of dexterity and human-like design is a critical milestone. The hand is one of the hardest parts of building a truly useful humanoid robot — and Tesla is iterating extremely fast.

Tesla Owners Silicon Valley

251,147 views • 2 months ago

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is **Lovie** Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like **Claude Code, Cursor, and Windsurf** through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat **$29/month**, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is Lovie Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like Claude Code, Cursor, and Windsurf through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat $29/month, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

Parul Gautam

41,751 views • 2 months ago

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,454 views • 10 months ago

$🚨UNIPHICS NEWS🚨: Light doesn’t slow down in glass — time does. And that explains every rainbow you’ve ever seen 🧨 For centuries, we’ve been taught that light slows down when it enters glass, water, or any transparent material, and that this slowing causes refraction and the splitting of colors in rainbows and prisms. The refractive index is treated as a material property, and photons are pictured as particles mysteriously changing speed inside matter. Uniphics offers a much cleaner and more fundamental picture. Light is a propagating spin-wave mode in the ξM-field. When this wave enters a material like glass, the material increases the local energy density. Because time flow is directly tied to energy density (t_flow = k / E_d), time flows more slowly inside the glass than in air. The spin-wave pattern of light therefore takes longer to advance through the region of slower time flow. This change in the rate of time progression across the boundary causes the wave to bend — exactly what we observe as refraction. Different wavelengths (colors) interact slightly differently with the energy-density environment, so they bend by different amounts, creating rainbows. Nothing actually slows down in the classical sense. The wave simply experiences a different rate of time flow inside the material. The same principle that explains gravitational lensing also explains ordinary lenses and rainbows. This turns one of the most familiar phenomena in optics into a direct consequence of variable time flow caused by energy density gradients. How might realizing that refraction and rainbows are caused by local changes in time flow rather than photons slowing down change the way we think about light, materials, or the design of new optical technologies? A Theory of Everything should be able to answer everything. Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia #Uniphics #Refraction #Rainbows #TimeFlow #Light Grok xAI$

🚨UNIPHICS NEWS🚨: Light doesn’t slow down in glass — time does. And that explains every rainbow you’ve ever seen 🧨 For centuries, we’ve been taught that light slows down when it enters glass, water, or any transparent material, and that this slowing causes refraction and the splitting of colors in rainbows and prisms. The refractive index is treated as a material property, and photons are pictured as particles mysteriously changing speed inside matter. Uniphics offers a much cleaner and more fundamental picture. Light is a propagating spin-wave mode in the ξM-field. When this wave enters a material like glass, the material increases the local energy density. Because time flow is directly tied to energy density (t_flow = k / E_d), time flows more slowly inside the glass than in air. The spin-wave pattern of light therefore takes longer to advance through the region of slower time flow. This change in the rate of time progression across the boundary causes the wave to bend — exactly what we observe as refraction. Different wavelengths (colors) interact slightly differently with the energy-density environment, so they bend by different amounts, creating rainbows. Nothing actually slows down in the classical sense. The wave simply experiences a different rate of time flow inside the material. The same principle that explains gravitational lensing also explains ordinary lenses and rainbows. This turns one of the most familiar phenomena in optics into a direct consequence of variable time flow caused by energy density gradients. How might realizing that refraction and rainbows are caused by local changes in time flow rather than photons slowing down change the way we think about light, materials, or the design of new optical technologies? A Theory of Everything should be able to answer everything. Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia #Uniphics #Refraction #Rainbows #TimeFlow #Light Grok xAI

Paul Maley

26,426 views • 3 months ago

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.

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 views • 4 months ago

i tried Framer Agent to build a brand guidelines website template, something i've meant to do for a while. a site i can swap a logo, nudge a colour, update the type, and send a link instead of re-exporting a pdf nobody opens. a few observations on the AI process: - i started with references. pulled together a handful of minimalist grid layouts and had Claude describe the visual style back to me. you can do the same inside Framer, feeding it the references directly. - from there i had it write a detailed prompt aimed at that exact style, then asked it to break it into a few smaller steps. then i fed those into the agent, one at a time. - the scaffolding stage is the satisfying part. for something this grid-driven (the columns, the spans, the whole underlying structure) watching it land in seconds is hard to look away from. - but then i still have to sweat the details: text alignment, line-heights, image sizes. i don't mind it at all; it's the part i like, making these design decisions. the strength of the agent is the mundane work. point it at the stuff that eats your time: cleaning up the build, adding responsiveness, dropping in small effects, checking text and colour styles stay consistent, writing alt text for every image. then i get the time back for the parts of web design that are actually fun. 🤝

charlota

37,427 views • 2 months ago

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

58,978 views • 4 months ago

Wow! Send A File By Just The Camera! A vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:

Wow! Send A File By Just The Camera! A vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:

Brian Roemmele

16,658 views • 1 month ago

Elon Musk is building a data center that no grid has to power and no county has to approve. Musk: “Think of it as a rack of compute in space.” Orbital hardware has always been custom. Each satellite is its own program, its own team, its own decade. SpaceX’s AI1 satellites are copies of each other. Each is built around a single Nvidia NVL72, the same rack-scale architecture running the largest data centers on the ground, redesigned to fly. Space stops being a program and becomes an inventory. Designing for orbit made the rack simpler and cheaper than the ground version, so SpaceX plans to run the space design in terrestrial data centers too. Racks alone have never made a data center. A thousand of them in a building with no network between them is a thousand computers. The interconnect is what turns them into one machine. Training a frontier model means moving enormous volumes of data between processors without pause, and the whole system runs at the speed of its slowest link. That is why the fabric between chips is fought over as hard as the chips themselves. Musk: “And then you can connect these racks of compute to either each other by the laser links, or directly to the Starlink constellation.” Light moves through glass at about two thirds of its speed in a vacuum, and fiber optic cable is glass. Every backbone route and undersea cable on Earth pays that tax. A laser between two satellites pays nothing. Over long distances, an orbital link arrives sooner than the same run in fiber. Orbit was supposed to be a trade, unlimited power in exchange for a worse network. The network is faster. The compute plugs into a delivery layer that is already finished. Thousands of satellites are up there right now carrying laser links, terminating at ground stations and dishes across most of the planet. The last mile was built years before the thing it delivers existed. Anyone else attempting this needs the rocket, the satellite bus, the solar manufacturing, the laser mesh, the ground network, and a supply line to a chipmaker. SpaceX had five of them before the project had a name. None of it was built for this. Starlink existed to sell internet subscriptions. The lasers existed so those subscriptions would work over open ocean. Starship existed for Mars. Nobody planned this. It assembled itself out of problems solved for other reasons. That is what twenty years of hard engineering leaves behind. Every previous expansion of human capability ran on a resource sitting inside somebody’s borders. Coal, oil, water, land. Sunlight in orbit belongs to nobody and never runs out. Compute still has an address. A building in Virginia, a substation, a county that had to approve it, a grid connection someone waited four years for. He is describing a version that only has an altitude.

Elon Musk is building a data center that no grid has to power and no county has to approve. Musk: “Think of it as a rack of compute in space.” Orbital hardware has always been custom. Each satellite is its own program, its own team, its own decade. SpaceX’s AI1 satellites are copies of each other. Each is built around a single Nvidia NVL72, the same rack-scale architecture running the largest data centers on the ground, redesigned to fly. Space stops being a program and becomes an inventory. Designing for orbit made the rack simpler and cheaper than the ground version, so SpaceX plans to run the space design in terrestrial data centers too. Racks alone have never made a data center. A thousand of them in a building with no network between them is a thousand computers. The interconnect is what turns them into one machine. Training a frontier model means moving enormous volumes of data between processors without pause, and the whole system runs at the speed of its slowest link. That is why the fabric between chips is fought over as hard as the chips themselves. Musk: “And then you can connect these racks of compute to either each other by the laser links, or directly to the Starlink constellation.” Light moves through glass at about two thirds of its speed in a vacuum, and fiber optic cable is glass. Every backbone route and undersea cable on Earth pays that tax. A laser between two satellites pays nothing. Over long distances, an orbital link arrives sooner than the same run in fiber. Orbit was supposed to be a trade, unlimited power in exchange for a worse network. The network is faster. The compute plugs into a delivery layer that is already finished. Thousands of satellites are up there right now carrying laser links, terminating at ground stations and dishes across most of the planet. The last mile was built years before the thing it delivers existed. Anyone else attempting this needs the rocket, the satellite bus, the solar manufacturing, the laser mesh, the ground network, and a supply line to a chipmaker. SpaceX had five of them before the project had a name. None of it was built for this. Starlink existed to sell internet subscriptions. The lasers existed so those subscriptions would work over open ocean. Starship existed for Mars. Nobody planned this. It assembled itself out of problems solved for other reasons. That is what twenty years of hard engineering leaves behind. Every previous expansion of human capability ran on a resource sitting inside somebody’s borders. Coal, oil, water, land. Sunlight in orbit belongs to nobody and never runs out. Compute still has an address. A building in Virginia, a substation, a county that had to approve it, a grid connection someone waited four years for. He is describing a version that only has an altitude.

Dustin

12,888 views • 17 days ago

DOM3 is made from HTML, CSS and Javascript, closer to a document than a canvas. It's a strange contemporary material that shapes much of our digital lives. The rhythm of endless creation and deletion in DOM3 is a self-driving version of what happens when you browse the web. "The Document Object Model (DOM) connects web pages to scripts or programming languages by representing the structure of a document—such as the HTML representing a web page—in memory." A html element, and a rectangle on a canvas, are not the same thing. It's a different material. Even if it all ends up controlling the same pixels, the process is different. The html element kind of actually exists, in this abstract context of a document object model. And in memory, in the moment. If it was possible to measure its weight, it would be heavier than a pixel on a canvas.

DOM3 is made from HTML, CSS and Javascript, closer to a document than a canvas. It's a strange contemporary material that shapes much of our digital lives. The rhythm of endless creation and deletion in DOM3 is a self-driving version of what happens when you browse the web. "The Document Object Model (DOM) connects web pages to scripts or programming languages by representing the structure of a document—such as the HTML representing a web page—in memory." A html element, and a rectangle on a canvas, are not the same thing. It's a different material. Even if it all ends up controlling the same pixels, the process is different. The html element kind of actually exists, in this abstract context of a document object model. And in memory, in the moment. If it was possible to measure its weight, it would be heavier than a pixel on a canvas.

Leander Herzog

11,484 views • 11 months ago

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

ard

429,067 views • 1 month ago