Loading video...
Video Failed to Load
Xerox Photocopy Effect Tutorial in AE (No plugins needed) 1. Import Image 2. Adjustment Layer 3. Noise 4. Gaussian Blur 5. CC Threshold 6. Tint 7. Posterize Time #niqteworks #aftereffects #ae #motiongraphics #motiondesign #mv #tutorial
83,661 views • 1 month ago •via X (Twitter)
0 Comments
No comments available
Comments from the original post will appear here
![IF I WAS FORCED to build a $20K/month AI creative agency using nothing but Photoshop, starting from 0, here's exactly what I would do in steps: The production setup (Days 1–3) 1. Download the Higgsfield plugin inside Photoshop — takes 5 minutes 2. You now have: sketch-to-image, layer decomposer, mockup studio, relight, upscale, face swap, character swap, background removal, AI stylist — all in 1 tool 3. Old creative agency workflow: designer + photographer + editor + 3–5 day turnaround 4. New workflow: 1 person, Photoshop, 30 minutes per deliverable The offer (Days 3–7) 5. Pick 1 niche — ecom brands, real estate agents, or course creators all need visuals constantly 6. Build a simple offer: "10 ad creatives delivered in 24 hours — $500" 7. Old agencies charge $2,000–$5,000/month for the same output 8. Your cost to deliver: $0 beyond the plugin. Pure margin. 9. Create 3 sample mockups using the tool — drop a product image in, generate 9 variations, pick the best 3 10. That's your portfolio. Built in under 1 hour. Cost: $0. The client machine (Days 7–20) 11. Go on X and search "[niche] + need a designer" or "[niche] + creatives" 12. DM 50 people per day — "I'll make you 3 free ad creatives in 24 hours, no catch" 13. Deliver them in 30 minutes using the plugin 14. 50 DMs/day × 14 days = 700 outreach messages 15. Conservative 3% conversion = 21 people see the free work 16. Close 5 of them at $500 = $2,500 in week 3 The scale (Days 20–30) 17. Upsell every client to a $1,500/month retainer — 10 creatives/week, unlimited revisions 18. 1 client per day in Photoshop takes 45 minutes max 19. 10 retainer clients × $1,500 = $15,000/month 20. Add 3 one-off clients at $500/month = $1,500 21. Add a $997 "AI creative system" course teaching other people this exact workflow = $3,000+/month from 3 sales The math: 50 DMs/day × 30 days = 1,500 outreach messages 3% book a call = 45 calls 40% close at $1,500/month retainer = 18 clients 18 × $1,500 = $27,000/month recurring Time per client per day: 45 minutes Total daily work: 4–5 hours Every mockup — AI. Every restyle — AI. Every layer rebuild — AI. Every variation — AI. No photographer. No designer and no reshoot. Start it here. 👇](https://image.24vids.com/tw-2066729067907588566/amplify_video_thumb/2066728481002864640/img/ycqZl87f4-zGMo4j.jpg)
![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!](https://image.24vids.com/tw-2079570896423911664/ext_tw_video_thumb/2079570876530298880/pu/img/w_TJ6G4bD69KBKuz.jpg)
![I created this desk calendar as a source of inspiration for anyone learning AI in 2026. It includes 24 AI algorithms and architectures, all drawn and calculated by hand. ✍️ 𝗝𝗮𝗻𝘂𝗮𝗿𝘆: [1] Matrix Multiplication; [2] Discrete Fourier Transform (DFT) 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆: [3] Support Vector Machine (SVM); [4] Vector Database 𝗠𝗮𝗿𝗰𝗵: [5] Multi-Layer Perceptron (MLP); [6] Backpropagation 𝗔𝗽𝗿𝗶𝗹: [7] Batchnorm; [8] Dropout 𝗠𝗮𝘆: [9] Recurrent Neural Network (RNN); [10] Long-Short Term Memory (LSTM) 𝗝𝘂𝗻𝗲: [11] Residual Network (ResNet); [12] Graph Convolutional Network (GCN) 𝗝𝘂𝗹𝘆: [13] Autoencoder; [14] Variational Autoencoder (VAE) 𝗔𝘂𝗴𝘂𝘀𝘁: [15] Generative Adversarial Network (GAN); [16] U-Net 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿: [17] Transformer; [18] Self Attention 𝗢𝗰𝘁𝗼𝗯𝗲𝗿: [19] Reinforcement Learning with Human Feedback (RLHF); [20] Contrastive Language-Image Pre-training (CLIP) 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿: [21] Diffusion Transformer; [22] Switch Transformer 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿: [23] Sparse Autoencoder; [24] BitNet](https://image.24vids.com/tw-1991252477052850273/amplify_video_thumb/1991251902600998912/img/QoUOwu6Of-ub0fc7.jpg)
