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3Blue1Brown’s new video explains why every LLM is actually a compression machine. everyone describes pre-training as “next token prediction” but that’s just the surface-level objective. in reality it is a means to making the most efficient text compressor. prediction and compression are two sides of the same coin. when...

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ℏεsam

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119,751 просмотров • 1 месяц назад •via X (Twitter)

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🚨 ANOTHER MASTERCLASS FROM Grant Sanderson The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use. Grant’s new video reframes Shannon’s entropy through one elegant lens: Prediction IS compression. → The better you predict the next word, the fewer bits you need to store it → Shannon measured English at ~1 bit per character: astonishingly compressible → This is exactly what GPT-style models optimize → Intelligence, in this framing, is compression FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄 Decades later, that same concept became the bedrock of modern AI. Deep-dive resources in the 🧵 ↓
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🚨 ANOTHER MASTERCLASS FROM Grant Sanderson The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use. Grant’s new video reframes Shannon’s entropy through one elegant lens: Prediction IS compression. → The better you predict the next word, the fewer bits you need to store it → Shannon measured English at ~1 bit per character: astonishingly compressible → This is exactly what GPT-style models optimize → Intelligence, in this framing, is compression FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄 Decades later, that same concept became the bedrock of modern AI. Deep-dive resources in the 🧵 ↓

Charly Wargnier

318,104 просмотров • 1 месяц назад

🚨 Grant Sanderson DID IT AGAIN Language compressibility is not just a neat math trick: it is the core engine of modern LLMs. Grant's latest video boils Shannon's entropy down to a single, powerful idea: Prediction IS compression. → Predict the next word better, use fewer bits to store it → Shannon found English is astonishingly compressible (~1 bit per character) → This is the exact mechanism GPT models run on → Under this framing, intelligence equals compression FUN FACT: Von Neumann told Shannon to use the term "entropy" because no one really understands it. Today, it powers the AI revolution. Deep-dive resources in the 🧵↓
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🚨 Grant Sanderson DID IT AGAIN Language compressibility is not just a neat math trick: it is the core engine of modern LLMs. Grant's latest video boils Shannon's entropy down to a single, powerful idea: Prediction IS compression. → Predict the next word better, use fewer bits to store it → Shannon found English is astonishingly compressible (~1 bit per character) → This is the exact mechanism GPT models run on → Under this framing, intelligence equals compression FUN FACT: Von Neumann told Shannon to use the term "entropy" because no one really understands it. Today, it powers the AI revolution. Deep-dive resources in the 🧵↓

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160,113 просмотров • 16 дней назад

🚨| Sir Lewis Hamilton on Mercedes' engine: "What's clear is that they didn't show the engine power through any of the practice, because there's the whole talk of compression ratio, and they've obviously done a really solid job with their engine, which we have as well, but it would be interesting to... I want to understand why it's like two-tenths or more just through power, per sector. And so, if it is a compression thing, I want to understand why the FIA haven't done anything, what's being done to rectify it, but if it's not, and it's just pure [ped], then we have to do a better job." #F1 || #AustralianGP
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🚨| Sir Lewis Hamilton on Mercedes' engine: "What's clear is that they didn't show the engine power through any of the practice, because there's the whole talk of compression ratio, and they've obviously done a really solid job with their engine, which we have as well, but it would be interesting to... I want to understand why it's like two-tenths or more just through power, per sector. And so, if it is a compression thing, I want to understand why the FIA haven't done anything, what's being done to rectify it, but if it's not, and it's just pure [ped], then we have to do a better job." #F1 || #AustralianGP

sim

38,390 просмотров • 4 месяцев назад

🚨 | Lewis Hamilton: “If it is the compression thing, I wanna understand why the FIA haven’t done anything, what’s been done to rectify it but if it’s not and it’s just pure pace, then we have to do a better job.”
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🚨 | Lewis Hamilton: “If it is the compression thing, I wanna understand why the FIA haven’t done anything, what’s been done to rectify it but if it’s not and it’s just pure pace, then we have to do a better job.”

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🚨 | Lewis Hamilton on Mercedes’ compression ratio: “What’s clear is that they didn’t show the engine power through any of the practice because of the whole talk on the compression issue. They’ve done a really solid job with their engine, which we have as well but I’m trying to understand why it's 2 tenths or more just through power per sector... And so, if it is the compression thing, I wanna understand why the FIA haven't done anything [or] what’s being done to rectify it.”
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🚨 | Lewis Hamilton on Mercedes’ compression ratio: “What’s clear is that they didn’t show the engine power through any of the practice because of the whole talk on the compression issue. They’ve done a really solid job with their engine, which we have as well but I’m trying to understand why it's 2 tenths or more just through power per sector... And so, if it is the compression thing, I wanna understand why the FIA haven't done anything [or] what’s being done to rectify it.”

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959,964 просмотров • 4 месяцев назад

Mr Shift: What's the true innovation with prediction markets? Kalshi CEO: There's many ways to answer that question. Um there's the Kalshi piece of it, but then there's the prediction market piece of it, which are not exactly the same. “But the... the true innovation in prediction markets, I think, is this notion, like... and, and I think it has been built through a lot of literature, and then I think the prediction markets have come in our generation, like Kalshi and some of the others have proven it” “The best way to answer like all these most pressing questions is like you have to ask the crowd”
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Mr Shift: What's the true innovation with prediction markets? Kalshi CEO: There's many ways to answer that question. Um there's the Kalshi piece of it, but then there's the prediction market piece of it, which are not exactly the same. “But the... the true innovation in prediction markets, I think, is this notion, like... and, and I think it has been built through a lot of literature, and then I think the prediction markets have come in our generation, like Kalshi and some of the others have proven it” “The best way to answer like all these most pressing questions is like you have to ask the crowd”

ShaDow | Clipur.com

23,860 просмотров • 1 месяц назад

Understanding Iliotibial Band Pathology: When discussing the iliotibial band, it's important to note that although its anatomical insertion is at Gerdy's tubercle, this isn't typically the site where pathology develops. Instead, problems often arise at the level of the lateral femoral condyle, particularly with repeated knee flexion. Long-distance runners** are a classic example. With ongoing flexion, the vastus lateralis muscle compresses the fat pad situated between the iliotibial band and the lateral femoral condyle. This compression leads to changes in the fat pad, often observed as echogenic and hyperaemic fat on imaging—hallmarks of iliotibial band compression syndrome. Not Just Friction—It's Compression: It’s crucial to clarify that this condition is not a friction syndrome, as is commonly believed. Rather, it is a *compression syndrome*, with the underlying mechanism being the direct pressure on the fat pad rather than friction between the band and bone. Understanding the precise location and mechanism of iliotibial band pathology helps us better diagnose and manage athletes and active individuals who present with lateral knee pain. Video Original Source: Sonography_Secrets You Tube
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Understanding Iliotibial Band Pathology: When discussing the iliotibial band, it's important to note that although its anatomical insertion is at Gerdy's tubercle, this isn't typically the site where pathology develops. Instead, problems often arise at the level of the lateral femoral condyle, particularly with repeated knee flexion. Long-distance runners** are a classic example. With ongoing flexion, the vastus lateralis muscle compresses the fat pad situated between the iliotibial band and the lateral femoral condyle. This compression leads to changes in the fat pad, often observed as echogenic and hyperaemic fat on imaging—hallmarks of iliotibial band compression syndrome. Not Just Friction—It's Compression: It’s crucial to clarify that this condition is not a friction syndrome, as is commonly believed. Rather, it is a *compression syndrome*, with the underlying mechanism being the direct pressure on the fat pad rather than friction between the band and bone. Understanding the precise location and mechanism of iliotibial band pathology helps us better diagnose and manage athletes and active individuals who present with lateral knee pain. Video Original Source: Sonography_Secrets You Tube

Benoy Mathew

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(flashing lights warning!!!!) 'Artifacts' is my tribute to the outsidery and computery aesthetics of Flash animation - viewable ONLY in flash (link in the next tweet) because as you can see, video compression *completely* nukes it!
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(flashing lights warning!!!!) 'Artifacts' is my tribute to the outsidery and computery aesthetics of Flash animation - viewable ONLY in flash (link in the next tweet) because as you can see, video compression *completely* nukes it!

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WHOS READY TO RUN IT BACK TURBO?! Introducing FUTURE DAO! So we got DAOs ✅ We got Prediction markets ✅ We got Ai ✅ We got meme-coins✅ We got a pre sale✅ Prediction markets have been a bit boring to me TBH, I WANT TO HIT 100Xs not like do 10hrs of research on Greenland politics or something. So I created this DAO to give people some leverage, and make it fun. Basically how it works, is we do a pre sale, people get tokens, the rest of the token supply goes into the DAO. Like a classic DAOs fun but you can redeem the AUM at any time,(but you incur a penalty) . You have the AUM, and our proprietary Ai does the trading for you. Just hold the token and watch it go up(hopefully), the Ai trades with guidance from Token holder votes. The token holders also get to vote/guide the Ai into outcomes they think will benefit the DAO/token. AUM Goes up. Token Goes up. Everyone happy. Or AUM flat/Down. Token Goes down. People Redeem and get % of their money back. better than zeroing out on meme coins. The token will all be launched via a semi-gated pre sale, And using my own method of launching to showcase how its done and to ensure there is a good group of "seed DAO" members to help the Ai. it will run 8 weeks, and people can decide to shut it down and get $$ back. or keep it going forever. end of the day this is a meme coin guys, but I think its a cool experiment.

Skely

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"When you go to spaces like VR or BCI, everybody knows each other, and that means there's a high density high quality founders, and whoever makes it work in the bear market -- they are going to crush it when the market turns better." Felix Hartmann explains why some of the most compelling asymmetric opportunities lie in ecosystems like VR and brain-computer interfaces. Watch the full episode of Angels to Allocators:
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"When you go to spaces like VR or BCI, everybody knows each other, and that means there's a high density high quality founders, and whoever makes it work in the bear market -- they are going to crush it when the market turns better." Felix Hartmann explains why some of the most compelling asymmetric opportunities lie in ecosystems like VR and brain-computer interfaces. Watch the full episode of Angels to Allocators:

Hartmann Capital

378,971 просмотров • 11 месяцев назад

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sooyoung trying to convince yuri to yield the center position to her 😂😭 yuri: but you’re too tall sooyoung: because i’m taller, the picture of me in the center brings the stable feeling sometimes hyoyeon: when you’re in the center, it’s like swearing 😭 people just feel bad about it sooyoung: but if yuri’s in the center, it looks like do-re-mi, it feels unstable and like a downhill… yuri: still better than looking like a middle finger ^^ *both yuri and hyo means the 3 of them will look like this ㅗ (similar shape of a middle finger) if syoo is in the center 😭 MY POOR SOOYOUNG

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150,095 просмотров • 1 месяц назад

AI EXPLAINS WHAT IT MEANS TO BE CHOSEN NOT EVERYONE IS CHOSEN & THATS WHY THEY CANT SEE THE TRUTH, OR EVEN WANT TO NEITHER DOES THEIR VIBRATION RESONATE ON THE SAME LEVEL AS A CHOSEN ONE WE. ARE. NOT. THE. SAME.
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AI EXPLAINS WHAT IT MEANS TO BE CHOSEN NOT EVERYONE IS CHOSEN & THATS WHY THEY CANT SEE THE TRUTH, OR EVEN WANT TO NEITHER DOES THEIR VIBRATION RESONATE ON THE SAME LEVEL AS A CHOSEN ONE WE. ARE. NOT. THE. SAME.

Lesgo Brando

168,043 просмотров • 5 месяцев назад

.Naval: Look at reality the way it is. Try to take yourself out of the equation. “Labels like pessimist, optimist, cynic, introvert, extrovert—these are very self limiting. Humans are very dynamic. There are times when you feel like being introverted. There are times when you feel like being extroverted. There are contexts in which you’ll be pessimistic. There are contexts in which you’ll be optimistic. Leave all those labels alone. It’s better just to look at the problem at hand. Look at reality the way it is. Try to take yourself out of the equation, in a sense. Obviously you’re involved. But motivated reasoning is the worst kind of reasoning. You’re not going to find truth through highly motivated reasoning. You have to be objective. And objective means trying to take yourself out of it as much as possible, or at least your personality out of it as much as possible. And so to the extent you run with this thick identity and personality, it’s going to cloud your judgment. It’s going to try and lock you into the past. If you say, ‘I’m a depressed, unhappy person—I’m going to be unhappy.’ That’s a way of locking yourself into your past. Even saying, ‘I have trauma, I have PTSD,’ yeah, you feel something—there are memories, there are flashes, there are occasional bad feelings. But don’t define yourself by it because then you’ll lock it into your identity and you’re just going to loop on it. It’s better to stay flexible because reality is always changing and you have to be able to adapt to it. Adaptation is also intelligence, adaptation is survival. Adaptation is kind of how you’re here. You’re here because you’re an adapter and your ancestors were adapters. So to adapt you’ll have to see things clearly and if you’re seeing them through your own identity, it’s going to cloud your judgment.”
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.Naval: Look at reality the way it is. Try to take yourself out of the equation. “Labels like pessimist, optimist, cynic, introvert, extrovert—these are very self limiting. Humans are very dynamic. There are times when you feel like being introverted. There are times when you feel like being extroverted. There are contexts in which you’ll be pessimistic. There are contexts in which you’ll be optimistic. Leave all those labels alone. It’s better just to look at the problem at hand. Look at reality the way it is. Try to take yourself out of the equation, in a sense. Obviously you’re involved. But motivated reasoning is the worst kind of reasoning. You’re not going to find truth through highly motivated reasoning. You have to be objective. And objective means trying to take yourself out of it as much as possible, or at least your personality out of it as much as possible. And so to the extent you run with this thick identity and personality, it’s going to cloud your judgment. It’s going to try and lock you into the past. If you say, ‘I’m a depressed, unhappy person—I’m going to be unhappy.’ That’s a way of locking yourself into your past. Even saying, ‘I have trauma, I have PTSD,’ yeah, you feel something—there are memories, there are flashes, there are occasional bad feelings. But don’t define yourself by it because then you’ll lock it into your identity and you’re just going to loop on it. It’s better to stay flexible because reality is always changing and you have to be able to adapt to it. Adaptation is also intelligence, adaptation is survival. Adaptation is kind of how you’re here. You’re here because you’re an adapter and your ancestors were adapters. So to adapt you’ll have to see things clearly and if you’re seeing them through your own identity, it’s going to cloud your judgment.”

Arjun Khemani

251,772 просмотров • 1 год назад

Threadguy explains why Apple will capture ALL of the value from AI “Models have become a commodity, we'll be able to just use the best model at any given time” "The more data AI has access to, the better it is. Everyone already has an iPhone, the next iPhone is just going to launch with some OpenClaw esque assistant utilising the best model plugged into it and its probably going to be the best AI experience you’ve ever had”
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Threadguy explains why Apple will capture ALL of the value from AI “Models have become a commodity, we'll be able to just use the best model at any given time” "The more data AI has access to, the better it is. Everyone already has an iPhone, the next iPhone is just going to launch with some OpenClaw esque assistant utilising the best model plugged into it and its probably going to be the best AI experience you’ve ever had”

Jack

24,179 просмотров • 4 месяцев назад

Emotions come and go—that’s the nature of being human. One moment you feel joy, the next, anger or sadness. That doesn’t mean something’s wrong with you. It means you’re alive. The key is not to resist what you feel… but to understand it. To sit with it. To learn from it without letting it define you. Even the wisest man has storms within— he’s just learned how to sail through them.
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Emotions come and go—that’s the nature of being human. One moment you feel joy, the next, anger or sadness. That doesn’t mean something’s wrong with you. It means you’re alive. The key is not to resist what you feel… but to understand it. To sit with it. To learn from it without letting it define you. Even the wisest man has storms within— he’s just learned how to sail through them.

𖣂JIK𖣂

175,333 просмотров • 1 год назад

I love that in my line of work I get to pour all my love, preparation, goals, and dreams into shaping the next generation. It is finding ways to reach their hearts, to teach in the best way they receive it. It is a privilege to invest countless hours into building not just a better athlete, but a better man. This is more than a game, it’s an opportunity to better the world, one student- athlete at a time. Why not have some fun while we’re at it 😎!! Thank you Vandals Nation! #RelentlessEffort #MyBrothersKeeper #FightForward #WorkDay
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I love that in my line of work I get to pour all my love, preparation, goals, and dreams into shaping the next generation. It is finding ways to reach their hearts, to teach in the best way they receive it. It is a privilege to invest countless hours into building not just a better athlete, but a better man. This is more than a game, it’s an opportunity to better the world, one student- athlete at a time. Why not have some fun while we’re at it 😎!! Thank you Vandals Nation! #RelentlessEffort #MyBrothersKeeper #FightForward #WorkDay

Coach Hebron (Loni) Fangupo

45,110 просмотров • 1 год назад

🚨 NASA RELEASES “ARTEMIS CREW” FOOTAGE — BUT SOMETHING IS VERY WRONG A man breaking down the official video says something isn’t adding up. Not just “it looks weird.” Something specific. Watch closely. He says this isn’t compression. You can record a video multiple times, it won’t erase subjects from screens or create inconsistencies like this. And now others are starting to notice the same thing. So what exactly are we looking at here? And why release footage like this in the first place?
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🚨 NASA RELEASES “ARTEMIS CREW” FOOTAGE — BUT SOMETHING IS VERY WRONG A man breaking down the official video says something isn’t adding up. Not just “it looks weird.” Something specific. Watch closely. He says this isn’t compression. You can record a video multiple times, it won’t erase subjects from screens or create inconsistencies like this. And now others are starting to notice the same thing. So what exactly are we looking at here? And why release footage like this in the first place?

HustleBitch

106,472 просмотров • 3 месяцев назад

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:
2:52

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

125,146 просмотров • 1 год назад

Elon Musk explains why AI could be capable of discovering new physics. “You can think of intelligence like a compression problem. For any given field, it can be physics, or any field, you can distill things down to their most basic axiomatic elements, the things that are extremely likely to be true. And then, you can reason up from there to reach a cogent conclusion. I think if you apply that sort of first-principles compression to a field, and then you decompress it and contest the results against reality, then AI is capable of innovation and discovering new physics.” Town Hall in Philadelphia, October 18, 2024
0:37

Elon Musk explains why AI could be capable of discovering new physics. “You can think of intelligence like a compression problem. For any given field, it can be physics, or any field, you can distill things down to their most basic axiomatic elements, the things that are extremely likely to be true. And then, you can reason up from there to reach a cogent conclusion. I think if you apply that sort of first-principles compression to a field, and then you decompress it and contest the results against reality, then AI is capable of innovation and discovering new physics.” Town Hall in Philadelphia, October 18, 2024

ELON CLIPS

1,054,036 просмотров • 1 год назад

I'll tell it like it is: they're lying. Robots are getting better and better, and the goal now is not to support human labor, as suggested in the video, but in the long term they will replace human labor. In a market economy, profit is the only thing that counts, whether you like to criticize that or not. The fact is, however, that robots will be cheaper than human labor and will therefore be preferred. Moreover, robots are getting better and better, they will work longer hours than humans, are less prone to errors, do not fall ill and do not need vacations. They are superior to human labor.
1:49

I'll tell it like it is: they're lying. Robots are getting better and better, and the goal now is not to support human labor, as suggested in the video, but in the long term they will replace human labor. In a market economy, profit is the only thing that counts, whether you like to criticize that or not. The fact is, however, that robots will be cheaper than human labor and will therefore be preferred. Moreover, robots are getting better and better, they will work longer hours than humans, are less prone to errors, do not fall ill and do not need vacations. They are superior to human labor.

Chubby♨️

65,163 просмотров • 1 год назад