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Laminar flow in fluid dynamics is when fluid particles move in parallel layers without significant mixing. At low speeds, these layers slide past each other smoothly, resembling frozen motion when observed on camera.

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9,325,047 次观看 • 2 年前 •via X (Twitter)

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Bernoulli's Principle It’s a key idea in fluid dynamics named after Daniel Bernoulli. In simple terms: For a fluid flowing smoothly, an increase in the fluid’s speed happens at the same time as a decrease in pressure or a decrease in the fluid’s potential energy. Where: - P = pressure in the fluid - ρ = fluid density - v = flow velocity - g = acceleration due to gravity - h = height above a reference point Bernoulli's Principle basically says that for a fluid that's flowing smoothly, when its speed goes up, its pressure goes down. Think of it like this: fast-moving air or water pushes less sideways than slow-moving air or water. The energy in the fluid stays the same overall, but it shifts between motion, pressure, and height. That's why an airplane wing creates lift. The air moves faster over the curved top of the wing than underneath, so the pressure on top is lower. The higher pressure below pushes the wing up.

Bernoulli's Principle It’s a key idea in fluid dynamics named after Daniel Bernoulli. In simple terms: For a fluid flowing smoothly, an increase in the fluid’s speed happens at the same time as a decrease in pressure or a decrease in the fluid’s potential energy. Where: - P = pressure in the fluid - ρ = fluid density - v = flow velocity - g = acceleration due to gravity - h = height above a reference point Bernoulli's Principle basically says that for a fluid that's flowing smoothly, when its speed goes up, its pressure goes down. Think of it like this: fast-moving air or water pushes less sideways than slow-moving air or water. The energy in the fluid stays the same overall, but it shifts between motion, pressure, and height. That's why an airplane wing creates lift. The air moves faster over the curved top of the wing than underneath, so the pressure on top is lower. The higher pressure below pushes the wing up.

Sam Mathverse

27,791 次观看 • 2 个月前

🚨 BREAKING: Electrons just stopped behaving like particles, and started flowing like a perfect liquid. In graphene, scientists just observed a Dirac fluid a state where electrons move collectively with almost zero resistance. Even crazier? It breaks a fundamental law of physics (the Wiedemann–Franz law) by over 200x. This isn’t just a material discovery. It means: Electricity can behave like fluid dynamics Heat and charge can decouple Quantum systems can act like perfect liquids Now think bigger: If electrons can flow like this. what else in physics isn’t actually “particle-based” at all? Follow me this is where physics starts rewriting itself.

🚨 BREAKING: Electrons just stopped behaving like particles, and started flowing like a perfect liquid. In graphene, scientists just observed a Dirac fluid a state where electrons move collectively with almost zero resistance. Even crazier? It breaks a fundamental law of physics (the Wiedemann–Franz law) by over 200x. This isn’t just a material discovery. It means: Electricity can behave like fluid dynamics Heat and charge can decouple Quantum systems can act like perfect liquids Now think bigger: If electrons can flow like this. what else in physics isn’t actually “particle-based” at all? Follow me this is where physics starts rewriting itself.

TheNewPhysics

70,012 次观看 • 3 个月前

“when this place was built on all of you feeling each other up in an on call room” and merder look at eo... these FREAKS

“when this place was built on all of you feeling each other up in an on call room” and merder look at eo... these FREAKS

pook 🔪

322,390 次观看 • 4 个月前

‼️🇺🇸🇻🇪 Venezuela is on high alert as reports confirm the presence of US forces gathering in the Caribbean Sea. Tensions are rising in the region, fueling speculation about Washington’s next move. Military buildup continues as both sides monitor each other closely. The situation remains fluid, with regional stability hanging in the balance.

‼️🇺🇸🇻🇪 Venezuela is on high alert as reports confirm the presence of US forces gathering in the Caribbean Sea. Tensions are rising in the region, fueling speculation about Washington’s next move. Military buildup continues as both sides monitor each other closely. The situation remains fluid, with regional stability hanging in the balance.

Defense Intelligence

46,777 次观看 • 8 个月前

UV Flow is the fastest way to unwrap. Cut loops that stop at other seams. Drag to cut through poles and tris. Auto-unwrap with tons of extra options. Work in layers. Unwrap to real world scale. Pack by material. And so much more! 25% off rn on BM:

UV Flow is the fastest way to unwrap. Cut loops that stop at other seams. Drag to cut through poles and tris. Auto-unwrap with tons of extra options. Work in layers. Unwrap to real world scale. Pack by material. And so much more! 25% off rn on BM:

Jonathan Lampel

66,433 次观看 • 1 年前

Bullet time effect using Seedance 2.0 on SocialSight Prompt: Bullet time effect. A businessman in white shirt and black tie slipping and falling backwards on an icy wet street in Wall Street, New York. Coffee cup standing on ground, liquid exploding outward frozen in mid-air. Ice chunks, water droplets, and coffee splash all completely suspended time is frozen. Tall buildings on both sides creating a canyon effect. The camera smoothly orbits 360 degrees around the falling man at low ground level angle, only the camera moves while everything else remains perfectly still. Cinematic, overcast dramatic lighting, wide angle lens distortion.

Bullet time effect using Seedance 2.0 on SocialSight Prompt: Bullet time effect. A businessman in white shirt and black tie slipping and falling backwards on an icy wet street in Wall Street, New York. Coffee cup standing on ground, liquid exploding outward frozen in mid-air. Ice chunks, water droplets, and coffee splash all completely suspended time is frozen. Tall buildings on both sides creating a canyon effect. The camera smoothly orbits 360 degrees around the falling man at low ground level angle, only the camera moves while everything else remains perfectly still. Cinematic, overcast dramatic lighting, wide angle lens distortion.

Oogie

59,495 次观看 • 3 个月前

Bullet time effect using Seedance 2.0 Prompt: Bullet time effect. A businessman in white shirt and black tie slipping and falling backwards on icy wet street in Wall Street, New York. Coffee cup standing on ground, liquid exploding outward frozen in mid-air. Ice chunks, water droplets, and coffee splash all completely suspended — time is frozen. Tall buildings on both sides creating a canyon effect. Camera smoothly orbits 360 degrees around the falling man at low ground level angle, only the camera moves while everything else remains perfectly still. Cinematic, overcast dramatic lighting, wide angle lens distortion.

Bullet time effect using Seedance 2.0 Prompt: Bullet time effect. A businessman in white shirt and black tie slipping and falling backwards on icy wet street in Wall Street, New York. Coffee cup standing on ground, liquid exploding outward frozen in mid-air. Ice chunks, water droplets, and coffee splash all completely suspended — time is frozen. Tall buildings on both sides creating a canyon effect. Camera smoothly orbits 360 degrees around the falling man at low ground level angle, only the camera moves while everything else remains perfectly still. Cinematic, overcast dramatic lighting, wide angle lens distortion.

Oogie

88,051 次观看 • 3 个月前

d’Alembert’s Paradox: The 1752 Paradox That Stood in the Way of Real Aerodynamics In 1752, d’Alembert found a result that still feels wrong at first sight: An ideal fluid can flow around a body and produce no drag at all. In the first animation, viscosity is set to zero. The fluid bends around the object, accelerates, slows down, and stitches itself back together downstream. You get no wake, no loss and no scar in the flow. The equations have removed the very thing that would let the body leave a trace. That is Euler flow: ρ(∂u/∂t + (u · ∇)u) = −∇p ∇ · u = 0 For steady irrotational flow, u = ∇φ and ∇²φ = 0. The pressure field stays perfectly symmetric front to back, so when you integrate pressure over the body, the drag cancels: D = 0 The second animation changes one thing: viscosity is allowed back in. ρ(∂u/∂t + (u · ∇)u) = −∇p + μ∇²u ∇ · u = 0 Now, the surface can grip the fluid. A boundary layer forms. It can separate, vorticity rolls off the body and wake appears. Drag is now built by the flow. Therefore, making viscosity tiny is not the same as deleting it. ν → 0 still has a boundary layer. ν = 0 has no memory.

d’Alembert’s Paradox: The 1752 Paradox That Stood in the Way of Real Aerodynamics In 1752, d’Alembert found a result that still feels wrong at first sight: An ideal fluid can flow around a body and produce no drag at all. In the first animation, viscosity is set to zero. The fluid bends around the object, accelerates, slows down, and stitches itself back together downstream. You get no wake, no loss and no scar in the flow. The equations have removed the very thing that would let the body leave a trace. That is Euler flow: ρ(∂u/∂t + (u · ∇)u) = −∇p ∇ · u = 0 For steady irrotational flow, u = ∇φ and ∇²φ = 0. The pressure field stays perfectly symmetric front to back, so when you integrate pressure over the body, the drag cancels: D = 0 The second animation changes one thing: viscosity is allowed back in. ρ(∂u/∂t + (u · ∇)u) = −∇p + μ∇²u ∇ · u = 0 Now, the surface can grip the fluid. A boundary layer forms. It can separate, vorticity rolls off the body and wake appears. Drag is now built by the flow. Therefore, making viscosity tiny is not the same as deleting it. ν → 0 still has a boundary layer. ν = 0 has no memory.

Mathelirium

43,784 次观看 • 3 个月前

Have you ever heard of the three-body problem? This video explains a really fascinating and complex concept in a simple way! Here's the idea: When two stars orbit each other, their motion is very orderly and predictable. But when a third star enters the picture, everything falls apart! The gravity of each star affects the other two, creating a completely chaotic and unpredictable cosmic dance. It's like three people trying to dance together with no rules, constantly bumping into each other! No one can say exactly what their next move will be.

Have you ever heard of the three-body problem? This video explains a really fascinating and complex concept in a simple way! Here's the idea: When two stars orbit each other, their motion is very orderly and predictable. But when a third star enters the picture, everything falls apart! The gravity of each star affects the other two, creating a completely chaotic and unpredictable cosmic dance. It's like three people trying to dance together with no rules, constantly bumping into each other! No one can say exactly what their next move will be.

Kekius Maximus

41,949 次观看 • 6 个月前

The fascinating concept of Non-Newtonian fluids, which transition from a liquid state to a solid-like state when pressure is applied, has a rich history that spans several centuries. The study and understanding of these peculiar fluids have evolved over time, leading to a wide range of practical applications and scientific insights. One of the earliest references to Non-Newtonian behavior in fluids dates back to the 17th century when Sir Isaac Newton formulated the basic principles of fluid mechanics. Newton's laws of fluid motion primarily applied to Newtonian fluids, which exhibit constant viscosity and flow behavior regardless of the applied force or pressure. However, it soon became apparent that not all fluids behaved in this predictable manner. In the mid-19th century, a scientist named Thomas Andrews made significant contributions to the understanding of Non-Newtonian fluids. Andrews conducted groundbreaking experiments with carbon dioxide, revealing that under high pressure, this gas could transform into a liquid. This observation marked one of the earliest instances of pressure-induced phase changes in fluids. The term "Non-Newtonian" itself was coined in the 20th century to describe fluids that did not adhere to Newton's classical laws of fluid dynamics. These fluids exhibited a variety of behaviors, but one of the most intriguing was their ability to solidify or increase in viscosity when subjected to stress or pressure. One of the most famous examples of such behavior is cornstarch mixed with water, which forms a substance known as "oobleck" that becomes more solid when pressure is applied. In the modern era, Non-Newtonian fluids have found applications in various fields, including food science, engineering, and material science. They are used in products like quicksand, body armor, and even in the development of impact-resistant materials. One of the key insights that emerged from the study of Non-Newtonian fluids is the importance of understanding the relationship between stress and strain, as well as the influence of time-dependent properties on their behavior. This knowledge has led to advancements in rheology, the study of flow and deformation in materials, and has practical implications in areas such as industrial processing, medicine, and the design of everyday products.

The fascinating concept of Non-Newtonian fluids, which transition from a liquid state to a solid-like state when pressure is applied, has a rich history that spans several centuries. The study and understanding of these peculiar fluids have evolved over time, leading to a wide range of practical applications and scientific insights. One of the earliest references to Non-Newtonian behavior in fluids dates back to the 17th century when Sir Isaac Newton formulated the basic principles of fluid mechanics. Newton's laws of fluid motion primarily applied to Newtonian fluids, which exhibit constant viscosity and flow behavior regardless of the applied force or pressure. However, it soon became apparent that not all fluids behaved in this predictable manner. In the mid-19th century, a scientist named Thomas Andrews made significant contributions to the understanding of Non-Newtonian fluids. Andrews conducted groundbreaking experiments with carbon dioxide, revealing that under high pressure, this gas could transform into a liquid. This observation marked one of the earliest instances of pressure-induced phase changes in fluids. The term "Non-Newtonian" itself was coined in the 20th century to describe fluids that did not adhere to Newton's classical laws of fluid dynamics. These fluids exhibited a variety of behaviors, but one of the most intriguing was their ability to solidify or increase in viscosity when subjected to stress or pressure. One of the most famous examples of such behavior is cornstarch mixed with water, which forms a substance known as "oobleck" that becomes more solid when pressure is applied. In the modern era, Non-Newtonian fluids have found applications in various fields, including food science, engineering, and material science. They are used in products like quicksand, body armor, and even in the development of impact-resistant materials. One of the key insights that emerged from the study of Non-Newtonian fluids is the importance of understanding the relationship between stress and strain, as well as the influence of time-dependent properties on their behavior. This knowledge has led to advancements in rheology, the study of flow and deformation in materials, and has practical implications in areas such as industrial processing, medicine, and the design of everyday products.

Historic Vids

2,632,483 次观看 • 2 年前

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1 and W2 — the foundational prerequisites — stay frozen. Only W3 — the layer closest to your task-specific output — gets a ΔW.

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1 and W2 — the foundational prerequisites — stay frozen. Only W3 — the layer closest to your task-specific output — gets a ΔW.

Tom Yeh

27,587 次观看 • 3 个月前

You are never truly standing still. Even when everything feels motionless, you are carried by Earth’s rotation, swept along as it orbits the Sun, and pulled through the Milky Way along with billions of other stars. From a cosmic perspective, stillness is an illusion created by our limited human scale of perception, too small to notice the vast layers of motion we are part of. Taken together, these motions place us in constant transit through space at speeds far beyond anything we can feel. Yet there is no wind, no vibration, and no sense of rushing. We do not experience this journey because everything around us shares it, locked into the same immense and silent choreography of the universe. Credit: physicsj

You are never truly standing still. Even when everything feels motionless, you are carried by Earth’s rotation, swept along as it orbits the Sun, and pulled through the Milky Way along with billions of other stars. From a cosmic perspective, stillness is an illusion created by our limited human scale of perception, too small to notice the vast layers of motion we are part of. Taken together, these motions place us in constant transit through space at speeds far beyond anything we can feel. Yet there is no wind, no vibration, and no sense of rushing. We do not experience this journey because everything around us shares it, locked into the same immense and silent choreography of the universe. Credit: physicsj

Cosmos Archive

12,746 次观看 • 2 个月前

Seedance 2.0 4K on Higgsfield Prompt: Scene: A seamless ultra-cinematic one-take shot starting from deep space and ending in an intimate café moment. The sequence emphasizes speed, scale, and immersive transition from cosmic to human scale. Subject / Character: Final subject is irene galbraith — a young woman sitting in an open-air café named “LUMOS”, wearing denim shorts and a white shirt, casually eating a hamburger with a drink beside her. Action Timeline (TOTAL: 15s): 0–4s (Space → Earth Approach): Wide cinematic shot of Earth in deep space. Camera accelerates forward smoothly. Earth rapidly grows in frame. Subtle light streaks, atmospheric glow becomes visible. Motion builds gradually but feels grand. 4–8s (Atmospheric Entry → Fast Descent): Camera pierces atmosphere with intense glow and motion blur. Clouds rush past. Continents and terrain sweep underneath at high speed. Strong sense of acceleration. 8–11s (City Dive → Street Flow): Camera locks onto a city and dives sharply. Skyscrapers rise fast. Transition into street-level movement — fluid glide through streets, passing buildings, corners, and urban elements with dynamic motion. 11–12s (Café Target Lock): Camera spots outdoor café with large “LUMOS” sign. Rapid but smooth deceleration begins. Focus tightens. 12–15s (Final Scene – Character): Camera settles into a medium shot of irene galbraith. She sits casually, eating a hamburger, drink on table. Natural motion (taking a bite, relaxed posture). Warm, calm contrast to previous high-speed sequence. Camera: One continuous shot, no cuts. Extreme speed ramping: slow → ultra-fast → controlled slowdown. Wide lens in space → natural cinematic lens at final shot. Smooth stabilization with slight handheld realism at the end. Audio: Cinematic rise from deep space ambience → intense whooshing during descent → city ambience → soft café sounds (light chatter, ambient noise). Style: Cinematic color grading: cool tones in space/descent → gradually warmer tones at street/café. High contrast, subtle film grain, volumetric lighting, atmospheric particles, ultra-detailed, 8K, photorealistic.

Seedance 2.0 4K on Higgsfield Prompt: Scene: A seamless ultra-cinematic one-take shot starting from deep space and ending in an intimate café moment. The sequence emphasizes speed, scale, and immersive transition from cosmic to human scale. Subject / Character: Final subject is irene galbraith — a young woman sitting in an open-air café named “LUMOS”, wearing denim shorts and a white shirt, casually eating a hamburger with a drink beside her. Action Timeline (TOTAL: 15s): 0–4s (Space → Earth Approach): Wide cinematic shot of Earth in deep space. Camera accelerates forward smoothly. Earth rapidly grows in frame. Subtle light streaks, atmospheric glow becomes visible. Motion builds gradually but feels grand. 4–8s (Atmospheric Entry → Fast Descent): Camera pierces atmosphere with intense glow and motion blur. Clouds rush past. Continents and terrain sweep underneath at high speed. Strong sense of acceleration. 8–11s (City Dive → Street Flow): Camera locks onto a city and dives sharply. Skyscrapers rise fast. Transition into street-level movement — fluid glide through streets, passing buildings, corners, and urban elements with dynamic motion. 11–12s (Café Target Lock): Camera spots outdoor café with large “LUMOS” sign. Rapid but smooth deceleration begins. Focus tightens. 12–15s (Final Scene – Character): Camera settles into a medium shot of irene galbraith. She sits casually, eating a hamburger, drink on table. Natural motion (taking a bite, relaxed posture). Warm, calm contrast to previous high-speed sequence. Camera: One continuous shot, no cuts. Extreme speed ramping: slow → ultra-fast → controlled slowdown. Wide lens in space → natural cinematic lens at final shot. Smooth stabilization with slight handheld realism at the end. Audio: Cinematic rise from deep space ambience → intense whooshing during descent → city ambience → soft café sounds (light chatter, ambient noise). Style: Cinematic color grading: cool tones in space/descent → gradually warmer tones at street/café. High contrast, subtle film grain, volumetric lighting, atmospheric particles, ultra-detailed, 8K, photorealistic.

simeon-sanai

58,500 次观看 • 1 个月前

When you hit a GB in the infield your job is to run your “best speed” & read the 1st baseman’s feet. If they elevate or move towards you, slide. IMO this is totally on the batter-runner & not the 1st base coach. It must be repped at game speed.

When you hit a GB in the infield your job is to run your “best speed” & read the 1st baseman’s feet. If they elevate or move towards you, slide. IMO this is totally on the batter-runner & not the 1st base coach. It must be repped at game speed.

Jerry Weinstein

42,725 次观看 • 6 个月前

What Looks Like A Complex Set Of Spinning Parts Is The Core Of How Trucks Convert Engine Power Into Motion. A drivetrain system transfers torque from the engine through the transmission, driveshaft, and differential to the wheels. Each component plays a specific role. The transmission adjusts torque and speed, the driveshaft carries rotational force, and the differential distributes power between wheels while allowing them to rotate at different speeds during turns. Seeing the system in motion reveals how precisely synchronized these components must be to handle heavy loads and maintain efficiency under stress. In Heavy Vehicles, Power Is Only Useful When It Is Controlled.

What Looks Like A Complex Set Of Spinning Parts Is The Core Of How Trucks Convert Engine Power Into Motion. A drivetrain system transfers torque from the engine through the transmission, driveshaft, and differential to the wheels. Each component plays a specific role. The transmission adjusts torque and speed, the driveshaft carries rotational force, and the differential distributes power between wheels while allowing them to rotate at different speeds during turns. Seeing the system in motion reveals how precisely synchronized these components must be to handle heavy loads and maintain efficiency under stress. In Heavy Vehicles, Power Is Only Useful When It Is Controlled.

Mechanical Knowledge

66,718 次观看 • 3 个月前

She wasn't dancing for anyone until the whole street joined in. One girl. One beat. And suddenly everyone's in. Made with GPT Image 2 and Seedance 2.0 on BudgetPixel AI Prompt: Video Prompt — 15 seconds, 16:9, Solo female street dancer Jessy (described above) dancing continuously and energetically from the very first frame to a viral trending upbeat pop/afrobeat-style track (130 BPM, catchy hook-driven viral sound), full choreography visible throughout — no static feet-only opening, no frozen ending. Sunlit outdoor market street, colorful stalls, soft crowd blur, photorealistic, 8K detail, consistent facial identity, cinematic natural daylight, fluid motion blur, perfect temporal consistency. 0:00–0:03 — Low-angle tracking shot rising into a full-body view; Jessy is already mid-move, doing a bouncy shuffle-step with sharp arm swings and a hip pop on the beat drop, ponytail whipping, face clearly visible and expressive. 0:03–0:06 — Whip-pan into a Dutch-angle mid-shot; Jessy spins into a body-roll combo, arms cutting sharply through the air, handheld camera pulsing with the beat. 0:06–0:09 — Crash-zoom to a high-angle crane shot; Jessy drops into a low groove-step with a floor-touch and pop back up, dust kicking softly at her sneakers, still mid-motion. 0:09–0:12 — Snap-cut to eye-level tracking shot circling her; nearby onlookers — two women and one man, each with distinct clearly-visible faces, different hairstyles, and different outfits from Jessy and each other — start mirroring her moves and join in, forming a loose group dance, all faces on camera (face cards visible, no obstruction). 0:12–0:15 — Wide dynamic shot pulling back as the whole group dances together in sync, Jessy in the center still moving with sharp bounce and a playful spin, camera continuing to move (slow pull-back with slight handheld shake) right up to the final frame — no freeze, motion carries through to the last moment, energetic smile toward camera.

She wasn't dancing for anyone until the whole street joined in. One girl. One beat. And suddenly everyone's in. Made with GPT Image 2 and Seedance 2.0 on BudgetPixel AI Prompt: Video Prompt — 15 seconds, 16:9, Solo female street dancer Jessy (described above) dancing continuously and energetically from the very first frame to a viral trending upbeat pop/afrobeat-style track (130 BPM, catchy hook-driven viral sound), full choreography visible throughout — no static feet-only opening, no frozen ending. Sunlit outdoor market street, colorful stalls, soft crowd blur, photorealistic, 8K detail, consistent facial identity, cinematic natural daylight, fluid motion blur, perfect temporal consistency. 0:00–0:03 — Low-angle tracking shot rising into a full-body view; Jessy is already mid-move, doing a bouncy shuffle-step with sharp arm swings and a hip pop on the beat drop, ponytail whipping, face clearly visible and expressive. 0:03–0:06 — Whip-pan into a Dutch-angle mid-shot; Jessy spins into a body-roll combo, arms cutting sharply through the air, handheld camera pulsing with the beat. 0:06–0:09 — Crash-zoom to a high-angle crane shot; Jessy drops into a low groove-step with a floor-touch and pop back up, dust kicking softly at her sneakers, still mid-motion. 0:09–0:12 — Snap-cut to eye-level tracking shot circling her; nearby onlookers — two women and one man, each with distinct clearly-visible faces, different hairstyles, and different outfits from Jessy and each other — start mirroring her moves and join in, forming a loose group dance, all faces on camera (face cards visible, no obstruction). 0:12–0:15 — Wide dynamic shot pulling back as the whole group dances together in sync, Jessy in the center still moving with sharp bounce and a playful spin, camera continuing to move (slow pull-back with slight handheld shake) right up to the final frame — no freeze, motion carries through to the last moment, energetic smile toward camera.

Jessica Collins

31,429 次观看 • 12 天前

Seedance 2.0 Tip: This is how I use grids to give just enough freedom for AI to work with while maintaining a controlled set of moves/techniques. Great for anything that is choreographed. Workflow: Use Grok to generate a list of techniques that you can pick from. choose 9 variations. Use banana to make it into a grid. Specify that the pose in each grid should be at the moment most representative of the tenique. SD2 Prompt: An olympic skater is performing her beautiful fast paced skate choreography. Choose at least 3 moves at random from @图片2 throughout her performance. No stopping between moves. Each move should be sharp and flow smoothly into the next move. Moves must not last longer than 4 seconds each. Emphasize her speed and accuracy of execution with dramatic cuts that shows her facial expression for emotional highlights. Must include at least 1 spin and 1 jump. Camera work should include fast 360 orbits, rapid jet cuts, ECUs to when she lands her jumps, dynamic push/pulls where her skate glides right infront of the camera, or any other that you think would be fitting. Perfect performance of a worldclass skater.

Seedance 2.0 Tip: This is how I use grids to give just enough freedom for AI to work with while maintaining a controlled set of moves/techniques. Great for anything that is choreographed. Workflow: Use Grok to generate a list of techniques that you can pick from. choose 9 variations. Use banana to make it into a grid. Specify that the pose in each grid should be at the moment most representative of the tenique. SD2 Prompt: An olympic skater is performing her beautiful fast paced skate choreography. Choose at least 3 moves at random from @图片2 throughout her performance. No stopping between moves. Each move should be sharp and flow smoothly into the next move. Moves must not last longer than 4 seconds each. Emphasize her speed and accuracy of execution with dramatic cuts that shows her facial expression for emotional highlights. Must include at least 1 spin and 1 jump. Camera work should include fast 360 orbits, rapid jet cuts, ECUs to when she lands her jumps, dynamic push/pulls where her skate glides right infront of the camera, or any other that you think would be fitting. Perfect performance of a worldclass skater.

Jason W - AI

45,912 次观看 • 3 个月前

🚨 BREAKING: We just broke one of superconductivity’s biggest limits… with a lightweight element. No heavy exotic materials. No extreme redesign of physics. Just 3 atomic layers of gallium… sandwiched between graphene and silicon carbide. And suddenly Superconductivity survives strong magnetic fields Beyond the expected physical limit In a system that shouldn’t be able to do it Why? Because the interface changes everything. The electrons don’t behave normally anymore. Their spin locks in place… protecting the flow. Meaning: No resistance. Even when physics says it should fail. Now think bigger What if materials aren’t defined by what they are. but by how they’re structured at the smallest scale? What else could we unlock just by changing the layers? The real question is How many “limits” in physics… are actually just design problems? What other possibilities are out there? Follow me this is where physics gets real.

🚨 BREAKING: We just broke one of superconductivity’s biggest limits… with a lightweight element. No heavy exotic materials. No extreme redesign of physics. Just 3 atomic layers of gallium… sandwiched between graphene and silicon carbide. And suddenly Superconductivity survives strong magnetic fields Beyond the expected physical limit In a system that shouldn’t be able to do it Why? Because the interface changes everything. The electrons don’t behave normally anymore. Their spin locks in place… protecting the flow. Meaning: No resistance. Even when physics says it should fail. Now think bigger What if materials aren’t defined by what they are. but by how they’re structured at the smallest scale? What else could we unlock just by changing the layers? The real question is How many “limits” in physics… are actually just design problems? What other possibilities are out there? Follow me this is where physics gets real.

TheNewPhysics

23,801 次观看 • 3 个月前