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[Sc〇t Animation Scene ] #4 oneshota double facesitting scat Scene data from URL! Patreon : fanbox : Pixiv : Pixiv(sc〇t) : #コイカツ #koikatsu

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SkyArcadia

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68,706 次观看 • 22 天前 •via X (Twitter)

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Toy Story 4 opening scene was literally Pixar flexing how far their Renderman software has come. That rain and water animation…Holy Sh!t

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Guinaifen Possessed During Stream~ 👻 After wrapping up Stream Guinaifen is possessed by some suspicious malware on her phone... SFX OpenNSFW Moan Pack MOO🩷COW This animation is shorter than my other ones due to me testing some new plugins I got for Koikatsu and I wanted to time myself on how long it takes start to finish to make an animation, so I could more accurately price them. More info soon on my Twitter. 4K60, Nude, POV, Phone Camera, No Overlay Version, and Scene Data on Patreon! Discord #TSF #憑依 #乗っ取り

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The Branch-Point Loom Some equations do not draw curves but instead draw weave sheets. This scene comes from the algebraic curve y² = Δ(z,t), where Δ(z,t) = Πⱼ(z - βⱼ(t)). The moving points βⱼ are branch points. These are special places where two sheets of the curve touch and swap identities. In the animation, those branch points appear as glowing mineral spindles moving across a dark surface. The landscape is shaped by -log|Δ(z,t)|. Therefore, the branch points rise into sharp seams while the surrounding surface folds around them. The bright threads follow the horizontal trajectories of the quadratic differential Δ(z,t)dz². #Mathematics #ComplexAnalysis #AlgebraicGeometry #MathArt #MathematicalArt #Geometry #PhysicsVisualization #STEM #Art
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The Branch-Point Loom Some equations do not draw curves but instead draw weave sheets. This scene comes from the algebraic curve y² = Δ(z,t), where Δ(z,t) = Πⱼ(z - βⱼ(t)). The moving points βⱼ are branch points. These are special places where two sheets of the curve touch and swap identities. In the animation, those branch points appear as glowing mineral spindles moving across a dark surface. The landscape is shaped by -log|Δ(z,t)|. Therefore, the branch points rise into sharp seams while the surrounding surface folds around them. The bright threads follow the horizontal trajectories of the quadratic differential Δ(z,t)dz². #Mathematics #ComplexAnalysis #AlgebraicGeometry #MathArt #MathematicalArt #Geometry #PhysicsVisualization #STEM #Art

Mathelirium

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The irony of law and order start Ice T getting pulled over and having heated exchange with Hudson County Deputy. Case of real like imitating art. Body cam footage of Ice T getting pulled over for a missing front plate escalated quickly as the deputy discovered his registration and license were also expired. After a heated exchange the sergeant arrived on scene and helped de-escalte. Things went from car getting towed and 4 tickets to car getting parked and Ice T can walk to the dmv to get his car and license legal. Hearing the interaction and the final outcome, it's clear that celebrities do get some special treatment.
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The irony of law and order start Ice T getting pulled over and having heated exchange with Hudson County Deputy. Case of real like imitating art. Body cam footage of Ice T getting pulled over for a missing front plate escalated quickly as the deputy discovered his registration and license were also expired. After a heated exchange the sergeant arrived on scene and helped de-escalte. Things went from car getting towed and 4 tickets to car getting parked and Ice T can walk to the dmv to get his car and license legal. Hearing the interaction and the final outcome, it's clear that celebrities do get some special treatment.

Giggling Ganon

107,615 次观看 • 3 个月前

Quantum Mechanics Series Lecture 4 Lecture 1 established that ρ(x,t) = |ψ(x,t)|² behaves like a conserved probability density. Lecture 2 showed what drives that flow. We also saw that writing ψ = r exp(iθ) makes the probability current proportional to the phase gradient, making it clear that phase geometry literally steers the motion. Lecture 3 then showed that the centroid of that flow can move almost classically when the packet is tight and the external potential is smooth. However, that raises yet another question. If the centroid can look classical, why does the full wave still spread, bend, split, and interfere in ways no classical particle cloud would? This is because the wave is not driven only by the external potential. It is also driven by its own curvature. Write ψ(x,t) = r(x,t) exp(iθ(x,t)) with ρ = r². Then Schrödinger’s equation gives two coupled real equations. One is the continuity equation you already know. The other looks like a Hamilton-Jacobi equation, but with one extra term: Q = −(1/2m) ∇²r / r This is the so-called Quantum Potential. It depends entirely on how the amplitude bends across space. So, the wave is being shaped not only by V(x,t), but also by the geometry of its own envelope. In the animation, the upper surface is still |ψ| and its skin is still colored by arg(ψ). The glowing threads still trace the probability current. But now a second membrane hangs underneath. That lower membrane encodes the quantum potential Q itself. The porcelain bead marks the quantum centroid. The amber bead follows a classical centroid under the same external V. When those paths separate, the lower membrane tells you why. The difference is not magic but the extra term classical mechanics does not have. The math breakdown: Start from Schrödinger evolution in units with ħ = 1: i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Write the state in polar form: ψ = r exp(iθ) Then ρ = |ψ|² = r² From the imaginary part, you recover probability conservation: ∂ρ/∂t + ∇·j = 0 with j = (1/m) Im(ψ* ∇ψ) = (ρ/m) ∇θ So the local velocity field is v = j / ρ = ∇θ / m Now take the real part of Schrödinger’s equation. That gives ∂θ/∂t + |∇θ|² / (2m) + V + Q = 0 where Q = −(1/2m) ∇²r / r This is the classical Hamilton-Jacobi equation with one extra term. That extra term is what makes quantum motion locally different from classical motion. Take a gradient of that phase equation and use v = ∇θ / m. Then the flow obeys an Euler-like equation: ∂v/∂t + (v·∇)v = −(1/m) ∇(V + Q) In other words, there are really two forces in the problem. One comes from the external potential V. The other comes from the wave’s own curvature through Q. That is why Ehrenfest is only approximate. The centroid can still satisfy d⟨x⟩/dt = ⟨p⟩/m d⟨p⟩/dt = −⟨∇V⟩ but the internal shape of the packet evolves under the combined influence of V and Q. When the packet stays broad and smooth, Q is gentle and the motion looks more classical. When the packet develops sharp curvature or interference structure, Q becomes strong and the classical picture breaks down. That is what this scene is designed to show live. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #EhrenfestTheorem #QuantumPotential #Madelung #HamiltonJacobi #MathematicalPhysics #Mathematics #Physics
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Quantum Mechanics Series Lecture 4 Lecture 1 established that ρ(x,t) = |ψ(x,t)|² behaves like a conserved probability density. Lecture 2 showed what drives that flow. We also saw that writing ψ = r exp(iθ) makes the probability current proportional to the phase gradient, making it clear that phase geometry literally steers the motion. Lecture 3 then showed that the centroid of that flow can move almost classically when the packet is tight and the external potential is smooth. However, that raises yet another question. If the centroid can look classical, why does the full wave still spread, bend, split, and interfere in ways no classical particle cloud would? This is because the wave is not driven only by the external potential. It is also driven by its own curvature. Write ψ(x,t) = r(x,t) exp(iθ(x,t)) with ρ = r². Then Schrödinger’s equation gives two coupled real equations. One is the continuity equation you already know. The other looks like a Hamilton-Jacobi equation, but with one extra term: Q = −(1/2m) ∇²r / r This is the so-called Quantum Potential. It depends entirely on how the amplitude bends across space. So, the wave is being shaped not only by V(x,t), but also by the geometry of its own envelope. In the animation, the upper surface is still |ψ| and its skin is still colored by arg(ψ). The glowing threads still trace the probability current. But now a second membrane hangs underneath. That lower membrane encodes the quantum potential Q itself. The porcelain bead marks the quantum centroid. The amber bead follows a classical centroid under the same external V. When those paths separate, the lower membrane tells you why. The difference is not magic but the extra term classical mechanics does not have. The math breakdown: Start from Schrödinger evolution in units with ħ = 1: i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Write the state in polar form: ψ = r exp(iθ) Then ρ = |ψ|² = r² From the imaginary part, you recover probability conservation: ∂ρ/∂t + ∇·j = 0 with j = (1/m) Im(ψ* ∇ψ) = (ρ/m) ∇θ So the local velocity field is v = j / ρ = ∇θ / m Now take the real part of Schrödinger’s equation. That gives ∂θ/∂t + |∇θ|² / (2m) + V + Q = 0 where Q = −(1/2m) ∇²r / r This is the classical Hamilton-Jacobi equation with one extra term. That extra term is what makes quantum motion locally different from classical motion. Take a gradient of that phase equation and use v = ∇θ / m. Then the flow obeys an Euler-like equation: ∂v/∂t + (v·∇)v = −(1/m) ∇(V + Q) In other words, there are really two forces in the problem. One comes from the external potential V. The other comes from the wave’s own curvature through Q. That is why Ehrenfest is only approximate. The centroid can still satisfy d⟨x⟩/dt = ⟨p⟩/m d⟨p⟩/dt = −⟨∇V⟩ but the internal shape of the packet evolves under the combined influence of V and Q. When the packet stays broad and smooth, Q is gentle and the motion looks more classical. When the packet develops sharp curvature or interference structure, Q becomes strong and the classical picture breaks down. That is what this scene is designed to show live. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #EhrenfestTheorem #QuantumPotential #Madelung #HamiltonJacobi #MathematicalPhysics #Mathematics #Physics

Mathelirium

20,456 次观看 • 4 个月前

Grok Imagine, right now is in my opinion best and fastest ai video generator for the masses. sure, is not perfect, but Rome wasn't built in a day. Maybe ppl from xai or Elon Musk would look on our posts and suggestions for future improvements. What is a must (for advanced users into ai video generation, been doing this game since 2022) .. 1. for longer movies , we need an option to organize like a project style, and to be able to add main prompts like the niche of the current movie, the character description and to be able to select a custom seed so we can have consistency of the characters. 2. we have now 6 seconds generation ( saw Elon promised 15 seconds soon).. BUT when we generate long movies, we end up with lots of scenes... what grok needs for the same project of the movie, would be a First Frame -Last frame scene interpolation between the scenes (take last frame from scene one, and first frame from scene 2 and generate a mid scene that would merge scene 1 with scene 2 .. and continue for the other scenes (this could be very easy implemented with some python lines of code , like before spitting final video, select all scenes.. extract frames etc etc etc etc.. simple af, when u have all scenes + the interpolation scenes combine evrything with ffmpeg ). 3.. list is long... and i dind't finished my coffee yet, so here is a grok TEXT to video short movie (coz lol u hit the limit for today). Prompts i used for each scene are a little more advanced, so i can see what grok is able to do .. the prompts used are like this (can;t post all due to X limits ) : { "scene_1": { "global_cinematography": "Ultra-realistic Hollywood cyberpunk thriller in the vein of The Matrix (1999) and Blade Runner 2049 (2017), shot on Arri Alexa LF with anamorphic lenses for widescreen 2.39:1 aspect ratio, 24fps for fluid motion, desaturated palette dominated by cool blues, greens, and high-contrast neon reds piercing perpetual smog-choked night. Consistent VFX pipeline: Procedural green code cascades, photorealistic cybernetic augmentations with subsurface scattering, physics-based rain and particle simulations. Lighting paradigm: Volumetric god rays through haze, practical lens flares from holograms, rim lighting on metallic surfaces for depth. Sound integration: Pulsing industrial synth score with digital glitches, rain patter syncing to code interference, metallic echoes underscoring dialogue. Transitions: Seamless glitch wipes or matrix symbol dissolves ensuring narrative continuity, each scene's final beat priming the next for unbroken tension flow. Continuity directive: Scenes chain via lingering elements—rain droplets from prior shots persisting, Nova's silhouette echoing across cuts, HUD overlays threading flashbacks to present, escalating glitch distortions building to climax rupture—maintaining spatial and temporal cohesion in Neo-Tokyo's underbelly.", "shot": { "composition": "Wide aerial drone shot with 35mm wide-angle anamorphic lens on Arri Alexa LF, high dynamic range capturing smog gradients and rain refraction for immersive dystopian establishment, foreground skyscraper edges framing the descent path", "camera_motion": "Controlled descending tilt-push through layered haze, subtle forward momentum building velocity into street-level convergence, priming alley reveal for Scene 2 silhouette emergence" }, "subject": { "description": "Neo-Tokyo's jagged circuit-board skyscrapers thrusting into smog-veiled void, rain-lashed surfaces mirroring erratic neon pulses; faint pedestrian phantoms below as harbingers of oblivious simulation", "wardrobe": "null" }, "scene": { "location": "Shadowed aerial vantage over Neo-Tokyo underbelly, continuity hook from global haze motif", "time_of_day": "Perpetual neon-twilight under storm overcast, syncing with all scenes' eternal dusk", "environment": "Thick smog banks parting reluctantly, acid rain sheets cascading in synchronized sheets with volumetric depth, holographic billboards stuttering in the distance to echo Scene 7 flicker" }, "visual_details": { "action": "Drone pierces urban canopy, unveiling rain-assaulted sprawl where neon bleeds into puddles like corrupted signals, distant alley haze teasing Nova's imminent step-forward in Scene 2", "props": "Circuit-etched tower facades with embedded LED veins flickering erratically, overflowing industrial gutters spewing iridescent chemical runoff, wind-scattered debris hinting at skirmish aftermath", "action_sequence": [ {"0-1s": "High hover frames smog-piercing spires, rain droplets streak lens in slow-mo refraction"}, {"1-2s": "Descent accelerates, haze thins to reveal neon-veined edges glowing faintly blue"}, {"2-3s": "Tilt reveals grid below, rooftops hammered in static-burst impacts syncing to score pulse"}, {"3-4s": "Forward push threads alley corridors, Mandarin signs initial flicker priming Scene 7"}, {"4-5s": "Pedestrians sharpen as wireframe ghosts, AR visors glinting obliviously"}, {"5-6s": "Level to ground haze, Nova's trench silhouette materializes at frame's vanishing point, coat billow lingering into Scene 2 track"} ] }, "cinematography": { "lighting": "Desaturated neon primaries with volumetric god rays slicing haze for ethereal isolation, rain speculars adding dynamic highlights consistent across wet surfaces", "tone": "Oppressive immersion yielding to rebellious spark—global cyber-noir dread laced with glitch anticipation, flowing seamlessly to Nova's personal emergence" } }, "scene_2": { "global_cinematography": "Ultra-realistic Hollywood cyberpunk thriller in the vein of The Matrix (1999) and Blade Runner 2049 (2017), shot on Arri Alexa LF with anamorphic lenses for widescreen 2.39:1 aspect ratio, 24fps for fluid motion, desaturated palette dominated by cool blues, greens, and high-contrast neon reds piercing perpetual smog-choked night. Consistent VFX pipeline: Procedural green code cascades, photorealistic cybernetic augmentations with subsurface scattering, physics-based rain and particle simulations. Lighting paradigm: Volumetric god rays through haze, practical lens flares from holograms, rim lighting on metallic surfaces for depth. Sound integration: Pulsing industrial synth score with digital glitches, rain patter syncing to code interference, metallic echoes underscoring dialogue. Transitions: Seamless glitch wipes or matrix symbol dissolves ensuring narrative continuity, each scene's final beat priming the next for unbroken tension flow. Continuity directive: Scenes chain via lingering elements—rain droplets from prior shots persisting, Nova's silhouette echoing across cuts, HUD overlays threading flashbacks to present, escalating glitch distortions building to climax rupture—maintaining spatial and temporal cohesion in Neo-Tokyo's underbelly.", "shot": { "composition": "Low-angle tracking push with 50mm anamorphic prime on Arri Alexa LF, heroic distortion compressing background alley into claustrophobic funnel, foreground rain blur veiling initial fog for continuity from Scene 1 descent", "camera_motion": "Fluid forward Steadicam arc from lingering Scene 1 haze, subtle left profile tilt to frame Nova against graffiti wall, pulling back slightly to hold environmental depth into Scene 3 orbit" }, "subject": { "description": "Nova, 30s hybrid rebel with scarred synthetic pallor, cropped black hair rain-matted, holographic irises scanning with latent data flickers; sleek titanium limbs rune-etched in dormant blue", "wardrobe": "Sodden black trench coat with frayed hems from Scene 1 debris scatter, high collar shadowing jawline for motif continuity" }, "scene": { "location": "Graffiti-choked alley continuation from Scene 1 street convergence, Neo-Tokyo underbelly", "time_of_day": "Eternal neon-dusk syncing global palette", "environment": "Fog banks rolling from industrial vents as Scene 1 smog extension, wet cobblestones rippling with residual aerial rain patterns" }, "visual_details": { "action": "Nova materializes from Scene 1's terminal haze, striding assertively into sodium glow with metallic glint, coat hem dragging puddles to splash forward—teasing Scene 3 facial trace", "props": "Luminescent 'GLITCH THE SYSTEM' graffiti echoing from Scene 1 signs, overhead hover-traffic hum persisting from aerial hum", "action_sequence": [ {"0-1s": "Fog swirl from Scene 1 yields Nova's silhouette, boot first impacting puddle"}, {"1-2s": "Full stride forward, coat hem trails iridescent wake linking to blood drip in Scene 9"}, {"2-3s": "Titanium forearm catches neon, runes sequential-pulse awakening blue continuity"}, {"3-4s": "Holographic eyes iris-scan, reflecting alley code fragments priming Scene 4 overlay"}, {"4-5s": "Rain beads contour synthetic skin, parting at seams for Scene 3 macro journey"}, {"5-6s": "Profile lean against wall, vapor breath hangs, posture straightening into Scene 5 OTS"} ] }, "cinematography": { "lighting": "Harsh sodium sidelight rimming form per global motif, cool rune fill softening human remnants, prismatic rain refractions tying to Scene 1 aerial streaks", "tone": "Defiant grace in simulated decay—cyber-noir intimacy building personal stakes, camera arc ensuring spatial flow to close-up revelation" } }, etc etc etc up to scene 16. you got the point
1:34

Grok Imagine, right now is in my opinion best and fastest ai video generator for the masses. sure, is not perfect, but Rome wasn't built in a day. Maybe ppl from xai or Elon Musk would look on our posts and suggestions for future improvements. What is a must (for advanced users into ai video generation, been doing this game since 2022) .. 1. for longer movies , we need an option to organize like a project style, and to be able to add main prompts like the niche of the current movie, the character description and to be able to select a custom seed so we can have consistency of the characters. 2. we have now 6 seconds generation ( saw Elon promised 15 seconds soon).. BUT when we generate long movies, we end up with lots of scenes... what grok needs for the same project of the movie, would be a First Frame -Last frame scene interpolation between the scenes (take last frame from scene one, and first frame from scene 2 and generate a mid scene that would merge scene 1 with scene 2 .. and continue for the other scenes (this could be very easy implemented with some python lines of code , like before spitting final video, select all scenes.. extract frames etc etc etc etc.. simple af, when u have all scenes + the interpolation scenes combine evrything with ffmpeg ). 3.. list is long... and i dind't finished my coffee yet, so here is a grok TEXT to video short movie (coz lol u hit the limit for today). Prompts i used for each scene are a little more advanced, so i can see what grok is able to do .. the prompts used are like this (can;t post all due to X limits ) : { "scene_1": { "global_cinematography": "Ultra-realistic Hollywood cyberpunk thriller in the vein of The Matrix (1999) and Blade Runner 2049 (2017), shot on Arri Alexa LF with anamorphic lenses for widescreen 2.39:1 aspect ratio, 24fps for fluid motion, desaturated palette dominated by cool blues, greens, and high-contrast neon reds piercing perpetual smog-choked night. Consistent VFX pipeline: Procedural green code cascades, photorealistic cybernetic augmentations with subsurface scattering, physics-based rain and particle simulations. Lighting paradigm: Volumetric god rays through haze, practical lens flares from holograms, rim lighting on metallic surfaces for depth. Sound integration: Pulsing industrial synth score with digital glitches, rain patter syncing to code interference, metallic echoes underscoring dialogue. Transitions: Seamless glitch wipes or matrix symbol dissolves ensuring narrative continuity, each scene's final beat priming the next for unbroken tension flow. Continuity directive: Scenes chain via lingering elements—rain droplets from prior shots persisting, Nova's silhouette echoing across cuts, HUD overlays threading flashbacks to present, escalating glitch distortions building to climax rupture—maintaining spatial and temporal cohesion in Neo-Tokyo's underbelly.", "shot": { "composition": "Wide aerial drone shot with 35mm wide-angle anamorphic lens on Arri Alexa LF, high dynamic range capturing smog gradients and rain refraction for immersive dystopian establishment, foreground skyscraper edges framing the descent path", "camera_motion": "Controlled descending tilt-push through layered haze, subtle forward momentum building velocity into street-level convergence, priming alley reveal for Scene 2 silhouette emergence" }, "subject": { "description": "Neo-Tokyo's jagged circuit-board skyscrapers thrusting into smog-veiled void, rain-lashed surfaces mirroring erratic neon pulses; faint pedestrian phantoms below as harbingers of oblivious simulation", "wardrobe": "null" }, "scene": { "location": "Shadowed aerial vantage over Neo-Tokyo underbelly, continuity hook from global haze motif", "time_of_day": "Perpetual neon-twilight under storm overcast, syncing with all scenes' eternal dusk", "environment": "Thick smog banks parting reluctantly, acid rain sheets cascading in synchronized sheets with volumetric depth, holographic billboards stuttering in the distance to echo Scene 7 flicker" }, "visual_details": { "action": "Drone pierces urban canopy, unveiling rain-assaulted sprawl where neon bleeds into puddles like corrupted signals, distant alley haze teasing Nova's imminent step-forward in Scene 2", "props": "Circuit-etched tower facades with embedded LED veins flickering erratically, overflowing industrial gutters spewing iridescent chemical runoff, wind-scattered debris hinting at skirmish aftermath", "action_sequence": [ {"0-1s": "High hover frames smog-piercing spires, rain droplets streak lens in slow-mo refraction"}, {"1-2s": "Descent accelerates, haze thins to reveal neon-veined edges glowing faintly blue"}, {"2-3s": "Tilt reveals grid below, rooftops hammered in static-burst impacts syncing to score pulse"}, {"3-4s": "Forward push threads alley corridors, Mandarin signs initial flicker priming Scene 7"}, {"4-5s": "Pedestrians sharpen as wireframe ghosts, AR visors glinting obliviously"}, {"5-6s": "Level to ground haze, Nova's trench silhouette materializes at frame's vanishing point, coat billow lingering into Scene 2 track"} ] }, "cinematography": { "lighting": "Desaturated neon primaries with volumetric god rays slicing haze for ethereal isolation, rain speculars adding dynamic highlights consistent across wet surfaces", "tone": "Oppressive immersion yielding to rebellious spark—global cyber-noir dread laced with glitch anticipation, flowing seamlessly to Nova's personal emergence" } }, "scene_2": { "global_cinematography": "Ultra-realistic Hollywood cyberpunk thriller in the vein of The Matrix (1999) and Blade Runner 2049 (2017), shot on Arri Alexa LF with anamorphic lenses for widescreen 2.39:1 aspect ratio, 24fps for fluid motion, desaturated palette dominated by cool blues, greens, and high-contrast neon reds piercing perpetual smog-choked night. Consistent VFX pipeline: Procedural green code cascades, photorealistic cybernetic augmentations with subsurface scattering, physics-based rain and particle simulations. Lighting paradigm: Volumetric god rays through haze, practical lens flares from holograms, rim lighting on metallic surfaces for depth. Sound integration: Pulsing industrial synth score with digital glitches, rain patter syncing to code interference, metallic echoes underscoring dialogue. Transitions: Seamless glitch wipes or matrix symbol dissolves ensuring narrative continuity, each scene's final beat priming the next for unbroken tension flow. Continuity directive: Scenes chain via lingering elements—rain droplets from prior shots persisting, Nova's silhouette echoing across cuts, HUD overlays threading flashbacks to present, escalating glitch distortions building to climax rupture—maintaining spatial and temporal cohesion in Neo-Tokyo's underbelly.", "shot": { "composition": "Low-angle tracking push with 50mm anamorphic prime on Arri Alexa LF, heroic distortion compressing background alley into claustrophobic funnel, foreground rain blur veiling initial fog for continuity from Scene 1 descent", "camera_motion": "Fluid forward Steadicam arc from lingering Scene 1 haze, subtle left profile tilt to frame Nova against graffiti wall, pulling back slightly to hold environmental depth into Scene 3 orbit" }, "subject": { "description": "Nova, 30s hybrid rebel with scarred synthetic pallor, cropped black hair rain-matted, holographic irises scanning with latent data flickers; sleek titanium limbs rune-etched in dormant blue", "wardrobe": "Sodden black trench coat with frayed hems from Scene 1 debris scatter, high collar shadowing jawline for motif continuity" }, "scene": { "location": "Graffiti-choked alley continuation from Scene 1 street convergence, Neo-Tokyo underbelly", "time_of_day": "Eternal neon-dusk syncing global palette", "environment": "Fog banks rolling from industrial vents as Scene 1 smog extension, wet cobblestones rippling with residual aerial rain patterns" }, "visual_details": { "action": "Nova materializes from Scene 1's terminal haze, striding assertively into sodium glow with metallic glint, coat hem dragging puddles to splash forward—teasing Scene 3 facial trace", "props": "Luminescent 'GLITCH THE SYSTEM' graffiti echoing from Scene 1 signs, overhead hover-traffic hum persisting from aerial hum", "action_sequence": [ {"0-1s": "Fog swirl from Scene 1 yields Nova's silhouette, boot first impacting puddle"}, {"1-2s": "Full stride forward, coat hem trails iridescent wake linking to blood drip in Scene 9"}, {"2-3s": "Titanium forearm catches neon, runes sequential-pulse awakening blue continuity"}, {"3-4s": "Holographic eyes iris-scan, reflecting alley code fragments priming Scene 4 overlay"}, {"4-5s": "Rain beads contour synthetic skin, parting at seams for Scene 3 macro journey"}, {"5-6s": "Profile lean against wall, vapor breath hangs, posture straightening into Scene 5 OTS"} ] }, "cinematography": { "lighting": "Harsh sodium sidelight rimming form per global motif, cool rune fill softening human remnants, prismatic rain refractions tying to Scene 1 aerial streaks", "tone": "Defiant grace in simulated decay—cyber-noir intimacy building personal stakes, camera arc ensuring spatial flow to close-up revelation" } }, etc etc etc up to scene 16. you got the point

NFK

3,351,561 次观看 • 9 个月前

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub
1:02

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub

Davide Scaramuzza

12,028 次观看 • 5 个月前

‼️🙏DOUBLE SCRIPT ➕ THE MANIPULATION BEHIND THE DEATH "MY JOURNEY OF SELF INVESTIGATION"🌸 💁‍♀️Right in the first post of "RED STAR NEWS", it is clear that the murder was staged - Papaprazzi reporter of "RED STAR NEWS" (No 2 time fall but 2 scenes in a staging.) 🙏➕➕ (I'm not sure whether the account "Red Star News" works for capitalists, the state, the party, or if they are truly just reporting news. A witness walking their dog and found, it was not correct. The first person to discover it was a janitor. And this issue is still in doubt.) ❗️The video and photo released on September 11 include: ( Happened and filmed after.) 1. A shadow inside YML's room, which was just the police investigating the scene + the iron window fully opened (a drunk person could not have opened that iron grille, it's very difficult to open). 2.Analysis (the photo of clothes - blue T-shirt, white long pants) 👕👖 - On September 11, reporters captured the scene of (1) blue T-shirt and white pants hanging out to dry, which matches the photo circulating online showing YML after the "second fall" and passing away. - Therefore, my observation is that the person lying there is indeed Yu Menglong (in my view, he died during torture, there was no suicide, he only died after the surgery to remove the USB. This is the first staged scene). (❗️The rumor they released in writing is true. The plan involved two time killings, "two time falls from the building", so indeed it happened twice.) Like two falls, two tortures, two stagings. The same thing, this is how the perpetrators want us to be messed up.) - Then, on September 11, there was a photo of (2) digging up the grass on the ground. Very strange. Previously said he was tortured, thrown there and still breathing. (That's a rumor.) ‼️👉Therefore, conversely, this is actually the final crime scene when they (the police) secretly took Yu Menglong away. - He was tortured to retrieve the USB, and the staged suicide was planned twice, which confused the paparazzi between real and fake. - He was murdered after brutal torture and thrown onto the "dug-up ground", with the staff there doing it to fabricate a fake crime scene. 👉 And based on reasoning about the final sequence of events: (unprocessed crime scene) 1. They placed his body below the 5th floor and claimed it was a suicide. It was a staged scene. The police must have taken photos at the actual crime scene. There will always be an original, untouched crime scene. 2. He was lying on the dug-up ground and secretly taken away by the police, after which the police concluded it was a fall, a slip, or an accident… without any video recording. 👉 the possibility of number 2 is higher, because they conclude quickly. When Yu Menglong's body is no longer intact. While at the crime scene, there were also two characters sitting near the police, Jin Quang and Xin Qi or (Cheng) ‼️️ 👉This is definitely a murder attempt in which the police are the ones who hide the crime. If you want the community to stop analyzing and speculating, show us the conclusion of the case. There is no infor autopsy, what else? (✅) THANK YOU "RED STAR NEWS"🙂 Note: Analysis based on the video of red star news on September 11 at the scene. Note: Refer to the timeline below. 👇 ❗️In short: I mean that some things have been messed up by some, but in general it still follows a certain direction and a result. - Torture - take usb - murder -> Staging, staging 2 times (pull the body to the scene) 👉 Anyway, it still only comes to one result. ( Analysis of : 미나문🌙 🇰🇷🇹🇼 Remember to write the source when take it out ) #于朦朧墜樓事件 #YuMenglongFallingEvent #justiceforyumenglong #于朦胧坠楼 #YuMenglong #于朦胧 #AlanYu #YuMenglong #JusticeforYuMenglong #YuMenglong于朦胧 #于朦胧
1:10

‼️🙏DOUBLE SCRIPT ➕ THE MANIPULATION BEHIND THE DEATH "MY JOURNEY OF SELF INVESTIGATION"🌸 💁‍♀️Right in the first post of "RED STAR NEWS", it is clear that the murder was staged - Papaprazzi reporter of "RED STAR NEWS" (No 2 time fall but 2 scenes in a staging.) 🙏➕➕ (I'm not sure whether the account "Red Star News" works for capitalists, the state, the party, or if they are truly just reporting news. A witness walking their dog and found, it was not correct. The first person to discover it was a janitor. And this issue is still in doubt.) ❗️The video and photo released on September 11 include: ( Happened and filmed after.) 1. A shadow inside YML's room, which was just the police investigating the scene + the iron window fully opened (a drunk person could not have opened that iron grille, it's very difficult to open). 2.Analysis (the photo of clothes - blue T-shirt, white long pants) 👕👖 - On September 11, reporters captured the scene of (1) blue T-shirt and white pants hanging out to dry, which matches the photo circulating online showing YML after the "second fall" and passing away. - Therefore, my observation is that the person lying there is indeed Yu Menglong (in my view, he died during torture, there was no suicide, he only died after the surgery to remove the USB. This is the first staged scene). (❗️The rumor they released in writing is true. The plan involved two time killings, "two time falls from the building", so indeed it happened twice.) Like two falls, two tortures, two stagings. The same thing, this is how the perpetrators want us to be messed up.) - Then, on September 11, there was a photo of (2) digging up the grass on the ground. Very strange. Previously said he was tortured, thrown there and still breathing. (That's a rumor.) ‼️👉Therefore, conversely, this is actually the final crime scene when they (the police) secretly took Yu Menglong away. - He was tortured to retrieve the USB, and the staged suicide was planned twice, which confused the paparazzi between real and fake. - He was murdered after brutal torture and thrown onto the "dug-up ground", with the staff there doing it to fabricate a fake crime scene. 👉 And based on reasoning about the final sequence of events: (unprocessed crime scene) 1. They placed his body below the 5th floor and claimed it was a suicide. It was a staged scene. The police must have taken photos at the actual crime scene. There will always be an original, untouched crime scene. 2. He was lying on the dug-up ground and secretly taken away by the police, after which the police concluded it was a fall, a slip, or an accident… without any video recording. 👉 the possibility of number 2 is higher, because they conclude quickly. When Yu Menglong's body is no longer intact. While at the crime scene, there were also two characters sitting near the police, Jin Quang and Xin Qi or (Cheng) ‼️️ 👉This is definitely a murder attempt in which the police are the ones who hide the crime. If you want the community to stop analyzing and speculating, show us the conclusion of the case. There is no infor autopsy, what else? (✅) THANK YOU "RED STAR NEWS"🙂 Note: Analysis based on the video of red star news on September 11 at the scene. Note: Refer to the timeline below. 👇 ❗️In short: I mean that some things have been messed up by some, but in general it still follows a certain direction and a result. - Torture - take usb - murder -> Staging, staging 2 times (pull the body to the scene) 👉 Anyway, it still only comes to one result. ( Analysis of : 미나문🌙 🇰🇷🇹🇼 Remember to write the source when take it out ) #于朦朧墜樓事件 #YuMenglongFallingEvent #justiceforyumenglong #于朦胧坠楼 #YuMenglong #于朦胧 #AlanYu #YuMenglong #JusticeforYuMenglong #YuMenglong于朦胧 #于朦胧

미나문🌙 🇰🇷🇹🇼

129,573 次观看 • 11 个月前

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.
0:30

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,843 次观看 • 3 个月前

CSS Tip! 🎠 You can create a responsive infinite marquee animation with container queries and no duplicate items 🤙 li{ animation: slide; } @​keyframes slide { to { translate: 0% calc(var(--i) * -100%);}} The trick is animating the items, not the list 😎 More tricks 👇 To get this one working, you need to animate the items and not the list (Watch the video first?). Each item needs to know its row index (--i) in the list and the parent needs to know how many rows are in the list: ul { --count: 12; } li:nth-of-type(1), li:nth-of-type(2) { --i: 0; } li:nth-of-type(3), li:nth-of-type(4) { --i: 1; } Once you have that, translate each item based on its row index in the list li { translate: 0% calc((var(--count) - var(--i)) * 100%); } Now for the animation. The key here is that each row has an animation-delay calculated from its index (--i). That number is offset to make it negative so the animation start is offset ✨ ul { --duration: 10s; } li { --delay: calc((var(--duration) / var(--count)) * (var(--i) - 8)); animation: slide var(--duration) var(--delay) infinite linear; } Make sure to wrap that animation in: @​media (prefers-reduced-motion: no-preference) { ... } Lastly, the fun parts! 🤓 To create the "vignette" mask. Use a layered mask on the container 😷 .scene { --buff: 3rem; height: 100%; width: 100%; mask: linear-gradient(transparent, white var(--buff) calc(100% - var(--buff)), transparent), linear-gradient(90deg, transparent, white var(--buff) calc(100% - var(--buff)), transparent); mask-composite: intersect; } To create the 3D skewed effect, use a chained transform (Try toggling it in the demo ⚡️): .grid { transform: rotateX(20deg) rotateZ(-20deg) skewX(20deg); } As for the responsive part, use container queries! 🔥 article { container-type: inline-size; } When the article (card) is narrower than 400px update the grid and animation settings 🤙 Double the rows means double the duration! @​container (width < 400px) { .grid { --count: 12; grid-template-columns: 1fr; } li:nth-of-type(1) { --i: 0; } li:nth-of-type(2) { --i: 1; } li:nth-of-type(3) { --i: 2; } li:nth-of-type(4) { --i: 3; } li { --duration: 20s; } } CSS has the magic to be able to update those animations at runtime based on your custom property values 😎 An added bonus in this demo is that it doesn't require any JavaScript at all, for any of it 🤯 We can use CSS :has() for those toggles that update the styles, even the theme toggle! 🫶 Any questions, let me know! Make sure to check out the video. Will do a walkthrough one to follow-up 🤙 CodePen.IO link below! 👇
1:04

CSS Tip! 🎠 You can create a responsive infinite marquee animation with container queries and no duplicate items 🤙 li{ animation: slide; } @​keyframes slide { to { translate: 0% calc(var(--i) * -100%);}} The trick is animating the items, not the list 😎 More tricks 👇 To get this one working, you need to animate the items and not the list (Watch the video first?). Each item needs to know its row index (--i) in the list and the parent needs to know how many rows are in the list: ul { --count: 12; } li:nth-of-type(1), li:nth-of-type(2) { --i: 0; } li:nth-of-type(3), li:nth-of-type(4) { --i: 1; } Once you have that, translate each item based on its row index in the list li { translate: 0% calc((var(--count) - var(--i)) * 100%); } Now for the animation. The key here is that each row has an animation-delay calculated from its index (--i). That number is offset to make it negative so the animation start is offset ✨ ul { --duration: 10s; } li { --delay: calc((var(--duration) / var(--count)) * (var(--i) - 8)); animation: slide var(--duration) var(--delay) infinite linear; } Make sure to wrap that animation in: @​media (prefers-reduced-motion: no-preference) { ... } Lastly, the fun parts! 🤓 To create the "vignette" mask. Use a layered mask on the container 😷 .scene { --buff: 3rem; height: 100%; width: 100%; mask: linear-gradient(transparent, white var(--buff) calc(100% - var(--buff)), transparent), linear-gradient(90deg, transparent, white var(--buff) calc(100% - var(--buff)), transparent); mask-composite: intersect; } To create the 3D skewed effect, use a chained transform (Try toggling it in the demo ⚡️): .grid { transform: rotateX(20deg) rotateZ(-20deg) skewX(20deg); } As for the responsive part, use container queries! 🔥 article { container-type: inline-size; } When the article (card) is narrower than 400px update the grid and animation settings 🤙 Double the rows means double the duration! @​container (width < 400px) { .grid { --count: 12; grid-template-columns: 1fr; } li:nth-of-type(1) { --i: 0; } li:nth-of-type(2) { --i: 1; } li:nth-of-type(3) { --i: 2; } li:nth-of-type(4) { --i: 3; } li { --duration: 20s; } } CSS has the magic to be able to update those animations at runtime based on your custom property values 😎 An added bonus in this demo is that it doesn't require any JavaScript at all, for any of it 🤯 We can use CSS :has() for those toggles that update the styles, even the theme toggle! 🫶 Any questions, let me know! Make sure to check out the video. Will do a walkthrough one to follow-up 🤙 CodePen.IO link below! 👇

jhey ʕ•ᴥ•ʔ

542,234 次观看 • 2 年前

If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod
0:23

If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod

Alok

31,765 次观看 • 1 个月前

This is a shocking reminder of why you can never let your guard down, even when dealing with retail employees at a major service provider. ​In a disturbing breach of trust, a Metro by T-Mobile store associate took advantage of a customer who was simply trying to upgrade a phone. While pretending to assist with the device and transfer data, the worker took the customer’s unlocked phone and secretly opened their Cash App. ​The employee successfully transferred $400 directly to her own account. Greedy for more, she immediately tried to drain another $500. Fortunately, the bank flagged the second back-to-back transaction as highly suspicious and denied the transfer, stopping the theft from escalating. Police were called to the scene, and bodycam footage captured the investigation that ultimately exposed the employee’s pattern of predatory behavior. ​This incident serves as a massive wake-up call for everyone. Your phone is your digital wallet, and handing it over to anyone—even a retail worker in uniform—comes with severe risks. ​To protect your money and your identity, always follow these critical security steps: ​1. Enable a security lock (Face ID, fingerprint, or a strict PIN) explicitly for all financial apps like Cash App, Venmo, and mobile banking. Even if your phone is unlocked, the apps themselves should require a separate login. 2. Never give your phone’s master passcode to a retail employee. They do not need it to activate a device. 3. Log out of your financial and password-manager apps before handing your phone over for tech support. 4. Watch the employee like a hawk. If they need to take your phone to a back room, tell them you prefer the work be done right in front of you. ​Don't let a routine errand turn into a financial nightmare. Stay vigilant and lock your data.
7:52

This is a shocking reminder of why you can never let your guard down, even when dealing with retail employees at a major service provider. ​In a disturbing breach of trust, a Metro by T-Mobile store associate took advantage of a customer who was simply trying to upgrade a phone. While pretending to assist with the device and transfer data, the worker took the customer’s unlocked phone and secretly opened their Cash App. ​The employee successfully transferred $400 directly to her own account. Greedy for more, she immediately tried to drain another $500. Fortunately, the bank flagged the second back-to-back transaction as highly suspicious and denied the transfer, stopping the theft from escalating. Police were called to the scene, and bodycam footage captured the investigation that ultimately exposed the employee’s pattern of predatory behavior. ​This incident serves as a massive wake-up call for everyone. Your phone is your digital wallet, and handing it over to anyone—even a retail worker in uniform—comes with severe risks. ​To protect your money and your identity, always follow these critical security steps: ​1. Enable a security lock (Face ID, fingerprint, or a strict PIN) explicitly for all financial apps like Cash App, Venmo, and mobile banking. Even if your phone is unlocked, the apps themselves should require a separate login. 2. Never give your phone’s master passcode to a retail employee. They do not need it to activate a device. 3. Log out of your financial and password-manager apps before handing your phone over for tech support. 4. Watch the employee like a hawk. If they need to take your phone to a back room, tell them you prefer the work be done right in front of you. ​Don't let a routine errand turn into a financial nightmare. Stay vigilant and lock your data.

✨️Serenitee♡Sam✨️

448,160 次观看 • 3 个月前

Made this cinematic sequence using Seedance 2.5 The realism, camera movements, water physics, explosions, and intense atmosphere are on another level. AI video generation is getting seriously cinematic. Prompt ⤵️ Create a 30-second ultra-realistic cinematic disaster sequence with a dark, intense, Hollywood-blockbuster atmosphere. Main character: A young woman with short, messy dark hair, wearing a soaked white T-shirt and light-colored shorts. Keep her appearance consistent throughout the entire video. Her face should remain realistic and expressive, showing fear, shock, exhaustion, and determination. Scene 1 — 0–5 seconds: Open with an extreme close-up of the woman running toward the camera through a dark industrial shipping yard at night. Heavy rain is falling, her hair and clothes are completely wet, and she is breathing heavily. Behind her, bright industrial lights glow through thick smoke and mist. The camera moves backward smoothly while maintaining focus on her face. Add realistic rain droplets on the camera lens, dramatic backlighting, atmospheric fog, and handheld cinematic movement. Scene 2 — 5–10 seconds: Cut to a wider shot as she runs through the flooded container yard. Large shipping containers surround her, emergency lights flash in the distance, and explosions/fire erupt behind her. She looks over her shoulder in panic while continuing to run. Water splashes dramatically around her legs with every step. Use realistic fire, smoke, debris, rain, and volumetric lighting. Scene 3 — 10–15 seconds: She suddenly loses her balance and falls into the flooded ground. Show the impact in slow motion for a moment, with water splashing around her. She quickly pushes herself back up while terrified people run in the background. A massive wave of smoke, debris, and water moves through the shipping yard behind them. Use a low-angle camera close to the ground for a powerful disaster-movie perspective. Scene 4 — 15–21 seconds: She gets back on her feet and starts sprinting toward safety. The camera tracks alongside her at high speed. Containers shake, debris flies through the air, vehicles and objects are pushed around by the powerful force behind her. Keep her face and body consistent. Alternate between close-ups of her frightened expression and wide shots showing the enormous scale of destruction. Scene 5 — 21–25 seconds: She reaches a large modern building filled with terrified people. The camera follows her inside as everyone rushes toward safety. People are falling, crawling, and helping each other while water and debris can be seen outside through the entrance. The lighting changes from cold blue-gray exterior lighting to dramatic warm interior lighting. Scene 6 — 25–30 seconds: Suddenly transition to a massive luxury yacht in the middle of a violent ocean. Huge dark waves surround the vessel under a stormy sky. The camera starts behind the yacht and slowly reveals an enormous shark-like sea creature emerging from the ocean directly behind it, creating a terrifying final reveal. The creature rises through the waves with water cascading from its body. End with a gigantic wave crashing toward the yacht. Visual style: photorealistic Hollywood disaster film, cinematic color grading, realistic skin texture, physically accurate water and rain, volumetric fog, dramatic practical lighting, realistic fire and smoke, detailed environments, natural motion blur, shallow depth of field, dynamic camera movement, high contrast, atmospheric storm clouds, extremely detailed CGI, 4K cinematic quality. Camera: mixture of handheld close-ups, smooth tracking shots, wide establishing shots, low-angle disaster shots, slow-motion impact moments, and dramatic aerial/wide shots. Sound design: heavy rainfall, thunder, distant explosions, sirens, screaming crowds, footsteps splashing through water, crashing metal, deep cinematic bass, roaring waves, and an intense rising orchestral score that builds toward the final creature reveal.
0:30

Made this cinematic sequence using Seedance 2.5 The realism, camera movements, water physics, explosions, and intense atmosphere are on another level. AI video generation is getting seriously cinematic. Prompt ⤵️ Create a 30-second ultra-realistic cinematic disaster sequence with a dark, intense, Hollywood-blockbuster atmosphere. Main character: A young woman with short, messy dark hair, wearing a soaked white T-shirt and light-colored shorts. Keep her appearance consistent throughout the entire video. Her face should remain realistic and expressive, showing fear, shock, exhaustion, and determination. Scene 1 — 0–5 seconds: Open with an extreme close-up of the woman running toward the camera through a dark industrial shipping yard at night. Heavy rain is falling, her hair and clothes are completely wet, and she is breathing heavily. Behind her, bright industrial lights glow through thick smoke and mist. The camera moves backward smoothly while maintaining focus on her face. Add realistic rain droplets on the camera lens, dramatic backlighting, atmospheric fog, and handheld cinematic movement. Scene 2 — 5–10 seconds: Cut to a wider shot as she runs through the flooded container yard. Large shipping containers surround her, emergency lights flash in the distance, and explosions/fire erupt behind her. She looks over her shoulder in panic while continuing to run. Water splashes dramatically around her legs with every step. Use realistic fire, smoke, debris, rain, and volumetric lighting. Scene 3 — 10–15 seconds: She suddenly loses her balance and falls into the flooded ground. Show the impact in slow motion for a moment, with water splashing around her. She quickly pushes herself back up while terrified people run in the background. A massive wave of smoke, debris, and water moves through the shipping yard behind them. Use a low-angle camera close to the ground for a powerful disaster-movie perspective. Scene 4 — 15–21 seconds: She gets back on her feet and starts sprinting toward safety. The camera tracks alongside her at high speed. Containers shake, debris flies through the air, vehicles and objects are pushed around by the powerful force behind her. Keep her face and body consistent. Alternate between close-ups of her frightened expression and wide shots showing the enormous scale of destruction. Scene 5 — 21–25 seconds: She reaches a large modern building filled with terrified people. The camera follows her inside as everyone rushes toward safety. People are falling, crawling, and helping each other while water and debris can be seen outside through the entrance. The lighting changes from cold blue-gray exterior lighting to dramatic warm interior lighting. Scene 6 — 25–30 seconds: Suddenly transition to a massive luxury yacht in the middle of a violent ocean. Huge dark waves surround the vessel under a stormy sky. The camera starts behind the yacht and slowly reveals an enormous shark-like sea creature emerging from the ocean directly behind it, creating a terrifying final reveal. The creature rises through the waves with water cascading from its body. End with a gigantic wave crashing toward the yacht. Visual style: photorealistic Hollywood disaster film, cinematic color grading, realistic skin texture, physically accurate water and rain, volumetric fog, dramatic practical lighting, realistic fire and smoke, detailed environments, natural motion blur, shallow depth of field, dynamic camera movement, high contrast, atmospheric storm clouds, extremely detailed CGI, 4K cinematic quality. Camera: mixture of handheld close-ups, smooth tracking shots, wide establishing shots, low-angle disaster shots, slow-motion impact moments, and dramatic aerial/wide shots. Sound design: heavy rainfall, thunder, distant explosions, sirens, screaming crowds, footsteps splashing through water, crashing metal, deep cinematic bass, roaring waves, and an intense rising orchestral score that builds toward the final creature reveal.

Noor 🌸

22,287 次观看 • 9 天前

I'm going to show you a really strange scene and provide tons of context that won't help you at all. -They're the only 2 on Earth from another dimension -Neither knows they are -The cop is running a psyop -Other people are running a psyop on the cop -The other already killed himself -Their memories are sort of not right -They now live in a totalitarian state with constant surveillance -The cop has more power than he currently knows. -The man who silently rules the reality is owner of an electric car company called "Treer" which starts with a T and has 5 letters...and who invented built a perpetual motion machine with limitless energy that runs his electric devices remotely from the ocean controlled by him alone. They call him Baron. He also built an unusual flying luxury vehicle called the mega zeppelin that can hold an absurd amount of elites which runs off his energy and frequently fines with politicians who give him whatever he wants and let him control world events, even though he often undermines them anyway. World leaders submit to him because they need him after using all of their own energy resources for wars, the last of which was WW3 where America wiped out Iran, Syria, Afghanistan, and North Korea in retaliation for a nuclear attack in Texas which America was told was "terrorism" by a coalition of those 4 countries but was really by persons unknown... but is implied that it was a false flag to manipulate future events which is definitely was. Movie from 2006 Intriguing isn't it?
2:50

I'm going to show you a really strange scene and provide tons of context that won't help you at all. -They're the only 2 on Earth from another dimension -Neither knows they are -The cop is running a psyop -Other people are running a psyop on the cop -The other already killed himself -Their memories are sort of not right -They now live in a totalitarian state with constant surveillance -The cop has more power than he currently knows. -The man who silently rules the reality is owner of an electric car company called "Treer" which starts with a T and has 5 letters...and who invented built a perpetual motion machine with limitless energy that runs his electric devices remotely from the ocean controlled by him alone. They call him Baron. He also built an unusual flying luxury vehicle called the mega zeppelin that can hold an absurd amount of elites which runs off his energy and frequently fines with politicians who give him whatever he wants and let him control world events, even though he often undermines them anyway. World leaders submit to him because they need him after using all of their own energy resources for wars, the last of which was WW3 where America wiped out Iran, Syria, Afghanistan, and North Korea in retaliation for a nuclear attack in Texas which America was told was "terrorism" by a coalition of those 4 countries but was really by persons unknown... but is implied that it was a false flag to manipulate future events which is definitely was. Movie from 2006 Intriguing isn't it?

Invisidon

12,267 次观看 • 6 个月前

The OncoAlert WEEKLY RoundUp 🚨 Covering the TOP of the week Sept 19-25, 2025 REGISTER at OR Discussing: ✅evERA Update : Oral SERD giredestrant + everolimus improves PFS in ER+/HER2– advanced Breast #Cancer ✅FDA Approval🇺🇸SC pembrolizumab approved for solid tumor indications. ✅PCS-9: Adding SBRT to ADT + enzalutamide doubles rPFS in oligometastatic CR #ProstateCancer ✅ARON-3 : ^177Lu-PSMA outperforms cabazitaxel w/ higher responses, longer survival & similar safety in PC ✅ #PancreaticCancer Review: Advances in early detection, neoadjuvant therapy, and emerging treatments aim to overcome resistance. ✅SBRT☢️offers high local control, bridge-to-transplant use, and synergy with immunotherapy in #HepatocellularCarcinoma ✅NSCLC 50-Year Review🫁 From no options to molecular profiling, screening, and targeted therapies ✅COMPEL : Osimertinib + chemo improves PFS & OS beyond progression🆚chemo alone in🧬EGFR+ #NSCLC ✅ Platinum chemo with PD-(L)1 increases resistance: CTLA-4 adds no benefit. Non Small Cell #LungCancer ✅ in early #BreastCancer HR+/HER2– patients gain recurrence benefit from anthracyclines + taxane chemo. ✅SAFE Trial ❤️Cardioprotective therapy preserves LV & RV function during anthracycline-based chemo. ✅Genomic HRD-based #OvarianCancer model predicts PARPi response better than current assessments ✅Proton craniospinal irradiation☢️improves CNS-PFS & OS🆚 photon IFRT in leptomeningeal metastasis🧠 Dr Rishabh Jain Komal Jhaveri, MD, FACP, FASCO Stephanie A. Haddad, DO Sarah Premji, MD Sergio Cifuentes Sherene Loi, MD Naoto T Ueno, MD, PhD Double Whammy Elvina Almuradova Abi Siva MD Rita Nanda Icro Meattini iacopo olivotto レ点🧬💉💊 Carlotta Becherini Viola Salvestrini Dr. Tamim Niazi Andrew Feifer MD MPH Sean Collins Prostate SBRT @ USF Health Álvaro Pinto Gustavo Thomas Zilli Fabio Cury Andrew Farach, MD Eleni Efstathiou Yüksel Ürün Maite Bourlon Fernando Sabino, MD, PhD Francesco Massari Daniel Castellano Nieves Martinez Lago MD PhD zuleyha akgun Aakash Desai, MD, MPH, FASCO Stephen V Liu, MD Diego A. Díaz-García Dr. Estela Rodriguez Dr. Antonio Calles 🫁🚭 ALFONSO DUEÑAS GONZALEZ Dr B.S.Bhati BSB 🇮🇳 OsmanKostekMD Roberto Ferrara Giannis Mountzios Niccolò Giaj Levra よし兄 Yoshi Mustafa Özdoğan, MD Dr Sarah Sammons Alex Shteynshlyuger MD Dario Trapani Matteo Lambertini, MD PhD
4:57

The OncoAlert WEEKLY RoundUp 🚨 Covering the TOP of the week Sept 19-25, 2025 REGISTER at OR Discussing: ✅evERA Update : Oral SERD giredestrant + everolimus improves PFS in ER+/HER2– advanced Breast #Cancer ✅FDA Approval🇺🇸SC pembrolizumab approved for solid tumor indications. ✅PCS-9: Adding SBRT to ADT + enzalutamide doubles rPFS in oligometastatic CR #ProstateCancer ✅ARON-3 : ^177Lu-PSMA outperforms cabazitaxel w/ higher responses, longer survival & similar safety in PC ✅ #PancreaticCancer Review: Advances in early detection, neoadjuvant therapy, and emerging treatments aim to overcome resistance. ✅SBRT☢️offers high local control, bridge-to-transplant use, and synergy with immunotherapy in #HepatocellularCarcinoma ✅NSCLC 50-Year Review🫁 From no options to molecular profiling, screening, and targeted therapies ✅COMPEL : Osimertinib + chemo improves PFS & OS beyond progression🆚chemo alone in🧬EGFR+ #NSCLC ✅ Platinum chemo with PD-(L)1 increases resistance: CTLA-4 adds no benefit. Non Small Cell #LungCancer ✅ in early #BreastCancer HR+/HER2– patients gain recurrence benefit from anthracyclines + taxane chemo. ✅SAFE Trial ❤️Cardioprotective therapy preserves LV & RV function during anthracycline-based chemo. ✅Genomic HRD-based #OvarianCancer model predicts PARPi response better than current assessments ✅Proton craniospinal irradiation☢️improves CNS-PFS & OS🆚 photon IFRT in leptomeningeal metastasis🧠 Dr Rishabh Jain Komal Jhaveri, MD, FACP, FASCO Stephanie A. Haddad, DO Sarah Premji, MD Sergio Cifuentes Sherene Loi, MD Naoto T Ueno, MD, PhD Double Whammy Elvina Almuradova Abi Siva MD Rita Nanda Icro Meattini iacopo olivotto レ点🧬💉💊 Carlotta Becherini Viola Salvestrini Dr. Tamim Niazi Andrew Feifer MD MPH Sean Collins Prostate SBRT @ USF Health Álvaro Pinto Gustavo Thomas Zilli Fabio Cury Andrew Farach, MD Eleni Efstathiou Yüksel Ürün Maite Bourlon Fernando Sabino, MD, PhD Francesco Massari Daniel Castellano Nieves Martinez Lago MD PhD zuleyha akgun Aakash Desai, MD, MPH, FASCO Stephen V Liu, MD Diego A. Díaz-García Dr. Estela Rodriguez Dr. Antonio Calles 🫁🚭 ALFONSO DUEÑAS GONZALEZ Dr B.S.Bhati BSB 🇮🇳 OsmanKostekMD Roberto Ferrara Giannis Mountzios Niccolò Giaj Levra よし兄 Yoshi Mustafa Özdoğan, MD Dr Sarah Sammons Alex Shteynshlyuger MD Dario Trapani Matteo Lambertini, MD PhD

OncoAlert

36,952 次观看 • 11 个月前

This newly released body cam footage from the March 2025 DOGE raid on the US Institute of Peace shows the insane resistance Trump faced from day one. As Edward Coristine (aka Big Balls) told Jesse Watters last year, this was the “least peaceful” of all the agencies to deal with, and that it used taxpayer funds for private jets, an armory filled with weapons, and contracts with the former members of the Taliban (!!) These are grown adults in full meltdown mode because they realized the gravy train was coming to an end. They referred to lawful orders as "jackbooted police state actions" while refusing to vacate the premises after President Trump issued an order firing them and sending in his DOGE team to clean house. What you see in this video isn’t democracy, it’s an insight into how wild and entitled the DC swamp had grown. Even the officers who were on the scene to ensure order became impatient after realizing these defiant bureaucrats weren't even USIP employees when one said, "we're their lawyers" who were trying to regain access to "classified information" pertaining to "other cases" outside of USIP’s purview. George Foote, one of the lawyers there, even pushed to have Trump officials arrested as "unauthorized intruders." Elon Musk later revealed on X that USIP's chief accountant attempted to wipe over a terabyte of financial data in a desperate cover-up - but DOGE recovered it, and it's now with the DOJ for review. Unelected ideologues fleecing taxpayer dollars shouldn't block the people's choice. This is what draining the swamp looks like, and it’s horrifying just how out of control it had all become. H/T: Footage FOIA’d by Marisa Kabas at The Handbasket Co.
5:32

This newly released body cam footage from the March 2025 DOGE raid on the US Institute of Peace shows the insane resistance Trump faced from day one. As Edward Coristine (aka Big Balls) told Jesse Watters last year, this was the “least peaceful” of all the agencies to deal with, and that it used taxpayer funds for private jets, an armory filled with weapons, and contracts with the former members of the Taliban (!!) These are grown adults in full meltdown mode because they realized the gravy train was coming to an end. They referred to lawful orders as "jackbooted police state actions" while refusing to vacate the premises after President Trump issued an order firing them and sending in his DOGE team to clean house. What you see in this video isn’t democracy, it’s an insight into how wild and entitled the DC swamp had grown. Even the officers who were on the scene to ensure order became impatient after realizing these defiant bureaucrats weren't even USIP employees when one said, "we're their lawyers" who were trying to regain access to "classified information" pertaining to "other cases" outside of USIP’s purview. George Foote, one of the lawyers there, even pushed to have Trump officials arrested as "unauthorized intruders." Elon Musk later revealed on X that USIP's chief accountant attempted to wipe over a terabyte of financial data in a desperate cover-up - but DOGE recovered it, and it's now with the DOJ for review. Unelected ideologues fleecing taxpayer dollars shouldn't block the people's choice. This is what draining the swamp looks like, and it’s horrifying just how out of control it had all become. H/T: Footage FOIA’d by Marisa Kabas at The Handbasket Co.

Andrew Kolvet

1,531,376 次观看 • 5 个月前

BONGINO: “This is Jesse Ventura, former wrestler, former governor of Minnesota. Many of you know who he is… He's a, was a big cultural figure, went into politics later. Jesse Ventura went on Piers Morgan Show, which has become like a, I don't know what is a cesspool for lunatics… He goes on the show and this is what happens when you bathe in chaos and you never produce actual data. You get moments like this where Jesse Ventura says publicly on Piers Show, he doesn't believe the assassination attempt and murder scene in Butler, Pennsylvania with President Trump and sadly, Cory Comparatore. He doesn't believe it happened! His family wants Comperatore remembered as more than the person killed in the assassination attempt of the former president. Two others were shot during the rally for former President Trump back on July 13th. James Copenhaver and David Dutch, both of them are still recovering from their gunshot wounds. I thought, I thought it was a hoax, Jesse. I thought it was a hoax. You think the other victim, President Donald Trump, feels the same way? Having to think every day if he had just turned his head one more degree to the right or probably even left, he would have been shot either directly in the face or in the cranium. Do you ever, you ever ask them if they feel that way? …I've been doing an enormous amount of reading secondary to the show on how this type of embrace and love affair with chaos amongst a group of basically cultists, how it metastasizes into larger and much more horrible things. And I've said to you from my first day back, we're not going to be any part of it here. We're going to do everything we can to make sure that this cancer is cut out.” H/T: MAZE Dan Bongino Piers Morgan
3:45

BONGINO: “This is Jesse Ventura, former wrestler, former governor of Minnesota. Many of you know who he is… He's a, was a big cultural figure, went into politics later. Jesse Ventura went on Piers Morgan Show, which has become like a, I don't know what is a cesspool for lunatics… He goes on the show and this is what happens when you bathe in chaos and you never produce actual data. You get moments like this where Jesse Ventura says publicly on Piers Show, he doesn't believe the assassination attempt and murder scene in Butler, Pennsylvania with President Trump and sadly, Cory Comparatore. He doesn't believe it happened! His family wants Comperatore remembered as more than the person killed in the assassination attempt of the former president. Two others were shot during the rally for former President Trump back on July 13th. James Copenhaver and David Dutch, both of them are still recovering from their gunshot wounds. I thought, I thought it was a hoax, Jesse. I thought it was a hoax. You think the other victim, President Donald Trump, feels the same way? Having to think every day if he had just turned his head one more degree to the right or probably even left, he would have been shot either directly in the face or in the cranium. Do you ever, you ever ask them if they feel that way? …I've been doing an enormous amount of reading secondary to the show on how this type of embrace and love affair with chaos amongst a group of basically cultists, how it metastasizes into larger and much more horrible things. And I've said to you from my first day back, we're not going to be any part of it here. We're going to do everything we can to make sure that this cancer is cut out.” H/T: MAZE Dan Bongino Piers Morgan

MAGA Kitty

343,830 次观看 • 4 个月前

CHARLIE KIRK ASSASSINATION NARRATIVE TAKES ANOTHER HIT! DESTRUCTION OF THE CRIME SCENE WAS NOT PRE-SCHEDULED MAINTENANCE 4 days after Charlie Kirk was assassinated, they ripped up the landscaping & paved it over. We were originally told it was just a pre-scheduled project proceeding as planned. I've said repeatedly in Twitter spaces since then that it's extremely unusual for work like this to be done on a Sunday in Utah County—where even gas stations are closed, gas pumps are turned off and vending machines have been known to be deactivated! It's the heart of LDS/Mormon country and they take the sabbath very seriously. *Scheduled* maintenence work like this would logically & routinely have begun on a Monday, or some other day of the week. NOT ON SUNDAY. I said that this was an indication that it wasn't pre-planned, but done on the fly, and therefore must probably a cover-up operation. Now, we find out that it indeed WAS done on a Sunday—by a brother who admits he typically doesn't do this work on Sundays! And it wasn't pre-scheduled, but spur-of-the-moment & directly due to the assassination! (video via HustleBitch) I. WAS. RIGHT. But there's more: Google Trends shows that the man who did the work, Daniel Merrell—owner of Hardscape Utah—was searched from israeli IP addresses earlier this summer! And in the exact same time frame that people in DC & israel were searching for "Timpanogas Regional Hospital" (the hospital where Charlie was taken), and Deirde Amaro (the new Utah state medical examiner who performed the autopsy). What?! (h/t: 🇺🇸 The Light Revival💡) Now, to be clear, I'm not accusing Daniel Merrell of anything nefarious. This all doesn't mean he's in on any plot. I'm sure he was just doing what was asked of him, like he said. But don't you find all this very strange and extremely suspicious? The JFK, RFK & MLK crime scenes were all quickly destroyed after those conspiracy/cover-up assassinations as well. You might remember how the FBI hosed down the Thomas Crooks scene last summer immediately after the assassination attempt on Donald Trump. Rapid destruction of a high-profile assassination crime scene is a tell-tale sign of a cover-up. That is the simplest/Occam's Razor explanation here. I'm sure they'll all say they just meant well and wanted to honor Charlie, or maybe they'll say that given the circumstances it wasn't the best choice, in hindsight, to act so soon after. Perhaps they'll say that whoever said it was scheduled maintenance spoke in error. Etc. If we had a real uncompromised FBI & law enforcement, they'd question Daniel & all involved in this debacle and search their records to see if any clues could be found that lead back to the perps. DON'T LET THEM GET AWAY WITH THIS.
1:30

CHARLIE KIRK ASSASSINATION NARRATIVE TAKES ANOTHER HIT! DESTRUCTION OF THE CRIME SCENE WAS NOT PRE-SCHEDULED MAINTENANCE 4 days after Charlie Kirk was assassinated, they ripped up the landscaping & paved it over. We were originally told it was just a pre-scheduled project proceeding as planned. I've said repeatedly in Twitter spaces since then that it's extremely unusual for work like this to be done on a Sunday in Utah County—where even gas stations are closed, gas pumps are turned off and vending machines have been known to be deactivated! It's the heart of LDS/Mormon country and they take the sabbath very seriously. *Scheduled* maintenence work like this would logically & routinely have begun on a Monday, or some other day of the week. NOT ON SUNDAY. I said that this was an indication that it wasn't pre-planned, but done on the fly, and therefore must probably a cover-up operation. Now, we find out that it indeed WAS done on a Sunday—by a brother who admits he typically doesn't do this work on Sundays! And it wasn't pre-scheduled, but spur-of-the-moment & directly due to the assassination! (video via HustleBitch) I. WAS. RIGHT. But there's more: Google Trends shows that the man who did the work, Daniel Merrell—owner of Hardscape Utah—was searched from israeli IP addresses earlier this summer! And in the exact same time frame that people in DC & israel were searching for "Timpanogas Regional Hospital" (the hospital where Charlie was taken), and Deirde Amaro (the new Utah state medical examiner who performed the autopsy). What?! (h/t: 🇺🇸 The Light Revival💡) Now, to be clear, I'm not accusing Daniel Merrell of anything nefarious. This all doesn't mean he's in on any plot. I'm sure he was just doing what was asked of him, like he said. But don't you find all this very strange and extremely suspicious? The JFK, RFK & MLK crime scenes were all quickly destroyed after those conspiracy/cover-up assassinations as well. You might remember how the FBI hosed down the Thomas Crooks scene last summer immediately after the assassination attempt on Donald Trump. Rapid destruction of a high-profile assassination crime scene is a tell-tale sign of a cover-up. That is the simplest/Occam's Razor explanation here. I'm sure they'll all say they just meant well and wanted to honor Charlie, or maybe they'll say that given the circumstances it wasn't the best choice, in hindsight, to act so soon after. Perhaps they'll say that whoever said it was scheduled maintenance spoke in error. Etc. If we had a real uncompromised FBI & law enforcement, they'd question Daniel & all involved in this debacle and search their records to see if any clues could be found that lead back to the perps. DON'T LET THEM GET AWAY WITH THIS.

Sam Parker 🇺🇸🧯

128,312 次观看 • 10 个月前