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Luna Snow Small Vore Belly Just a short test with the models from Marvel Rivals! (feel like I'm making these too fast lol) #vore #voreart #voreanimation #vorebelly #animation #fortnite #blender3d

Tht1Gurl28

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29,914 views • 2 days ago •via X (Twitter)

Arts Gaming #vore #voreart #voreanimation #vorebelly #animation #fortnite #blender3d

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That's not how you use a shield potion... Testing out some audio and SFX, also wanted to try Object vore lol #vore #voreart #voreanimation #vorebelly #animation #fortnite #blender3d

That's not how you use a shield potion... Testing out some audio and SFX, also wanted to try Object vore lol #vore #voreart #voreanimation #vorebelly #animation #fortnite #blender3d

Tht1Gurl28

85,804 views • 1 day ago

The only thing I'm excited about with Doomsday is that I'm 100% convinced we're getting Doctor Doom as a playable character for the anniversary because of the Path to Doomsday event. Doctor Doom is basically Marvel Rivals' mascot, so I fully expect him to get special treatment, probably even more than Luna. I wouldn't be surprised if they already have seven skins ready for his launch. I'm almost certain God Emperor Doom will be one of them too. Both Fortnite and Marvel Tokon Fighting Souls have it already as a recolor, and it's just essentially a recolor anyways, so it seems like an easy choice.

The only thing I'm excited about with Doomsday is that I'm 100% convinced we're getting Doctor Doom as a playable character for the anniversary because of the Path to Doomsday event. Doctor Doom is basically Marvel Rivals' mascot, so I fully expect him to get special treatment, probably even more than Luna. I wouldn't be surprised if they already have seven skins ready for his launch. I'm almost certain God Emperor Doom will be one of them too. Both Fortnite and Marvel Tokon Fighting Souls have it already as a recolor, and it's just essentially a recolor anyways, so it seems like an easy choice.

Sage VALE (Mina Ha thrall)

103,041 views • 1 month ago

Sanju Samson and Thala Dhoni were honored with flower bouquets. A few moments later, Sanju gently took the bouquet from Dhoni, making him feel at ease. Small gestures like these speak volumes about the admiration and respect Sanju has for the big man. Chettan - you're one of us💛!!

Sanju Samson and Thala Dhoni were honored with flower bouquets. A few moments later, Sanju gently took the bouquet from Dhoni, making him feel at ease. Small gestures like these speak volumes about the admiration and respect Sanju has for the big man. Chettan - you're one of us💛!!

𝑻𝑯𝑨𝑳𝑨

95,538 views • 1 year ago

A supposed Avengers: Doomsday trailer is making the rounds online, with footage claiming to be from the movie — but there’s a major catch. The “leaked” footage isn’t an official Marvel Studios trailer. It’s an AI-generated recreation, created to look like the real CinemaCon footage and match descriptions that have been circulating online. And honestly… some of these AI recreations are getting way too convincing. Marvel has not officially released the trailer, so treat any “leaked trailer” currently circulating with caution. Avengers: Doomsday arrives in theaters December 18, 2026. #AvengersDoomsday #Marvel #Avengers

A supposed Avengers: Doomsday trailer is making the rounds online, with footage claiming to be from the movie — but there’s a major catch. The “leaked” footage isn’t an official Marvel Studios trailer. It’s an AI-generated recreation, created to look like the real CinemaCon footage and match descriptions that have been circulating online. And honestly… some of these AI recreations are getting way too convincing. Marvel has not officially released the trailer, so treat any “leaked trailer” currently circulating with caution. Avengers: Doomsday arrives in theaters December 18, 2026. #AvengersDoomsday #Marvel #Avengers

Ashish Kumar

12,874 views • 15 days ago

Andrej Karpathy calls AI Agents slop "Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing. And it's not—it's slop! And I think they are not coming to terms with it. And maybe they are trying to fundraise or something like that, I'm not sure what's going on."

Andrej Karpathy calls AI Agents slop "Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing. And it's not—it's slop! And I think they are not coming to terms with it. And maybe they are trying to fundraise or something like that, I'm not sure what's going on."

Lisan al Gaib

3,129,978 views • 10 months ago

Wait… this is AI animation? The short film 《AKUMA》 from Dreamina × Annecy honestly caught my attention because it feels less like a random generated clip and more like a scene someone actually directed. The prison wagon setup has such a strong mood. The warm light coming through the bars, the rough wood, the small chain rattles, and that subtle wagon movement all make the scene feel alive without doing too much. What I liked most is Saku’s reaction when he wakes up. It is quiet and controlled. He does not panic. He just checks his bandages, looks around, then notices the shackles on his hands. That small acting choice makes the moment feel much more real. The camera also keeps it simple, which works. Close shots, readable faces, little cuts between the characters, and just enough movement to remind you the wagon is still rolling. That is why Dreamina feels interesting to me. Dreamina Octo helps shape the idea and creative direction, while Seedance 2.0 brings it into motion with mood, character acting, and atmosphere. Dreamina AI → Dreamina Octo Creative Agent + Dreamina Seedance 2.0 Animation Model. Dreamina AI #DreaminaAtAnnecy #MadeWithDreamina #AnimationFilm #DreaminaAI

Wait… this is AI animation? The short film 《AKUMA》 from Dreamina × Annecy honestly caught my attention because it feels less like a random generated clip and more like a scene someone actually directed. The prison wagon setup has such a strong mood. The warm light coming through the bars, the rough wood, the small chain rattles, and that subtle wagon movement all make the scene feel alive without doing too much. What I liked most is Saku’s reaction when he wakes up. It is quiet and controlled. He does not panic. He just checks his bandages, looks around, then notices the shackles on his hands. That small acting choice makes the moment feel much more real. The camera also keeps it simple, which works. Close shots, readable faces, little cuts between the characters, and just enough movement to remind you the wagon is still rolling. That is why Dreamina feels interesting to me. Dreamina Octo helps shape the idea and creative direction, while Seedance 2.0 brings it into motion with mood, character acting, and atmosphere. Dreamina AI → Dreamina Octo Creative Agent + Dreamina Seedance 2.0 Animation Model. Dreamina AI #DreaminaAtAnnecy #MadeWithDreamina #AnimationFilm #DreaminaAI

Marry claire

14,091 views • 2 months ago

Yooooo what's up with Fortnite bots in chapter 7?! They've evolved like CRAZY! Look at this movement. Bro literally cracked my shield and had pretty good movement. I have a working theory that there are actually A LOT more bots than we think there are in matches. not the 30-50% they tell us there are but actually like 90%. I'm not talking about just for low skilled players like me, I think the high tier players have these next level bots or even better all over their matches too. What do you think?

Yooooo what's up with Fortnite bots in chapter 7?! They've evolved like CRAZY! Look at this movement. Bro literally cracked my shield and had pretty good movement. I have a working theory that there are actually A LOT more bots than we think there are in matches. not the 30-50% they tell us there are but actually like 90%. I'm not talking about just for low skilled players like me, I think the high tier players have these next level bots or even better all over their matches too. What do you think?

Quantum Camper

51,758 views • 2 months ago

Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

KIRI Engine - 3D Scanner App

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The Audi S1 was a pocket-sized quattro hot hatch that felt almost too interesting for its era. Short wheelbase, serious traction, and a level of drivetrain character that Audi never really returned to in this size. It sat against cars like the F56 Mini Cooper JCW and the Polo WRC, small performance machines built around real intent rather than just numbers. The S1 disappeared quietly, replaced by softer, more standardised models. Today the most powerful model shares the powertrain with the Polo GTI, yet itself is not positioned as a true hot hatch. It’s one of those cars that makes you wonder why brands stop making the fun versions first, even when the formula clearly worked.

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Autowelt

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In a masterclass at Sequoia Capital AI Ascent, Jim Fan laid out the "Great Parallel": how robotics is speedrunning the LLM playbook. 🔹 VLA → WAM: Moving from language-heavy models to "World Action Models" that dream in physics. 🔹 Teleop → EgoScale: Replacing manual data with human egocentric video. 🔹 Simulation 2.0: Using neural simulators like DreamDojo to turn compute into environments. "Our generation was born too late to explore the earth and too early to explore the stars. But we are born just in time to solve robotics." He believes that robots will pass the Physical Turing Test in the coming 2–3 years.

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Humanoids daily

12,123 views • 3 months ago

Olivia celebrating ‘drivers license’’s anniversary via her Instagram 💜 “happy 5 years of drivers license!!! looking back at these pics I feel like I look like a baby but it also feels like just yesterday lol. this song has totally changed my life in ways I still can't totally wrap my head around. I'm forever grateful for this magical chapter and I can't wait to scream red lights stop signs with u guys at every concert I play for the rest of my life! happy birthday DL 🎂🎂🎂”

Olivia celebrating ‘drivers license’’s anniversary via her Instagram 💜 “happy 5 years of drivers license!!! looking back at these pics I feel like I look like a baby but it also feels like just yesterday lol. this song has totally changed my life in ways I still can't totally wrap my head around. I'm forever grateful for this magical chapter and I can't wait to scream red lights stop signs with u guys at every concert I play for the rest of my life! happy birthday DL 🎂🎂🎂”

Olivia Rodrigo Daily

17,054 views • 7 months ago

What did I do wrong 🤦🏻‍♂️ Alright ladies and gentlemen. These buds have been curing for right at 2 weeks. They still smell like grass. Hardly any cannabis smell. I dried them for just over 2 weeks. Did the snap test. Both snapped like a dried toothpick. I feel like I did it wrong. Or at least did something wrong, if not everything. Load this in 4K and let me know what I did wrong. I know the buds are small. This is my first ever grow so be kind if you don’t mind lol. I know it’s ugly. Thanks in advance for any help y’all give me. I have 58% humidity packs in my jars. Stored under my restroom that’s dark & cool.

What did I do wrong 🤦🏻‍♂️ Alright ladies and gentlemen. These buds have been curing for right at 2 weeks. They still smell like grass. Hardly any cannabis smell. I dried them for just over 2 weeks. Did the snap test. Both snapped like a dried toothpick. I feel like I did it wrong. Or at least did something wrong, if not everything. Load this in 4K and let me know what I did wrong. I know the buds are small. This is my first ever grow so be kind if you don’t mind lol. I know it’s ugly. Thanks in advance for any help y’all give me. I have 58% humidity packs in my jars. Stored under my restroom that’s dark & cool.

Chase

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My little pack of rescued babies 🐾 ❤️ 🐕 Luna Fortuna Black Ops Perimeter Check was still in her mamas belly when they were flown off a reservation to save them from being drowned 😭 Bolt was beaten and bounced around from shelter to shelter so by the time we rescued him he just sat in the corner and shook for the first month 😭 Miss Leila was rescued by one of my dear friends after a life of abuse and abandonment and she is so brave and learning all about love and what doggo life should be like 😭 I absolutely adore every single one of them and I am so blessed to have them feel safe with me ❤️ Also if I ever meet the people who hurt these precious souls I’m 1000% going to jail…with a big smile on my face 🥰🥰🥰

My little pack of rescued babies 🐾 ❤️ 🐕 Luna Fortuna Black Ops Perimeter Check was still in her mamas belly when they were flown off a reservation to save them from being drowned 😭 Bolt was beaten and bounced around from shelter to shelter so by the time we rescued him he just sat in the corner and shook for the first month 😭 Miss Leila was rescued by one of my dear friends after a life of abuse and abandonment and she is so brave and learning all about love and what doggo life should be like 😭 I absolutely adore every single one of them and I am so blessed to have them feel safe with me ❤️ Also if I ever meet the people who hurt these precious souls I’m 1000% going to jail…with a big smile on my face 🥰🥰🥰

M2

56,946 views • 8 months ago

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

Martin Ziqiao Ma

33,847 views • 2 months ago

004/100 Buttons. A bit of the process on building an animation. When looking at a finished animation or in this example a finished button, it can look quite complex inside the CSS. But when building it, it’s more like a lot of simple steps, one after another. Here I had the idea to make some kind of text animation like the footer logo on the Osmo site. I try to add the base animation with no complex easing, for example transition: translate 0.4s ease. Starting with just moving the one text from bottom to top and the other text to top. Adding a stagger, play around with it. Searching for a way to make it more circular. On the research I found the sin() function inside CSS which can build a more smooth non linear curve for the stagger which creates this circular effect. And step by step adding more complexity like, different easing for hover/hover-out, opacity, 3D transform and more. I use also the sin() function to rotate the letters, so the middle ones are getting more rotated than the outer ones. Another thing which helps is to add a small delay on hover, for example 0.05s or 0.1s, you don’t really see the difference, but when you hover pretty fast on and out it doesn’t get that jumpy. I’m using here GSAP’s SplitText to split every char into spans. And then I’m adding a CSS index variable to every span, starting from the center. SplitText can provide CSS index variables, but you cannot tell it from which direction. For the sin() it’s also important to have a max length, so I add another CSS variable with the max char number on it. Crafting 100 Buttons with Osmo ⏳ Total time: 63h

Eduard Bodak

166,023 views • 3 months ago

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,441 views • 1 month ago

One Polymarket trader turned $10 into $450K with his script ClawdBot wrote him a working Bitcoin betting script in 10 hours - that’s just crazy. No strong programming skills He’s not friends with Elon Musk He just built a working script that delivers results Wallet → Copytrade → When I opened the code, my surprise was huge. No huge databases. No insanely complex infrastructure. Nothing rocket-science level. HIS FULL STRATEGY: 1. Low-risk “NO” positions The bot mostly bets against outcomes that are extremely unlikely to happen. Instead of chasing massive payouts, it stacks many small, high-probability wins. It behaves more like disciplined risk management than traditional gambling. 2. Exploiting logical price gaps If event A clearly suggests event B should also move in probability, but the market hasn’t adjusted yet, the bot enters instantly. By reacting in seconds, it captures short-lived mispricings before human traders can even process the news. 3. Main edge: sports and politics These markets are filled with retail traders who often react emotionally or too late. The bot operates inside the spread, repeatedly taking tiny profits from small inconsistencies in odds. Scale effect Instead of making a few large trades, the system executes tens of thousands of micro-trades each month. Each trade earns only cents, but the volume allows profits to compound into large totals over time. Final take There is an ongoing “bot war” on prediction platforms like Polymarket. Crypto markets are already slowed down by fees and heavy competition, while sports and political markets remain more chaotic - which can favor traders who automate fast, data-driven strategies.

One Polymarket trader turned $10 into $450K with his script ClawdBot wrote him a working Bitcoin betting script in 10 hours - that’s just crazy. No strong programming skills He’s not friends with Elon Musk He just built a working script that delivers results Wallet → Copytrade → When I opened the code, my surprise was huge. No huge databases. No insanely complex infrastructure. Nothing rocket-science level. HIS FULL STRATEGY: 1. Low-risk “NO” positions The bot mostly bets against outcomes that are extremely unlikely to happen. Instead of chasing massive payouts, it stacks many small, high-probability wins. It behaves more like disciplined risk management than traditional gambling. 2. Exploiting logical price gaps If event A clearly suggests event B should also move in probability, but the market hasn’t adjusted yet, the bot enters instantly. By reacting in seconds, it captures short-lived mispricings before human traders can even process the news. 3. Main edge: sports and politics These markets are filled with retail traders who often react emotionally or too late. The bot operates inside the spread, repeatedly taking tiny profits from small inconsistencies in odds. Scale effect Instead of making a few large trades, the system executes tens of thousands of micro-trades each month. Each trade earns only cents, but the volume allows profits to compound into large totals over time. Final take There is an ongoing “bot war” on prediction platforms like Polymarket. Crypto markets are already slowed down by fees and heavy competition, while sports and political markets remain more chaotic - which can favor traders who automate fast, data-driven strategies.

winkle.

37,649 views • 6 months ago