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Layer 1; spine board. Layer 2 thick heavy rubber mask. Layer 3 bodybag connected to the spine board. Layer 4 Carara belt locked and loaded layer 5 nipple clamps ^^ 💦💦💦😈😈😈🔒🔒 dammm no movement. But 🐷🐷🐷

GearNL

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14,490 görüntüleme • 2 yıl önce •via X (Twitter)

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: 😋

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looks good😈😈

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Facking hot

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Good way of storage 😈

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Lay me there please

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would enjoy to be this prisoner,Sir

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🟢Substance Painter Tip With "Anchor Points" you can link layers together (e.g. use one layer as a mask for another), which makes workflows much faster. Example: You have a shadow layer and want to add an outline around it. Instead of repainting the outline every time you change the shadows, use an Anchor Point: 1. Add an Anchor Point to the shadow layer 2. Add a Fill Filter for the shadow outline layer 3. For the Fill grayscale, select the Anchor Point 4. Add a "Mask Outline" filter 5. If you paint new shadows now, the outline will adjust automatically!

🟢Substance Painter Tip With "Anchor Points" you can link layers together (e.g. use one layer as a mask for another), which makes workflows much faster. Example: You have a shadow layer and want to add an outline around it. Instead of repainting the outline every time you change the shadows, use an Anchor Point: 1. Add an Anchor Point to the shadow layer 2. Add a Fill Filter for the shadow outline layer 3. For the Fill grayscale, select the Anchor Point 4. Add a "Mask Outline" filter 5. If you paint new shadows now, the outline will adjust automatically!

Keeki | 3D Artist

38,950 görüntüleme • 3 ay önce

🎓Learn how to create a powerful Torn Fabric smart material in a matter of seconds in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. ____________________________________________________ In this video, the steps are as follows: 1. Create a base fill layer containing no information. We will use this layer to call out the core effects. Add a black mask to this layer and inside that mask add a paint layer and draw a simple pill shape. 2. Next, add a blur directional and be sure the direction is the same direction that your fabric is flowing to. 3. Add a UV border generator set to subtract to mask out any uv seams followed by an anchorpoint. Additionally, add a messy Fibers 3 fill layer set to overlay. 4. Use a levels to adjust the mask along with a sharpen filter. A warp filter should also be added to introduce some randomness. Add an anchor point at the top of the mask as well. 5. Create another fill layer with its opacity channel set to black and apply a black mask to the fill layer. Inside its mask retrieve the anchorpoint information from the previous fill layer. 6. Create an additional fill layer with a bright diffuse color along with a black mask applied to it. Add the anchorpoint information from the previous mask into its mask as well and this should give us some white fibers on the edges. Now we have a torn fabric effect wherever we paint using the paint layer created inside the callout mask! ____________________________________________________ More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #tutorial #3dmodeling #hardsurface #texturing

🎓Learn how to create a powerful Torn Fabric smart material in a matter of seconds in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. In this video, the steps are as follows: 1. Create a base fill layer containing no information. We will use this layer to call out the core effects. Add a black mask to this layer and inside that mask add a paint layer and draw a simple pill shape. 2. Next, add a blur directional and be sure the direction is the same direction that your fabric is flowing to. 3. Add a UV border generator set to subtract to mask out any uv seams followed by an anchorpoint. Additionally, add a messy Fibers 3 fill layer set to overlay. 4. Use a levels to adjust the mask along with a sharpen filter. A warp filter should also be added to introduce some randomness. Add an anchor point at the top of the mask as well. 5. Create another fill layer with its opacity channel set to black and apply a black mask to the fill layer. Inside its mask retrieve the anchorpoint information from the previous fill layer. 6. Create an additional fill layer with a bright diffuse color along with a black mask applied to it. Add the anchorpoint information from the previous mask into its mask as well and this should give us some white fibers on the edges. Now we have a torn fabric effect wherever we paint using the paint layer created inside the callout mask! More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #tutorial #3dmodeling #hardsurface #texturing

Cohen Brawley

78,816 görüntüleme • 2 yıl önce

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Tom Yeh

959,393 görüntüleme • 1 ay önce

🎓Learn how to create a powerful Dynamic Embers smart material in a matter of seconds, in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. ___________________________________________________ In this video, the steps are as follows: 1. Create a fill layer to then apply the base maps for our asset into it. 2. Bake out the world space, AO, Curvature, and Position maps. These maps will be crucial for the smart material to function. Also, be sure to add an emissive channel to the project. 3. Next, create another fill layer labeled (Fire animation gradient) with a black mask and inside the mask apply a paint layer. Select the paint layer and paint a simple pill shape using a soft brush. Add an anchorpoint to the mask and disable all channels in this fill layer. 4. Create a folder labeled (embers smart material) and set it to pass through. Add a paint layer inside this folder and set it to pass through as well followed by an anchorpoint. 5. Create another fill layer and apply the paint layers anchorpoint information into its color channel. From here an HSL filter should be added to desaturate and darken the colors. 6. Next, create an additional fill layer with an orange-colored emissive channel. Add a black mask to this fill layer and inside its mask apply a dirt generator. This will give us the illusion of the embers glowing from inside the charred tree. Duplicate this fill layer and tweak its emissive color to a yellowish hue and reduce its mask amount. Do this a second time but with a white-colored emissive channel. This will now create an effect of the embers appearing brighter the deeper they are inside the tree. 7. Lastly, add these layers into a folder with a black mask and apply the (Fire Animation Gradient) anchorpoint into its mask via a fill layer. Now we have a charred embers effect wherever we paint using the paint layer created inside the gradient mask! This gradient mask can also be exported into Unreal engine to create some stunning real-time dynamic animations or the new material can simply be used for in-engine vertex painting. ____________________________________________________ More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #3dmodeling #texturing #madewithsubstance #ue5 #unrealengine #unity #vfx

🎓Learn how to create a powerful Dynamic Embers smart material in a matter of seconds, in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. _ In this video, the steps are as follows: 1. Create a fill layer to then apply the base maps for our asset into it. 2. Bake out the world space, AO, Curvature, and Position maps. These maps will be crucial for the smart material to function. Also, be sure to add an emissive channel to the project. 3. Next, create another fill layer labeled (Fire animation gradient) with a black mask and inside the mask apply a paint layer. Select the paint layer and paint a simple pill shape using a soft brush. Add an anchorpoint to the mask and disable all channels in this fill layer. 4. Create a folder labeled (embers smart material) and set it to pass through. Add a paint layer inside this folder and set it to pass through as well followed by an anchorpoint. 5. Create another fill layer and apply the paint layers anchorpoint information into its color channel. From here an HSL filter should be added to desaturate and darken the colors. 6. Next, create an additional fill layer with an orange-colored emissive channel. Add a black mask to this fill layer and inside its mask apply a dirt generator. This will give us the illusion of the embers glowing from inside the charred tree. Duplicate this fill layer and tweak its emissive color to a yellowish hue and reduce its mask amount. Do this a second time but with a white-colored emissive channel. This will now create an effect of the embers appearing brighter the deeper they are inside the tree. 7. Lastly, add these layers into a folder with a black mask and apply the (Fire Animation Gradient) anchorpoint into its mask via a fill layer. Now we have a charred embers effect wherever we paint using the paint layer created inside the gradient mask! This gradient mask can also be exported into Unreal engine to create some stunning real-time dynamic animations or the new material can simply be used for in-engine vertex painting. __ More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #3dmodeling #texturing #madewithsubstance #ue5 #unrealengine #unity #vfx

Cohen Brawley

46,070 görüntüleme • 2 yıl önce

🎓 Learn how to create a Cherry Wood Material in a matter of seconds in my latest video series (AAA) Pro Tips! ____________________________________________________ In this video, the steps are as follows: 1. Create a slightly red-colored fill layer with high gloss to act as the woods base material. 2. Add a second fill layer with a black mask, and inside that mask add a stripes greyscale set to planar projection. These stripes will act as the wood rings and should be projected onto your mesh in a natural way. The stripes should also be set to max softness, and followed by a Directional Noise 3 with a blend mode set to Difference. 3. Add a levels to tweak the mask and add an anchor point to the mask as well. 4. Create another fill layer with a dark brown diffuse, and store the information from the anchor point into its mask. Apply a levels to increase its intensity, and then add a fill layer up top with the anchorpoint information applied again, but set its blending mode to Subtract. 5. Create an additional fill layer with a very dark diffuse channel. Add a mask to the fill layer and inside its mask add a fill layer containing the anchorpoint information, from the Wood Rings. This fill layer can now be used to add some dark contrast to the center of the wood rings 6. A warp filter should also be used to apply some subtle imperfections to the wood rings and grain. Now we are left with a very nice result of Cherry wood! ____________________________________________________ More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips. Let me know below what you would like to learn in future videos! 👇 Happy Texturing! 💚 #gameart #texturing

🎓 Learn how to create a Cherry Wood Material in a matter of seconds in my latest video series (AAA) Pro Tips! In this video, the steps are as follows: 1. Create a slightly red-colored fill layer with high gloss to act as the woods base material. 2. Add a second fill layer with a black mask, and inside that mask add a stripes greyscale set to planar projection. These stripes will act as the wood rings and should be projected onto your mesh in a natural way. The stripes should also be set to max softness, and followed by a Directional Noise 3 with a blend mode set to Difference. 3. Add a levels to tweak the mask and add an anchor point to the mask as well. 4. Create another fill layer with a dark brown diffuse, and store the information from the anchor point into its mask. Apply a levels to increase its intensity, and then add a fill layer up top with the anchorpoint information applied again, but set its blending mode to Subtract. 5. Create an additional fill layer with a very dark diffuse channel. Add a mask to the fill layer and inside its mask add a fill layer containing the anchorpoint information, from the Wood Rings. This fill layer can now be used to add some dark contrast to the center of the wood rings 6. A warp filter should also be used to apply some subtle imperfections to the wood rings and grain. Now we are left with a very nice result of Cherry wood! More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips. Let me know below what you would like to learn in future videos! 👇 Happy Texturing! 💚 #gameart #texturing

Cohen Brawley

25,219 görüntüleme • 2 yıl önce

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 görüntüleme • 2 yıl önce

🎓Learn how to create a powerful Worn Leather smart material in a matter of seconds in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. ____________________________________________________ In this video, the steps are as follows: 1. Create a fill layer with a black mask applied. Inside its mask paint a simple pill shape and use a blur slope filter to introduce some random shapes. Apply a marble veins fill layer on top with its blending mode set to color burn. Apply a warp filter as well to introduce some randomness followed by an anchorpoint. 2. Create another fill layer using the same technique to apply some dirt in the crevices of the wear. 3. Next, Inside the main fill layer apply a tiling raw leather texture to its Color and roughness channel 4. Create an additional fill layer up top and apply a black mask to it.Inside this mask retrieve the anchorpoint information followed by a blur filter with a value of 6. Use a levels to increase the spread of the mask. Apply another marble veins matching the values of the marble veins fill layer that we previously added and set its blending mode to multiply. Lastly retrieve the anchorpoint information from the previous fill layer again but this time set its blending mode to subtract. This fill layers properties should have a dark diffuse color and a matte roughness. 5. Add another fill layer with a bright diffuse and height properties with a black mask applied to it. Inside its mask retrieve the anchorpoint information again followed by a blur slope. Apply the anchorpoint information again but this time set its blending mode to subtract. Now we have a worn leather effect wherever we paint using the paint layer created inside the callout mask! ____________________________________________________ More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #3dmodeling #3dartist #texturing #UE5 #unity #artstation

🎓Learn how to create a powerful Worn Leather smart material in a matter of seconds in my latest video series (AAA) Pro Tips! This smart material can be used on virtually any 3D asset. In this video, the steps are as follows: 1. Create a fill layer with a black mask applied. Inside its mask paint a simple pill shape and use a blur slope filter to introduce some random shapes. Apply a marble veins fill layer on top with its blending mode set to color burn. Apply a warp filter as well to introduce some randomness followed by an anchorpoint. 2. Create another fill layer using the same technique to apply some dirt in the crevices of the wear. 3. Next, Inside the main fill layer apply a tiling raw leather texture to its Color and roughness channel 4. Create an additional fill layer up top and apply a black mask to it.Inside this mask retrieve the anchorpoint information followed by a blur filter with a value of 6. Use a levels to increase the spread of the mask. Apply another marble veins matching the values of the marble veins fill layer that we previously added and set its blending mode to multiply. Lastly retrieve the anchorpoint information from the previous fill layer again but this time set its blending mode to subtract. This fill layers properties should have a dark diffuse color and a matte roughness. 5. Add another fill layer with a bright diffuse and height properties with a black mask applied to it. Inside its mask retrieve the anchorpoint information again followed by a blur slope. Apply the anchorpoint information again but this time set its blending mode to subtract. Now we have a worn leather effect wherever we paint using the paint layer created inside the callout mask! More AAA Game Dev Tips can be found on my YouTube channel here: Stay tuned for more weekly Tips! Happy Texturing!💚 #gamedev #gameart #3dmodeling #3dartist #texturing #UE5 #unity #artstation

Cohen Brawley

88,726 görüntüleme • 2 yıl önce

Canada paid $24 million to help a Chinese company make baby formula and ship 85% of it to China. The Canada Royal Milk story has layers. Layer 1: a Chinese dairy giant built Canada's largest infant formula plant in Kingston Ontario. Layer 2: Canadian taxpayers contributed at least $24 million to the project. Layer 3: early planning documents show 85% of production was intended for export to China from day one. Layer 4: it used supply-managed Canadian milk priced and controlled to protect Canadian food sovereignty. Layer 5: supply management advocates defend this system against American trade pressure every CUSMA cycle. Layer 6: supply management is a major trade irritant cited in the CUSMA negotiations. Bottom line: the system designed to keep Americans out subsidized Chinese exports. If you were the Americans at the negotiating table, wouldn’t this make you furious? And as a Canadian, doesn’t it anger you that we ship baby formula made with Canadian milk out of the country while parents here face sky-high prices and shortages?

Canada paid $24 million to help a Chinese company make baby formula and ship 85% of it to China. The Canada Royal Milk story has layers. Layer 1: a Chinese dairy giant built Canada's largest infant formula plant in Kingston Ontario. Layer 2: Canadian taxpayers contributed at least $24 million to the project. Layer 3: early planning documents show 85% of production was intended for export to China from day one. Layer 4: it used supply-managed Canadian milk priced and controlled to protect Canadian food sovereignty. Layer 5: supply management advocates defend this system against American trade pressure every CUSMA cycle. Layer 6: supply management is a major trade irritant cited in the CUSMA negotiations. Bottom line: the system designed to keep Americans out subsidized Chinese exports. If you were the Americans at the negotiating table, wouldn’t this make you furious? And as a Canadian, doesn’t it anger you that we ship baby formula made with Canadian milk out of the country while parents here face sky-high prices and shortages?

The Really Big Show

41,831 görüntüleme • 2 ay önce

JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer 1 : Hurst exponent. Below 0.45 fades work. Above 0.55 momentum works. Medallion switches strategy automatically based on regime. Layer 2 : Factor decomposition. Renaissance runs 200+ factors privately. High alpha plus low R-squared means your edge is unexplained by known factors. Layer 3 : Markov Chain regime modeling. 76% Bear probability over 5 periods means no long positions. The model decides before you do. Layer 4 : Neural net. Three outputs : long, short, flat. Not predicting price. Predicting direction probability. 52% accuracy Kelly-sized correctly still prints money. Layer 5 : Execution. Half-Kelly sizing. Hard 2% risk cap. Edge must exceed execution cost by 1.5x minimum. This is where 80% of quants bleed out. 400 people. One shared model. The edge was never a single formula. It was always the pipeline.

JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer 1 : Hurst exponent. Below 0.45 fades work. Above 0.55 momentum works. Medallion switches strategy automatically based on regime. Layer 2 : Factor decomposition. Renaissance runs 200+ factors privately. High alpha plus low R-squared means your edge is unexplained by known factors. Layer 3 : Markov Chain regime modeling. 76% Bear probability over 5 periods means no long positions. The model decides before you do. Layer 4 : Neural net. Three outputs : long, short, flat. Not predicting price. Predicting direction probability. 52% accuracy Kelly-sized correctly still prints money. Layer 5 : Execution. Half-Kelly sizing. Hard 2% risk cap. Edge must exceed execution cost by 1.5x minimum. This is where 80% of quants bleed out. 400 people. One shared model. The edge was never a single formula. It was always the pipeline.

Lummox

24,134 görüntüleme • 2 ay önce

Before you even see a U.S. Navy ship, it's already tracking you. Layer 1: VLS missile cells. Loaded and ready to reach out and kill you from 50+ miles away before you've even registered the ship on radar. Layer 2: A Gatling gun that doesn't need a human. It sees you at 10 miles, locks at 5, and puts 4,500 rounds per minute in the air before you blink. Layer 3: 11 launchers, 90% kill accuracy, infrared-guided, designed specifically to catch whatever the other two missed. Three fully independent layers, all capable of firing simultaneously, all capable of operating without a single sailor pressing a button. AI Telly

Before you even see a U.S. Navy ship, it's already tracking you. Layer 1: VLS missile cells. Loaded and ready to reach out and kill you from 50+ miles away before you've even registered the ship on radar. Layer 2: A Gatling gun that doesn't need a human. It sees you at 10 miles, locks at 5, and puts 4,500 rounds per minute in the air before you blink. Layer 3: 11 launchers, 90% kill accuracy, infrared-guided, designed specifically to catch whatever the other two missed. Three fully independent layers, all capable of firing simultaneously, all capable of operating without a single sailor pressing a button. AI Telly

Mario Nawfal

144,706 görüntüleme • 4 ay önce

“AI is no longer a narrative, it's a way to build the future.” Shahaf Bar-Geffen, CEO of COTI Foundation, dives into the launch of their privacy-centric Layer 2, the role of AI and cryptography in real-world use cases, and what success looks like for COTI. 0:37 – Launching a Privacy-Focused Layer 2 1:27 – Is Privacy an Obstacle? 2:26 – How Garbled Circuits Power Their Tech 3:00 – Real-World Test Case 4:47 – COTI’s Global Ambitions 8:48 – Defining Success for the Network Watch the full episode below! 🎥

“AI is no longer a narrative, it's a way to build the future.” Shahaf Bar-Geffen, CEO of COTI Foundation, dives into the launch of their privacy-centric Layer 2, the role of AI and cryptography in real-world use cases, and what success looks like for COTI. 0:37 – Launching a Privacy-Focused Layer 2 1:27 – Is Privacy an Obstacle? 2:26 – How Garbled Circuits Power Their Tech 3:00 – Real-World Test Case 4:47 – COTI’s Global Ambitions 8:48 – Defining Success for the Network Watch the full episode below! 🎥

Genzio Media | Rolly Mode

20,489 görüntüleme • 1 yıl önce

Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember

Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember

Radek Sienkiewicz

94,359 görüntüleme • 5 ay önce

$ONDS FULL 2026 ACQUISITION LIST 1. Roboteam adds unmanned ground vehicle layer with rugged tactical UGVs already deployed by military & security forces 2. Sentrycs brings counter-drone protection validated by its selection to help defend venues at the 2026 FIFA World Cup 3. 4M Defense adds AI-enabled land intelligence for demining & border security backed by ~$80M in active Israeli tender value 4. World View brings stratospheric ISR through long-endurance Stratollite sensing extending Ondas from air & ground into the stratosphere 5. Mistral gives Ondas U.S. defense prime contractor status, direct access to Army & Special Operations IDIQ contracts & $264M in backlog. 6. Omnisys adds battlefield command-and-control software layer connecting sensors, autonomous systems & workflows into one operating layer.

$ONDS FULL 2026 ACQUISITION LIST 1. Roboteam adds unmanned ground vehicle layer with rugged tactical UGVs already deployed by military & security forces 2. Sentrycs brings counter-drone protection validated by its selection to help defend venues at the 2026 FIFA World Cup 3. 4M Defense adds AI-enabled land intelligence for demining & border security backed by ~$80M in active Israeli tender value 4. World View brings stratospheric ISR through long-endurance Stratollite sensing extending Ondas from air & ground into the stratosphere 5. Mistral gives Ondas U.S. defense prime contractor status, direct access to Army & Special Operations IDIQ contracts & $264M in backlog. 6. Omnisys adds battlefield command-and-control software layer connecting sensors, autonomous systems & workflows into one operating layer.

Shay Boloor

262,639 görüntüleme • 3 ay önce

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

Ask Perplexity

64,013 görüntüleme • 10 ay önce

Pt1 was the Cloning Operation. Pt2 goes deeper. Everyone is focused on Layer 1 (predation) and Layer 2 (blackmail). Layer 3 was Epstein operating a genetics pipeline through Silicon Valley... but the question is HOW? Blackmail alone doesn't explain how one man controls an entire circle of tech billionaires. Layer 4 is the Inner Circle. There is no more denying it. Secret societies have been operating behind the scenes, unknown to the public, for hundreds of years. We caught them. It's in the files. Secret societies are not a meme. They're a historical fact. You can learn about them and do something about it, or you can keep pretending it's fantasy while nothing changes. IT'S TIME TO HOLD THESE PEOPLE ACCOUNTABLE. Thanks to everyone who shared Pt1. Let's keep going. Let's expose this whole inner circle together. I'm posting the full video from my YouTube here. All I ask is you share the message, and if you feel like it, check out the channel. Everything drops there first. Full video dropping now. Stay tuned for Pt 3... You won't believe it... Science fiction... is Science Fact. #EpsteinFiles #Epstein #SecretSocieties #EpsteinIsland #TruthMovement #InnerCircle #EpsteinDidntKillHimself #MindUnveiled

Pt1 was the Cloning Operation. Pt2 goes deeper. Everyone is focused on Layer 1 (predation) and Layer 2 (blackmail). Layer 3 was Epstein operating a genetics pipeline through Silicon Valley... but the question is HOW? Blackmail alone doesn't explain how one man controls an entire circle of tech billionaires. Layer 4 is the Inner Circle. There is no more denying it. Secret societies have been operating behind the scenes, unknown to the public, for hundreds of years. We caught them. It's in the files. Secret societies are not a meme. They're a historical fact. You can learn about them and do something about it, or you can keep pretending it's fantasy while nothing changes. IT'S TIME TO HOLD THESE PEOPLE ACCOUNTABLE. Thanks to everyone who shared Pt1. Let's keep going. Let's expose this whole inner circle together. I'm posting the full video from my YouTube here. All I ask is you share the message, and if you feel like it, check out the channel. Everything drops there first. Full video dropping now. Stay tuned for Pt 3... You won't believe it... Science fiction... is Science Fact. #EpsteinFiles #Epstein #SecretSocieties #EpsteinIsland #TruthMovement #InnerCircle #EpsteinDidntKillHimself #MindUnveiled

Mind Unveiled

40,539 görüntüleme • 6 ay önce

$PLTR cofounder Joe Lonsdale on investing in the 5 Layers of AI 🔮 “Over the next 10 years, every Fortune 500 company should benefit.” Layer 1: Chips Layer 2: Datacenters Level 3: Modeling (OpenAI, Anthropic, xAI) Level 4: Infrastructure & Deployment ( $META Llama, $PLTR 🔮(kinda) Level 5: Companies actually performing services in the economy “The big argument in venture is: is level 4 going to be eaten by level 3? Are the model companies themselves going to build out the infrastructure and own it? “The level 5 is about building things in the service economy. For example, healthcare billing is a $280B revenue industry in the U.S… We’re already making it 3x more productive. 3x higher margins… Via The Compound

$PLTR cofounder Joe Lonsdale on investing in the 5 Layers of AI 🔮 “Over the next 10 years, every Fortune 500 company should benefit.” Layer 1: Chips Layer 2: Datacenters Level 3: Modeling (OpenAI, Anthropic, xAI) Level 4: Infrastructure & Deployment ( $META Llama, $PLTR 🔮(kinda) Level 5: Companies actually performing services in the economy “The big argument in venture is: is level 4 going to be eaten by level 3? Are the model companies themselves going to build out the infrastructure and own it? “The level 5 is about building things in the service economy. For example, healthcare billing is a $280B revenue industry in the U.S… We’re already making it 3x more productive. 3x higher margins… Via The Compound

Jawwwn

151,179 görüntüleme • 1 yıl önce

ComfyUI 3D Node Improvements 03/18 (Multi-layer Output) Recently, I've made several changes and improvements to Load3D: 1. Support for simultaneous output of multiple layers - original, mask, normal maps, lineart 2. Performance improvements - rendering only occurs when the mouse is over the node 3. UI changes - using menus to manage the buttons in the top left corner 4. UI changes - temporarily removed depth mode from material options 5. Many backend code changes. #ComfyUI

ComfyUI 3D Node Improvements 03/18 (Multi-layer Output) Recently, I've made several changes and improvements to Load3D: 1. Support for simultaneous output of multiple layers - original, mask, normal maps, lineart 2. Performance improvements - rendering only occurs when the mouse is over the node 3. UI changes - using menus to manage the buttons in the top left corner 4. UI changes - temporarily removed depth mode from material options 5. Many backend code changes. #ComfyUI

jtydhr88

17,581 görüntüleme • 1 yıl önce

Are you lifting but neglecting movement work? DON’T. Here are 12 upper & lower movements you should do 1. Half kneeling pigeon rock 2. Half kneeling wall t-spine rotation 3. V sit-up 4. Half kneeling t spine to pec opener 5. Hand supported SL RDL to hip sweep 6. Seated t-spine rotation w/ board 7. SL Hip gate (w/ IR) w/ overhead hold 8. Thread the needle w/ foam roller 9. Med ball rocking 90/90 10. Chin-up dead hang 11. Inchworm to side lunge 12. Thread the needle w/ KB

Are you lifting but neglecting movement work? DON’T. Here are 12 upper & lower movements you should do 1. Half kneeling pigeon rock 2. Half kneeling wall t-spine rotation 3. V sit-up 4. Half kneeling t spine to pec opener 5. Hand supported SL RDL to hip sweep 6. Seated t-spine rotation w/ board 7. SL Hip gate (w/ IR) w/ overhead hold 8. Thread the needle w/ foam roller 9. Med ball rocking 90/90 10. Chin-up dead hang 11. Inchworm to side lunge 12. Thread the needle w/ KB

Gerry DeFilippo

102,889 görüntüleme • 1 yıl önce

You have a big goal. You think about it often. But if someone asked you how today's to-do list moves you closer to it, you'd struggle to answer. The solution is a goal stack. A goal stack is a reverse-engineered chain that connects a distant ambition to what you do today. Each layer feeds the one above it: Layer 1: High Hard Goal (1-5 years). An ambitious target that pushes you past your comfort zone. Layer 2: Break it down into annual targets, quarterly milestones, monthly objectives, and weekly tasks. Each level builds on the previous one, creating a logical progression toward the High Hard Goal. Layer 3: Daily Goal-Directed Actions. The single most important task you can do today, the one domino that triggers a chain reaction toward your High Hard Goal. At the end of each workday, take 10 minutes to plan tomorrow. Identify three goal-directed actions. Set wildly specific micro-goals for each. Then take the first step of the first step before you stop for the night. Open the document. Title it. Save it in the right folder. When you do this, your daily actions connect upward through the stack to the thing you actually care about. More on this in the article below: - Rian

You have a big goal. You think about it often. But if someone asked you how today's to-do list moves you closer to it, you'd struggle to answer. The solution is a goal stack. A goal stack is a reverse-engineered chain that connects a distant ambition to what you do today. Each layer feeds the one above it: Layer 1: High Hard Goal (1-5 years). An ambitious target that pushes you past your comfort zone. Layer 2: Break it down into annual targets, quarterly milestones, monthly objectives, and weekly tasks. Each level builds on the previous one, creating a logical progression toward the High Hard Goal. Layer 3: Daily Goal-Directed Actions. The single most important task you can do today, the one domino that triggers a chain reaction toward your High Hard Goal. At the end of each workday, take 10 minutes to plan tomorrow. Identify three goal-directed actions. Set wildly specific micro-goals for each. Then take the first step of the first step before you stop for the night. Open the document. Title it. Save it in the right folder. When you do this, your daily actions connect upward through the stack to the thing you actually care about. More on this in the article below: - Rian

Rian Doris

46,001 görüntüleme • 5 ay önce

Hey athletes! Are you lifting but neglecting movement work? DON’T. Here are 12 upper & lower movements you should do 1. Half kneeling pigeon rock 2. Half kneeling wall t-spine rotation 3. V sit-up 4. Half kneeling t spine to pec opener 5. Hand supported SL RDL to hip sweep 6. Seated t-spine rotation w/ board 7. SL Hip gate (w/ IR) w/ overhead hold 8. Thread the needle w/ foam roller 9. Med ball rocking 90/90 10. Chin-up dead hang 11. Inchworm to side lunge 12. Thread the needle w/ KB

Hey athletes! Are you lifting but neglecting movement work? DON’T. Here are 12 upper & lower movements you should do 1. Half kneeling pigeon rock 2. Half kneeling wall t-spine rotation 3. V sit-up 4. Half kneeling t spine to pec opener 5. Hand supported SL RDL to hip sweep 6. Seated t-spine rotation w/ board 7. SL Hip gate (w/ IR) w/ overhead hold 8. Thread the needle w/ foam roller 9. Med ball rocking 90/90 10. Chin-up dead hang 11. Inchworm to side lunge 12. Thread the needle w/ KB

Gerry DeFilippo

38,020 görüntüleme • 1 yıl önce