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Can Adobe Firefly's "Edit" feature compete with Photoshop? Select a region, describe the fix, keep the rest of the frame intact. That's the pitch. Here’s what we observed across 4 sessions and 8 targeted edits with real working creatives: >1 edit landed cleanly 🥇 >5 landed partial 🥈 >2...

13,452 views • 3 months ago •via X (Twitter)

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I built a clone of the Yeezy store with Next.js. This was a fun challenge — the site has some smooth animations and feels very fast. But it was bothering me that I couldn't use the browser back button. Can we do better? So I rebuilt the site with v0 and Motion. Here's how it works: 1. When you click on a product, Motion is able to animate the original position of the product in the grid, to the zoomed in product detail page. 2. During this transition, we also shallow update the URL with the `/p/slug` route for the page. 3. If you press the back button in the navbar, or use the browser back button, or press escape — all options will take you back to the main product listing page. 4. If you reload the page while looking at a product, or someone sends you a link to a specific product, it still works! This is the best parts of a SPA and MPA mixed together. In the future, I can make this even better with View Transitions (I wasn't able to get the product animation just right, but if you can I'd love to see it!). I also took some creative liberties from the original design. The whole 1/2/3 size thing, where you needed to click the "?" to see SM/MD/LG was strange, so I just went directly to those sizes. Similarly, I prefered the more traditional style sheet/modal with the background color change, versus the full screen takeover. If you wanted to actually hook this up to Shopify now, you can swap the cart implementation with Next.js Commerce, which has all the APIs you need + optimistic writes 🔥 Should I make a video walking through the code?

Lee Robinson

148,937 views • 1 year ago

Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!

Tom Yeh

20,848 views • 1 month ago

As I sit here in DC this week, we are closer to something I was not sure I would ever see. I have been working in this industry since 2015. For most of those years, the defining feature of crypto in Washington was not policy. It was the absence of it. A gray zone where serious people built serious things under a constant cloud, never quite sure which rules applied or whether the ground would move beneath them. This week the CLARITY Act sits on the Senate calendar. A federal framework for digital asset market structure, the thing this industry has wanted for the better part of a decade, is closer than it has ever been. It is not law yet, and there are real hurdles left. But the distance between where we stood a few years ago and where we are sitting today is hard to put into words. I keep thinking about the work that got us here. Over the past year I watched Chainlink move from outside these conversations to inside them. Sergey at the White House for the signing of the GENIUS Act. The Department of Commerce putting government economic data onchain. Meetings with the SEC that became real interpretive guidance. Conversations with the lawmakers now writing the rules. None of that happens by accident. It happens because people keep showing up, year after year, and make the case in rooms where it is not yet obvious. And there is something fitting in it. The entire premise of what we build is verification. Making truth provable. Removing the question of what is real. The work here in DC is the same thing in a different form. Trading a decade of ambiguity for something the industry has never actually had. We are not at the finish line. But sitting here, it is hard not to feel the weight of it. The gray zone is ending. What comes next is something this industry has never had. Clarity.

Chris Barrett

14,798 views • 3 months ago