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Add some motion to your static dither graphics. The trick is using a "Blue Noise" dither underneath the ASCII dither. Blue noise is a very uniformly distributed random that feels almost as smooth as procedural noise. The result is this pleasing shimmer effect.

George Hastings

4,595 subscribers

67,522 次观看 • 7 个月前 •via X (Twitter)

艺术科学技术

Anya Rossi• Live Now

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A quick and easy way to add the cronchy PS1-style dither effect to your texture maps in Photoshop 1. Lower image size (512 px is good) 2. Select Indexed Color mode 3. Set palette to Local (Perceptual), Colors to 16, Forced to Black and White, Dither to Pattern 4. Profit #psx

A quick and easy way to add the cronchy PS1-style dither effect to your texture maps in Photoshop 1. Lower image size (512 px is good) 2. Select Indexed Color mode 3. Set palette to Local (Perceptual), Colors to 16, Forced to Black and White, Dither to Pattern 4. Profit #psx

sodaraptor ➤ wishlist Hypnagogia 壊れた夢

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Noise driven ripple effect, some heavy CA too. It is kept at sdf as progress goes to the next phase. #MetalShader

Noise driven ripple effect, some heavy CA too. It is kept at sdf as progress goes to the next phase. #MetalShader

Minsang Choi

14,463 次观看 • 6 个月前

Wow. He is pretty good with that. What the difference between a sound effect and a noise.

Wow. He is pretty good with that. What the difference between a sound effect and a noise.

Johnny Midnight ⚡️

19,525 次观看 • 2 个月前

Gerhard Berger reunited with his 1989 #Ferrari 640 as he laps #Goodwood. What a noise. This is the #F1 engine noise we miss! #Tifosi #81MM

Gerhard Berger reunited with his 1989 #Ferrari 640 as he laps #Goodwood. What a noise. This is the #F1 engine noise we miss! #Tifosi #81MM

Goodwood Road & Racing

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That is 100% a OWA. The noise is so distinctive.

That is 100% a OWA. The noise is so distinctive.

SA Defensa

177,015 次观看 • 8 个月前

tried to reduce the background noise as much as i can to get the original audio 😭 this new song is such a banger!!!

tried to reduce the background noise as much as i can to get the original audio 😭 this new song is such a banger!!!

~merci beaucoup~

20,709 次观看 • 4 个月前

When the enemy makes noise he is trying to pull you in a different direction. The wisdom is not to chase the noise. The wisdom is to stay still and keep moving exactly where you were already going. Because that is where the blessing has always been. The noise is the distraction. Not the destination.

When the enemy makes noise he is trying to pull you in a different direction. The wisdom is not to chase the noise. The wisdom is to stay still and keep moving exactly where you were already going. Because that is where the blessing has always been. The noise is the distraction. Not the destination.

VT

20,133 次观看 • 10 天前

This is the noise of a Goat 🐐 😤

This is the noise of a Goat 🐐 😤

JOKAMRREDPILLZ

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What the awakened feels in a crowd… The noise is loud, but the emptiness is louder. Too many bodies. Too little presence. You feel the static, the anxiety, the unconscious loops. Sound familiar? 👁️✨

What the awakened feels in a crowd… The noise is loud, but the emptiness is louder. Too many bodies. Too little presence. You feel the static, the anxiety, the unconscious loops. Sound familiar? 👁️✨

vegastar

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Southwest Missouri is going to receive a slap. My leather coat is almost making more noise than the dialogue

Southwest Missouri is going to receive a slap. My leather coat is almost making more noise than the dialogue

LocalMan Weather

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Luca explains how Noise is solving a core problem with memecoins “Noise is tied to underlying information using Google trends and Kaito’s mindshare. You're able to trade on top of this data almost like its an oracle”

Luca explains how Noise is solving a core problem with memecoins “Noise is tied to underlying information using Google trends and Kaito’s mindshare. You're able to trade on top of this data almost like its an oracle”

Jack

25,574 次观看 • 1 年前

“The noise is just a lot of people who don’t know what they’re talking about.. we understand the outside noise is just noise. We would never go ask anybody who’s giving the noise their opinion about how to gameplan or how to play football.” - DeMeco Ryans

“The noise is just a lot of people who don’t know what they’re talking about.. we understand the outside noise is just noise. We would never go ask anybody who’s giving the noise their opinion about how to gameplan or how to play football.” - DeMeco Ryans

Houston Stressans

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Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.

Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.

Rossst.03

214,907 次观看 • 2 个月前

THE MANIPULATION IS ALMOST OVER. NO MORE SELL PRESSURE. ACCUMULATE AS MUCH AS YOU CAN. IGNORE THE NOISE AND THE FEAR. 2026 IS ABOUT TO BE THE BIGGEST RUN IN THE HISTORY OF CRYPTO.

THE MANIPULATION IS ALMOST OVER. NO MORE SELL PRESSURE. ACCUMULATE AS MUCH AS YOU CAN. IGNORE THE NOISE AND THE FEAR. 2026 IS ABOUT TO BE THE BIGGEST RUN IN THE HISTORY OF CRYPTO.

Crypto Zenkai

12,176 次观看 • 1 个月前

SORA by Hand ✍️ OpenAI’s #SORA took over the Internet when it was announced earlier this year. The technology behind Sora is the Diffusion Transformer (DiT) developed by William Peebles and Shining Xie. How does DiT work? 𝗚𝗼𝗮𝗹: Generate a video conditioned by a text prompt and a series of diffusion steps [1] Given ↳ Video ↳ Prompt: "sora is sky" ↳ Diffusion step: t = 3 [2] Video → Patches ↳ Divide all pixels in all frames into 4 spacetime patches [3] Visual Encoder: Pixels 🟨 → Latent 🟩 ↳ Multiply the patches with weights and biases, followed by ReLU ↳ The result is a latent feature vector per patch ↳ The purpose is dimension reduction from 4 (2x2x1) to 2 (2x1). ↳ In the paper, the reduction is 196,608 (256x256x3)→ 4096 (32x32x4) [4] ⬛ Add Noise ↳ Sample a noise according to the diffusion time step t. Typically, the larger the t, the smaller the noise. ↳ Add the Sampled Noise to latent features to obtain Noised Latent. ↳ The goal is to purposely add noise to a video and ask the model to guess what that noise is. ↳ This is analogous to training a language model by purposely deleting a word in a sentence and ask the model to guess what the deleted word was. [5-7] 🟪 Conditioning by Adaptive Layer Norm [5] Encode Conditions ↳ Encode "sora is sky" into a text embedding vector [0,1,-1]. ↳ Encode t = 3 to as a binary vector [1,1]. ↳ Concatenate the two vectors in to a 5D column vector. [6] Estimate Scale/Shift ↳ Multiply the combined vector with weights and biases ↳ The goal is to estimate the scale [2,-1] and shift [-1,5]. ↳ Copy the result to (X) and (+) [7] Apply Scale/Sift ↳ Scale the noised latent by [2,-1] ↳ Shifted the scaled noised latent by [-1, 5] ↳ The result is "conditioned" noise latent. [8-10] Transformer [8] Self-Attention ↳ Feed the conditioned noised latent to Query-Key function to obtain a self-attention matrix ↳ Value is omitted for simplicity [9] Attention Pooling ↳ Multiply the conditioned noised latent with the self-attention matrix ↳ The result are attention weighted features [10] Pointwise Feed Forward Network ↳ Multiply the attention weighted features with weights and biases ↳ The result is the Predicted Noise 🏋️‍♂️ 𝗧𝗿𝗮𝗶𝗻 [11] ↳ Calculate MSE loss gradients by taking the different between the Predicted Noise and the Sampled Noise (ground truth). ↳ Use the loss gradients to kick off backpropagation to update all learnable parameters (red borders) ↳ Note the visual encoder and decoder's parameters are frozen (blue borders) 🎨 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 (𝗦𝗮𝗺𝗽𝗹𝗲) [12] Denoise ↳ Subtract the predicted noise from the noised latent to obtain the noise-free latent [13] Visual Decoder: Latent 🟩 → Pixels 🟨 ↳ Multiply the patches with weights and biases, followed by ReLU [14] Patches → Video ↳ Rearrange patches into a sequence of video frames.

SORA by Hand ✍️ OpenAI’s #SORA took over the Internet when it was announced earlier this year. The technology behind Sora is the Diffusion Transformer (DiT) developed by William Peebles and Shining Xie. How does DiT work? 𝗚𝗼𝗮𝗹: Generate a video conditioned by a text prompt and a series of diffusion steps [1] Given ↳ Video ↳ Prompt: "sora is sky" ↳ Diffusion step: t = 3 [2] Video → Patches ↳ Divide all pixels in all frames into 4 spacetime patches [3] Visual Encoder: Pixels 🟨 → Latent 🟩 ↳ Multiply the patches with weights and biases, followed by ReLU ↳ The result is a latent feature vector per patch ↳ The purpose is dimension reduction from 4 (2x2x1) to 2 (2x1). ↳ In the paper, the reduction is 196,608 (256x256x3)→ 4096 (32x32x4) [4] ⬛ Add Noise ↳ Sample a noise according to the diffusion time step t. Typically, the larger the t, the smaller the noise. ↳ Add the Sampled Noise to latent features to obtain Noised Latent. ↳ The goal is to purposely add noise to a video and ask the model to guess what that noise is. ↳ This is analogous to training a language model by purposely deleting a word in a sentence and ask the model to guess what the deleted word was. [5-7] 🟪 Conditioning by Adaptive Layer Norm [5] Encode Conditions ↳ Encode "sora is sky" into a text embedding vector [0,1,-1]. ↳ Encode t = 3 to as a binary vector [1,1]. ↳ Concatenate the two vectors in to a 5D column vector. [6] Estimate Scale/Shift ↳ Multiply the combined vector with weights and biases ↳ The goal is to estimate the scale [2,-1] and shift [-1,5]. ↳ Copy the result to (X) and (+) [7] Apply Scale/Sift ↳ Scale the noised latent by [2,-1] ↳ Shifted the scaled noised latent by [-1, 5] ↳ The result is "conditioned" noise latent. [8-10] Transformer [8] Self-Attention ↳ Feed the conditioned noised latent to Query-Key function to obtain a self-attention matrix ↳ Value is omitted for simplicity [9] Attention Pooling ↳ Multiply the conditioned noised latent with the self-attention matrix ↳ The result are attention weighted features [10] Pointwise Feed Forward Network ↳ Multiply the attention weighted features with weights and biases ↳ The result is the Predicted Noise 🏋️‍♂️ 𝗧𝗿𝗮𝗶𝗻 [11] ↳ Calculate MSE loss gradients by taking the different between the Predicted Noise and the Sampled Noise (ground truth). ↳ Use the loss gradients to kick off backpropagation to update all learnable parameters (red borders) ↳ Note the visual encoder and decoder's parameters are frozen (blue borders) 🎨 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 (𝗦𝗮𝗺𝗽𝗹𝗲) [12] Denoise ↳ Subtract the predicted noise from the noised latent to obtain the noise-free latent [13] Visual Decoder: Latent 🟩 → Pixels 🟨 ↳ Multiply the patches with weights and biases, followed by ReLU [14] Patches → Video ↳ Rearrange patches into a sequence of video frames.

Tom Yeh

238,303 次观看 • 2 年前

THE MANIPULATION IS ALMOST OVER. NO MORE SELL PRESSURE. ACCUMULATE AS MUCH AS YOU CAN. IGNORE THE NOISE AND THE FEAR. 2026 IS ABOUT TO BE THE BIGGEST RUN IN THE HISTORY OF CRYPTO.

THE MANIPULATION IS ALMOST OVER. NO MORE SELL PRESSURE. ACCUMULATE AS MUCH AS YOU CAN. IGNORE THE NOISE AND THE FEAR. 2026 IS ABOUT TO BE THE BIGGEST RUN IN THE HISTORY OF CRYPTO.

Crypto Zenkai

22,592 次观看 • 2 个月前

Good morning T&T 🇹🇹 Noise pollution is a nuisance. I instructed that all fetes at the Brian Lara Stadium be stopped as the noise has become unbearable for residents. For far too long, the issue of noise pollution and its negative impact on communities has been ignored. My Government is committed to addressing this, as promised. Before the end of this year, we will pass the Fireworks Legislation in Parliament. In addition, enhanced noise pollution laws will be introduced to regulate designated quiet hours, as well as new restrictions on fetes, music trucks, residential parties, and bars.

Good morning T&T 🇹🇹 Noise pollution is a nuisance. I instructed that all fetes at the Brian Lara Stadium be stopped as the noise has become unbearable for residents. For far too long, the issue of noise pollution and its negative impact on communities has been ignored. My Government is committed to addressing this, as promised. Before the end of this year, we will pass the Fireworks Legislation in Parliament. In addition, enhanced noise pollution laws will be introduced to regulate designated quiet hours, as well as new restrictions on fetes, music trucks, residential parties, and bars.

Kamla Persad-Bissessar

156,463 次观看 • 9 个月前

The noise from outside Ashton Gate after Bristol City beat Sunderland on Friday What a noise that is 😳 #BristolCity

The noise from outside Ashton Gate after Bristol City beat Sunderland on Friday What a noise that is 😳 #BristolCity

The 44 ⚽️

99,877 次观看 • 1 年前