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🎓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 #artstationshow more

Cohen Brawley

2,455 subscribers

88,602 views • 2 years ago •via X (Twitter)

Education Science & Technology Arts

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5 Comments

Artys2 years ago

Cool!

Siddhant2 years ago

Invaluable content!!

Cohen Brawley2 years ago

Cheers!🍻

V2 years ago

🤯

Midnight Mayhem2 years ago

Looks amazing!

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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 views • 2 months ago

Here is a breakdown (kind of) how I did this effect for live2d Vtuber model art. I’ve drawn the lips, you would have to separate top and bottom though and added a cyan layer which will be my mask and the mask for the rigger to clip on. Then I added a shiny add layer on top and used the cyan layer as a mask, just to test the effect in procreate, you would have to delete the black/white procreate mask for the rigger because this can cause issues. If you have any questions feels free to ask. 💕

Here is a breakdown (kind of) how I did this effect for live2d Vtuber model art. I’ve drawn the lips, you would have to separate top and bottom though and added a cyan layer which will be my mask and the mask for the rigger to clip on. Then I added a shiny add layer on top and used the cyan layer as a mask, just to test the effect in procreate, you would have to delete the black/white procreate mask for the rigger because this can cause issues. If you have any questions feels free to ask. 💕

Sandrina 😈🎀 | VArtist | †øм

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How annoying is it when you can't find a company's logo in SVG format? I use a quick hack in Figma that lets me easily change the logo's color to whatever I need. 1. Add a rectangle layer on top of the logo layer. 2. Right-click and create a mask for the logo layer. 3. Voilà.

James McDonald

27,883 views • 1 year ago

Tips: Inverted Hull Method #Live2D You can create an anime-style outline with a few simple steps. Here's how: 1. Open your paint tool and select the hair parts. Select the hair lineart and register it as the selected area. Select the hair base layer and erase the color from the selected parts. 2. Next, fill the inside with a dark color and move the lineart behind the base hair layer Repeat the same steps for the other bang strands. After adjusting the layer order, Place all hull lineart layers behind all base hair layers. 3. Export the PSD to Live2D and rig the hair motions. The result looks just like anime! Sound video⬇️

Tips: Inverted Hull Method #Live2D You can create an anime-style outline with a few simple steps. Here's how: 1. Open your paint tool and select the hair parts. Select the hair lineart and register it as the selected area. Select the hair base layer and erase the color from the selected parts. 2. Next, fill the inside with a dark color and move the lineart behind the base hair layer Repeat the same steps for the other bang strands. After adjusting the layer order, Place all hull lineart layers behind all base hair layers. 3. Export the PSD to Live2D and rig the hair motions. The result looks just like anime! Sound video⬇️

Ninta(ニンタ) Art&Live2D (Vgen comm)

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🌐 O3 Layer Incentivized Testnet – Applications Now Open O3 Layer is launching its incentivized testnet, inviting dApps and developers to be part of the first modular Layer 3 ecosystem on Bitcoin. ✅ Test and optimize your dApp in a scalable, high-performance environment ✅ Gain early access, incentives, and ecosystem support ✅ Join a pioneering network driving Bitcoin’s next evolution Apply now by filling out the form below: 🔗 Be among the first to explore the potential of Bitcoin’s modular future with O3 Layer. Apply now!

🌐 O3 Layer Incentivized Testnet – Applications Now Open O3 Layer is launching its incentivized testnet, inviting dApps and developers to be part of the first modular Layer 3 ecosystem on Bitcoin. ✅ Test and optimize your dApp in a scalable, high-performance environment ✅ Gain early access, incentives, and ecosystem support ✅ Join a pioneering network driving Bitcoin’s next evolution Apply now by filling out the form below: 🔗 Be among the first to explore the potential of Bitcoin’s modular future with O3 Layer. Apply now!

O3 Layer

29,671 views • 1 year ago

Whiteboard Fox now can now fill an area with a block of color! Click Draw > Style > Fill to enter Fill mode and define the bounds to be filled. This feature is currently only available to Pro subscribers, but will be free for all users in 6 months.

Whiteboard Fox now can now fill an area with a block of color! Click Draw > Style > Fill to enter Fill mode and define the bounds to be filled. This feature is currently only available to Pro subscribers, but will be free for all users in 6 months.

Whiteboard Fox

213,941 views • 2 years ago

CSS Trick! 🤙 You can create gradient borders on translucent elements using mask-clip and mask-composite with a pseudo-element 🔥 .gradient-border::after { mask-clip: padding-box, border-box; mask-composite: intersect; mask: linear-gradient(transparent, transparent), linear-gradient(white, white); } It's the same "Transparent border trick" from before. But, now you apply it to a pseudo-element 😎 The trick is to create a pseudo-element with a gradient background and then mask it so we only see the part we want, the border ✨ mask-clip defines the area affected by a mask. Similar to how you can define background-size. Using padding-box and border-box constrains the two masks. mask-composite is the magic part ✨ It defines a compositing operation for stacked mask layers. Using intersect means that the parts that overlap get replaced. And this seems to work in all browsers 🙌 As for the rest of the styles... – Make sure you set pointer-events: none on the pseudo-element – Make sure it fills the parent element. You can use position: absolute and inset: 0 – Make sure the background fills the space including the border-width. You can use calc to achieve that: --bg-size: calc(100% + (2px * var(--border))); background: var(--gradient) center center / var(--bg-size) var(--bg-size); That's it! 🚀 Gradient borders on translucent elements. You can set all the backdrop-filter: blur() you like! 😅 CodePen.IO link below! 👇

CSS Trick! 🤙 You can create gradient borders on translucent elements using mask-clip and mask-composite with a pseudo-element 🔥 .gradient-border::after { mask-clip: padding-box, border-box; mask-composite: intersect; mask: linear-gradient(transparent, transparent), linear-gradient(white, white); } It's the same "Transparent border trick" from before. But, now you apply it to a pseudo-element 😎 The trick is to create a pseudo-element with a gradient background and then mask it so we only see the part we want, the border ✨ mask-clip defines the area affected by a mask. Similar to how you can define background-size. Using padding-box and border-box constrains the two masks. mask-composite is the magic part ✨ It defines a compositing operation for stacked mask layers. Using intersect means that the parts that overlap get replaced. And this seems to work in all browsers 🙌 As for the rest of the styles... – Make sure you set pointer-events: none on the pseudo-element – Make sure it fills the parent element. You can use position: absolute and inset: 0 – Make sure the background fills the space including the border-width. You can use calc to achieve that: --bg-size: calc(100% + (2px * var(--border))); background: var(--gradient) center center / var(--bg-size) var(--bg-size); That's it! 🚀 Gradient borders on translucent elements. You can set all the backdrop-filter: blur() you like! 😅 CodePen.IO link below! 👇

jhey ʕ•ᴥ•ʔ

269,847 views • 2 years ago

$CSS Tip! 🤙 You can use mask-composite and some JavaScript to create this pointer proximity following glow border ✨ .glow { mask-composite: intersect; mask-clip: padding-box, border-box; mask: linear-gradient(#0000, #0000), conic-gradient(#0000 0deg, #fff, #0000 45deg); } The trick is to mask a background-image with a combination of mask layers. mask-composite: intersect; means the mask used will be the intersection of the layers 🔥 use source-in, xor; in browsers that don't support intersect; In this demo, you can use pseudoelements and rely on scoped custom properties to do a lot of the heavy lifting for you 🙌 Once you've masked the background, you need to update the starting angle of the conic-gradient on pointermove 👆 You can work that out by getting the center point of each card and then calculating the angle between that and the pointer with Math.atan2 🤓 let ANGLE = Math.atan2( event?.y - CARD_CENTER[1], event?.x - CARD_CENTER[0] ) * 180 / Math.PI ANGLE = ANGLE < 0 ? ANGLE + 360 : ANGLE; CARD.style.setProperty('--start', ANGLE + 90) You plug that into your conic-gradient mask as a custom property accounting for --spread ⚡️ conic-gradient(from calc((var(--angle) - (var(--spread) * 0.5)) * 1deg), #000 0deg, #fff, #0000 calc(var(--spread) * 1deg)); To get the blur, you apply a blur to the glow container on each card 🤙 .glows { filter: blur(calc(var(--blur) * 1px); } That's it! Layers of masks that are clipped and composited before being blurred 😎 The added trick is to fade each one in when the pointer is in the defined proximity of the card. For example, don't show unless within 100px of a card. You can see that in the video. Check out the JavaScript code for that 🫶 Couldn't resist making this one 😁 CodePen.IO link below! 👇$

CSS Tip! 🤙 You can use mask-composite and some JavaScript to create this pointer proximity following glow border ✨ .glow { mask-composite: intersect; mask-clip: padding-box, border-box; mask: linear-gradient(#0000, #0000), conic-gradient(#0000 0deg, #fff, #0000 45deg); } The trick is to mask a background-image with a combination of mask layers. mask-composite: intersect; means the mask used will be the intersection of the layers 🔥 use source-in, xor; in browsers that don't support intersect; In this demo, you can use pseudoelements and rely on scoped custom properties to do a lot of the heavy lifting for you 🙌 Once you've masked the background, you need to update the starting angle of the conic-gradient on pointermove 👆 You can work that out by getting the center point of each card and then calculating the angle between that and the pointer with Math.atan2 🤓 let ANGLE = Math.atan2( event?.y - CARD_CENTER[1], event?.x - CARD_CENTER[0] ) * 180 / Math.PI ANGLE = ANGLE < 0 ? ANGLE + 360 : ANGLE; CARD.style.setProperty('--start', ANGLE + 90) You plug that into your conic-gradient mask as a custom property accounting for --spread ⚡️ conic-gradient(from calc((var(--angle) - (var(--spread) * 0.5)) * 1deg), #000 0deg, #fff, #0000 calc(var(--spread) * 1deg)); To get the blur, you apply a blur to the glow container on each card 🤙 .glows { filter: blur(calc(var(--blur) * 1px); } That's it! Layers of masks that are clipped and composited before being blurred 😎 The added trick is to fade each one in when the pointer is in the defined proximity of the card. For example, don't show unless within 100px of a card. You can see that in the video. Check out the JavaScript code for that 🫶 Couldn't resist making this one 😁 CodePen.IO link below! 👇

jhey ʕ•ᴥ•ʔ

1,180,047 views • 2 years ago

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 views • 2 years ago

When I saw the mask "Tribes of the Calf" from Kanbas I knew I had to make it into reality. The jewelry and gold really made it stand out for me. Since Sam Spratt's The Masquerade was revealed, I have been spending time sculpting and dissecting the mask to recreate it in 3D as faithfully as possible. I delved into the creation of this mask for many reasons. I love a good challenge and this mask surely was one for me. Creating something in 3D from a 2D image is not easy, and especially when the source has generative nature, some stuff is hard to interpret, but I tried my best to make sure the visual integrity of the mask is as close to the original as possible. Splitting the whole mask into parts, filling the missing pieces so I can build the textures was quite a lot of work. I tried to present the mask in my own style with a slightly different colorway to adapt to the mask itself. Please enjoy this short animation, and turn on sound🔊 This piece is my statement that I am here to stay. That I have a voice that often feels being lost in the void. That I have been creating and posting digital art for over 20 years now and will continue until I'm gone. I have a story to tell and I want to be heard. The space we have here is small, and is shrinking day by day. It doesn't have to be like that. We need to support each other and push ourselves and people here, otherwise we are all doomed. As Kanbas has put in their observation of the mask: "Inspirational. Emotional. Natural." This is what our space can be, and this is me making a statement with this homage. I will share a 4k still below as well as a short video showing the 3D GLB interactive model together with a yt link to the 4k video since compression here is pretty bad.

When I saw the mask "Tribes of the Calf" from Kanbas I knew I had to make it into reality. The jewelry and gold really made it stand out for me. Since Sam Spratt's The Masquerade was revealed, I have been spending time sculpting and dissecting the mask to recreate it in 3D as faithfully as possible. I delved into the creation of this mask for many reasons. I love a good challenge and this mask surely was one for me. Creating something in 3D from a 2D image is not easy, and especially when the source has generative nature, some stuff is hard to interpret, but I tried my best to make sure the visual integrity of the mask is as close to the original as possible. Splitting the whole mask into parts, filling the missing pieces so I can build the textures was quite a lot of work. I tried to present the mask in my own style with a slightly different colorway to adapt to the mask itself. Please enjoy this short animation, and turn on sound🔊 This piece is my statement that I am here to stay. That I have a voice that often feels being lost in the void. That I have been creating and posting digital art for over 20 years now and will continue until I'm gone. I have a story to tell and I want to be heard. The space we have here is small, and is shrinking day by day. It doesn't have to be like that. We need to support each other and push ourselves and people here, otherwise we are all doomed. As Kanbas has put in their observation of the mask: "Inspirational. Emotional. Natural." This is what our space can be, and this is me making a statement with this homage. I will share a 4k still below as well as a short video showing the 3D GLB interactive model together with a yt link to the 4k video since compression here is pretty bad.

shoneec

17,531 views • 1 year ago

How do we enable single-layer PCBs to support the routing complexity of multi-layer PCBs? We're making a "Bridge-Programmable Interconnect" chip. This chip is "programmed" by traces etched on the top layer of the PCB, it allows any configurable pin to connect to any other configurable pin Why is this a big deal? Single-layer PCB etching can be done in minutes on a UV laser, but because they don't have vias it is impossible to change layers. Using an interconnect chip, you are guaranteed to be able to route any signal over any other signal. By placing these on your single-layer PCB, you can reconfigure wiring such that you don't need any vias!

How do we enable single-layer PCBs to support the routing complexity of multi-layer PCBs? We're making a "Bridge-Programmable Interconnect" chip. This chip is "programmed" by traces etched on the top layer of the PCB, it allows any configurable pin to connect to any other configurable pin Why is this a big deal? Single-layer PCB etching can be done in minutes on a UV laser, but because they don't have vias it is impossible to change layers. Using an interconnect chip, you are guaranteed to be able to route any signal over any other signal. By placing these on your single-layer PCB, you can reconfigure wiring such that you don't need any vias!

Seve

30,046 views • 8 months ago

These stainless-steel beads form a powder that is over six times finer than flour, meaning each particle is too small to see with the naked eye. Powders like this are the basic building material of a type of metal 3D printing called "laser powder bed fusion" or (PBF-LB). It works by spreading out a thin layer of metal powder and using a powerful laser to melt the powder together into a specific shape. Then the process continues by laying down a fresh layer of powder on top and repeating. Each layer is leveled off by a wiper. It’s like the way you might level off a cup of flour with the back of a knife. To make a good 3D-printed part, the powder must be spread in an even, consistent way. Now, thanks to NIST research, we can watch the powder spreading and study its behavior. This work will improve the consistency and quality of metal 3D printed parts. Video Credit: National Institute of Standards and Technology #engineering #technology #3dprinting #additivemanufacturing

These stainless-steel beads form a powder that is over six times finer than flour, meaning each particle is too small to see with the naked eye. Powders like this are the basic building material of a type of metal 3D printing called "laser powder bed fusion" or (PBF-LB). It works by spreading out a thin layer of metal powder and using a powerful laser to melt the powder together into a specific shape. Then the process continues by laying down a fresh layer of powder on top and repeating. Each layer is leveled off by a wiper. It’s like the way you might level off a cup of flour with the back of a knife. To make a good 3D-printed part, the powder must be spread in an even, consistent way. Now, thanks to NIST research, we can watch the powder spreading and study its behavior. This work will improve the consistency and quality of metal 3D printed parts. Video Credit: National Institute of Standards and Technology #engineering #technology #3dprinting #additivemanufacturing

Wevolver

73,188 views • 2 months ago

I think I found the most intuitive Blender texturing add-on for artists who work with NPR models relying on hand-painting 🥹 This is “Paint System” and I found it really simple to use and navigate! It really feels like using PS/CSP in Blender ദ്ദി(ᵔᗜᵔ) Here’s a simple video showing how layers work (very simple clipping mask & alpha locking too), changing layer blend mode, and you can then bake the layers into 1 & export image! Opacity of each layer can also be adjusted with a slider like in PS/CSP but I forgot to show it hahah It also comes with 2 very nice brushes!! I know there are other add-ons for the same job or can do even more things but just wanted to share this since I found it the most intuitive as someone who hand paints textures a lot in other art programs and have non-existent knowledge over nodes in Blender ;-; Link to the add-on in replies and happy painting!!!! (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ (yes it’s free! with an option to donate to the creator to support them too)

I think I found the most intuitive Blender texturing add-on for artists who work with NPR models relying on hand-painting 🥹 This is “Paint System” and I found it really simple to use and navigate! It really feels like using PS/CSP in Blender ദ്ദി(ᵔᗜᵔ) Here’s a simple video showing how layers work (very simple clipping mask & alpha locking too), changing layer blend mode, and you can then bake the layers into 1 & export image! Opacity of each layer can also be adjusted with a slider like in PS/CSP but I forgot to show it hahah It also comes with 2 very nice brushes!! I know there are other add-ons for the same job or can do even more things but just wanted to share this since I found it the most intuitive as someone who hand paints textures a lot in other art programs and have non-existent knowledge over nodes in Blender ;-; Link to the add-on in replies and happy painting!!!! (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ (yes it’s free! with an option to donate to the creator to support them too)

𝗞𝗔𝗥𝗜𝗛𝗔 🌘🍀 3D Artist ☻

19,713 views • 5 months ago

QuickSwap is now live on X Layer Mainnet ⚡️ The birth of a new Citadel on the dragon DEX, expanding the Polygon CDK family. Swap and LP on X Layer (farms coming soon) via QuickSwap to experience DeFi on the network at its very best. X Layer is ZK-powered Layer 2 network designed to connect the OKX and Ethereum communities, built using the Polygon CDK tech stack. It's designed to have seamless interoperability with other Layer 2 chains. Now plugged into the AggLayer, OKX’s massive community of 50+ million users is fully interoperable with all interconnected chains and can now tap into its unified liquidity. The network also uses zk-SNARK to make transactions significantly cheaper and far more compact. X Layer brings other benefits to users, such as near-instant finality, unified liquidity, robust security, high scalability... the list goes on. With this mainnet deployment and more to come, QuickSwap is leading the charge to ignite activity and growth on X Layer at rapid speeds. Going full throttle to further unite the Polygon | POL and OKX communities. This is only the beginning of a bright future for QuickSwap, X Layer, and the broader Polygon and Ethereum ecosystems. Get ready to rumble.

QuickSwap is now live on X Layer Mainnet ⚡️ The birth of a new Citadel on the dragon DEX, expanding the Polygon CDK family. Swap and LP on X Layer (farms coming soon) via QuickSwap to experience DeFi on the network at its very best. X Layer is ZK-powered Layer 2 network designed to connect the OKX and Ethereum communities, built using the Polygon CDK tech stack. It's designed to have seamless interoperability with other Layer 2 chains. Now plugged into the AggLayer, OKX’s massive community of 50+ million users is fully interoperable with all interconnected chains and can now tap into its unified liquidity. The network also uses zk-SNARK to make transactions significantly cheaper and far more compact. X Layer brings other benefits to users, such as near-instant finality, unified liquidity, robust security, high scalability... the list goes on. With this mainnet deployment and more to come, QuickSwap is leading the charge to ignite activity and growth on X Layer at rapid speeds. Going full throttle to further unite the Polygon | POL and OKX communities. This is only the beginning of a bright future for QuickSwap, X Layer, and the broader Polygon and Ethereum ecosystems. Get ready to rumble.

QuickSwap 🐲 DragonFi 2.0

20,857 views • 2 years ago

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

952,379 views • 12 days ago

DINO-v3 has a single high-magnitude channel on its residual pathway, channel 416. Turning off this single channel affects DINO's entire output by 50-80%. For context, turning off a random channel has an effect of less than one percent. The model builds up channel 416 in its last two layer-scale operations, using a single high-magnitude weight in each op to drastically ramp up channel 416's magnitude. This channel doesn't depend on the input, every image fires with a constant overlay. After bringing channel 416 up to a value of about ten thousand, DINO-v3 then scales it down in the final layer-norm to almost nothing, removing it without a trace.

DINO-v3 has a single high-magnitude channel on its residual pathway, channel 416. Turning off this single channel affects DINO's entire output by 50-80%. For context, turning off a random channel has an effect of less than one percent. The model builds up channel 416 in its last two layer-scale operations, using a single high-magnitude weight in each op to drastically ramp up channel 416's magnitude. This channel doesn't depend on the input, every image fires with a constant overlay. After bringing channel 416 up to a value of about ten thousand, DINO-v3 then scales it down in the final layer-norm to almost nothing, removing it without a trace.

Rudy Gilman

85,984 views • 11 months ago

ResNet by hand ✍️ ~ 10 steps walkthrough below "Deep Residual Learning for Image Recognition" (Kaiming He, CVPR 2016) is among the most cited papers in all of deep learning. Why does it matter so much? It fixed the exploding and vanishing gradients that kept deep networks from being deep, and made thousands of layers possible. How simple was the fix? An identity matrix. Goal: push three input vectors through a residual block, then through a transformer encoder block, filling in every cell yourself. = 1. Given = A mini batch of three input vectors, 3D, and the weights of the layers ahead. = 2. Linear layer = Let us multiply by the weights, add the bias, and apply ReLU so negatives become 0. Three feature vectors out. This is F(X). = 3. Concatenate = Now the trick. Stack an identity matrix beside the second layer's weights, and stack the input vectors under the features. Draw the lines between rows and columns: those are the skip connections. The identity is the residual. = 4. Linear layer + identity = We multiply the two stacked matrices. The identity carries X straight through while the weights transform it, so a single multiplication computes F(X) + X. Apply ReLU and hand it to the next block. Now watch the same trick inside a transformer, first in attention. = 5. Attention = Let us take three input vectors in 2D, compute the attention matrix, and multiply to get attention weighted vectors. = 6. Concatenate = We stack two identities this time, two residuals, which is how you get 1 + 1, and stack the input vectors with the attention weighted ones. = 7. Add = Multiply the stacked matrices. The identity adds attention to its own input, across the columns, which is how positions get combined. And again in the feed forward layer. = 8. First layer = Let us multiply by the feed forward weights and bias, then ReLU. Three feature vectors. = 9. Concatenate = Stack and link exactly as in step 3: the residual again. = 10. Second layer + identity = We multiply, apply ReLU, and pass the result to the next encoder block. This identity adds across the rows, combining features rather than positions. Takeaway: one simple "add" is what made really deep networks possible. 💾 Save this post!

ResNet by hand ✍️ ~ 10 steps walkthrough below "Deep Residual Learning for Image Recognition" (Kaiming He, CVPR 2016) is among the most cited papers in all of deep learning. Why does it matter so much? It fixed the exploding and vanishing gradients that kept deep networks from being deep, and made thousands of layers possible. How simple was the fix? An identity matrix. Goal: push three input vectors through a residual block, then through a transformer encoder block, filling in every cell yourself. = 1. Given = A mini batch of three input vectors, 3D, and the weights of the layers ahead. = 2. Linear layer = Let us multiply by the weights, add the bias, and apply ReLU so negatives become 0. Three feature vectors out. This is F(X). = 3. Concatenate = Now the trick. Stack an identity matrix beside the second layer's weights, and stack the input vectors under the features. Draw the lines between rows and columns: those are the skip connections. The identity is the residual. = 4. Linear layer + identity = We multiply the two stacked matrices. The identity carries X straight through while the weights transform it, so a single multiplication computes F(X) + X. Apply ReLU and hand it to the next block. Now watch the same trick inside a transformer, first in attention. = 5. Attention = Let us take three input vectors in 2D, compute the attention matrix, and multiply to get attention weighted vectors. = 6. Concatenate = We stack two identities this time, two residuals, which is how you get 1 + 1, and stack the input vectors with the attention weighted ones. = 7. Add = Multiply the stacked matrices. The identity adds attention to its own input, across the columns, which is how positions get combined. And again in the feed forward layer. = 8. First layer = Let us multiply by the feed forward weights and bias, then ReLU. Three feature vectors. = 9. Concatenate = Stack and link exactly as in step 3: the residual again. = 10. Second layer + identity = We multiply, apply ReLU, and pass the result to the next encoder block. This identity adds across the rows, combining features rather than positions. Takeaway: one simple "add" is what made really deep networks possible. 💾 Save this post!

Tom Yeh

16,658 views • 4 days ago

CSS Tip! ✨ It's 2024 and you have a new way to make animated borders 🚀 .glow::after { offset-path: rect(0 100% 100% 0 round var(--radius)); animation: loop; } @keyframes loop { to { offset-distance: 100%; }} Using the offset-* properties you can animate elements along the perimeter of others 😍 The rect() value gained support in Safari 17.2 🙌 To start, you create an element and put it inside your main element. For example, you put a span inside the button 🤙 Click me! Make the element fill its parent with absolute positioning and inset [data-glow] { position: absolute; inset: 0; } Now the good part, you use a pseudoelement on that element and define an offset-path [data-glow]::after { content: ""; offset-path: rect(0 auto auto 0 round var(--radius)); animation: loop 2.6s infinite linear; } With the rect value, you are saying the path fills the parent container: top: 0 right: auto || 100% bottom: auto || 100% left: 0 Then you can use round to make sure the path uses the same radius as whatever the parent has The @keyframes animation merely animates the offset-distance of that pseudoelement to 100% @keyframes loop { to { offset-distance: 100%; }} You can see this more clearly in the video 🫶 The offset-* properties also include an offset-anchor property. This allows you to dictate which point of the element follows the path. For example: anchor-offset: 100% 50%; This means that the "right, center" of the element will follow the perimeter of the parent element 🤙 Lastly, the visuals 🎨 For color, you can use a gradient such as a linear gradient to fill the pseudo-element. [data-glow]::after { background: radial-gradient( circle at right, hsl(320 90% 100%), transparent 50% ); } Then clip away everything so you only have the border and can still have translucent backgrounds, etc. Use a mask with mask-composite ✨ A little transparent border trick: [data-glow] { border: 2px solid transparent; mask: linear-gradient(transparent, transparent), linear-gradient(white, white); mask-clip: padding-box, border-box; mask-composite: intersect; } Bit of a long one. Hope you find it useful 🙏 CodePen.IO link below 👇

CSS Tip! ✨ It's 2024 and you have a new way to make animated borders 🚀 .glow::after { offset-path: rect(0 100% 100% 0 round var(--radius)); animation: loop; } @keyframes loop { to { offset-distance: 100%; }} Using the offset-* properties you can animate elements along the perimeter of others 😍 The rect() value gained support in Safari 17.2 🙌 To start, you create an element and put it inside your main element. For example, you put a span inside the button 🤙 Click me! Make the element fill its parent with absolute positioning and inset [data-glow] { position: absolute; inset: 0; } Now the good part, you use a pseudoelement on that element and define an offset-path [data-glow]::after { content: ""; offset-path: rect(0 auto auto 0 round var(--radius)); animation: loop 2.6s infinite linear; } With the rect value, you are saying the path fills the parent container: top: 0 right: auto || 100% bottom: auto || 100% left: 0 Then you can use round to make sure the path uses the same radius as whatever the parent has The @keyframes animation merely animates the offset-distance of that pseudoelement to 100% @keyframes loop { to { offset-distance: 100%; }} You can see this more clearly in the video 🫶 The offset-* properties also include an offset-anchor property. This allows you to dictate which point of the element follows the path. For example: anchor-offset: 100% 50%; This means that the "right, center" of the element will follow the perimeter of the parent element 🤙 Lastly, the visuals 🎨 For color, you can use a gradient such as a linear gradient to fill the pseudo-element. [data-glow]::after { background: radial-gradient( circle at right, hsl(320 90% 100%), transparent 50% ); } Then clip away everything so you only have the border and can still have translucent backgrounds, etc. Use a mask with mask-composite ✨ A little transparent border trick: [data-glow] { border: 2px solid transparent; mask: linear-gradient(transparent, transparent), linear-gradient(white, white); mask-clip: padding-box, border-box; mask-composite: intersect; } Bit of a long one. Hope you find it useful 🙏 CodePen.IO link below 👇

jhey ʕ•ᴥ•ʔ

283,498 views • 2 years ago

Excited to share a new vibe coded prototype: an interactive lesson on ants and their habitat, designed for elementary school students. AI allowed me to finalize this demo quickly. Some educational details still need review, but the main chambers of an anthill are all represented. Everything was made in Omma AI , a Spline product. What's inside: - 3D models created with Omma, all I needed was one image of the anthill's exterior and another of its cross-section to generate both 3D models - Clouds done with sprites - A custom shader mask to reveal the inside of the anthill This was a really fun one to work on!

Excited to share a new vibe coded prototype: an interactive lesson on ants and their habitat, designed for elementary school students. AI allowed me to finalize this demo quickly. Some educational details still need review, but the main chambers of an anthill are all represented. Everything was made in Omma AI , a Spline product. What's inside: - 3D models created with Omma, all I needed was one image of the anthill's exterior and another of its cross-section to generate both 3D models - Clouds done with sprites - A custom shader mask to reveal the inside of the anthill This was a really fun one to work on!

Joseph Azar

19,853 views • 2 months ago

After an incredibly successful week at Token2049, the highlight was undoubtedly presenting our SPIN whitepaper at the Bleeding Edge Summit! While still waiting for the official video, we would like to share a recording from the audience. With SPIN, we're introducing a revolutionary approach to building proof-of-stake blockchains. There is a well-known chicken and egg problem when it comes to launching a new proof-of-stake blockchain. You need high value at stake to achieve economic security, but at the same time, for achieving that, you already need to be secure. With SPIN, you can borrow security from well-established networks such as Polkadot or Ethereum. Unlike a Layer 2, which completely gives up chain sovereignty and frequently sacrifices scalability and throughput to satisfy Layer 1 validation protocols, SPIN maintains full sovereignty of the Fast Chain while still benefiting from the security of the Anchor Chain. Our SPIN protocol addresses this fundamental challenge by enabling a fast, sovereign chain to maintain and grow its economic security with its own validator network, while simultaneously leveraging an established anchor chain to provide a second layer of finality. With SPIN, we take an unlimited set of stakers, reduce it to a limited set of validators who can run very quickly, producing a consecutive sequence of blocks during their staker period. This design allows for blocks with very small block times, as low as 0.1 seconds and beyond. Our full SPIN whitepaper will be released soon.

After an incredibly successful week at Token2049, the highlight was undoubtedly presenting our SPIN whitepaper at the Bleeding Edge Summit! While still waiting for the official video, we would like to share a recording from the audience. With SPIN, we're introducing a revolutionary approach to building proof-of-stake blockchains. There is a well-known chicken and egg problem when it comes to launching a new proof-of-stake blockchain. You need high value at stake to achieve economic security, but at the same time, for achieving that, you already need to be secure. With SPIN, you can borrow security from well-established networks such as Polkadot or Ethereum. Unlike a Layer 2, which completely gives up chain sovereignty and frequently sacrifices scalability and throughput to satisfy Layer 1 validation protocols, SPIN maintains full sovereignty of the Fast Chain while still benefiting from the security of the Anchor Chain. Our SPIN protocol addresses this fundamental challenge by enabling a fast, sovereign chain to maintain and grow its economic security with its own validator network, while simultaneously leveraging an established anchor chain to provide a second layer of finality. With SPIN, we take an unlimited set of stakers, reduce it to a limited set of validators who can run very quickly, producing a consecutive sequence of blocks during their staker period. This design allows for blocks with very small block times, as low as 0.1 seconds and beyond. Our full SPIN whitepaper will be released soon.

QF Network

25,547 views • 1 year ago