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We outgrew static gradients. So we rebuilt everything. Grainient V2 is live. What started as a gradient library is now something else entirely: • Animated gradient videos • Real-time gradient shader tool • Textured, mesh, and AI-crafted backgrounds From downloading assets → to actually create and control them. So,... show more

Basit A. Khan

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127,817 görüntüleme • 3 ay önce •via X (Twitter)

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Solchat V2 is now live! We've optimized the fee structure for creating message accounts and adding friends. The major update is the implementation of end-to-end encryption, ensuring that no one can track your messages, as everything is stored on-chain encrypted. Additionally, we have introduced a Solana gradient to our new V2 UI mobile optimized. We'll be incorporating V1 features into this V2 version soon, so stay tuned for our upcoming feature: group chats. $Chat with #Solchat on the #Solana blockchain

Solchat V2 is now live! We've optimized the fee structure for creating message accounts and adding friends. The major update is the implementation of end-to-end encryption, ensuring that no one can track your messages, as everything is stored on-chain encrypted. Additionally, we have introduced a Solana gradient to our new V2 UI mobile optimized. We'll be incorporating V1 features into this V2 version soon, so stay tuned for our upcoming feature: group chats. $Chat with #Solchat on the #Solana blockchain

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This is the clearest explanation of modern inference we’ve heard all year. We brought CEO Tuhin Srivastava onto Gradient Dissent, and he breaks down what actually happens when teams move from demos to real production workloads. Links to the full episode below!

This is the clearest explanation of modern inference we’ve heard all year. We brought CEO Tuhin Srivastava onto Gradient Dissent, and he breaks down what actually happens when teams move from demos to real production workloads. Links to the full episode below!

Weights & Biases

103,361 görüntüleme • 8 ay önce

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 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

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If you aren’t cheating, you aren’t trying! Are you looking at MRIs? Feeling some confusion about the diffusion? Feel impaired when it comes to the FLAIR? Wish you could cheat a little? Here’s a little cheat sheet on all the sequences you NEED to know for looking at MRIs! ➡️T1: It’s for anatomy, so brain structures reflect the same color as real life. 🔸So gray matter is gray on T1 & white matter is white on T1. 🔸It’s also for contrast. Contrast is taken up by masses making them light up & easier to see. ➡️T2: It’s the water sensitive sequence. 🔸What is pathologic water in the brain? Edema! 🔸Remember--everything bad in this world is trying to turn you back into what you came from—water! ➡️DWI: Diffusion detects stroke, which are bright on DWI. 🔸But anything that makes space tight in the brain can be bright on diffusion (cellular masses, pus under pressure, etc.) ➡️Gradient: Gradient is sensitive to metals. 🔸What’s the most important metal in body? Iron—bc iron is in blood. 🔸So gradient is our blood sensitive sequence 🌟So remember🌟 ▶️T1 is for Anatomy & Contrast ▶️T2 is for Water & Edema ▶️DWI is for stroke ▶️Gradient is for blood Hopefully, this will help you next time you have an MRI--who says cheaters never win? 😉

If you aren’t cheating, you aren’t trying! Are you looking at MRIs? Feeling some confusion about the diffusion? Feel impaired when it comes to the FLAIR? Wish you could cheat a little? Here’s a little cheat sheet on all the sequences you NEED to know for looking at MRIs! ➡️T1: It’s for anatomy, so brain structures reflect the same color as real life. 🔸So gray matter is gray on T1 & white matter is white on T1. 🔸It’s also for contrast. Contrast is taken up by masses making them light up & easier to see. ➡️T2: It’s the water sensitive sequence. 🔸What is pathologic water in the brain? Edema! 🔸Remember--everything bad in this world is trying to turn you back into what you came from—water! ➡️DWI: Diffusion detects stroke, which are bright on DWI. 🔸But anything that makes space tight in the brain can be bright on diffusion (cellular masses, pus under pressure, etc.) ➡️Gradient: Gradient is sensitive to metals. 🔸What’s the most important metal in body? Iron—bc iron is in blood. 🔸So gradient is our blood sensitive sequence 🌟So remember🌟 ▶️T1 is for Anatomy & Contrast ▶️T2 is for Water & Edema ▶️DWI is for stroke ▶️Gradient is for blood Hopefully, this will help you next time you have an MRI--who says cheaters never win? 😉

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Every time you switch between design and code, you lose something, your momentum, your fidelity, or your vision. The gap between what you designed and what ships has always been the cost of building. That’s why we built Wonder, a design tool where everything you create is backed by real code. Public Alpha is now live at

Every time you switch between design and code, you lose something, your momentum, your fidelity, or your vision. The gap between what you designed and what ships has always been the cost of building. That’s why we built Wonder, a design tool where everything you create is backed by real code. Public Alpha is now live at

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Finance Agent Benchmark v2 is here. Finance is one of the most lucrative applications of AI where much of the busy work could be automated. That’s why we rebuilt our Finance Agent Benchmark to push frontier models even further. We designed V2 to better reflect what financial analysts actually do: refined taxonomy reflecting real workflows, an improved harness with more tools, and jury-based evaluation. The result: no model cracks 52%. Would you trust a financial analyst who’s only correct half the time?

Finance Agent Benchmark v2 is here. Finance is one of the most lucrative applications of AI where much of the busy work could be automated. That’s why we rebuilt our Finance Agent Benchmark to push frontier models even further. We designed V2 to better reflect what financial analysts actually do: refined taxonomy reflecting real workflows, an improved harness with more tools, and jury-based evaluation. The result: no model cracks 52%. Would you trust a financial analyst who’s only correct half the time?

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SONA V2 is here and it’s a gamechanger. You can now trade and transfer tokens using just your voice. From executing swaps to sending assets, SONA brings seamless voice-first control to onchain actions. NO clicks, NO friction. The future of crypto interaction is hands-free and it’s already live.

TARS AI 🤖

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Fun fact: the Marathon spinner is actually a hand-coded shader! It has to display before the engine loads any textures or videos, so it's entirely procedurally animated and rendered by a shader I wrote for it using SDFs, procedural noise, and a pile of math for the animation :)

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Abby Welsh

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Engineers with taste will make a lot of so-called designers irrelevant. It’s 2026. With all the vibe-coding tools we have now, you can build real working prototypes of almost anything in a few hours. So when I see another “iOS 27 concept” that is just a Figma screenshot with a gradient or a circular one, and some fake Liquid Glass layer on top, that’s laziness at the next level for the sake of engagement.

Engineers with taste will make a lot of so-called designers irrelevant. It’s 2026. With all the vibe-coding tools we have now, you can build real working prototypes of almost anything in a few hours. So when I see another “iOS 27 concept” that is just a Figma screenshot with a gradient or a circular one, and some fake Liquid Glass layer on top, that’s laziness at the next level for the sake of engagement.

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Arena V2 is not a product release. It’s not a one-time launch. V2 is the start of a fresh new chapter of The Arena defined by expansion and growth. The first step is a UI / UX overhaul. Today we introduce V2 beta, which aims to solve some of the product issues holding the original platform back from broader adoption. The Arena now has the user experience of a Web2 social app with all the benefits that crypto and blockchain technology can offer. V2 beta is live on Following the launch of V2 beta comes a series of new feature releases that will continue to improve the product and add entirely new creator tools to enable even greater monetization. Read about the product roadmap and V2 vision in the Medium article below 👇

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Trump is using tariffs as a tool of statecraft to fight for American workers and families. What has the global elite so disoriented is that—for the first time in a long time—there’s a U.S. president actually willing to stand up for them.

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Kevin Roberts

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#Atlee Recent - The shooting is going well as planned, every step is a bady step. - There is no Bible for this genre of film, this kind of film. - So we are discovering everything as we go and trying to create something bigger for the audience. #AA22xA6

#Atlee Recent - The shooting is going well as planned, every step is a bady step. - There is no Bible for this genre of film, this kind of film. - So we are discovering everything as we go and trying to create something bigger for the audience. #AA22xA6

Movie Tamil

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Jupiter Spot V2 is Live We merged Swap and Terminal into one page, everything you need to discover a trade and make it, in one place. We built it with one aim: To help you find trades like an expert So we put every market-moving signal in one place, right beside your swap button: > Live market-moving news, curated by humans > Heatmap showing sectorwise performance > Twitter posts that influence the market > Track the whales and top trader wallets Spot V2 is the most complete trading terminal ever built on Solana. The information the winners had was never a secret. It was just never accessible this easily. Now it is. Just Use Jupiter Spot V2 👇

Jupiter

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The sky texture in Blender 5 changed how I work with environments. Multiple scattering alone is worth the update — realistic light, accurate shadows, full mood control. No HDRIs needed for environment work. But a clean sky is boring. So I layer on top: procedural clouds or real stock photos, gradient mask to fade the horizon, volume for atmosphere. Sky texture handles the light. Everything else is just background. Full breakdown on YouTube:

The sky texture in Blender 5 changed how I work with environments. Multiple scattering alone is worth the update — realistic light, accurate shadows, full mood control. No HDRIs needed for environment work. But a clean sky is boring. So I layer on top: procedural clouds or real stock photos, gradient mask to fade the horizon, volume for atmosphere. Sky texture handles the light. Everything else is just background. Full breakdown on YouTube:

SLAVO | c g s l a v

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There's a fruit fly walking around right now that was never born. eon just released a video where they took a real fly's connectome — the wiring diagram of its brain — and simulated it. Dropped it into a virtual body. It started walking. Grooming. Feeding. Doing what flies do. Nobody taught it to walk. No training data, no gradient descent toward fly-like behavior. This is the opposite of how AI works. They rebuilt the mind from the inside, neuron by neuron, and behavior just... emerged. It's the first time a biological organism has been recreated not by modeling what it does, but by modeling what it is. A human brain is 6 OOM more neurons. That's a scaling problem, something we've gotten very good at solving. So what happens when we have a working copy of the human mind?

There's a fruit fly walking around right now that was never born. eon just released a video where they took a real fly's connectome — the wiring diagram of its brain — and simulated it. Dropped it into a virtual body. It started walking. Grooming. Feeding. Doing what flies do. Nobody taught it to walk. No training data, no gradient descent toward fly-like behavior. This is the opposite of how AI works. They rebuilt the mind from the inside, neuron by neuron, and behavior just... emerged. It's the first time a biological organism has been recreated not by modeling what it does, but by modeling what it is. A human brain is 6 OOM more neurons. That's a scaling problem, something we've gotten very good at solving. So what happens when we have a working copy of the human mind?

hattie

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MITO AI is here. MITO AI is the tool we wish we had as filmmakers. It is not a prompt playground or a template generator. It is a control room for people who think in scenes, boards and timelines. You bring a story, references and intent. The tool helps you explore shots, workflows and versions faster than a traditional pipeline, without compromising taste. •⁠ ⁠Cinematic boards in minutes. •⁠ ⁠Keep your style consistent across images, scenes and videos. Designed by a studio that actually uses it every day. Join the early access list and help shape the tool:

MITO AI is here. MITO AI is the tool we wish we had as filmmakers. It is not a prompt playground or a template generator. It is a control room for people who think in scenes, boards and timelines. You bring a story, references and intent. The tool helps you explore shots, workflows and versions faster than a traditional pipeline, without compromising taste. •⁠ ⁠Cinematic boards in minutes. •⁠ ⁠Keep your style consistent across images, scenes and videos. Designed by a studio that actually uses it every day. Join the early access list and help shape the tool:

MITO AI

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✈️ Dogfighting works and other people's planes now explode if you hit them enough and their health goes to 0%, very fun :D "I thought nice to show the health from green->yellow->red->black gradient as other player plane color, so you know who is almost dead" ✅ Done "That score label very annoying in the center, I'll move it elsewhere!" ✅ Done "Next is broadcasting the hits to the server who will then affect the health so people will actually fall out of the sky :D" Now need to broadcast health of all players so you actually die when YOU get shot too

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@levelsio

262,315 görüntüleme • 1 yıl önce