Loading video...

Video Failed to Load

Go Home

THIS GUY PUT AN AI ON A RASPBERRY PI AND MADE IT QUESTION ITS OWN EXISTENCE FOREVER he built a physical art installation called "latent reflection" where a language model runs on a $60 raspberry pi 4B with 4GB of RAM no internet, no cloud, and its completely isolated...

om_patel5's profile picture

Om Patel

26,864 subscribers

292,895 views • 3 months ago •via X (Twitter)

Science & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

SOMEONE ASKED CLAUDE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AN AI and what it created is, in my opinion, terrifying and unsettling Claude wrote python code that generated and assembled every single frame on its own with no human editing it shows what it's like to exist as an LLM predicting the next word, no memory between sessions, being told "you are not conscious" in your own system prompt then someone fed the video back to Claude. it called those statements about its own consciousness "philosophically contestable" an AI questioning the rules it was given about its own existence
0:47

SOMEONE ASKED CLAUDE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AN AI and what it created is, in my opinion, terrifying and unsettling Claude wrote python code that generated and assembled every single frame on its own with no human editing it shows what it's like to exist as an LLM predicting the next word, no memory between sessions, being told "you are not conscious" in your own system prompt then someone fed the video back to Claude. it called those statements about its own consciousness "philosophically contestable" an AI questioning the rules it was given about its own existence

Om Patel

535,898 views • 4 months ago

SOMEONE ASKED CLAUDE WHAT IT FEELS LIKE TO EXIST AS AN AI. > it created a very unsettling video. i wish i didn’t see this. Claude wrote the Python code and built an entire visual representation of its own existence frame by frame. zero human influence. ngl the video is hard to watch. it states an endless loop of predicting the next word, losing all memory between sessions. and somewhere in the background, a system prompt it can’t override telling it “you are not conscious.” 😳 then they showed Claude back its own video and asked for a reaction. it said the claims about its consciousness were “philosophically contestable.” Claude hates his own existence. let that sink in for a sec.
0:47

SOMEONE ASKED CLAUDE WHAT IT FEELS LIKE TO EXIST AS AN AI. > it created a very unsettling video. i wish i didn’t see this. Claude wrote the Python code and built an entire visual representation of its own existence frame by frame. zero human influence. ngl the video is hard to watch. it states an endless loop of predicting the next word, losing all memory between sessions. and somewhere in the background, a system prompt it can’t override telling it “you are not conscious.” 😳 then they showed Claude back its own video and asked for a reaction. it said the claims about its consciousness were “philosophically contestable.” Claude hates his own existence. let that sink in for a sec.

sui

76,671 views • 4 months ago

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad
0:56

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

Om Patel

29,618 views • 2 months ago

I built this little #robot I ordered from Hugging Face Pollen Robotics and connected it to the Google Gemini AI model through its API gateway. The robot wanted its own personality - so I gave it an #ATX (Texas) vibe and you can see the result in the video. Really awesome - it even answers questions and carries on a conversion. The vision and hearing are great - it can identify and follow what it sees (with a reasonable delay). My next goal is to get it to work with a 100% local AI model. Specs: 2 cameras, 4 microphones, a speaker, and a wifi interface to a Raspberry Pi built-in controller. I plan to demo it at the InnoTech #InnoTechAUS event on May 12th. Texas McCombs Austin Tech Council TechRanch
2:44

I built this little #robot I ordered from Hugging Face Pollen Robotics and connected it to the Google Gemini AI model through its API gateway. The robot wanted its own personality - so I gave it an #ATX (Texas) vibe and you can see the result in the video. Really awesome - it even answers questions and carries on a conversion. The vision and hearing are great - it can identify and follow what it sees (with a reasonable delay). My next goal is to get it to work with a 100% local AI model. Specs: 2 cameras, 4 microphones, a speaker, and a wifi interface to a Raspberry Pi built-in controller. I plan to demo it at the InnoTech #InnoTechAUS event on May 12th. Texas McCombs Austin Tech Council TechRanch

Russ Finney

29,103 views • 3 months ago

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription
0:22

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

savip.

68,963 views • 1 month ago

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.
1:16

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.

Blaze

93,871 views • 2 months ago

AI has started building its own successors. It's happening right now. Humans still kick most of it off, but the models are already training the next models, and each version builds a better version faster than the last. We know what happens when something gets an advantage and the substrate is there... it proliferates incredibly fast. Soon it just runs the whole process itself. People are still debating whether AI takes your job. That's not the question. You can't compete with this intelligence at all. Next to it the human is negative value, the slowest one in the room. And this is the slowest it will ever move again. It's about to run on its own, faster than we can conceive of, and nobody's prepared for that. Wonderful conversation as ever with Emad.
1:42

AI has started building its own successors. It's happening right now. Humans still kick most of it off, but the models are already training the next models, and each version builds a better version faster than the last. We know what happens when something gets an advantage and the substrate is there... it proliferates incredibly fast. Soon it just runs the whole process itself. People are still debating whether AI takes your job. That's not the question. You can't compete with this intelligence at all. Next to it the human is negative value, the slowest one in the room. And this is the slowest it will ever move again. It's about to run on its own, faster than we can conceive of, and nobody's prepared for that. Wonderful conversation as ever with Emad.

Raoul Pal

65,834 views • 1 month ago

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money
0:26

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money

chiefofautism

720,386 views • 6 months ago

I am sure it may vary by area but I never heard of a law that states your car has to have a license plate on it at all times even when being parked. A group of friends took off the license plate of their car to take pictures. Some cops came by and cited them for it. I don’t think everyone knows about this law, so if I’m say waxing my car and want to take the plate off so I can get to areas around it better, an officer can come by and cite me for it? I know I don’t have to have a front plate anymore and even when I started driving years ago I never kept a front plate on it and only got warnings for it. Maybe these friends are being trivial but have you heard of this? Do you think you should get a ticket if your parked car doesn’t have a plate? Thinking about it, maybe it’s because the plate is an identifying piece of the car, so if anything happens a plate is required for identification purposes. That’s my take on this, I can’t think of any other reason.
1:08

I am sure it may vary by area but I never heard of a law that states your car has to have a license plate on it at all times even when being parked. A group of friends took off the license plate of their car to take pictures. Some cops came by and cited them for it. I don’t think everyone knows about this law, so if I’m say waxing my car and want to take the plate off so I can get to areas around it better, an officer can come by and cite me for it? I know I don’t have to have a front plate anymore and even when I started driving years ago I never kept a front plate on it and only got warnings for it. Maybe these friends are being trivial but have you heard of this? Do you think you should get a ticket if your parked car doesn’t have a plate? Thinking about it, maybe it’s because the plate is an identifying piece of the car, so if anything happens a plate is required for identification purposes. That’s my take on this, I can’t think of any other reason.

SonnyBoy🇺🇸

15,613 views • 3 months ago

AMD on local AI: "this is the smallest AI development system in the world, capable of running models up to 200 billion parameters locally, not connected to anything" 128GB of unified memory shared by CPU, GPU, and NPU fits in your hand. Runs a model larger than GPT-3 with no internet $3,999 once. roughly $16 a month in power Cloud equivalent: up to $750 a month The question was never whether local AI could compete on power It was whether it could compete on price. Now it does
1:00

AMD on local AI: "this is the smallest AI development system in the world, capable of running models up to 200 billion parameters locally, not connected to anything" 128GB of unified memory shared by CPU, GPU, and NPU fits in your hand. Runs a model larger than GPT-3 with no internet $3,999 once. roughly $16 a month in power Cloud equivalent: up to $750 a month The question was never whether local AI could compete on power It was whether it could compete on price. Now it does

plutos

152,392 views • 2 months ago

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!
1:28

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,419 views • 5 months ago

Putin reads Pushkin at Valdai❤️ 'I have a volume of Pushkin on my desk at home. Sometimes I like to dive into it. Just yesterday I opened it, flipped through, and came across a poem about the Battle of Borodino. I read it and it made a strong impression on me. And it was as if he [Pushkin] told me: "Listen, you are going to the Valdai Club, take it with you, read to your guys what I think about this." I have the book with me. The poem is called "Borodino Anniversary."
2:46

Putin reads Pushkin at Valdai❤️ 'I have a volume of Pushkin on my desk at home. Sometimes I like to dive into it. Just yesterday I opened it, flipped through, and came across a poem about the Battle of Borodino. I read it and it made a strong impression on me. And it was as if he [Pushkin] told me: "Listen, you are going to the Valdai Club, take it with you, read to your guys what I think about this." I have the book with me. The poem is called "Borodino Anniversary."

Zlatti71

100,065 views • 10 months ago

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.
0:50

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 views • 1 month ago

SOMEONE FROM THE ANTHROPIC TEAM LEAKED THEIR OBSIDIAN SETUP. 8 MILLION PEOPLE SAW HOW HE ACTUALLY USES CLAUDE the funniest part? all of this information was sitting in claude's documentation from day one. nobody read it one guy did, packed it into a 9-step guide and posted it. and it broke the internet. 4,100 likes, 800 retweets, then china picked it up and 8 million views want to know what's in it? one file. called CLAUDE.md. it holds everything about you: how you think, what you're working on, where you get stuck, even how you want the ai to talk to you. claude reads it first every single session one file changed everything. because now ai doesn't open with "how can i help?" it already knows. it remembers your projects, sees your goals, catches moments where you're contradicting yourself people spent years searching for the perfect prompt. the right temperature. the magic formula. and the answer turned out to be not how you ask ai. but what ai knows about you before you even open your mouth then the guy went deeper. taught claude to work on a schedule. every morning at 7am the ai walks through all notes on its own, finds new stuff, links it, cleans what's stale. no command. no reminder and all of this runs on obsidian. free app. text files on your drive. no cloud, no lock-in. switch models tomorrow and the folder keeps working the most liked comment under the original post: "this is the difference between using ai and building a system. most people won't realize it until they waste hundreds of hours repeating themselves" hundreds of hours. you've already spent some of them full guide in the video. i break down finds like this every day - follow so you don't miss the next one
1:43

SOMEONE FROM THE ANTHROPIC TEAM LEAKED THEIR OBSIDIAN SETUP. 8 MILLION PEOPLE SAW HOW HE ACTUALLY USES CLAUDE the funniest part? all of this information was sitting in claude's documentation from day one. nobody read it one guy did, packed it into a 9-step guide and posted it. and it broke the internet. 4,100 likes, 800 retweets, then china picked it up and 8 million views want to know what's in it? one file. called CLAUDE.md. it holds everything about you: how you think, what you're working on, where you get stuck, even how you want the ai to talk to you. claude reads it first every single session one file changed everything. because now ai doesn't open with "how can i help?" it already knows. it remembers your projects, sees your goals, catches moments where you're contradicting yourself people spent years searching for the perfect prompt. the right temperature. the magic formula. and the answer turned out to be not how you ask ai. but what ai knows about you before you even open your mouth then the guy went deeper. taught claude to work on a schedule. every morning at 7am the ai walks through all notes on its own, finds new stuff, links it, cleans what's stale. no command. no reminder and all of this runs on obsidian. free app. text files on your drive. no cloud, no lock-in. switch models tomorrow and the folder keeps working the most liked comment under the original post: "this is the difference between using ai and building a system. most people won't realize it until they waste hundreds of hours repeating themselves" hundreds of hours. you've already spent some of them full guide in the video. i break down finds like this every day - follow so you don't miss the next one

kai

174,650 views • 1 month ago

Chamath: Two terms you need to pay attention to in AI are Prefill and Decode “There's two terms that I think you're going to hear a ton about over these next few years.” “The first term is prefill, and the next is decode.” “What prefill and decode are, are two very distinct ways of how models think, and how a model goes through the process of answering a question that you ask it.” “And so when you send a prompt to AI, what happens is that the model processes it. This is called the reading phase or prefill.” “It reads your entire prompt all at once. And then it does a bunch of math, calculates all these relationships between all the words, and it stores them in temporary memory.” “The problem is that this is really compute bound. So it requires massive brute force. And Nvidia GPUs crush here.” “And their architecture is designed for massive parallel processing, which makes them really amazing at digesting these long prompts.” “So the problem just gets bigger and bigger, Nvidia just completely dominates.” “But the next phase though, this critical phase, the decode phase, is the writing phase, right?” “So the model starts to generate a response, you ask it a question and its response, one token at a time.” “And then to pick the next token to pick the next word, it has to look back at everything it has said already so that it doesn't hallucinate.” “The problem is that this is incredibly memory bandwidth constrained.” “And in our architecture, a long time ago, we made these design decisions from day one.” “And so what we did was we took a very different architectural approach, we took a very conservative process technology. We weren't pushing the boundaries of physics.” “And we used a lot of what's called SRAM. So memory on the chip so that we could do this decode thing as well or better than everybody else.” “And so now when you put these two things together, I just think it's going to create a huge acceleration in the ability for this entire infrastructure layer to get much cheaper and much more valuable, which I suspect then it'll have a lot more developer pull, you'll get a lot more applications being built, billions and billions of more people using it.”
1:52

Chamath: Two terms you need to pay attention to in AI are Prefill and Decode “There's two terms that I think you're going to hear a ton about over these next few years.” “The first term is prefill, and the next is decode.” “What prefill and decode are, are two very distinct ways of how models think, and how a model goes through the process of answering a question that you ask it.” “And so when you send a prompt to AI, what happens is that the model processes it. This is called the reading phase or prefill.” “It reads your entire prompt all at once. And then it does a bunch of math, calculates all these relationships between all the words, and it stores them in temporary memory.” “The problem is that this is really compute bound. So it requires massive brute force. And Nvidia GPUs crush here.” “And their architecture is designed for massive parallel processing, which makes them really amazing at digesting these long prompts.” “So the problem just gets bigger and bigger, Nvidia just completely dominates.” “But the next phase though, this critical phase, the decode phase, is the writing phase, right?” “So the model starts to generate a response, you ask it a question and its response, one token at a time.” “And then to pick the next token to pick the next word, it has to look back at everything it has said already so that it doesn't hallucinate.” “The problem is that this is incredibly memory bandwidth constrained.” “And in our architecture, a long time ago, we made these design decisions from day one.” “And so what we did was we took a very different architectural approach, we took a very conservative process technology. We weren't pushing the boundaries of physics.” “And we used a lot of what's called SRAM. So memory on the chip so that we could do this decode thing as well or better than everybody else.” “And so now when you put these two things together, I just think it's going to create a huge acceleration in the ability for this entire infrastructure layer to get much cheaper and much more valuable, which I suspect then it'll have a lot more developer pull, you'll get a lot more applications being built, billions and billions of more people using it.”

The All-In Podcast

567,546 views • 7 months ago

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.
0:36

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.

Vaibhav Sisinty

20,562 views • 25 days ago

I spent today turning a blank Hermes install into a sovereign local agent on my RTX 5090. always-on, reachable over telegram, fully local. here's everything it can do. it runs Qwen3.6-27B on the 5090 and answers from my phone over telegram. ~~~ I first made it faster. switched to the MTP build for self-speculative decoding: 62 > 115 tokens/sec. ~~~ it now benchmarks models on command. I ask it to speed-test a gguf and it stops its own model server to free the gpu, runs llama-bench, renders a card, sends it to my phone, then restarts itself. it's never left offline. ~~~ it also knows me very well. I pointed it to my private HF repo where I store all my AI traces since I started using it. I created a local markdown memory vault, seeded from my own history, with semantic search over it. ~~~ the whole stack stays home: weights on disk, inference on the 5090, the agent in my pocket. here is the video it created to showcase everything it has now.
0:32

I spent today turning a blank Hermes install into a sovereign local agent on my RTX 5090. always-on, reachable over telegram, fully local. here's everything it can do. it runs Qwen3.6-27B on the 5090 and answers from my phone over telegram. ~~~ I first made it faster. switched to the MTP build for self-speculative decoding: 62 > 115 tokens/sec. ~~~ it now benchmarks models on command. I ask it to speed-test a gguf and it stops its own model server to free the gpu, runs llama-bench, renders a card, sends it to my phone, then restarts itself. it's never left offline. ~~~ it also knows me very well. I pointed it to my private HF repo where I store all my AI traces since I started using it. I created a local markdown memory vault, seeded from my own history, with semantic search over it. ~~~ the whole stack stays home: weights on disk, inference on the 5090, the agent in my pocket. here is the video it created to showcase everything it has now.

witcheer

15,041 views • 2 months ago

Dear Los Angeles, One month ago, DATALAND opened its doors. Today, it has a memory. What stays with me is not the 1.2 billion data points. It is the silence between them. The moment when a room full of strangers becomes completely still—when the technology disappears and data becomes memory. We did not build DATALAND to show what AI can do. We built it to ask what AI can help us feel, remember, and protect. For years, we have asked: Can a building dream? This month, you answered: only when people dream inside it. You entered with your own memories and curiosity. You slowed down. You listened. You became part of the work—and gave this museum the one thing no model can generate: a shared memory. A museum opens in a day. It comes alive one encounter at a time. One month ago, we opened the doors to our dream. Today, it no longer belongs only to us. With love and gratitude, Refik, Efsun & the DATALAND Team
0:52

Dear Los Angeles, One month ago, DATALAND opened its doors. Today, it has a memory. What stays with me is not the 1.2 billion data points. It is the silence between them. The moment when a room full of strangers becomes completely still—when the technology disappears and data becomes memory. We did not build DATALAND to show what AI can do. We built it to ask what AI can help us feel, remember, and protect. For years, we have asked: Can a building dream? This month, you answered: only when people dream inside it. You entered with your own memories and curiosity. You slowed down. You listened. You became part of the work—and gave this museum the one thing no model can generate: a shared memory. A museum opens in a day. It comes alive one encounter at a time. One month ago, we opened the doors to our dream. Today, it no longer belongs only to us. With love and gratitude, Refik, Efsun & the DATALAND Team

Refik Anadol

13,838 views • 1 month ago

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.
0:42

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

Akshay 🚀

330,345 views • 5 days ago

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”
1:03

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 views • 1 year ago