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that’s satisfying: Real-time destruction physics in game engines usually force developers to choose between performance and visual fidelity. VFX artist U R B A N - B / Urban Bradesko and Grayson Cotrell are running a workshop breaking down high-performance real-time destruction techniques for game development. Instead of relying...

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3DxDEV

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63,624 görüntüleme • 16 gün önce •via X (Twitter)

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MOST PEOPLE DON’T KNOW THIS IS EVEN POSSIBLE. You can swap your face live on camera in real-time using just ONE image. OPEN SOURCE. FREE TO INSTALL. No expensive tools, studio setup or complex pipelines. Real-time face swap. Live webcam. VIDEO DEEPFAKE. Here is what is going on. Most creators struggle with content production. You need editing, VFX tools, time, and skills to create something engaging. The problem has always been this: AI face swap tools are slow, offline, or require heavy editing workflows. Upload → wait → render → export → repeat. Someone just killed that entire workflow. It is called Deep Live Cam. You connect a single image to your webcam, and it swaps your face live - instantly. No rendering. No waiting. No editing. And then it acts in real time. swaps faces live on webcam or video supports multiple faces at once keeps natural expressions with mouth masking works with CPU, GPU, or Mac lets you stream, record, or create content instantly You can literally: go live as anyone create viral memes in seconds run live shows with different identities replace actors in videos in real time prank, entertain, or build content at scale Professional studios spend thousands on VFX pipelines to do this. This does it in seconds. Someone built a real-time deepfake engine… and put it on the internet for free.
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MOST PEOPLE DON’T KNOW THIS IS EVEN POSSIBLE. You can swap your face live on camera in real-time using just ONE image. OPEN SOURCE. FREE TO INSTALL. No expensive tools, studio setup or complex pipelines. Real-time face swap. Live webcam. VIDEO DEEPFAKE. Here is what is going on. Most creators struggle with content production. You need editing, VFX tools, time, and skills to create something engaging. The problem has always been this: AI face swap tools are slow, offline, or require heavy editing workflows. Upload → wait → render → export → repeat. Someone just killed that entire workflow. It is called Deep Live Cam. You connect a single image to your webcam, and it swaps your face live - instantly. No rendering. No waiting. No editing. And then it acts in real time. swaps faces live on webcam or video supports multiple faces at once keeps natural expressions with mouth masking works with CPU, GPU, or Mac lets you stream, record, or create content instantly You can literally: go live as anyone create viral memes in seconds run live shows with different identities replace actors in videos in real time prank, entertain, or build content at scale Professional studios spend thousands on VFX pipelines to do this. This does it in seconds. Someone built a real-time deepfake engine… and put it on the internet for free.

Kanika

17,835 görüntüleme • 5 ay önce

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:
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A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

16,064 görüntüleme • 19 gün önce

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."
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This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

Rohan Paul

12,761 görüntüleme • 8 ay önce

We are sharing a major update to our General World Models efforts. GWM Worlds 2 can generate full, interactive, real-time video simulations in one continuous 720p stream at 24 fps and audio at 48000 Hz, responding to your inputs as you explore. It is the first world model that generalizes to arbitrary actions rather than a fixed set of actions or just navigation. Dynamic actions are what actually matter for building gaming and interactive experiences, and for training agents in these simulations. We also introduced WorldPrompt, a way to tell GWM-2 that a world can be split into two kinds of state: what persists and what changes over time. For example: persistent gravity, physics, and light, alongside actions that describe movement, gestures, object interactions, speech, and sound.
0:15

We are sharing a major update to our General World Models efforts. GWM Worlds 2 can generate full, interactive, real-time video simulations in one continuous 720p stream at 24 fps and audio at 48000 Hz, responding to your inputs as you explore. It is the first world model that generalizes to arbitrary actions rather than a fixed set of actions or just navigation. Dynamic actions are what actually matter for building gaming and interactive experiences, and for training agents in these simulations. We also introduced WorldPrompt, a way to tell GWM-2 that a world can be split into two kinds of state: what persists and what changes over time. For example: persistent gravity, physics, and light, alongside actions that describe movement, gestures, object interactions, speech, and sound.

Cristóbal Valenzuela

249,665 görüntüleme • 27 gün önce

Two cinematic prompts for Seedance 2.0 right here 👇 [STYLE + CAMERA + ATMOSPHERE] Ultra-photorealistic cinematic 15-second action sequence from a grounded action thriller film. Nighttime at an abandoned industrial train yard. Old trains, rusted tracks, broken platforms and warehouses. Strong moonlight and industrial lighting. The visual language is gritty and realistic with hyper-realistic practical destruction. Camera is handheld-dynamic with urgent moves. [IMAGE REFERENCES] No reference images provided. Generate a fully consistent lead character: a male fugitive in his mid-30s, short hair, intense expression. He wears a dark jacket, jeans and boots. Maintain exact same appearance and clothing throughout the clip. [TIMELINE SECOND BY SECOND] 0-3s: [Dynamic handheld tracking shot from side-rear] The man runs across the train yard as a freight train derails violently after hitting a collapsed section of track. Massive train cars tip over and crash into the ground, sending metal, wood and cargo flying. The camera tracks him urgently as the destruction spreads. 3-7s: [Slow continuous orbiting camera moving around the frozen destruction] At the peak of the derailment, everything freezes in perfect realistic physics. Huge train cars, twisted metal, wooden debris and clouds of dust hang motionless in the air with accurate weight and trajectories. The camera performs a smooth, continuous orbit through the frozen chaos, moving between large suspended train cars and passing close to sharp metal fragments while the man remains visible. 7-15s: [Dynamic tracking shot as time resumes] At the 7-second mark time snaps back to forward motion with a slight realistic temporal residue. Some outer debris shows a very subtle reverse lag before continuing. The man has used the frozen moment to dive behind a concrete barrier. When motion resumes, several large pieces of metal miss him. He stays low and runs toward an open warehouse as debris finally crashes down. The camera tracks with him through the dust. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, hyper-realistic rigid body destruction with correct material properties (metal, wood, train parts), perfect frozen mid-air physics, seamless transition from freeze to resume, natural dust interaction, heavy cinematic motion blur on fast debris, stable character performance, movie-level practical destruction VFX quality.
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Two cinematic prompts for Seedance 2.0 right here 👇 [STYLE + CAMERA + ATMOSPHERE] Ultra-photorealistic cinematic 15-second action sequence from a grounded action thriller film. Nighttime at an abandoned industrial train yard. Old trains, rusted tracks, broken platforms and warehouses. Strong moonlight and industrial lighting. The visual language is gritty and realistic with hyper-realistic practical destruction. Camera is handheld-dynamic with urgent moves. [IMAGE REFERENCES] No reference images provided. Generate a fully consistent lead character: a male fugitive in his mid-30s, short hair, intense expression. He wears a dark jacket, jeans and boots. Maintain exact same appearance and clothing throughout the clip. [TIMELINE SECOND BY SECOND] 0-3s: [Dynamic handheld tracking shot from side-rear] The man runs across the train yard as a freight train derails violently after hitting a collapsed section of track. Massive train cars tip over and crash into the ground, sending metal, wood and cargo flying. The camera tracks him urgently as the destruction spreads. 3-7s: [Slow continuous orbiting camera moving around the frozen destruction] At the peak of the derailment, everything freezes in perfect realistic physics. Huge train cars, twisted metal, wooden debris and clouds of dust hang motionless in the air with accurate weight and trajectories. The camera performs a smooth, continuous orbit through the frozen chaos, moving between large suspended train cars and passing close to sharp metal fragments while the man remains visible. 7-15s: [Dynamic tracking shot as time resumes] At the 7-second mark time snaps back to forward motion with a slight realistic temporal residue. Some outer debris shows a very subtle reverse lag before continuing. The man has used the frozen moment to dive behind a concrete barrier. When motion resumes, several large pieces of metal miss him. He stays low and runs toward an open warehouse as debris finally crashes down. The camera tracks with him through the dust. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, hyper-realistic rigid body destruction with correct material properties (metal, wood, train parts), perfect frozen mid-air physics, seamless transition from freeze to resume, natural dust interaction, heavy cinematic motion blur on fast debris, stable character performance, movie-level practical destruction VFX quality.

TechHalla

16,316 görüntüleme • 2 ay önce

🚨 PHYSICISTS JUST CONFIRMED “NEGATIVE TIME” IS REAL IN A MIND-BENDING QUANTUM EXPERIMENT. Light can exit a cloud of atoms before it even enters. In a new experiment, researchers fired photons through a dense cloud of ultra-cold atoms and measured something that shouldn’t be possible in classical physics. Some photons appeared to spend a negative amount of time inside the cloud effectively leaving before they had fully arrived. Why this matters: • This isn’t time travel it’s a quantum effect involving how light interacts with matter at the deepest level • It comes from “weak measurements” that let scientists observe the system without fully disturbing it • The atoms themselves “report” spending negative time in an excited state • It challenges our everyday intuition about cause and effect in quantum systems The deeper implication is enormous: We are seeing the strange, non-intuitive nature of quantum mechanics play out in real experiments. Time at the quantum scale doesn’t always behave like the arrow we experience in daily life. Effects can appear to precede causes in measurable ways without breaking relativity or causality. This is one of the clearest experimental windows yet into how reality works at its most fundamental level. What do you think does “negative time” change how you see reality, or is it just another quantum quirk we’ll eventually get used to? Follow for more frontier physics and reality-bending discoveries.
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🚨 PHYSICISTS JUST CONFIRMED “NEGATIVE TIME” IS REAL IN A MIND-BENDING QUANTUM EXPERIMENT. Light can exit a cloud of atoms before it even enters. In a new experiment, researchers fired photons through a dense cloud of ultra-cold atoms and measured something that shouldn’t be possible in classical physics. Some photons appeared to spend a negative amount of time inside the cloud effectively leaving before they had fully arrived. Why this matters: • This isn’t time travel it’s a quantum effect involving how light interacts with matter at the deepest level • It comes from “weak measurements” that let scientists observe the system without fully disturbing it • The atoms themselves “report” spending negative time in an excited state • It challenges our everyday intuition about cause and effect in quantum systems The deeper implication is enormous: We are seeing the strange, non-intuitive nature of quantum mechanics play out in real experiments. Time at the quantum scale doesn’t always behave like the arrow we experience in daily life. Effects can appear to precede causes in measurable ways without breaking relativity or causality. This is one of the clearest experimental windows yet into how reality works at its most fundamental level. What do you think does “negative time” change how you see reality, or is it just another quantum quirk we’ll eventually get used to? Follow for more frontier physics and reality-bending discoveries.

TheNewPhysics

22,256 görüntüleme • 4 ay önce

Bye bye, VFX artists? Is this really the end? Why would a studio spend $10,000 on a single VFX shot… when tools like Seedance 2.0 can generate an entire cinematic scene in minutes? That’s the uncomfortable question no one wants to answer. We’re watching a shift happen in real time. Jobs are already being reshaped. Pipelines are changing. And the speed of evolution is hard to ignore. In just 3 years, AI went from experimental… to producing complex, high-quality visuals that were unthinkable before. Now compare that to the traditional path: A VFX artist can take years—often a decade— to reach a professional level. So what happens when that gap collapses? This isn’t just about replacing artists. It’s about redefining what “creating” even means. The barrier to entry is disappearing. The tools are getting exponentially better. And the timeline is accelerating. The real question isn’t if this changes the industry— It’s how fast.
0:15

Bye bye, VFX artists? Is this really the end? Why would a studio spend $10,000 on a single VFX shot… when tools like Seedance 2.0 can generate an entire cinematic scene in minutes? That’s the uncomfortable question no one wants to answer. We’re watching a shift happen in real time. Jobs are already being reshaped. Pipelines are changing. And the speed of evolution is hard to ignore. In just 3 years, AI went from experimental… to producing complex, high-quality visuals that were unthinkable before. Now compare that to the traditional path: A VFX artist can take years—often a decade— to reach a professional level. So what happens when that gap collapses? This isn’t just about replacing artists. It’s about redefining what “creating” even means. The barrier to entry is disappearing. The tools are getting exponentially better. And the timeline is accelerating. The real question isn’t if this changes the industry— It’s how fast.

nachos2d

43,813 görüntüleme • 5 ay önce

Game Day Priming for Marshall Baseball 🦬🔥 Biggest points of emphasis: ✅Add everything in a small dosage within a short time parameter. “Small doses stimulate. Large doses annihilate.” Tony Holler ✅Program exercises that maximize intent and output. The session should be High CNS and set them up for a great performance during games or scouting. ✅Add in needed concepts as determined necessary. Love adding in the Hunter Eisenhower Force System, especially in the warm up. For this session, the goal was to get a rotational rhythm and then experience force across different time parameters depending on the high or low catch for each limb. This also extends into the circuit, just built into a crescendo for our Fast Force exercises. Warm Up: Rhythm Med Ball Side to Side Hip Toss Chest-Supported Plate Y High Drop Catch 1 Plate Drop Low Catch Circuit: Heavy Med Ball Slam Light Med Ball Slam Trap Bar Pin Pull Reactive Trap Bar Jump Bent Knee Hamstring Rebound Straight Leg Small to Large Primetimes Sprint Rest #realtraining
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Game Day Priming for Marshall Baseball 🦬🔥 Biggest points of emphasis: ✅Add everything in a small dosage within a short time parameter. “Small doses stimulate. Large doses annihilate.” Tony Holler ✅Program exercises that maximize intent and output. The session should be High CNS and set them up for a great performance during games or scouting. ✅Add in needed concepts as determined necessary. Love adding in the Hunter Eisenhower Force System, especially in the warm up. For this session, the goal was to get a rotational rhythm and then experience force across different time parameters depending on the high or low catch for each limb. This also extends into the circuit, just built into a crescendo for our Fast Force exercises. Warm Up: Rhythm Med Ball Side to Side Hip Toss Chest-Supported Plate Y High Drop Catch 1 Plate Drop Low Catch Circuit: Heavy Med Ball Slam Light Med Ball Slam Trap Bar Pin Pull Reactive Trap Bar Jump Bent Knee Hamstring Rebound Straight Leg Small to Large Primetimes Sprint Rest #realtraining

Nat Sauberan

58,279 görüntüleme • 2 yıl önce

🚨 PHYSICISTS JUST SPLIT A SINGLE PHOTON AND IT TURNED INTO AN IMPROBABLE SWARM OF PARTICLES. In a striking experiment, researchers have shown that a photon can be split apart in such a way that it produces a large number of particles, creating what they describe as a “mixture from zero to infinity.” Instead of the usual clean splitting into two photons (as seen in spontaneous parametric down-conversion), this process generated a complex, broad swarm of particles. The result challenges conventional intuition about how photons behave when pushed into extreme nonlinear regimes. Why this matters: • It demonstrates a rare and complex form of photon splitting that was previously very difficult to observe cleanly • Such processes could help simulate high-energy particle physics in table-top experiments • It opens new possibilities for generating exotic quantum states of light • It provides deeper insight into nonlinear quantum electrodynamics (QED) in strong fields The deeper implication: Photons are usually thought of as indivisible quanta of light. But under the right extreme conditions, a single photon can effectively “break apart” into many particles. This isn’t just a curiosity it touches on fundamental questions about the nature of light and matter, and could eventually lead to new tools for quantum technologies and for studying physics that normally requires particle accelerators. We’re seeing light behave in ways that blur the line between a single quantum and a many-particle system. How do you think being able to controllably split photons into swarms of particles could impact quantum optics or fundamental physics research? Follow for more frontier quantum physics and breakthroughs in light-matter interaction.
0:10

🚨 PHYSICISTS JUST SPLIT A SINGLE PHOTON AND IT TURNED INTO AN IMPROBABLE SWARM OF PARTICLES. In a striking experiment, researchers have shown that a photon can be split apart in such a way that it produces a large number of particles, creating what they describe as a “mixture from zero to infinity.” Instead of the usual clean splitting into two photons (as seen in spontaneous parametric down-conversion), this process generated a complex, broad swarm of particles. The result challenges conventional intuition about how photons behave when pushed into extreme nonlinear regimes. Why this matters: • It demonstrates a rare and complex form of photon splitting that was previously very difficult to observe cleanly • Such processes could help simulate high-energy particle physics in table-top experiments • It opens new possibilities for generating exotic quantum states of light • It provides deeper insight into nonlinear quantum electrodynamics (QED) in strong fields The deeper implication: Photons are usually thought of as indivisible quanta of light. But under the right extreme conditions, a single photon can effectively “break apart” into many particles. This isn’t just a curiosity it touches on fundamental questions about the nature of light and matter, and could eventually lead to new tools for quantum technologies and for studying physics that normally requires particle accelerators. We’re seeing light behave in ways that blur the line between a single quantum and a many-particle system. How do you think being able to controllably split photons into swarms of particles could impact quantum optics or fundamental physics research? Follow for more frontier quantum physics and breakthroughs in light-matter interaction.

TheNewPhysics

25,874 görüntüleme • 3 ay önce

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.
0:19

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.

Alok

62,631 görüntüleme • 2 ay önce

We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺
0:10

We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺

Avalanche🔺

13,142 görüntüleme • 6 ay önce

Doesn’t get much better than texts like this. From the guys you know have been putting in the work. — Big time recruit. Middle of the order on a big time high school team. We spent the whole offseason shortening up his swing. More ready to swing, gap to gap. He also put real focus into skinny bat, 3 plate drill, and mixed pitches. The skill of HITTING, not just the swing. Both pieces matter. The swing (mechanical advantage) and the skill of hitting. Find what you need. Train what the game is telling you to get better at. > Send this to who needs it > Save this for when you need it.
0:15

Doesn’t get much better than texts like this. From the guys you know have been putting in the work. — Big time recruit. Middle of the order on a big time high school team. We spent the whole offseason shortening up his swing. More ready to swing, gap to gap. He also put real focus into skinny bat, 3 plate drill, and mixed pitches. The skill of HITTING, not just the swing. Both pieces matter. The swing (mechanical advantage) and the skill of hitting. Find what you need. Train what the game is telling you to get better at. > Send this to who needs it > Save this for when you need it.

Trey Hannam

21,030 görüntüleme • 4 ay önce

you are making video games all wrong for decades almost all game art time went into reverse-engineering the end result you start with concept art of what you WANT it to look like. then for YEARS you walk the computer backwards into something it can process: a rendering engine, 3d assets that fit that engine, and a gamer’s local hardware that has to turn all of that back into a high fidelity image incredibly slow. incredibly expensive. but it was the best we had ai makes that pipeline faster as everyone is figuring out today. but they’re missing the real unlock: YOU DON’T NEED THAT PIPELINE AT ALL if you have a reference for the end look, you don’t model it in 3d. you don’t reverse-engineer into an engine you keep a very basic greybox model of the world as the source of truth, and track every entity in real time. it doesn’t need to look good at all… the player never sees it! a fast video model turns that into the “game” in real time. it doesn’t have to look like a game. it can look like a 2d cartoon or cinematic live action fal’s h3 max is very fast. astra 6 low-effort too. neither is fully realtime yet but together they can generate continuous, coherent, player-controlled scenes, faster than playback early demo:
0:19

you are making video games all wrong for decades almost all game art time went into reverse-engineering the end result you start with concept art of what you WANT it to look like. then for YEARS you walk the computer backwards into something it can process: a rendering engine, 3d assets that fit that engine, and a gamer’s local hardware that has to turn all of that back into a high fidelity image incredibly slow. incredibly expensive. but it was the best we had ai makes that pipeline faster as everyone is figuring out today. but they’re missing the real unlock: YOU DON’T NEED THAT PIPELINE AT ALL if you have a reference for the end look, you don’t model it in 3d. you don’t reverse-engineer into an engine you keep a very basic greybox model of the world as the source of truth, and track every entity in real time. it doesn’t need to look good at all… the player never sees it! a fast video model turns that into the “game” in real time. it doesn’t have to look like a game. it can look like a 2d cartoon or cinematic live action fal’s h3 max is very fast. astra 6 low-effort too. neither is fully realtime yet but together they can generate continuous, coherent, player-controlled scenes, faster than playback early demo:

Udi Wertheimer

124,601 görüntüleme • 24 gün önce

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.
0:10

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

TheNewPhysics

21,772 görüntüleme • 5 ay önce

“Crash is so desperate for exit liquidity, he’s such a scammer” Step 1: Rolls up sleeves Step 2: share a +490k USD leverage Pnut trade this week in TG with my followers Step 3: Proceed to make +3M USD in the bloodiest week of 2025 Sit down and stop projecting People want to see me do so BAD bc my success makes them feel bad about themselves They want to bring me down, so they feel closer and better about themselves How about u just lift urself up? Instead of being mad at me or others The solution is to always - get better, instead of blame others or anything - be consistent - try ur best even when things are not going your way - do ur best to be a net positive to everyone you come across Stop focusing on me. Focus on you Hating on me is a waste of time and energy if u can’t tell by now I will always ultimately find a way to win for myself and others You should too
0:12

“Crash is so desperate for exit liquidity, he’s such a scammer” Step 1: Rolls up sleeves Step 2: share a +490k USD leverage Pnut trade this week in TG with my followers Step 3: Proceed to make +3M USD in the bloodiest week of 2025 Sit down and stop projecting People want to see me do so BAD bc my success makes them feel bad about themselves They want to bring me down, so they feel closer and better about themselves How about u just lift urself up? Instead of being mad at me or others The solution is to always - get better, instead of blame others or anything - be consistent - try ur best even when things are not going your way - do ur best to be a net positive to everyone you come across Stop focusing on me. Focus on you Hating on me is a waste of time and energy if u can’t tell by now I will always ultimately find a way to win for myself and others You should too

Crash

112,533 görüntüleme • 1 yıl önce

Nic is actually one of those people that looked perfect on the show but the more you keep up after, the more things just start… not adding up. It’s like everything he does has a layer of performance to it. The “good guy” image is a bit too polished, the way he moves is very calculated, and you can tell he’s very aware of how he wants to be perceived at all times. That kind of thing always cracks eventually. With Olandria I must laugh cause she’s blinded by her delusions, it’s nice on the surface, but if you’ve been paying attention, there are little moments that feel off. Not enough for people to clock immediately, but enough to make you side-eye. And that’s usually how it starts small inconsistencies before the real slip. I don’t think he’s as genuine as people want him to be, and honestly, it’s only a matter of time before something happens that people can’t defend or spin. When it does, I’m not even going to argue with anyone. I’ll just be here, because some of us already saw it coming. #LoveIslandUSA #olandria #NicholasVansteenberghe
0:19

Nic is actually one of those people that looked perfect on the show but the more you keep up after, the more things just start… not adding up. It’s like everything he does has a layer of performance to it. The “good guy” image is a bit too polished, the way he moves is very calculated, and you can tell he’s very aware of how he wants to be perceived at all times. That kind of thing always cracks eventually. With Olandria I must laugh cause she’s blinded by her delusions, it’s nice on the surface, but if you’ve been paying attention, there are little moments that feel off. Not enough for people to clock immediately, but enough to make you side-eye. And that’s usually how it starts small inconsistencies before the real slip. I don’t think he’s as genuine as people want him to be, and honestly, it’s only a matter of time before something happens that people can’t defend or spin. When it does, I’m not even going to argue with anyone. I’ll just be here, because some of us already saw it coming. #LoveIslandUSA #olandria #NicholasVansteenberghe

Chris ejiofor🔱🦋🐻

607,099 görüntüleme • 5 ay önce

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇
0:14

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇

Ridark

11,592 görüntüleme • 1 ay önce

Webhooks are LIVE on KEPLER 🛰️ Get real-time HTTP notifications directly from the blockchain. Say goodbye to constantly polling for data and managing heavy infrastructure. Setting up a webhook takes just a few clicks. Perfect for Developers & No-Code Builders • You can easily route webhooks directly into OpenClaw or platforms like Make or n8n. Build powerful, automated workflows in minutes without writing a single line of code. • For the classical builders, easily integrate real-time events into your existing Web2 backend using standard HTTP POST requests. Seamlessly bridge Web2 and Web3 to power custom indexing bots, trading algorithms, or dApp backends using our lightweight JSON payloads. Precision Filtering Cut through the noise by triggering webhooks only when your specific address, identifier, or topic conditions are met. Reliable Delivery If your endpoint or automation flow hiccups, our system automatically retries the delivery up to 4 times so you never miss an event. Cryptographic Security Every request includes an Ed25519 signature in the header, letting you natively verify that the payload is authentic and hasn't been tampered with.
0:12

Webhooks are LIVE on KEPLER 🛰️ Get real-time HTTP notifications directly from the blockchain. Say goodbye to constantly polling for data and managing heavy infrastructure. Setting up a webhook takes just a few clicks. Perfect for Developers & No-Code Builders • You can easily route webhooks directly into OpenClaw or platforms like Make or n8n. Build powerful, automated workflows in minutes without writing a single line of code. • For the classical builders, easily integrate real-time events into your existing Web2 backend using standard HTTP POST requests. Seamlessly bridge Web2 and Web3 to power custom indexing bots, trading algorithms, or dApp backends using our lightweight JSON payloads. Precision Filtering Cut through the noise by triggering webhooks only when your specific address, identifier, or topic conditions are met. Reliable Delivery If your endpoint or automation flow hiccups, our system automatically retries the delivery up to 4 times so you never miss an event. Cryptographic Security Every request includes an Ed25519 signature in the header, letting you natively verify that the payload is authentic and hasn't been tampered with.

Project X 🧬

11,155 görüntüleme • 7 ay önce