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As someone who builds with LLMs and diffusion models, this 14-minute heat equation video is the closest thing to a "why noise reversal actually works" explainer I've ever seen released for free. Everyone thinks diffusion is a novel AI concept. It's the heat equation from 1822. The forward process... show more

fintex

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15,535 görüntüleme • 7 gün önce •via X (Twitter)

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As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

As someone who's built with modern LLM architectures, this Laplace transform lecture is the closest thing to a "why Mamba works" explainer I've ever seen released for free. Everyone thinks transformers are the only path to modern LLMs. State space models like Mamba and S4 challenge that using math Laplace built in the 1780s. This video shows why. 25 minutes. Bookmark & watch today.

fintex

109,806 görüntüleme • 17 gün önce

As someone who trains LLMs, this 20-minute gradient descent video is the closest thing to a "why the memorization vs generalization debate matters" explainer I've ever seen released for free. Everyone thinks neural nets learn features. They often just memorize. The video walks through the 2017 paper that shocked the field: shuffle your labels randomly and modern networks will still fit them perfectly. 25 minutes. Bookmark & watch today.

As someone who trains LLMs, this 20-minute gradient descent video is the closest thing to a "why the memorization vs generalization debate matters" explainer I've ever seen released for free. Everyone thinks neural nets learn features. They often just memorize. The video walks through the 2017 paper that shocked the field: shuffle your labels randomly and modern networks will still fit them perfectly. 25 minutes. Bookmark & watch today.

fintex

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As someone who ships LLM systems in production, this Laplace transform video is the closest thing to a "why Mamba beats transformers" explainer I've ever seen released for free. Everyone thinks attention is what makes LLMs powerful. Laplace proved in 1785 that any signal breaks into exponential pieces - locate the poles, expose the frequencies. Mamba and Claude run on this exact trick. 34 minutes. Bookmark & watch today. The math is older than the United States.

As someone who ships LLM systems in production, this Laplace transform video is the closest thing to a "why Mamba beats transformers" explainer I've ever seen released for free. Everyone thinks attention is what makes LLMs powerful. Laplace proved in 1785 that any signal breaks into exponential pieces - locate the poles, expose the frequencies. Mamba and Claude run on this exact trick. 34 minutes. Bookmark & watch today. The math is older than the United States.

Eron

66,237 görüntüleme • 9 gün önce

As someone who ships LLM systems in production, this backpropagation video is the closest thing to a "why GPT actually learns" explainer I've ever seen released for free. Everyone thinks training a neural network is calculus. It's not. It's 175 billion weights getting nudged proportionally to how much each one caused the mistake. PyTorch does this backwards pass on every single training step. 20 minutes. Bookmark & watch today. The algorithm is older than the iPhone.

As someone who ships LLM systems in production, this backpropagation video is the closest thing to a "why GPT actually learns" explainer I've ever seen released for free. Everyone thinks training a neural network is calculus. It's not. It's 175 billion weights getting nudged proportionally to how much each one caused the mistake. PyTorch does this backwards pass on every single training step. 20 minutes. Bookmark & watch today. The algorithm is older than the iPhone.

Eron

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As someone who's been shipping LLMs since the GPT-2 days, this lecture on cross-entropy from a Stanford math grad is the closest thing to an ML PhD qualifying exam I've ever seen released publicly for free. Everyone thinks language models predict the next word. They don't. They compress language. Once you see the math, you can't unsee it. 33 minutes. Bookmark & watch today.

As someone who's been shipping LLMs since the GPT-2 days, this lecture on cross-entropy from a Stanford math grad is the closest thing to an ML PhD qualifying exam I've ever seen released publicly for free. Everyone thinks language models predict the next word. They don't. They compress language. Once you see the math, you can't unsee it. 33 minutes. Bookmark & watch today.

fintex

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As someone who reads every transformer paper that drops, this Fourier series lecture is the closest thing to a Google FNet explainer I've ever seen released for free. Everyone thinks attention is what makes transformers powerful. Google replaced attention with a 200-year-old Fourier transform. It nearly matched BERT and ran 7x faster. 25 minutes. Bookmark & watch today. Then read the article below - I broke down the 5 pieces of math the AI hype skips.

As someone who reads every transformer paper that drops, this Fourier series lecture is the closest thing to a Google FNet explainer I've ever seen released for free. Everyone thinks attention is what makes transformers powerful. Google replaced attention with a 200-year-old Fourier transform. It nearly matched BERT and ran 7x faster. 25 minutes. Bookmark & watch today. Then read the article below - I broke down the 5 pieces of math the AI hype skips.

fintex

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As someone who ships LLM systems in production, this hairy ball theorem video is the closest thing to a "why positional encoding in 3D robotics always breaks" explainer I've ever seen released for free. Everyone thinks orientation in 3D space is a solved problem. A theorem from 1912 proves there is always at least one direction where any continuous vector field hits zero — the exact reason every robotics LLM glitches at certain angles. 25 minutes. Bookmark & watch today. The math is older than the airplane.

As someone who ships LLM systems in production, this hairy ball theorem video is the closest thing to a "why positional encoding in 3D robotics always breaks" explainer I've ever seen released for free. Everyone thinks orientation in 3D space is a solved problem. A theorem from 1912 proves there is always at least one direction where any continuous vector field hits zero — the exact reason every robotics LLM glitches at certain angles. 25 minutes. Bookmark & watch today. The math is older than the airplane.

Eron

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As someone who's spent 3 years fine-tuning ML models, this lecture on neural networks from a Stanford math grad is the closest thing to a no-bullshit "day one of ML" briefing I've ever seen released publicly for free. Everyone thinks neural nets are magic. They aren't. They're 13,000 dials - a matrix multiplication, a sigmoid squish, done. Once you see the math, "AI" stops feeling mysterious. 18 minutes. Bookmark & watch today.

As someone who's spent 3 years fine-tuning ML models, this lecture on neural networks from a Stanford math grad is the closest thing to a no-bullshit "day one of ML" briefing I've ever seen released publicly for free. Everyone thinks neural nets are magic. They aren't. They're 13,000 dials - a matrix multiplication, a sigmoid squish, done. Once you see the math, "AI" stops feeling mysterious. 18 minutes. Bookmark & watch today.

fintex

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Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

Y Combinator

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LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

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Fourier wrapped a signal around a circle in 1822. Every Spotify equalizer, every noise filter in your AirPods, and every frequency layer inside ChatGPT runs on what he found. The trick is simple. Spin the signal at different speeds. When the spin matches a hidden frequency, the center of mass shifts. That shift is the answer. Claude and GPT use this same idea to separate relevant tokens from noise. 20 minutes. Bookmark & watch today. The math is older than the telephone.

Fourier wrapped a signal around a circle in 1822. Every Spotify equalizer, every noise filter in your AirPods, and every frequency layer inside ChatGPT runs on what he found. The trick is simple. Spin the signal at different speeds. When the spin matches a hidden frequency, the center of mass shifts. That shift is the answer. Claude and GPT use this same idea to separate relevant tokens from noise. 20 minutes. Bookmark & watch today. The math is older than the telephone.

Eron

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3DTopia-XL High-Quality 3D PBR Asset Generation via Primitive Diffusion demo: model: 3DTopia-XL scales high-quality 3D asset generation using Diffusion Transformer (DiT) built upon an expressive and efficient 3D representation, PrimX. The denoising process takes 5 seconds to generate a 3D PBR asset from text/image input which is ready for the graphics pipeline to use.

AK

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This is the craziest model you'll see this week! This model powers Figma. It's also behind Wayfair and a bunch of major e-commerce retailers. This model is a PIXEL-PERFECT image editing model. Literally, "pixel perfect". The model is capable of modifying one image while keeping everything else untouched. It's a model designed, developed, and deployed by the research team at Jasper. This beats OpenAI models, Nano Banana, and every single general image model I've seen so far. It's up to 100x less expensive to run, and up to 10X faster. I recorded a quick video to show you how impressive this is. There's a ton of research behind this model. I'm adding two links below so you can read about how this model works, specifically about "Latent Bridge Matching" and "Flash Diffusion".

This is the craziest model you'll see this week! This model powers Figma. It's also behind Wayfair and a bunch of major e-commerce retailers. This model is a PIXEL-PERFECT image editing model. Literally, "pixel perfect". The model is capable of modifying one image while keeping everything else untouched. It's a model designed, developed, and deployed by the research team at Jasper. This beats OpenAI models, Nano Banana, and every single general image model I've seen so far. It's up to 100x less expensive to run, and up to 10X faster. I recorded a quick video to show you how impressive this is. There's a ton of research behind this model. I'm adding two links below so you can read about how this model works, specifically about "Latent Bridge Matching" and "Flash Diffusion".

Santiago

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As someone who builds institutional quant systems, this is the only time I've seen a $2.2 trillion fund publicly show exactly how they use AI internally to win. Bookmark & watch this today. It's the most valuable 1 hour in quant AI right now.

As someone who builds institutional quant systems, this is the only time I've seen a $2.2 trillion fund publicly show exactly how they use AI internally to win. Bookmark & watch this today. It's the most valuable 1 hour in quant AI right now.

Roan

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I found this cooling plate on Amazon, and it looks like a gimmick but I ordered it anyway. This thing actually works SO WELL. The heat is all generated next to the cameras on most phones, but the cameras block coolers from reaching it. This metal plate extends so your cooler (sold separately) can absorb the heat coming out of it. The extended plate is freezing cold. (this is not sponsored, just trying to help people with heating problems). #WarzoneMobile #WZM

I found this cooling plate on Amazon, and it looks like a gimmick but I ordered it anyway. This thing actually works SO WELL. The heat is all generated next to the cameras on most phones, but the cameras block coolers from reaching it. This metal plate extends so your cooler (sold separately) can absorb the heat coming out of it. The extended plate is freezing cold. (this is not sponsored, just trying to help people with heating problems). #WarzoneMobile #WZM

Wynnsanity

13,047 görüntüleme • 2 yıl önce

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 görüntüleme • 2 yıl önce

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

ooaes

28,543 görüntüleme • 17 gün önce

A new Science Advances study shows offshore wind turbines actually warm the sea surface. Spikes near as much as one degree Celsius, which is as large as natural summer variability. Turbines slow the wind, explains the study. This weakens mixing, shuts down upwelling, and in turn traps heat at the surface. The warming appears within days and spreads tens of kilometers from the wind farm. These warm waters then heat the air above, creating extra upward heat flux and a more unstable boundary layer. Stack this onto bird kills, sediment disruption, underwater noise and microplastic shedding, and the picture is clear: Offshore wind isn't solving an environmental problem, it's becoming one.

A new Science Advances study shows offshore wind turbines actually warm the sea surface. Spikes near as much as one degree Celsius, which is as large as natural summer variability. Turbines slow the wind, explains the study. This weakens mixing, shuts down upwelling, and in turn traps heat at the surface. The warming appears within days and spreads tens of kilometers from the wind farm. These warm waters then heat the air above, creating extra upward heat flux and a more unstable boundary layer. Stack this onto bird kills, sediment disruption, underwater noise and microplastic shedding, and the picture is clear: Offshore wind isn't solving an environmental problem, it's becoming one.

Electroverse

92,243 görüntüleme • 3 ay önce

John: People tend to think AI is going to cure cancer with a prompt like, 'What's the cure for cancer?' and AI outputs some E = mc²-level math equation. More realistically, it might be that a guy gets really rich from AI image generation, then funnels that into a medical technology that can detect cancer a couple months earlier, so cancer rates decline a bunch. But it's not just like, you prompt it and get a magic pill. It's much more complicated than that. Because curing cancer, or reducing cancer rates, is a process — like an industrial process — more than a single clever trick.

John: People tend to think AI is going to cure cancer with a prompt like, 'What's the cure for cancer?' and AI outputs some E = mc²-level math equation. More realistically, it might be that a guy gets really rich from AI image generation, then funnels that into a medical technology that can detect cancer a couple months earlier, so cancer rates decline a bunch. But it's not just like, you prompt it and get a magic pill. It's much more complicated than that. Because curing cancer, or reducing cancer rates, is a process — like an industrial process — more than a single clever trick.

TBPN

40,835 görüntüleme • 1 ay önce

This 2 hour Stanford lecture by Andrew Ng and Laurence Moroney is the most honest AI career playbook I've ever seen. Why now is the best time to build in AI, and what actually gets you hired in 2026. Bookmark & watch tonight, best 2 hours you'll give your career this year.

This 2 hour Stanford lecture by Andrew Ng and Laurence Moroney is the most honest AI career playbook I've ever seen. Why now is the best time to build in AI, and what actually gets you hired in 2026. Bookmark & watch tonight, best 2 hours you'll give your career this year.

Phosphen

31,502 görüntüleme • 3 ay önce