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Introducing Better Gradient, a collaboration between Plugin Play and Ravie "The Gradient Guys" .co Now available at - Save gradient presets - Customize your color interpolation with graph easing - Significantly improved color rendering to get rid of the gross grays AE typically produces between color stops. - Animate... show more
17,600 görüntüleme • 5 gün önce •via X (Twitter)
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![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 👇](https://image.24vids.com/tw-1752903535488823789/ext_tw_video_thumb/1752900027591151616/pu/img/d0sViTqgbe8N-yux.jpg)
![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! 👇](https://image.24vids.com/tw-1734369933558010226/ext_tw_video_thumb/1734365662590418944/pu/img/320Dgq_k3RZoZOFV.jpg)
![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!](https://image.24vids.com/tw-2079208495094677511/ext_tw_video_thumb/2079208483669315584/pu/img/dDD0x6lHYHuV1POQ.jpg)
![Imagine reality as a closed electromagnetic system where the fundamental forces are not separate phenomena, but three expressions of the same field: Magnetism Dielectricity Electricity Within this model, the universe behaves like a spectrum of living light. The medium of this light has historically been called the Aether - a dynamic field through which vibration, charge, and form arise. Everything we observe can be understood as waveforms in the aetheric field. In this framework: Magnetism represents centripetal compression. Electricity represents radiative expansion. Dielectricity represents the neutral field potential between them. Together, they generate the harmonic structure of the cosmos. Color becomes more than perception - it becomes a map of energy states. The visible spectrum reflects different levels of vibrational density within the field. At the center of this spectrum lies green, traditionally associated with balance and equilibrium. In this symbolic model, Earth corresponds to the heart center of the system, where opposing forces reach harmonic balance. Above us, the atmosphere transitions toward higher electric potential and subtler states of matter. The sky appears blue because shorter wavelengths dominate in these higher, more rarefied regions of the field. Below us, matter becomes increasingly dense, magnetic, and structured as compression dominates. This gradient may also be reflected in the natural electrostatic potential of the atmosphere, where measurements show an average increase of roughly 100 volts per meter with elevation. In other words, the higher we go, the greater the electric potential relative to the ground. In this way, the atmosphere can be imagined as a living gradient between magnetism below and electricity above. From this perspective: Density seeks equilibrium. Energy circulates in toroidal fields. Color expresses the geometry of vibration. This video explores how color theory, electromagnetism, and ancient symbolic systems might all be describing the same underlying structure of reality. Not as dogma, but as a framework for thinking differently about the relationship between light, energy, and consciousness. [h/t phi.king.wizard11 on IG]](https://image.24vids.com/tw-2089396533355548713/amplify_video_thumb/2089396039757295616/img/t1bkugv7suBBovFG.jpg)
