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NVIDIA just dropped a crazy new research idea. Get ready. The same next-token trick behind ChatGPT got pointed at a ragdoll. Honestly the result is unsettling. No exotic new architecture. Chop human motion into a vocabulary of tiny movement tokens. Train a plain GPT to guess the next one,...

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clankr

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126,066 views • 1 month ago •via X (Twitter)

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Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.
2:05:17

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Rossst.03

147,913 views • 1 month ago

RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!
0:37

RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!

Tom Yeh

22,105 views • 1 month ago

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this
0:42

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,840 views • 2 months ago

The pull-up is the one exercise I see people quit on the fastest. They try a few, can't do them, decide they're just "not a pull-up person," and move on. But the thing is, strength is a skill. A lot of it is just your nervous system learning the exact movement, and your body only gets good at what you actually practise. That's why you can lift heavy on a barbell and still struggle to pull your own bodyweight up. It's a different skill, and it takes time. So no, you're not bad at pull-ups. You just haven't trained them yet. Start with regressions like slow negatives or banded reps, and try grease the groove method (thread below). That's exactly how a skill gets built. One day the bodyweight that felt impossible starts moving. Genuinely one of the best feelings in the gym.
0:23

The pull-up is the one exercise I see people quit on the fastest. They try a few, can't do them, decide they're just "not a pull-up person," and move on. But the thing is, strength is a skill. A lot of it is just your nervous system learning the exact movement, and your body only gets good at what you actually practise. That's why you can lift heavy on a barbell and still struggle to pull your own bodyweight up. It's a different skill, and it takes time. So no, you're not bad at pull-ups. You just haven't trained them yet. Start with regressions like slow negatives or banded reps, and try grease the groove method (thread below). That's exactly how a skill gets built. One day the bodyweight that felt impossible starts moving. Genuinely one of the best feelings in the gym.

Dr Neha Chawla || FreeGym

24,223 views • 2 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

One guy built this app in a month and now it makes him more than $1,000,000 a year. No team. No investors. No marketing department. One developer. One month. One clever idea. The app is a camera for events. In its first month it got 100,000 downloads and he did not spend a single dollar on ads. Here is how he did it because the most interesting part is the growth itself. He did not bolt marketing onto the product. He made using the product the marketing itself. After that things kick in that almost nobody figures out. 1. You cannot use the app alone. For it to work the host has to pull every guest into it. Each install drags in dozens more right away. 2. One wedding is not one user but a whole crowd at once. 200 people scan one code in an evening and install the app. No ad brings that many for the same money and here the money is zero. 3. The guest becomes the host. He liked it at someone else's wedding and a month later he throws his own event and brings his own people. The loop spins itself and for free. 4. It does not look like an ad. To the guest it is a gift not some app forced on him. So they install it gladly and all of them do. 5. It all runs on emotion. A wedding. Memories. Shared shots. People film it and show their own people and a new wave comes in. Now let us count the money plain and honest. The subscription runs from 2 to 50 dollars. Say only every 20th person pays. That is 5,000 people out of 100,000. The average check a modest 20 dollars. 5,000 times 20 is 100,000 dollars a month. More than 3,000 a day. More than $1,000,000 a year. And all of this is one guy in a month without a single dollar on ads. He did not win on budget and not on a team. He won by sewing distribution into the very use of the product. You can lift almost any product this way. Could you build something like this on your own or is it just luck?
0:23

One guy built this app in a month and now it makes him more than $1,000,000 a year. No team. No investors. No marketing department. One developer. One month. One clever idea. The app is a camera for events. In its first month it got 100,000 downloads and he did not spend a single dollar on ads. Here is how he did it because the most interesting part is the growth itself. He did not bolt marketing onto the product. He made using the product the marketing itself. After that things kick in that almost nobody figures out. 1. You cannot use the app alone. For it to work the host has to pull every guest into it. Each install drags in dozens more right away. 2. One wedding is not one user but a whole crowd at once. 200 people scan one code in an evening and install the app. No ad brings that many for the same money and here the money is zero. 3. The guest becomes the host. He liked it at someone else's wedding and a month later he throws his own event and brings his own people. The loop spins itself and for free. 4. It does not look like an ad. To the guest it is a gift not some app forced on him. So they install it gladly and all of them do. 5. It all runs on emotion. A wedding. Memories. Shared shots. People film it and show their own people and a new wave comes in. Now let us count the money plain and honest. The subscription runs from 2 to 50 dollars. Say only every 20th person pays. That is 5,000 people out of 100,000. The average check a modest 20 dollars. 5,000 times 20 is 100,000 dollars a month. More than 3,000 a day. More than $1,000,000 a year. And all of this is one guy in a month without a single dollar on ads. He did not win on budget and not on a team. He won by sewing distribution into the very use of the product. You can lift almost any product this way. Could you build something like this on your own or is it just luck?

Blaze

10,703 views • 1 month ago

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.
58:21

Mrityunjoy Chakraborty, IIT Kharagpur professor: "Jane Street pays a 22 year old $500,000 a year for one skill: turning uncertainty into a number you can bet on. that number is the first thing this professor teaches, and it is free" this free lecture is the foundation every quant bot and hedge fund model is secretly built on. before the AI, before the strategy, there is one question. what is the chance this happens, exactly. get that number right and every bet after it is just math. get it wrong and no model, no bot, no amount of compute can save you. Chakraborty builds probability from the axioms up, on a blackboard, in notation a 15 year old can follow. Wall Street sells this as a $500,000 edge. an IIT professor hands you the source code for nothing.

Rossst.03

688,996 views • 4 days ago

This is by far one of the best examples of how a droid would navigate a battle in real life. Every shot is on target, there is no wasted motion, and the only limiting factors on its kill potential are the firing rate of its blasters and how much ammunition it has. It does not run or move unnecessarily quickly; it is just measured and precise.
4:50

This is by far one of the best examples of how a droid would navigate a battle in real life. Every shot is on target, there is no wasted motion, and the only limiting factors on its kill potential are the firing rate of its blasters and how much ammunition it has. It does not run or move unnecessarily quickly; it is just measured and precise.

Best of Star Wars

173,805 views • 3 months ago

This is insane.🤯 Someone just open-sourced professional motion capture. The same tech behind every Marvel movie and AAA game now runs on any webcam. Free. The only thing it needs is a camera. You already have one. It's called FreeMoCap and it just became a GitHub link. The barrier was never the talent. It was always the tools.
0:21

This is insane.🤯 Someone just open-sourced professional motion capture. The same tech behind every Marvel movie and AAA game now runs on any webcam. Free. The only thing it needs is a camera. You already have one. It's called FreeMoCap and it just became a GitHub link. The barrier was never the talent. It was always the tools.

Vaibhav Sisinty

37,613 views • 4 months ago

As Erika Kirk put it, "This is not a ceremony, this is a commissioning." It is now up to each and every one of us to uphold Charlie's profound legacy. A life lived to its absolute fullest. A life spent fighting for the freedom, liberty, and opportunity of all Americans.
1:04

As Erika Kirk put it, "This is not a ceremony, this is a commissioning." It is now up to each and every one of us to uphold Charlie's profound legacy. A life lived to its absolute fullest. A life spent fighting for the freedom, liberty, and opportunity of all Americans.

Byron Donalds

52,619 views • 10 months ago

SPEC IS BECOMING THE PRODUCT An Anthropic engineer gave Claude a spec, pointed it to an Asana board and left for the weekend. Claude broke it into tickets and spun up a team of agents. The agents started picking up tasks on their own. No one told them to. They just did.
0:40

SPEC IS BECOMING THE PRODUCT An Anthropic engineer gave Claude a spec, pointed it to an Asana board and left for the weekend. Claude broke it into tickets and spun up a team of agents. The agents started picking up tasks on their own. No one told them to. They just did.

Shubham Saboo

285,667 views • 6 months ago

The magic is indescribable when you can stand up upon a stage and thousands of people can sing back to you lyrics of your song. It is a transcendent moment that elevates every human in the room. And for just one moment, all who are here share a sentiment which is… timeless.
0:53

The magic is indescribable when you can stand up upon a stage and thousands of people can sing back to you lyrics of your song. It is a transcendent moment that elevates every human in the room. And for just one moment, all who are here share a sentiment which is… timeless.

Brian Roemmele

65,785 views • 9 months ago

"each and every one of you is a part of this story. your names are written all over the credits of tonight. now give it up for..."
0:24

"each and every one of you is a part of this story. your names are written all over the credits of tonight. now give it up for..."

seungmin

56,228 views • 6 months ago

A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓
55:19

A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓

slash1s

237,718 views • 2 months ago

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓
39:31

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

433,431 views • 2 months ago

A robot just worked an 8-hour shift in China. Then it handled an error no human flagged, adapting to a conveyor that shifted under it and correcting the position deviation on its own before carrying on. This is AgiBot's G2 at Longcheer in Nanchang, where the fleet has now run 10 hours straight and sorted more than 3,000 units at a 100% success rate, talking to the factory's own software in real time and flagging defective parts by itself. We built every factory on earth around human limits: the eight-hour shift, the breaks, the way attention slips after hour six. AgiBot is making those limits optional, and the setting is the whole point. This is a live production line shipping real product next to human workers, not a staged demo. Figure ran a similar stream in California last month, so the milestone itself isn't unique. What's different here is that nothing is for the camera. A functioning factory, live, no cuts. I got back from China this month, and the pace of this one ecosystem is the thing the US is underrating most. The Terminator version of this was loud and apocalyptic. The real one just shows up to work, and gets a little better every shift.
0:44

A robot just worked an 8-hour shift in China. Then it handled an error no human flagged, adapting to a conveyor that shifted under it and correcting the position deviation on its own before carrying on. This is AgiBot's G2 at Longcheer in Nanchang, where the fleet has now run 10 hours straight and sorted more than 3,000 units at a 100% success rate, talking to the factory's own software in real time and flagging defective parts by itself. We built every factory on earth around human limits: the eight-hour shift, the breaks, the way attention slips after hour six. AgiBot is making those limits optional, and the setting is the whole point. This is a live production line shipping real product next to human workers, not a staged demo. Figure ran a similar stream in California last month, so the milestone itself isn't unique. What's different here is that nothing is for the camera. A functioning factory, live, no cuts. I got back from China this month, and the pace of this one ecosystem is the thing the US is underrating most. The Terminator version of this was loud and apocalyptic. The real one just shows up to work, and gets a little better every shift.

Shruti

11,014 views • 2 months ago

Protest vote is the only way to save our country. We have a big bully in the presidential seat right now and the only way to get him out is to bully him right back. When will we open our eyes guys. There is almost no eventuality when Museveni will wake up one day and decide to hand over power. He is drunk on it and he will never give it up without a fight. Ugandans need to vote and protect their vote. If we Ugandans can not stand up and protect our own vote, then no one will fight for it for us. Period.
2:03

Protest vote is the only way to save our country. We have a big bully in the presidential seat right now and the only way to get him out is to bully him right back. When will we open our eyes guys. There is almost no eventuality when Museveni will wake up one day and decide to hand over power. He is drunk on it and he will never give it up without a fight. Ugandans need to vote and protect their vote. If we Ugandans can not stand up and protect our own vote, then no one will fight for it for us. Period.

Solomon Kampala

73,197 views • 8 months ago

"you see, every second, each and every one of you is a part of this story. and now, give it up for..." AND THEY SHOWED THE NAMES OF STAYS WHO BOUGHT THE TICKETS??? OMG CINEMA WE LOVE YOU
1:13

"you see, every second, each and every one of you is a part of this story. and now, give it up for..." AND THEY SHOWED THE NAMES OF STAYS WHO BOUGHT THE TICKETS??? OMG CINEMA WE LOVE YOU

❀ tyne.

414,880 views • 1 year ago

When the sun goes down, one mouse comes out of hiding. It scurries onto a table inside the shed of 75-year-old Rodney Holbrook, and begins to tidy. This mouse is small, but it is mighty. It picks up nails, cable ties — even a screwdriver — with its tiny mouth and puts them away. One by one, it places each piece into a tray. “It happens every night without fail,” Holbrook said.
1:02

When the sun goes down, one mouse comes out of hiding. It scurries onto a table inside the shed of 75-year-old Rodney Holbrook, and begins to tidy. This mouse is small, but it is mighty. It picks up nails, cable ties — even a screwdriver — with its tiny mouth and puts them away. One by one, it places each piece into a tray. “It happens every night without fail,” Holbrook said.

The Washington Post

51,834 views • 2 years ago