Dither / halftone effects tutorial in After effects (No... plugins needed) 1. Create a Shape Layer (dot/circle) 2. Pre-comp it → add CC Tiler 3. Import your image/video 4. Add Card Dance to an Adjustment Layer 5. Set Gradient Layer 1 to your footage 6. Set X & Y Intensity to 1 Adjust Rows & Columns to tasteshow more

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 #tutorialshow more

Would love for Figma to introduce generative variants into... component sets: 1. Select a layer 2. Click generate variants 3. Figma generates variants into a new component set 4. Original layer turns into an instance of that component set Quick proto...show more

1. No, we can't recreate a universally perfect liquid... glass in Figma that will adapt to any UI on the background. 2. Yes, we can create a slightly fake version. The secret is to use 1 additional nested layer that will have 2 effects — blurring the background and deforming it. All additional lighting effects should be used on the main container where this background layer sits. These are my best numbers for it so far.show more

short thread to recap this new workflow on Kling... O1 1. choose an image or create it 2. create more cinematic angles in a grid (Higgsfield cinema studio or directly in NanoBanana) 3. add the grid into Kling O1 (add more references if needed)show more

How I use Claude for 2D website animations 👇... 1. Ask Claude to create CSS keyframes for floating or bounce effects 2. Use Claude to write fade-up animations on scroll 3. Ask Claude for parallax code for backgrounds 4. Generate hover effects like glow, scale, rotate buttons 5. Use Claude to animate text word by word 6. Paste the code, adjust timing, launch Save this tutorial 🚀show more

most people open Claude every morning and re-explain their... entire life. every. single. time. then I built 7 layers that remember everything: Layer 1: tell Claude who I am, once Layer 2: build separate brains for separate work Layer 3: turn on memory so it learns me Layer 4: upload 5 writing samples so it sounds like me Layer 5: dump my world into project files Layer 6: connect Gmail, Calendar, Drive, Slack Layer 7: schedule tasks that run while I sleep 60 minutes to set up. spread across one week. now Claude finishes my sentences. knows which client I mean from one word. catches mistakes I'd miss. it's not a chatbot anymore. it's a personal AI that knows me better than most coworkers do. your AI doesn't know you yet. this article fixes that.show more

[Backpropagation] by Hand✍️ [1] Forward Pass ↳ Given a... multi layer perceptron (3 levels), an input vector X, predictions Y^{Pred} = [0.5, 0.5, 0], and ground truth label Y^{Target} = [0, 1, 0]. [2] Backpropagation ↳ Insert cells to hold our calculations. [3] Layer 3 - Softmax (blue) ↳ Calculate ∂L / ∂z3 directly using the simple equation: Y^{Pred} - Y^{Target} = [0.5, -0.5, 0]. ↳ This simple equation is the benefit of using Softmax and Cross Entropy Loss together. [4] Layer 3 - Weights (orange) & Biases (black) ↳ Calculate ∂L / ∂W3 and ∂L / ∂b3 by multiplying ∂L / ∂z3 and [ a2 | 1 ]. [5] Layer 2 - Activations (green) ↳ Calculate ∂L / ∂a2 by multiplying ∂L / ∂z3 and W3. [6] Layer 2 - ReLU (blue) ↳ Calculate ∂L / ∂z2 by multiplying ∂L / ∂a2 with 1 for positive values and 0 otherwise. [7] Layer 2 - Weights (orange) & Biases (black) ↳ Calculate ∂L / ∂W2 and ∂L / ∂b2 by multiplying ∂L / ∂z2 and [ a1 | 1 ]. [8] Layer 1 - Activations (green) ↳ Calculate ∂L / ∂a1 by multiplying ∂L / ∂z2 and W2. [9] Layer 1 - ReLU (blue) ↳ Calculate ∂L / ∂z1 by multiplying ∂L / ∂a1 with 1 for positive values and 0 otherwise. [10] Layer 1 - Weights (orange) & Biases (black) ↳ Calculate ∂L / ∂W1 and ∂L / ∂b1 by multiplying ∂L / ∂z1 and [ x | 1 ]. [11] Gradient Descent ↳ Update weights and biases (typically a learning rate is applied here). 💡 Matrix Multiplication is All You Need: Just like in the forward pass, backpropagation is all about matrix multiplications. You can definitely do everything by hand as I demonstrated in this exercise, albeit slow and imperfect. This is why GPU's ability to multiply matrices efficiently plays such an important role in the deep learning evolution. This is why NVIDIA is now close to $1 trillion in valuation. 💡Exploding Gradients: We can already see the gradients are getting larger as we back-propagate up, even in this simple 3-layer network. This motivates using methods like skip connections to handle exploding (or diminishing) gradients as in the ResNet. I did the calculations entirely by hand. Please let me know if you spot any error or have any questions!show more

❓ What you need to do to participate in... Airdrop 2 TALENTRIX: 1. Subscribe to Talentrix 2. Retweet the Airdrop post on your X page: — Add a comment with the hashtags #TrixAirDrop #TrixToTheMoon #TrixChat — Tag 5 friends. Then at under Airdrop: 1. Enter your nickname (X) 2. Enter your Solana address 3. Insert retweet link 4. Press " >> TRANSMIT DATA <<" Wait for the launch and get ready to receive $TRIX!show more

Leftover Smoked Kielbasa and white bean soup from my... freezer. Baked in SouperCubes stoneware for 40 minutes at 350 and I have a beautiful homemade soup to eat since I’m feeling lazy. Here’s the recipe for the fresh soup: - 1 pound smoked kielbasa - 2 cans (15.5 oz) of great northern beans drained - 1 can (14.5 oz) diced tomatoes - 1 yellow onion - 3 cloves garlic minced - 2.5 cups of kale - 1 handful of parsley - 32 oz chicken stock - 2 tbsp Worcestershire sauce - 3 tbsp butter - 1 tbsp extra virgin olive oil Seasonings: Salt, pepper, oregano, thyme, rosemary, garlic powder. Steps: 1. Add your butter and EVOO to a pot and once melted cook your smoked kielbasa for 5-7 minutes. 2. Add your onion and garlic, cook another 2-3 minutes 3. Add some seasonings 4. Add in your Worcestershire sauce and cook another 2-3 minutes 5. Add more seasonings. 5. Add in your tomatoes and beans. Combine well. 6. Add in chicken stock and simmer for 20 minutes roughly. 7. Add in your parsley and kale and cook another 10-15 minutes. 8. Serve and enjoy.show more

[Deep RNN] by Hand ✍️ A Deep Recurrent Neural... Network (RNN) extends a basic single-layer RNN into multiple layers of hidden states, effectively incorporating deep learning into the RNN architecture. How does a Deep RNN work? [1] Given ↳ A sequence of four inputs X1, X2, X3, X4 ⬛️ ↳ Recurrent weights and biases for hidden layers a 🟩, b 🟧, c 🟪, and the output layer y 🟦. [2] Initialize Hidden States ↳ Set a0, b0, c0 to zeros — Process X1 (t = 1)— [3] First Hidden Layer (a) 🟩: a0 → a1 ↳ The transformation matrix is horizontal concatenation of input weights, hidden state weights and biases, visualized as [⬛️ | 🟩 | ⬜️] . ↳ The state matrix is vertical concatenation of input X1, previous hidden state a0, and an extra 1, visualized as [⬛️ ; 🟩 ; 1]. ↳ Multiply the two matrices to obtain new hidden state a1 = [0 ; 1]. [4] Second Hidden Layer (b) 🟪: b0 → b1 ↳ First layer a1 🟩 becomes the input. ↳ The transformation matrix is visualized as [🟩 | 🟪 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟩; 🟪 ; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [5] Third Hidden Layer (c) 🟧: c0 → c1 ↳ Second layer b 🟪 becomes the input. ↳ The transformation matrix is visualized as [🟪 | 🟧 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟪; 🟧; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [6] Output Layer (Y) 🟦 ↳ The transformation matrix is visualized as [🟧 | ⬜️]. ↳ The state matrix is the combination of c0 and , visualized as [🟧; 1]. ↳ Multiply the two matrices to obtain output Y1 = [3; 0; 3]. — Process X2 (t = 2)— [7] Previous Hidden States ↳ Copy the values of a1, b1, c1. [8] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y2 = [5; 0; 4] — Process X3 (t = 3)— [9] Previous Hidden States ↳ Copy the values of a2, b2, c2. [10] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y3 = [13; -1; 9] — Process X4 (t = 4)— [11] Previous Hidden States ↳ Copy the values of a3, b3, c3. [12] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y4 = [15; 7; 2]show more

HOW TO DODGE EVERY SKILLSHOT IN LEAGUE OF LEGENDS... SO YOU GET ACCUSED OF SCRIPTING - Script in your mind - Draw out how far, wide, fast an ability is relative to your character thats all the easy stuff that I have been preaching already you can find in my free discord for improvement however one thing that League coaches fail to explain is the human aspect of it every game you play in League of Legends, every single person in the game is constantly building their profile in a game on how they operate both sides are constantly trying to mind f*ck each other to land and dodge skillshots. I have broken it down into layers the three layers to dodging are layer 0 - no dodge (unconscious) layer 1 - dodge (conscious) layer 2 - no dodge (conscious) Notice how in the clip in a challenger game below Olaf shoots a layer 0 skillshot, but because I am playing at a layer 1, I dodge his axe. Now the Thresh hook gets a little deeper bare with me, because I built the profile that I will dodge an ability in that moment, he thinks that I won't dodge and is shooting a hook at a layer 2 thinking that I will dodge at a layer 2 also. However I know that he knows I will likely not juke and walk straight so I make the conscious choice to dodge AGAIN playing at a layer 1 resulting in me dodging the hook, of course he could be accounting for my tumble but the point still stands. There are many deeper things to consider like zoning abilities, environment etc but you generally want to always play at a layer 1 until you gain more data in a game to adapt. However one thing that always stays true throughout my 13 years of playing League is in teamfights that have gone on for awhile, human beings tend to panic and default to layer 0 of shooting abilities, so if your able to operate at layer 1 as a teamfight progresses, you will likely dodge that one final skillshot that wins you the game. study the saskio wayshow more

HE BUILT A 100% PRIVATE SECOND BRAIN IN OBSIDIAN... TO AUTOMATE HIS RESEARCH USING HERMES AGENT AND NOTEBOOKLM He visualizes his entire knowledge graph on-premise without paying for cloud subscriptions By connecting Hermes Agent and NotebookLM, developers can index hundreds of documents and generate content locally Four components of this local knowledge architecture: 1. Memory layer - set up an Obsidian PARA vault to store markdown notes and profiles 2. Agent layer - connect Hermes Agent to write files and run background scripts 3. Synthesis layer - use NotebookLM to create structured overviews from raw transcripts 4. Automation layer - trigger cron tasks to synchronize files and calendar standups The setup saves over $4,000 annually while keeping all private files offline Get the full step-by-step configuration guide and setup commands in the article below ↓show more

How to Morph Food 🍊↔🍎, 🍔↔🍕? Step 1: Use... any 2 food photos in same spot. 🍽️ Step 2: Import photos to First & Last Frame. 🎞️ Step 3: Type prompt “camera static, the (food 1) transforms into the (food 2)”. 🥕↔🥦 Step 4: Generate video and watch your transition!✨ Create your own in #LumaDreamMachine #tutorial #morphing #vfxshow more

How to Make Homemade Mayo. It’s creamy, delicious, and... avoids the usual risks of raw eggs. Ingredients (makes about 1–1.5 cups) 🥚 4–5 boiled eggs (peeled) 💧 Water (a splash, to help blending — roughly 2–4 tbsp) 🍋 Lemon juice (fresh, about 1–2 tbsp or to taste) 🫒 Olive oil (several tablespoons — added gradually, maybe ¼–⅓ cup total) 🧂 Pinch of salt (to taste) Optional add-ins (common for flavor): mustard, garlic powder, black pepper, or a touch of vinegar. Step-by-Step Instructions 1. Place the peeled boiled eggs in a wide-mouth jar or blending container. 2. Add a splash of water to loosen things up. 3. Pour in lemon juice and a pinch of salt. 4. Add olive oil (start with less and add more as needed for richness and emulsification). 5. Insert an immersion blender (stick blender) and blend everything until completely smooth and creamy. It transforms quickly into thick, classic-looking mayonnaise. 6. Taste and adjust seasoning (more salt, lemon, or oil if needed). Done! Store in the fridge for up to a week. Pro Tip: Use a narrow jar for the immersion blender so everything blends evenly without splatter. If it’s too thick, add a tiny bit more water or lemon juice.show more

3 epic phones for your 2023 adventures: Samsung Mobile... US Galaxy S23 Series. Enter for a chance to win: 1) Follow AT&T 2) Comment "💙📲" 3) Tag a fellow adventurer 4) Add #ATTSweepstakes No purch. nec. Rules: Pre-order yours:show more

Need an app to streamline your systematic review? Check... out for FREE. The free plan offers the following features: 1. Import studies 2. Screen titles and abstracts 3. Auto retrieval of open access PDFs in full text screening 4. Auto generated table of all extracted data 5. Auto generated PRISMA chart 6. Add collaborators 7. Unlimited literature reviewsshow more

MLP in PyTorch by hand ✍️ ~ 7 steps... walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearningshow more

The X algo is broken Right now quantity over... quality is being promoted Fight videos, toxicity, engagement farming spam are winning High quality content and small accounts are being suppressed Luckily I have a really easy fix for you Simple fix to dramatically improve your X: 1. Click Lists on the left hand side 2. Create a new List 3. Add accounts that don't try to poison your brain 4. Add accounts you strive to be like 5. Use this List to replace your home feed 6. Reply guy like crazy to everyone you want to network with Guarantee your X experience becomes 10x bettershow more

the great slippy takeover. $5000 in $SLIPPY. 1. build... your slippy with the PFP generator at 2. quote RT this tweet with your creation = you're in 3. add your telegram @ to the RT and set it as your telegram pfp = double ticket 10 winners, $500 each. two picked by Antoine himself.show more

In my class, I teach the autoencoder by asking... everyone to stand up. 🙆 Stretch your arms out wide. Imagine you are holding a heavy textbook (like Introduction to Algorithms by Prof. Cormen), the whole thing, every page. Now bring your hands slowly together until they almost touch your neck. The bottle "neck." The final exam is tomorrow and you are allowed one cheat sheet: whatever you can scribble on your palm. All nine hundred pages have to survive the squeeze. That is the encoder. Now imagine you sit in the exam. Push your arms back out to where they started. You try to rebuild the textbook from your palm notes. That is the decoder. Of course you cannot get every page back. What you get back is what mattered enough to write down, and the gap between the two is the loss the network is trying to shrink. I call it AI by Arms 🙆. It gets a laugh, and then it gets remembered. Goal: squeeze four numbers down to two, then rebuild the original four from them. 1. Given Let us start with four training examples: X1, X2, X3, X4. 2. Auto (copy to targets) We copy the training examples straight into the targets. That is the whole trick behind the name: "auto" is Greek for "self", and the data is its own label. 3. Encoder, layer 1 Let us multiply the inputs by the weights, add the biases, and apply ReLU. Negative values get crossed out and become zero. 4. Encoder, layer 2 (the bottleneck) We do it again, and now the four dimensions have become two. This layer is called the bottleneck, because everything has to fit through it. 5. Decoder, layer 1 Let us go back the other way: multiply, add, ReLU. This time there are no negatives to cross out. 6. Decoder, layer 2 We multiply once more and get the outputs Y. This is the decoder's attempt to rebuild the four original numbers from the two it was given. 7. MSE loss gradients Let us compare Y with the targets Y'. The gradient is 2 x (Y - Y'): subtract, then double. Those gradients kick off backpropagation, and the weights start to learn. Your entire education is all about encoding and decoding!show more