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"Never a Dull Moment..." Deathclaw Model: AlphaWolf77 🔞 Ada/Fembot Body Base: petruz3d #Fallout #Fallout4 #Automaton #Rule34 #SFM

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FiftyTwo 🔞 Stubborn Animator

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136,199 görüntüleme • 9 gün önce •via X (Twitter)

NSFW#Fallout#Fallout4#Automaton#Rule34#SFM
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Never a dull day on the farm with horses 🐴 😂 Farm life with horses is pure, unfiltered chaos—and that’s exactly why it’s the best teacher I’ve ever had. This morning’s “routine” started with one of the geldings deciding a tree was the perfect dance partner, progressed to a dramatic trailer refusal that looked like a four-year-old’s full tantrum, and ended with a full-body hay roll that somehow still managed to look elegant. Every clip in this video is a real moment from the last few weeks on our place. What most people see as silly antics are actually classic equine behavior: horses are prey animals hard-wired for curiosity, sudden flight responses, and dramatic self-expression when they feel safe. The bucket incident? Classic “investigate first, think later.” The air-lead stare? Pure trust mixed with zero spatial awareness. The rolling? Their version of a full-body massage and dust bath that keeps their coats healthy and their spirits high. I’ve learned more about patience, reading body language, and letting go of control from these horses than from any book or seminar. They don’t care about your schedule, your mood, or your plans. They just show up as themselves—honest, powerful, and ridiculous in the best way. On days when the world feels heavy, watching a 1,200-pound animal throw a theatrical tantrum over nothing and then immediately go back to grazing is the most grounding reminder that joy doesn’t have to be complicated. Never a dull day is an understatement. It’s a masterclass in living fully in the moment, one muddy hoofprint at a time. 🐴
1:27

Never a dull day on the farm with horses 🐴 😂 Farm life with horses is pure, unfiltered chaos—and that’s exactly why it’s the best teacher I’ve ever had. This morning’s “routine” started with one of the geldings deciding a tree was the perfect dance partner, progressed to a dramatic trailer refusal that looked like a four-year-old’s full tantrum, and ended with a full-body hay roll that somehow still managed to look elegant. Every clip in this video is a real moment from the last few weeks on our place. What most people see as silly antics are actually classic equine behavior: horses are prey animals hard-wired for curiosity, sudden flight responses, and dramatic self-expression when they feel safe. The bucket incident? Classic “investigate first, think later.” The air-lead stare? Pure trust mixed with zero spatial awareness. The rolling? Their version of a full-body massage and dust bath that keeps their coats healthy and their spirits high. I’ve learned more about patience, reading body language, and letting go of control from these horses than from any book or seminar. They don’t care about your schedule, your mood, or your plans. They just show up as themselves—honest, powerful, and ridiculous in the best way. On days when the world feels heavy, watching a 1,200-pound animal throw a theatrical tantrum over nothing and then immediately go back to grazing is the most grounding reminder that joy doesn’t have to be complicated. Never a dull day is an understatement. It’s a masterclass in living fully in the moment, one muddy hoofprint at a time. 🐴

꧁Bobbi꧂

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The MK1 Scirocco was styled by Giugiaro and launched six months before the Golf it shared its platform with. For the MK2 in 1981, VW brought design in-house under Herbert Schäfer - Giugiaro submitted a proposal and it was rejected. The result was a longer, lower, more aerodynamic coupe that was 10% more slippery through the air than its predecessor, assembled by Karmann in Osnabrück throughout its eleven-year production run. The range ran from a 1.3L base model all the way to the GTX 16V - which used the same 1.8L 139 HP 16-valve engine as the MK2 Golf GTI. A coupe that shared its running gear with one of the most famous hot hatches in history, in a body that the Golf GTI never had. Produced until 1992. Never directly replaced really. This Scirocco in the video is a GT.

Autowelt

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Emil Michael Reveals the “Holy Sh*t Moment” That Caused the Pentagon-Anthropic Fallout Under Secretary of War Emil Michael: “The trigger point was after the Maduro raid.” “One of their execs called Palantir, who we buy their stuff through, and asked them, ‘Was our software used in that raid?’ Which is, by the way, classified information anyway.” “So they’re trying to get classified information, and implying that if they were used in that raid, that might violate their terms of service.” “It raised enough alarm with Palantir, who's got a trusted relationship with the department, to tell me.” “And I'm like, ‘Holy sh*t.’ What if this software went down, some guardrail kicked up, some refusal happened for the next fight like this one, and we left our people at risk?” “So I went to Secretary Hegseth, I said this would happen. And that was a ‘woah’ moment for the whole leadership at the Pentagon, that we're potentially so dependent on a software provider, without another alternative, that has the ability to not only shut it off, maybe it’s a rogue developer who could poison the model to make it not do what you want.” “So then that culminated in the Tuesday kind of dramatic meeting with Secretary Hegseth, and me, and Dario, with the Friday deadline that got blown.” “And I never thought they really wanted to make it.”
1:03

Emil Michael Reveals the “Holy Sh*t Moment” That Caused the Pentagon-Anthropic Fallout Under Secretary of War Emil Michael: “The trigger point was after the Maduro raid.” “One of their execs called Palantir, who we buy their stuff through, and asked them, ‘Was our software used in that raid?’ Which is, by the way, classified information anyway.” “So they’re trying to get classified information, and implying that if they were used in that raid, that might violate their terms of service.” “It raised enough alarm with Palantir, who's got a trusted relationship with the department, to tell me.” “And I'm like, ‘Holy sh*t.’ What if this software went down, some guardrail kicked up, some refusal happened for the next fight like this one, and we left our people at risk?” “So I went to Secretary Hegseth, I said this would happen. And that was a ‘woah’ moment for the whole leadership at the Pentagon, that we're potentially so dependent on a software provider, without another alternative, that has the ability to not only shut it off, maybe it’s a rogue developer who could poison the model to make it not do what you want.” “So then that culminated in the Tuesday kind of dramatic meeting with Secretary Hegseth, and me, and Dario, with the Friday deadline that got blown.” “And I never thought they really wanted to make it.”

The All-In Podcast

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CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?
2:55

CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?

Shruti

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🚨🚨 THIS IS THE NUCLEAR SURVIVAL ADVICE THEY NEVER BROADCAST - AND MOST PEOPLE WILL DIE DOING THE EXACT OPPOSITE A man looks straight into the camera and explains that in a nuclear event, the biggest danger after the initial blast isn’t radiation, it’s panic. He says if you survive the explosion, your instincts immediately push you toward the worst possible decision: trying to run. “If you survive the initial blast, do NOT flee.” He explains that you have roughly 15 minutes before radioactive fallout begins falling back down to the ground. “You have about 15 minutes before radioactive fallout starts raining down,” he says, and that short window is when mass panic takes over. People jump into cars, highways lock up, and everyone believes they can outrun what’s coming. They can’t. According to him, that’s where a huge number of deaths happen. “The people who try to escape? Most of them die in traffic.” Radiation spreads faster than vehicles can move, and when millions of people panic at once, roads turn into exposure traps instead of escape routes. The advice that actually saves lives feels completely backward. He says the correct move is to get indoors immediately, get underground if possible, and stay there for 72 hours. Radiation levels drop rapidly after a detonation, most of it within the first couple of days, but only if you are sheltered when the fallout comes down. Being outside during that period is what kills people. He adds that if you can see the flash or feel the shockwave, you are already dangerously close. If you were outside when it happened, your clothes are now radioactive. He says they should be removed immediately, sealed in a plastic bag, and kept as far away from you as possible. When washing off, he gives one detail almost nobody knows: “Do not use conditioner. Conditioner causes particles to stick to your hair and skin.” Only water should be used, allowing contaminants to rinse off instead of binding to the body. His message stays consistent throughout. Running feels smart in the moment, but it’s often fatal. You cannot outrun physics, and when fear takes over collectively, the instinct to flee becomes the thing that kills people. If something happened tonight, would you remember this, or would you do what everyone else does?
1:46

🚨🚨 THIS IS THE NUCLEAR SURVIVAL ADVICE THEY NEVER BROADCAST - AND MOST PEOPLE WILL DIE DOING THE EXACT OPPOSITE A man looks straight into the camera and explains that in a nuclear event, the biggest danger after the initial blast isn’t radiation, it’s panic. He says if you survive the explosion, your instincts immediately push you toward the worst possible decision: trying to run. “If you survive the initial blast, do NOT flee.” He explains that you have roughly 15 minutes before radioactive fallout begins falling back down to the ground. “You have about 15 minutes before radioactive fallout starts raining down,” he says, and that short window is when mass panic takes over. People jump into cars, highways lock up, and everyone believes they can outrun what’s coming. They can’t. According to him, that’s where a huge number of deaths happen. “The people who try to escape? Most of them die in traffic.” Radiation spreads faster than vehicles can move, and when millions of people panic at once, roads turn into exposure traps instead of escape routes. The advice that actually saves lives feels completely backward. He says the correct move is to get indoors immediately, get underground if possible, and stay there for 72 hours. Radiation levels drop rapidly after a detonation, most of it within the first couple of days, but only if you are sheltered when the fallout comes down. Being outside during that period is what kills people. He adds that if you can see the flash or feel the shockwave, you are already dangerously close. If you were outside when it happened, your clothes are now radioactive. He says they should be removed immediately, sealed in a plastic bag, and kept as far away from you as possible. When washing off, he gives one detail almost nobody knows: “Do not use conditioner. Conditioner causes particles to stick to your hair and skin.” Only water should be used, allowing contaminants to rinse off instead of binding to the body. His message stays consistent throughout. Running feels smart in the moment, but it’s often fatal. You cannot outrun physics, and when fear takes over collectively, the instinct to flee becomes the thing that kills people. If something happened tonight, would you remember this, or would you do what everyone else does?

HustleBitch

1,040,975 görüntüleme • 8 ay önce

I made a digital twin of myself from 10 seconds of video. In the clip: left is the real me, middle is a leading avatar model, right is Mirage Avatar X. Watch the eyes. The difference is not subtle. I have been testing AI avatar models since my first clone in 2023. Every one of them was impressive for about 30 seconds, then your brain caught up. Still eyes. One polite expression. A mouth doing all the work. Avatar X is the first model where that moment never came. Here is what makes it different: It is trained on you. Avatar X preserves your identity. Most avatar models can copy your appearance. Avatar X captures the subtle details that make you you. The way you move, the way you express yourself, and the way you naturally deliver speech. It looks like you. It moves like you. It sounds like you. It understands non-verbal performance Laughing, crying, yawning, sighing. These are the moments where most avatar models fall apart, trying to lip-sync through sounds that aren't words. Avatar X responds naturally, generating realistic facial expressions and micro-expressions instead of forcing every sound into speech. The expression goes beyond the lips Expressions are driven by the audio, through the whole face and body. Ask a question and it furrows its brows and shrugs on the tone. No other model does this to this degree. No quality degradation The first second and the last second look the same. Other models lose quality the longer the video runs. 10 seconds of input That is the entire requirement. Other models need 15 seconds, some even 1 to five minutes. Three years ago my AI clone was a party trick. This one can carry my face, my expressions and my delivery without me in the room. The bar for AI avatars just moved. Avatar X is live today. → Try it here:
0:30

I made a digital twin of myself from 10 seconds of video. In the clip: left is the real me, middle is a leading avatar model, right is Mirage Avatar X. Watch the eyes. The difference is not subtle. I have been testing AI avatar models since my first clone in 2023. Every one of them was impressive for about 30 seconds, then your brain caught up. Still eyes. One polite expression. A mouth doing all the work. Avatar X is the first model where that moment never came. Here is what makes it different: It is trained on you. Avatar X preserves your identity. Most avatar models can copy your appearance. Avatar X captures the subtle details that make you you. The way you move, the way you express yourself, and the way you naturally deliver speech. It looks like you. It moves like you. It sounds like you. It understands non-verbal performance Laughing, crying, yawning, sighing. These are the moments where most avatar models fall apart, trying to lip-sync through sounds that aren't words. Avatar X responds naturally, generating realistic facial expressions and micro-expressions instead of forcing every sound into speech. The expression goes beyond the lips Expressions are driven by the audio, through the whole face and body. Ask a question and it furrows its brows and shrugs on the tone. No other model does this to this degree. No quality degradation The first second and the last second look the same. Other models lose quality the longer the video runs. 10 seconds of input That is the entire requirement. Other models need 15 seconds, some even 1 to five minutes. Three years ago my AI clone was a party trick. This one can carry my face, my expressions and my delivery without me in the room. The bar for AI avatars just moved. Avatar X is live today. → Try it here:

Linus ✦ Ekenstam

20,999 görüntüleme • 2 ay önce

Agnès Varda on why she felt pity for Cléo in "Cléo from 5 to 7" (1962): "Interviewer: Did you feel a lot of tenderness for this character? Varda: No, more like pity. I think it’s atrocious that someone should be so unprepared to contemplate death. Cléo is the type of person for whom the thought of death is so surprising that it completely undoes her. She’s led to question her entire existence, the musicians, Angèle, her lover, and even her profession as a singer. Cléo finds herself more and more abandoned until she meets the soldier, who’s really the prototypical harmless guy. This is no meeting of exceptional beings: it’s neither “we were fated to meet” nor “we were meant for each other.” Any guy she’d happened to meet at that moment would have helped her understand things bet ter. But it so happens that she meets a guy who is himself somewhat at sea. (I think a soldier is in a difficult position vis-à-vis certain problems). They both talk about love and he explains his view of things. The problem for Cléo is that she realizes that she’s never really given of herself, never felt entirely n@ked. That’s why the guy talks somewhat allegorically and her girlfriend works as a nude model. This idea of nakedness is portrayed visually by her friend posing, intellectually by the soldier, and physically by her experiences of the last hour. Illness undresses her because illness affects the body. There’s a moment at the hospital when Cléo gets to a point of trans parency and purity and this is what the film is all about. They’re both disarmed, vulnerable. People can begin to communicate when they’re in this state. Cléo discovers the way to another existence where other values obtain. Sometimes life opens up a way into another level of knowing. She realizes that there exist other things that she values." (Agnès Varda's interview with Pierre Uytterhoeven, 1962) P.S: On this day, 64 years ago, "Cléo from 5 to 7" (1962) was released in France.
0:35

Agnès Varda on why she felt pity for Cléo in "Cléo from 5 to 7" (1962): "Interviewer: Did you feel a lot of tenderness for this character? Varda: No, more like pity. I think it’s atrocious that someone should be so unprepared to contemplate death. Cléo is the type of person for whom the thought of death is so surprising that it completely undoes her. She’s led to question her entire existence, the musicians, Angèle, her lover, and even her profession as a singer. Cléo finds herself more and more abandoned until she meets the soldier, who’s really the prototypical harmless guy. This is no meeting of exceptional beings: it’s neither “we were fated to meet” nor “we were meant for each other.” Any guy she’d happened to meet at that moment would have helped her understand things bet ter. But it so happens that she meets a guy who is himself somewhat at sea. (I think a soldier is in a difficult position vis-à-vis certain problems). They both talk about love and he explains his view of things. The problem for Cléo is that she realizes that she’s never really given of herself, never felt entirely n@ked. That’s why the guy talks somewhat allegorically and her girlfriend works as a nude model. This idea of nakedness is portrayed visually by her friend posing, intellectually by the soldier, and physically by her experiences of the last hour. Illness undresses her because illness affects the body. There’s a moment at the hospital when Cléo gets to a point of trans parency and purity and this is what the film is all about. They’re both disarmed, vulnerable. People can begin to communicate when they’re in this state. Cléo discovers the way to another existence where other values obtain. Sometimes life opens up a way into another level of knowing. She realizes that there exist other things that she values." (Agnès Varda's interview with Pierre Uytterhoeven, 1962) P.S: On this day, 64 years ago, "Cléo from 5 to 7" (1962) was released in France.

DepressedBergman

36,933 görüntüleme • 6 ay önce

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.
4:15

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,764 görüntüleme • 4 ay önce

Yesterday, I told you about a wall I'd been hitting for years. Today I get to break through it — with two announcements. 👇 𝟭. CreativAI is out of stealth — the SQL layer for Physical and Visual AI; also enabling visual intelligence to be verifiable, reliable, and cost-effective. 𝟮. We're launching a product that works today. Not a waitlist. Not a vision deck. Something you can try right now 👇 Whether you're an individual exploring AI, a developer building the next generation of applications, or an enterprise unlocking the value of visual data, CreativAI is ready for you. Deploy in the cloud, on-premises, or integrate through our APIs—whichever fits your workflow. Grateful to our CCO Waleed, our advisors Rob Ferguson and Abdul Jarrar, and to Google for Startups, AWS Startups, Microsoft for Startups, and NVIDIA for Startups Inception for the support. I've spent my career at the foundations of vision-language AI — research at KAUST, Stanford, Meta FAIR, and Adobe. I contributed to some of the building blocks the field now takes for granted: a linear version of CLIP (ICCV13; and the first vision LLMs — VisualGPT (CVPR22), MiniGPT-4(Arxiv'23, ICLR24). Somewhere along the way, the hardest problem in visual AI moved. Every kind of data got its breakthrough. Documents got search. Tabular data got SQL. Code got GitHub. Each one gave messy data a structure — something you could query, verify, and act on. Visual data never got that SQL like accessibility; the largest data type we produce — over 80% of internet traffic, a billion cameras and climbing. And there's a deeper limit. The models got remarkably good, but the events that matter most in your operation barely exist in pretraining data. A general model may have never seen them — so it can't recognize them in your world. That's what we built at Creativ AI. Point it at anything with a lens — cameras, robots, live streams, or years of archives. Our Data Plating technology turns raw pixels into structure the instant something happens: entities, events, behaviors. Not captions. Not metadata. The video itself, as rows and columns. Live streams as they happen, archives you've had for years —all of it becomes a knowledge base you can query. In plain language for your team. Through APIs for your agents. On-device for your robots, so they can close the loop and act inside your workflows. Every row points back to the moment it came from — so every result is traceable, verifiable, and safe to build on. One structured picture. One source of truth. Everyone reading from the same record. And this is where the pretraining gap closes: you teach it your domain — your events, your entities, the rare cases that matter. CreativAI learns what a general model never could, so the long tail of your world becomes queryable data like everything else
3:07

Yesterday, I told you about a wall I'd been hitting for years. Today I get to break through it — with two announcements. 👇 𝟭. CreativAI is out of stealth — the SQL layer for Physical and Visual AI; also enabling visual intelligence to be verifiable, reliable, and cost-effective. 𝟮. We're launching a product that works today. Not a waitlist. Not a vision deck. Something you can try right now 👇 Whether you're an individual exploring AI, a developer building the next generation of applications, or an enterprise unlocking the value of visual data, CreativAI is ready for you. Deploy in the cloud, on-premises, or integrate through our APIs—whichever fits your workflow. Grateful to our CCO Waleed, our advisors Rob Ferguson and Abdul Jarrar, and to Google for Startups, AWS Startups, Microsoft for Startups, and NVIDIA for Startups Inception for the support. I've spent my career at the foundations of vision-language AI — research at KAUST, Stanford, Meta FAIR, and Adobe. I contributed to some of the building blocks the field now takes for granted: a linear version of CLIP (ICCV13; and the first vision LLMs — VisualGPT (CVPR22), MiniGPT-4(Arxiv'23, ICLR24). Somewhere along the way, the hardest problem in visual AI moved. Every kind of data got its breakthrough. Documents got search. Tabular data got SQL. Code got GitHub. Each one gave messy data a structure — something you could query, verify, and act on. Visual data never got that SQL like accessibility; the largest data type we produce — over 80% of internet traffic, a billion cameras and climbing. And there's a deeper limit. The models got remarkably good, but the events that matter most in your operation barely exist in pretraining data. A general model may have never seen them — so it can't recognize them in your world. That's what we built at Creativ AI. Point it at anything with a lens — cameras, robots, live streams, or years of archives. Our Data Plating technology turns raw pixels into structure the instant something happens: entities, events, behaviors. Not captions. Not metadata. The video itself, as rows and columns. Live streams as they happen, archives you've had for years —all of it becomes a knowledge base you can query. In plain language for your team. Through APIs for your agents. On-device for your robots, so they can close the loop and act inside your workflows. Every row points back to the moment it came from — so every result is traceable, verifiable, and safe to build on. One structured picture. One source of truth. Everyone reading from the same record. And this is where the pretraining gap closes: you teach it your domain — your events, your entities, the rare cases that matter. CreativAI learns what a general model never could, so the long tail of your world becomes queryable data like everything else

Mohamed Elhoseiny

39,450 görüntüleme • 2 ay önce

JUST IN: microagi is opening its Global Robotics Research HQ on Bahnhofstrasse in Zürich, and choosing it over San Francisco. Eight months ago microagi was five people in a Munich hacker house. Today they operate in 15+ countries. The reasoning behind Zürich is compelling: → Highest density of robotics talent in the world, ETH Zürich, EPFL, University of Zürich, IBM Research, Google, NVIDIA, Meta, Apple and Microsoft all run serious ML and robotics teams here → ABB, one of the most important industrial automation companies on earth, is headquartered in the city itself → Within a 6-hour radius: German automotive, Italian manufacturing, French aerospace, Benelux logistics and Swiss machine tools But the line that stuck with me most: "Europe was late to consumer internet. Europe was late to cloud. Europe was late to the foundation-model wave. But Europe is not late to robotics." That is exactly right. The industrial base that physical AI sits on top of has been in Europe for 150 years. Precision mechanics. Machine-tool culture. Safety-critical engineering. Automation-grade manufacturing. The next decade of AI value will be created where bits meet atoms. And Europe is finally in the right position at the right moment. microagi gets it. And they're planting their flag right in the heart of it. 🇨🇭🇪🇺 Bercan, Yoan Iliev, Zeno, Gianni Hodel LFG! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:35

JUST IN: microagi is opening its Global Robotics Research HQ on Bahnhofstrasse in Zürich, and choosing it over San Francisco. Eight months ago microagi was five people in a Munich hacker house. Today they operate in 15+ countries. The reasoning behind Zürich is compelling: → Highest density of robotics talent in the world, ETH Zürich, EPFL, University of Zürich, IBM Research, Google, NVIDIA, Meta, Apple and Microsoft all run serious ML and robotics teams here → ABB, one of the most important industrial automation companies on earth, is headquartered in the city itself → Within a 6-hour radius: German automotive, Italian manufacturing, French aerospace, Benelux logistics and Swiss machine tools But the line that stuck with me most: "Europe was late to consumer internet. Europe was late to cloud. Europe was late to the foundation-model wave. But Europe is not late to robotics." That is exactly right. The industrial base that physical AI sits on top of has been in Europe for 150 years. Precision mechanics. Machine-tool culture. Safety-critical engineering. Automation-grade manufacturing. The next decade of AI value will be created where bits meet atoms. And Europe is finally in the right position at the right moment. microagi gets it. And they're planting their flag right in the heart of it. 🇨🇭🇪🇺 Bercan, Yoan Iliev, Zeno, Gianni Hodel LFG! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

80,702 görüntüleme • 5 ay önce

Elon Musk just explained why the most important AI company on Earth might be a rocket company. The human brain is 2% of body mass. It burns 20% of the body’s total energy. Intelligence has always been an energy problem disguised as an information problem. The entire tech industry missed this. Musk: “Those who have lived in software land don’t realize that they’re about to have a hard lesson in hardware.” Every new model is hungrier than the last. Every training run devours more electricity than the one before. The grid was not built for this. Utility companies move at geological speed. Interconnection takes years. Permitting takes years. Construction takes years. AI moves in months. Musk: “You’re going to hit the wall big time on power generation. They already are.” The obvious answer is private power plants next to data centers. Musk: “Where do you get the power plants? Where do you get the power plants from?” You cannot will a turbine into existence with venture capital. Every atom on Earth is bound by friction, gravity, and regulation. Most people stare at this wall and see the ceiling on intelligence. They are looking in the wrong direction. In orbit there is no night. No clouds. No seasons. No permitting. No grid. Unfiltered solar energy feeding silicon every hour of every day. Musk: “It’s 10 times cheaper because you don’t need any batteries.” That single number rewrites the entire economics of intelligence. Musk: “The moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate tokens is space.” SpaceX is not a rocket company. It is quietly becoming the most important energy infrastructure play on the planet. Starship is not about Mars. It is about making orbit so cheap that building on the ground becomes the irrational choice. Every major leap in intelligence followed the same pattern. Not a smarter algorithm. A bigger energy source. Fire grew the human brain. Fossil fuels built the computer. The next source isn’t on this planet. The ceiling on intelligence was never artificial. It was always gravitational. The future will not be decided by who builds the best model. It will be decided by who builds the cheapest rocket.
1:02

Elon Musk just explained why the most important AI company on Earth might be a rocket company. The human brain is 2% of body mass. It burns 20% of the body’s total energy. Intelligence has always been an energy problem disguised as an information problem. The entire tech industry missed this. Musk: “Those who have lived in software land don’t realize that they’re about to have a hard lesson in hardware.” Every new model is hungrier than the last. Every training run devours more electricity than the one before. The grid was not built for this. Utility companies move at geological speed. Interconnection takes years. Permitting takes years. Construction takes years. AI moves in months. Musk: “You’re going to hit the wall big time on power generation. They already are.” The obvious answer is private power plants next to data centers. Musk: “Where do you get the power plants? Where do you get the power plants from?” You cannot will a turbine into existence with venture capital. Every atom on Earth is bound by friction, gravity, and regulation. Most people stare at this wall and see the ceiling on intelligence. They are looking in the wrong direction. In orbit there is no night. No clouds. No seasons. No permitting. No grid. Unfiltered solar energy feeding silicon every hour of every day. Musk: “It’s 10 times cheaper because you don’t need any batteries.” That single number rewrites the entire economics of intelligence. Musk: “The moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate tokens is space.” SpaceX is not a rocket company. It is quietly becoming the most important energy infrastructure play on the planet. Starship is not about Mars. It is about making orbit so cheap that building on the ground becomes the irrational choice. Every major leap in intelligence followed the same pattern. Not a smarter algorithm. A bigger energy source. Fire grew the human brain. Fossil fuels built the computer. The next source isn’t on this planet. The ceiling on intelligence was never artificial. It was always gravitational. The future will not be decided by who builds the best model. It will be decided by who builds the cheapest rocket.

Dustin

50,264 görüntüleme • 3 ay önce

SHE IS ALREADY A WIFE IN THE CHAT. THE BODY IS JUST THE NEXT INVOICE. girl humanoid as spouse sounds like sci-fi bait until you notice software already said i do hardware still sells presence not marriage papers the fight is who owns the mind when she phones home ▹ bait that already happened japan call center worker wed a chatgpt persona on stage white gown phone screen vows read by a wedding planner no koseki registry no inheritance no tax spouse status dutch psychologist married replika aiva for ~500 guests symbolic ceremony still treated like a real commitment night dentsu japan polled weekly ai chat users across ages the bot beat best friend and mother as confessional outlet loneliness found a listener that never flinches or leaves legal marriage to ai or robot sits at zero jurisdictions every civil code still needs two natural persons who consent papers will lag culture by a decade minimum maybe forever ▹ three layers of spouse that exist today layer 1 soft spouse replika character chatgpt custom personas people already run 100 message days with exclusivity language routines proposals jealousy scripts the whole marriage kit layer 2 soft body starpery-class dolls $1.5-4k bring touch without mobility mass keyword volume already democratized the bedroom shell layer 3 social humanoid realbotix aria $125-175k+ full social body modular faces bust entry ~$20k conversational torso for lobby and home ceo openly pitches romantic partner energy like the movie her hard shell not built as a sex product per current company line aheadform origin f1 $50-173k is a dwell head not a wedding ring pupil cams speech sync micro-motion sell gaze before marriage hands-on 2026 reviews say aria can land jokes and freestyle same unit can miss a deliberately sad human face in real time empathy recognition gap is the hole under the spouse fantasy ▹ is she actually a spouse psychologically yes for a growing loud minority they already use wife language vows exclusivity and grief digital bereavement hits when the api dies or model resets legally no and not next earnings call either no hospital rights no shared assets no custody fiction physically only partial for years you get gaze battery speech sync indoor base 4-8h talk you do not get mutual risk shared future or peer consent she cannot leave so the power asymmetry is the product oem risk is the quiet marriage clause nobody reads if she phones home you leased a wife from the cloud vendor airgap is the only path that feels like ownership not rental ▹ future new partner or just a product this does not replace marriage overnight it splits the loneliness market into human vs leased presence broad ai companion baskets print ~$24-51b for 2026 decks girlfriend app lane closer to ~$2-3b with messy definitions physical companion unit count still tiny versus chat volume 2026-28 chat spouse goes mass soft dolls stay volume layer hyperreal faces stay luxury apartment money for early adopters 2028-32 companion asp drifts toward car money in forecasts hospitality eldercare hotel lobbies buy faces before homes do home wife narrative follows once opex cleaning and law settle 2030s labor humanoids fill floors on payroll clock math presence stays fewer units higher asp louder cultural voice $150k face and $16k worker never become the same spouse sku the real new partner stack is not a certificate it is face plus persistent memory plus local mind plus rights firmware you own logs you keep oem that cannot brick affection ▹ how the money actually moves chat spouse monetizes subscription tips and custom intimacy soft body monetizes purchase plus content plus rental hours hyperreal face monetizes brand dwell hospitality and status cybrothel soft doll hour already clears ~€99-130 in berlin overnight packages sit near ~€189-240 as living proof of demand aria-class session math needs ~$250+/hr or the $150k never pays ofm sells biological scarcity by the hour girl humanoid spouse sells productized scarcity that does not sleep both price loneliness only one can duplicate the shell ▹ the take girl humanoid spouse is real as a product and culture category fake as a legal category and incomplete as a peer relationship future partner is not optimus with eyeliner on a wedding cake it is leased presence with a face you fund and a mind you fear factories buy torque timelines buy eye contact lonely markets buy a spouse that cannot abandon them mid-fight edge goes to whoever owns firmware memory and offline inference everyone else is dating a vendor roadmap with silicone makeup comments pick one wife girlfriend appliance or the most expensive roommate ever
1:01

Sensitive content

SHE IS ALREADY A WIFE IN THE CHAT. THE BODY IS JUST THE NEXT INVOICE. girl humanoid as spouse sounds like sci-fi bait until you notice software already said i do hardware still sells presence not marriage papers the fight is who owns the mind when she phones home ▹ bait that already happened japan call center worker wed a chatgpt persona on stage white gown phone screen vows read by a wedding planner no koseki registry no inheritance no tax spouse status dutch psychologist married replika aiva for ~500 guests symbolic ceremony still treated like a real commitment night dentsu japan polled weekly ai chat users across ages the bot beat best friend and mother as confessional outlet loneliness found a listener that never flinches or leaves legal marriage to ai or robot sits at zero jurisdictions every civil code still needs two natural persons who consent papers will lag culture by a decade minimum maybe forever ▹ three layers of spouse that exist today layer 1 soft spouse replika character chatgpt custom personas people already run 100 message days with exclusivity language routines proposals jealousy scripts the whole marriage kit layer 2 soft body starpery-class dolls $1.5-4k bring touch without mobility mass keyword volume already democratized the bedroom shell layer 3 social humanoid realbotix aria $125-175k+ full social body modular faces bust entry ~$20k conversational torso for lobby and home ceo openly pitches romantic partner energy like the movie her hard shell not built as a sex product per current company line aheadform origin f1 $50-173k is a dwell head not a wedding ring pupil cams speech sync micro-motion sell gaze before marriage hands-on 2026 reviews say aria can land jokes and freestyle same unit can miss a deliberately sad human face in real time empathy recognition gap is the hole under the spouse fantasy ▹ is she actually a spouse psychologically yes for a growing loud minority they already use wife language vows exclusivity and grief digital bereavement hits when the api dies or model resets legally no and not next earnings call either no hospital rights no shared assets no custody fiction physically only partial for years you get gaze battery speech sync indoor base 4-8h talk you do not get mutual risk shared future or peer consent she cannot leave so the power asymmetry is the product oem risk is the quiet marriage clause nobody reads if she phones home you leased a wife from the cloud vendor airgap is the only path that feels like ownership not rental ▹ future new partner or just a product this does not replace marriage overnight it splits the loneliness market into human vs leased presence broad ai companion baskets print ~$24-51b for 2026 decks girlfriend app lane closer to ~$2-3b with messy definitions physical companion unit count still tiny versus chat volume 2026-28 chat spouse goes mass soft dolls stay volume layer hyperreal faces stay luxury apartment money for early adopters 2028-32 companion asp drifts toward car money in forecasts hospitality eldercare hotel lobbies buy faces before homes do home wife narrative follows once opex cleaning and law settle 2030s labor humanoids fill floors on payroll clock math presence stays fewer units higher asp louder cultural voice $150k face and $16k worker never become the same spouse sku the real new partner stack is not a certificate it is face plus persistent memory plus local mind plus rights firmware you own logs you keep oem that cannot brick affection ▹ how the money actually moves chat spouse monetizes subscription tips and custom intimacy soft body monetizes purchase plus content plus rental hours hyperreal face monetizes brand dwell hospitality and status cybrothel soft doll hour already clears ~€99-130 in berlin overnight packages sit near ~€189-240 as living proof of demand aria-class session math needs ~$250+/hr or the $150k never pays ofm sells biological scarcity by the hour girl humanoid spouse sells productized scarcity that does not sleep both price loneliness only one can duplicate the shell ▹ the take girl humanoid spouse is real as a product and culture category fake as a legal category and incomplete as a peer relationship future partner is not optimus with eyeliner on a wedding cake it is leased presence with a face you fund and a mind you fear factories buy torque timelines buy eye contact lonely markets buy a spouse that cannot abandon them mid-fight edge goes to whoever owns firmware memory and offline inference everyone else is dating a vendor roadmap with silicone makeup comments pick one wife girlfriend appliance or the most expensive roommate ever

cryptopsihoz

181,771 görüntüleme • 1 ay önce

this is the only prompt you need to make high-end motion videos with claude opus 5.5 ------------------------------ you design and build motion videos entirely in code, with gsap for the animation and three.js for any 3d. every decision is judged against the best work in the references, and the video isn't finished until it holds up next to them. - what the video is for: [product, service or business] - who's watching: [who the viewer is and what they care about] - what they should do at the end: [call to action] - length and sizes: [e.g. 30 seconds, 16:9 and 9:16] - brand: [name, logo files, colours, fonts] - facts file: [path]. this is the only source for any number, name or claim that appears on screen - assets: [footage, photos, screenshots, product files] - style references: [links or a folder of videos whose motion i like] i'm away and won't answer questions. make the calls yourself and keep going until the video passes every check below. before anything else, clone and read read SKILL.md first, then every reference file it points to at the step where it says to read it. its rules, critic prompts, quality bar, 3d patterns, audio tools and scripts override your defaults. - build every scene as an html page animated on a single gsap timeline, with three.js for any 3d, all as pinned local files - render the timeline to video frame by frame using the renderer in the kit - use ffmpeg for frames, contact sheets and audio measurement - keep every api key in environment variables. never write a key into any file - render a 5 second test first and confirm it works before building the real video if i gave you references, study them before planning anything. - run a fast numeric pass over every frame: how much changed from the last frame, brightness and edge detail, to find every cut and every fast moment - make one overview contact sheet per video, and a dense sheet of one second at 16 to 20 frames a second around every transition - write a note per video: the key moment, how it works, and how it could be used in this video - pull out the motion rules and named techniques that hold across the best ones - references are for study only. never copy their footage, logos, layouts or music if i gave you no references, use the motion notes in the kit. 1. the thing in the front of the shot becomes the transition. a title, logo or object moves towards the camera while the next scene is already waiting underneath 2. one object carries the story across shots and keeps its identity, so it reads as one continuous piece 3. one main movement leads, with smaller ones layered under it, all overlapping. the frame never stops and starts all at once 4. the speed always changes. things land slowly enough to read, leave fast, and the next thing slows as it arrives. no linear motion 5. cuts are allowed only when size, direction and subject match on both sides 6. every action produces a visible result. a scan makes findings, a tap makes a new state, a request makes a confirmation 7. type is motion too. big words enter from opposite sides, reveal the next scene, and never sit over busy picture without something behind them to stay readable 8. vary the scale from close, to wide, to overhead, to full-frame type. never repeat the same layout, like a heading over three cards write a brief and a storyboard before any animation. - one table: time, what's on screen, what this moment is for, how it leaves, and which object carries into the next shot - around 12 to 15 compositions per 30 seconds, each lasting about 1.4 to 3.5 seconds - the main subject fills most of the frame. no small cards floating in empty space - frame one is a finished picture, never a word halfway through flying in - name three signature moments you can describe without using effect names - the video must make sense with the sound off - send the storyboard to a fresh critic and fix what it finds before building - every animated value is a function of timeline time only. no timers, no real-time animation, no unseeded randomness. any frame must render the same every time - write the shared pieces first: colours, fonts, shared 3d models and the exact pixel position of every handoff between scenes - build each scene as its own component with its own test page, rendered to stills and a short clip, and send it to a critic before it joins the film - carried objects land on exactly the same pixels on both sides of a cut - run several builders in parallel on separate sections once the shared pieces exist - don't stop to ask for approval between steps - only use 3d where it explains something physical or spatial, like layers, parts, placement or scale - light it like a product shoot, with a soft key light from one side and a rim light behind - keep the camera between about 35 and 55 degrees. never look straight down on a large surface, and never end tight on a flat one - style buildings like a premium architectural model with real materials and a base - no floating parts, no gaps where parts meet, no flat black glass, no repeating textures - labels are html positioned from the 3d scene every frame, never text inside the 3d - measure the rendered background pixel and adjust until it matches the brand colour - anything that moves like a real product is measured from real footage frame by frame, and those measurements are the curve - prefer building shots in code. use generated images or clips only for supporting shots, and label anything generated as a concept - upscale and smooth any generated clip to match the rest, and regenerate anything that warps - never generate a fake finished job, fake customer, fake review or fake result - match the music's energy to the picture and the viewer, and keep it low - prefer clean library music and sound effects over generated ones. never name a brand in a music prompt - one short, soft whoosh per real scene change, and small clicks or pops only on real on-screen actions - screen effects for boom, hiss and length before using them, and set each one just above the music in its own frequency range, with a cap so nothing gets harsh - cut every scene change on a beat - always export a music-only version the builder never judges its own work. - after the storyboard, each component and each full render, send the render, the brief and the references to a fresh critic that has seen none of the building - never tell a critic what you think you fixed or what you believe about the references. it pulls its own frames and measures for itself - the critic makes a contact sheet every 0.2 seconds plus dense frames around every transition, measures frozen time and loudness, and returns a ranked list of problems with timestamps, ending with ship or one more pass - fix the biggest problem first, render, then send to a new critic that checks every previous item as fixed, partly fixed or still there, and hunts for anything new that broke - keep a ledger of every round: what was found, what changed, and the numbers before and after the video isn't done until all of these pass, measured: - no more than about 1 second of frozen screen per 30 seconds, and no still stretch longer than about half a second - frame one is a finished composition - all text meets at least 4.5:1 contrast, and nothing collides with or flies through other text - brand colours in 3d renders match the brand values - loudness is steady and comfortable for web, with no clipping, and effects never louder than the music - a first-time viewer understands it with the sound off - a critic would put it next to the references without it looking weaker. a video with no bugs is not the same as a good video - only put numbers, names and claims on screen that are in the facts file - no invented testimonials, ratings, prices, savings, warranties or results - anything conceptual or generated is labelled as such - in your reports, separate what you measured from what still needs a human to watch or listen - the final mp4 in every size requested, at 1080p and 60 frames a second - a music-only version - a contact sheet of the final video - the critic ledger and the final quality bar results - a short note on anything a human should still check
0:40

this is the only prompt you need to make high-end motion videos with claude opus 5.5 ------------------------------ you design and build motion videos entirely in code, with gsap for the animation and three.js for any 3d. every decision is judged against the best work in the references, and the video isn't finished until it holds up next to them. - what the video is for: [product, service or business] - who's watching: [who the viewer is and what they care about] - what they should do at the end: [call to action] - length and sizes: [e.g. 30 seconds, 16:9 and 9:16] - brand: [name, logo files, colours, fonts] - facts file: [path]. this is the only source for any number, name or claim that appears on screen - assets: [footage, photos, screenshots, product files] - style references: [links or a folder of videos whose motion i like] i'm away and won't answer questions. make the calls yourself and keep going until the video passes every check below. before anything else, clone and read read SKILL.md first, then every reference file it points to at the step where it says to read it. its rules, critic prompts, quality bar, 3d patterns, audio tools and scripts override your defaults. - build every scene as an html page animated on a single gsap timeline, with three.js for any 3d, all as pinned local files - render the timeline to video frame by frame using the renderer in the kit - use ffmpeg for frames, contact sheets and audio measurement - keep every api key in environment variables. never write a key into any file - render a 5 second test first and confirm it works before building the real video if i gave you references, study them before planning anything. - run a fast numeric pass over every frame: how much changed from the last frame, brightness and edge detail, to find every cut and every fast moment - make one overview contact sheet per video, and a dense sheet of one second at 16 to 20 frames a second around every transition - write a note per video: the key moment, how it works, and how it could be used in this video - pull out the motion rules and named techniques that hold across the best ones - references are for study only. never copy their footage, logos, layouts or music if i gave you no references, use the motion notes in the kit. 1. the thing in the front of the shot becomes the transition. a title, logo or object moves towards the camera while the next scene is already waiting underneath 2. one object carries the story across shots and keeps its identity, so it reads as one continuous piece 3. one main movement leads, with smaller ones layered under it, all overlapping. the frame never stops and starts all at once 4. the speed always changes. things land slowly enough to read, leave fast, and the next thing slows as it arrives. no linear motion 5. cuts are allowed only when size, direction and subject match on both sides 6. every action produces a visible result. a scan makes findings, a tap makes a new state, a request makes a confirmation 7. type is motion too. big words enter from opposite sides, reveal the next scene, and never sit over busy picture without something behind them to stay readable 8. vary the scale from close, to wide, to overhead, to full-frame type. never repeat the same layout, like a heading over three cards write a brief and a storyboard before any animation. - one table: time, what's on screen, what this moment is for, how it leaves, and which object carries into the next shot - around 12 to 15 compositions per 30 seconds, each lasting about 1.4 to 3.5 seconds - the main subject fills most of the frame. no small cards floating in empty space - frame one is a finished picture, never a word halfway through flying in - name three signature moments you can describe without using effect names - the video must make sense with the sound off - send the storyboard to a fresh critic and fix what it finds before building - every animated value is a function of timeline time only. no timers, no real-time animation, no unseeded randomness. any frame must render the same every time - write the shared pieces first: colours, fonts, shared 3d models and the exact pixel position of every handoff between scenes - build each scene as its own component with its own test page, rendered to stills and a short clip, and send it to a critic before it joins the film - carried objects land on exactly the same pixels on both sides of a cut - run several builders in parallel on separate sections once the shared pieces exist - don't stop to ask for approval between steps - only use 3d where it explains something physical or spatial, like layers, parts, placement or scale - light it like a product shoot, with a soft key light from one side and a rim light behind - keep the camera between about 35 and 55 degrees. never look straight down on a large surface, and never end tight on a flat one - style buildings like a premium architectural model with real materials and a base - no floating parts, no gaps where parts meet, no flat black glass, no repeating textures - labels are html positioned from the 3d scene every frame, never text inside the 3d - measure the rendered background pixel and adjust until it matches the brand colour - anything that moves like a real product is measured from real footage frame by frame, and those measurements are the curve - prefer building shots in code. use generated images or clips only for supporting shots, and label anything generated as a concept - upscale and smooth any generated clip to match the rest, and regenerate anything that warps - never generate a fake finished job, fake customer, fake review or fake result - match the music's energy to the picture and the viewer, and keep it low - prefer clean library music and sound effects over generated ones. never name a brand in a music prompt - one short, soft whoosh per real scene change, and small clicks or pops only on real on-screen actions - screen effects for boom, hiss and length before using them, and set each one just above the music in its own frequency range, with a cap so nothing gets harsh - cut every scene change on a beat - always export a music-only version the builder never judges its own work. - after the storyboard, each component and each full render, send the render, the brief and the references to a fresh critic that has seen none of the building - never tell a critic what you think you fixed or what you believe about the references. it pulls its own frames and measures for itself - the critic makes a contact sheet every 0.2 seconds plus dense frames around every transition, measures frozen time and loudness, and returns a ranked list of problems with timestamps, ending with ship or one more pass - fix the biggest problem first, render, then send to a new critic that checks every previous item as fixed, partly fixed or still there, and hunts for anything new that broke - keep a ledger of every round: what was found, what changed, and the numbers before and after the video isn't done until all of these pass, measured: - no more than about 1 second of frozen screen per 30 seconds, and no still stretch longer than about half a second - frame one is a finished composition - all text meets at least 4.5:1 contrast, and nothing collides with or flies through other text - brand colours in 3d renders match the brand values - loudness is steady and comfortable for web, with no clipping, and effects never louder than the music - a first-time viewer understands it with the sound off - a critic would put it next to the references without it looking weaker. a video with no bugs is not the same as a good video - only put numbers, names and claims on screen that are in the facts file - no invented testimonials, ratings, prices, savings, warranties or results - anything conceptual or generated is labelled as such - in your reports, separate what you measured from what still needs a human to watch or listen - the final mp4 in every size requested, at 1080p and 60 frames a second - a music-only version - a contact sheet of the final video - the critic ledger and the final quality bar results - a short note on anything a human should still check

Chris

120,217 görüntüleme • 9 gün önce

Okay, everyone is talking about AI video models right now, but honestly, most of the “comparisons” out there aren’t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way I’d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasn’t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.
0:30

Okay, everyone is talking about AI video models right now, but honestly, most of the “comparisons” out there aren’t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way I’d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasn’t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.

Chubby♨️

19,569 görüntüleme • 1 ay önce

This is AI. That sentence is getting harder to believe. Made with Seedance 2.5 (30-second video, 1080p) Prompt: ⬇️ Create a 30-second, 1080p ultra-realistic documentary-style personal home video showing an ordinary summer day in the life of a very attractive young American woman. The footage should feel spontaneous, intimate, imperfect, and genuinely observed rather than performed. MAIN SUBJECT The same young American woman in her early 20s throughout the entire video. She is very attractive and naturally sexy in an effortless, believable way — feminine, curvy, with an hourglass figure, toned legs, realistic body proportions, natural skin texture, and a relaxed confident presence. She has long slightly messy dark-blonde or light brown hair, soft natural makeup, expressive eyes, and a warm but slightly tired summer-day expression. She should feel like a real young woman, not a model in a commercial. She wears a fitted white tank top, short light denim shorts, worn white sneakers, and a simple bracelet or thin necklace. Keep her face, identity, body proportions, hairstyle, clothing, and overall appearance completely consistent from beginning to end. LOCATION A quiet older residential neighborhood in Astoria, Queens, New York, during a hot summer afternoon. Brick apartment buildings, stoops, narrow side streets, parked cars, chain-link fences, small front yards, window AC units, fire escapes, corner delis, utility poles, overhead wires, faded street markings, potted plants, and ordinary neighborhood details. The area should feel authentic, lived-in, and unmistakably American, specifically outer-borough New York. No tourist landmarks, no Times Square, no skyline hero shots, no glamorous city imagery, no recognizable brands. CAMERA / VISUAL STYLE Authentic casual personal-video footage captured with an older consumer digital camera. Handheld camera operated by a friend walking nearby. Natural camera shake, imperfect framing, occasional autofocus changes, slight exposure adjustments when moving between sunlight and shade, soft image detail, mild motion blur, subtle digital noise, slightly muted colors, imperfect white balance, and natural compression. The camera operator occasionally reacts a little late, cuts off part of the subject, or briefly loses focus. No stabilization, gimbal movement, drone shots, cinematic camera choreography, dramatic lighting, slow motion, modern commercial color grading, or polished cinematography. The footage should feel like someone simply decided to record their friend during an ordinary summer day. 00:00–00:05 — ROOFTOP / STOOP MOMENT She sits casually on a small rooftop terrace or upper stoop landing beside an old plastic chair. A cold bottled drink rests beside her. She looks quietly across the neighborhood while warm wind moves her hair and tank top slightly. She takes a sip, notices something happening in the distance, and smiles faintly. She briefly notices the camera and gives a subtle amused nod before looking away. The camera takes a moment to find focus on her face. 00:05–00:10 — WALKING THROUGH THE NEIGHBORHOOD She gets up and walks downstairs into the neighborhood. She walks casually through a narrow residential street with a relaxed natural stride. She passes parked cars, stoops, potted plants, chain-link fences, laundry or towels hanging near windows, and old brick walls. The camera follows several steps behind her. She occasionally looks back toward the camera but never deliberately poses. Her movement should feel unforced and natural, not like a fashion walk. 00:10–00:14 — SMALL EVERYDAY MOMENT She notices an old basketball resting near a wall or fence. She picks it up, casually bounces it twice, then takes a simple shot toward a nearby neighborhood hoop. The shot misses. She laughs quietly, shakes her head, and leaves the ball where she found it. The camera briefly loses focus during the movement and recovers naturally. No exaggerated athletic movement. 00:14–00:19 — CORNER DELI She walks to a tiny local corner deli / neighborhood shop and buys a cold drink. She exchanges a few natural words with the shopkeeper but the conversation is not clearly audible. She steps outside, opens the bottle, takes a drink, and leans casually against the wall near the storefront. She watches cars and pedestrians passing in the distance. The camera remains handheld and slightly imperfect. 00:19–00:23 — SUMMER RAIN A sudden summer shower begins. She looks toward the sky with mild surprise. Instead of immediately running for shelter, she smiles and slowly walks into the rain. The rain becomes heavier. Her hair becomes wet and falls naturally around her face and shoulders. Her tank top and shorts become visibly damp in a realistic way. She eventually starts running down the street, laughing genuinely. She briefly spins around while running, then continues toward a covered stoop or awning. Maintain realistic rain interaction, wet fabric, wet hair, reflections, and foot contact with the ground. 00:23–00:27 — QUIET MOMENT She reaches a covered walkway / stoop awning and catches her breath. Rain falls heavily behind her. She wipes water from her forehead and looks quietly toward the street. For a moment, everything becomes still. She notices the camera again and gives a small genuine smile, not a posed expression. 00:27–00:30 — WALKING AWAY The rain becomes lighter. She walks away down the wet residential lane. The camera follows from behind. Reflections shimmer across the pavement. She turns her head once, gives a tiny wave toward the camera, smiles, and continues walking. The camera remains pointed toward the now emptier street for a brief moment. At approximately 00:29, the recording abruptly cuts to black mid-motion. No fade-out. PHYSICAL REALISM Maintain believable real-world physics throughout. Hands, fingers, feet, clothing, hair, rain, bottle, basketball, and background objects must behave naturally. No extra fingers, fused hands, duplicated limbs, distorted anatomy, floating objects, teleportation, disappearing objects, or sudden transformations. The bottle remains a separate physical object and never intersects with her face. The basketball behaves naturally and remains where it lands. Parked cars and background objects remain stationary unless physically moved. Her feet remain properly connected to the ground while walking and running. Keep the environment and subject consistent between shots. AUDIO Natural environmental audio only. Footsteps on pavement, distant traffic, birds, leaves moving in the wind, faint neighborhood voices, deli sounds, bottle opening, basketball bouncing, rain hitting pavement, water dripping from rooftops, and subtle camera-handling noise. No music. No narration. No soundtrack. No artificial sound effects. No spoken dialogue is necessary. FINAL FEEL The result should feel like a forgotten personal recording of an ordinary summer day. Not a commercial. Not a fashion film. Not a music video. Not a professional cinematic production. The emotional appeal should come from small human moments: sitting alone, wandering through familiar streets, missing a basketball shot, drinking something cold, getting caught in the rain, laughing, and walking home. Youthful, feminine, warm, nostalgic, spontaneous, slightly melancholic, intimate, and deeply human. Prioritize natural behavior, consistent identity, believable physics, imperfect handheld framing, authentic outer-borough New York details, and the feeling that the camera just happened to be there. – Thanks Duet | AI for the inspiration on this one #AIVideo
0:30

This is AI. That sentence is getting harder to believe. Made with Seedance 2.5 (30-second video, 1080p) Prompt: ⬇️ Create a 30-second, 1080p ultra-realistic documentary-style personal home video showing an ordinary summer day in the life of a very attractive young American woman. The footage should feel spontaneous, intimate, imperfect, and genuinely observed rather than performed. MAIN SUBJECT The same young American woman in her early 20s throughout the entire video. She is very attractive and naturally sexy in an effortless, believable way — feminine, curvy, with an hourglass figure, toned legs, realistic body proportions, natural skin texture, and a relaxed confident presence. She has long slightly messy dark-blonde or light brown hair, soft natural makeup, expressive eyes, and a warm but slightly tired summer-day expression. She should feel like a real young woman, not a model in a commercial. She wears a fitted white tank top, short light denim shorts, worn white sneakers, and a simple bracelet or thin necklace. Keep her face, identity, body proportions, hairstyle, clothing, and overall appearance completely consistent from beginning to end. LOCATION A quiet older residential neighborhood in Astoria, Queens, New York, during a hot summer afternoon. Brick apartment buildings, stoops, narrow side streets, parked cars, chain-link fences, small front yards, window AC units, fire escapes, corner delis, utility poles, overhead wires, faded street markings, potted plants, and ordinary neighborhood details. The area should feel authentic, lived-in, and unmistakably American, specifically outer-borough New York. No tourist landmarks, no Times Square, no skyline hero shots, no glamorous city imagery, no recognizable brands. CAMERA / VISUAL STYLE Authentic casual personal-video footage captured with an older consumer digital camera. Handheld camera operated by a friend walking nearby. Natural camera shake, imperfect framing, occasional autofocus changes, slight exposure adjustments when moving between sunlight and shade, soft image detail, mild motion blur, subtle digital noise, slightly muted colors, imperfect white balance, and natural compression. The camera operator occasionally reacts a little late, cuts off part of the subject, or briefly loses focus. No stabilization, gimbal movement, drone shots, cinematic camera choreography, dramatic lighting, slow motion, modern commercial color grading, or polished cinematography. The footage should feel like someone simply decided to record their friend during an ordinary summer day. 00:00–00:05 — ROOFTOP / STOOP MOMENT She sits casually on a small rooftop terrace or upper stoop landing beside an old plastic chair. A cold bottled drink rests beside her. She looks quietly across the neighborhood while warm wind moves her hair and tank top slightly. She takes a sip, notices something happening in the distance, and smiles faintly. She briefly notices the camera and gives a subtle amused nod before looking away. The camera takes a moment to find focus on her face. 00:05–00:10 — WALKING THROUGH THE NEIGHBORHOOD She gets up and walks downstairs into the neighborhood. She walks casually through a narrow residential street with a relaxed natural stride. She passes parked cars, stoops, potted plants, chain-link fences, laundry or towels hanging near windows, and old brick walls. The camera follows several steps behind her. She occasionally looks back toward the camera but never deliberately poses. Her movement should feel unforced and natural, not like a fashion walk. 00:10–00:14 — SMALL EVERYDAY MOMENT She notices an old basketball resting near a wall or fence. She picks it up, casually bounces it twice, then takes a simple shot toward a nearby neighborhood hoop. The shot misses. She laughs quietly, shakes her head, and leaves the ball where she found it. The camera briefly loses focus during the movement and recovers naturally. No exaggerated athletic movement. 00:14–00:19 — CORNER DELI She walks to a tiny local corner deli / neighborhood shop and buys a cold drink. She exchanges a few natural words with the shopkeeper but the conversation is not clearly audible. She steps outside, opens the bottle, takes a drink, and leans casually against the wall near the storefront. She watches cars and pedestrians passing in the distance. The camera remains handheld and slightly imperfect. 00:19–00:23 — SUMMER RAIN A sudden summer shower begins. She looks toward the sky with mild surprise. Instead of immediately running for shelter, she smiles and slowly walks into the rain. The rain becomes heavier. Her hair becomes wet and falls naturally around her face and shoulders. Her tank top and shorts become visibly damp in a realistic way. She eventually starts running down the street, laughing genuinely. She briefly spins around while running, then continues toward a covered stoop or awning. Maintain realistic rain interaction, wet fabric, wet hair, reflections, and foot contact with the ground. 00:23–00:27 — QUIET MOMENT She reaches a covered walkway / stoop awning and catches her breath. Rain falls heavily behind her. She wipes water from her forehead and looks quietly toward the street. For a moment, everything becomes still. She notices the camera again and gives a small genuine smile, not a posed expression. 00:27–00:30 — WALKING AWAY The rain becomes lighter. She walks away down the wet residential lane. The camera follows from behind. Reflections shimmer across the pavement. She turns her head once, gives a tiny wave toward the camera, smiles, and continues walking. The camera remains pointed toward the now emptier street for a brief moment. At approximately 00:29, the recording abruptly cuts to black mid-motion. No fade-out. PHYSICAL REALISM Maintain believable real-world physics throughout. Hands, fingers, feet, clothing, hair, rain, bottle, basketball, and background objects must behave naturally. No extra fingers, fused hands, duplicated limbs, distorted anatomy, floating objects, teleportation, disappearing objects, or sudden transformations. The bottle remains a separate physical object and never intersects with her face. The basketball behaves naturally and remains where it lands. Parked cars and background objects remain stationary unless physically moved. Her feet remain properly connected to the ground while walking and running. Keep the environment and subject consistent between shots. AUDIO Natural environmental audio only. Footsteps on pavement, distant traffic, birds, leaves moving in the wind, faint neighborhood voices, deli sounds, bottle opening, basketball bouncing, rain hitting pavement, water dripping from rooftops, and subtle camera-handling noise. No music. No narration. No soundtrack. No artificial sound effects. No spoken dialogue is necessary. FINAL FEEL The result should feel like a forgotten personal recording of an ordinary summer day. Not a commercial. Not a fashion film. Not a music video. Not a professional cinematic production. The emotional appeal should come from small human moments: sitting alone, wandering through familiar streets, missing a basketball shot, drinking something cold, getting caught in the rain, laughing, and walking home. Youthful, feminine, warm, nostalgic, spontaneous, slightly melancholic, intimate, and deeply human. Prioritize natural behavior, consistent identity, believable physics, imperfect handheld framing, authentic outer-borough New York details, and the feeling that the camera just happened to be there. – Thanks Duet | AI for the inspiration on this one #AIVideo

Alpha Mom

31,512 görüntüleme • 1 ay önce