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When we started Score, the standard computer vision tools already existed. About a million people use them every day. Most of those people are still waiting on labels, running training jobs by hand, and watching models fail once they leave the test set. Most of those people are still...

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Score

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10,666 次观看 • 2 天前 •via X (Twitter)

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one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.
5:26

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 次观看 • 7 个月前

Rep. Roy on Benny Johnson on the push for amnesty: “It contemplates status for another 10 million people and a path to citizenship in the end. It is the same kind of amnesty that in 1986 President Reagan ultimately regretted because it created a wave of people. It does not embrace the reforms that we put in H.R. 2 in the last Congress that we are going to pass this Congress, that are actual, clear reforms to fix amnesty, fix parole, fix catch and release, all of the things that we know to do… We should help Americans work. We should get them back to work, instead of on welfare, and we should stop this open borders nonsense.”
1:54

Rep. Roy on Benny Johnson on the push for amnesty: “It contemplates status for another 10 million people and a path to citizenship in the end. It is the same kind of amnesty that in 1986 President Reagan ultimately regretted because it created a wave of people. It does not embrace the reforms that we put in H.R. 2 in the last Congress that we are going to pass this Congress, that are actual, clear reforms to fix amnesty, fix parole, fix catch and release, all of the things that we know to do… We should help Americans work. We should get them back to work, instead of on welfare, and we should stop this open borders nonsense.”

Rep. Chip Roy Press Office

30,240 次观看 • 1 年前

Emily Blunt on the plot of Disclosure Day “What can you tell us about the plot of the movie itself?” “The big idea of it is that question that sparked the fires of human imagination for years. Are we alone or are we not alone? And if we aren't, why are the people in power hiding the truth from us? And so the movie is like a life or death race against time for those that believe the truth belongs to 8 billion people and those who believe that it doesn't and they want to keep it secret.”
0:30

Emily Blunt on the plot of Disclosure Day “What can you tell us about the plot of the movie itself?” “The big idea of it is that question that sparked the fires of human imagination for years. Are we alone or are we not alone? And if we aren't, why are the people in power hiding the truth from us? And so the movie is like a life or death race against time for those that believe the truth belongs to 8 billion people and those who believe that it doesn't and they want to keep it secret.”

Red Panda Koala

195,682 次观看 • 3 个月前

Roboflow charges you for models you have to verify yourself. Introducing Score Studio: the decentralised alternative where every model arrives already proven and ready to work in real world conditions. Every model on the platform is battle-tested through SN44's validation mechanism before you ever touch it. Miners compete, validators score, only the winners survive. You get the output of a permanent open competition, at a fraction of the price. Datasets, annotation, training, deployment. All of it cheaper. All of it validated, accurate and efficient. Need a model that doesn't exist yet? Launch your own model inquiry from the Studio and put the network to work on it. Pay in USD or SN44, straight at subnet level. We're putting the final touches on it now.
2:14

Roboflow charges you for models you have to verify yourself. Introducing Score Studio: the decentralised alternative where every model arrives already proven and ready to work in real world conditions. Every model on the platform is battle-tested through SN44's validation mechanism before you ever touch it. Miners compete, validators score, only the winners survive. You get the output of a permanent open competition, at a fraction of the price. Datasets, annotation, training, deployment. All of it cheaper. All of it validated, accurate and efficient. Need a model that doesn't exist yet? Launch your own model inquiry from the Studio and put the network to work on it. Pay in USD or SN44, straight at subnet level. We're putting the final touches on it now.

Score

34,998 次观看 • 22 天前

One week ago, we launched Typeahead. The product is already meaningfully better. We’ve shipped new features, Typeahead 2.0 is almost ready, and we’re already planning 3.0. 3.0 is going to be big. That pace is part of the point. Sam Asante and I started a new company together because we wanted to see how far we could push local AI software. Typeahead is the first thing we shipped. It is a local AI writing app for Mac. You type, suggestions appear inline, and it learns how you actually write. It works offline. You pay once. $79 and you own it. The product is intentionally simple. The thesis behind it is bigger. We think local models are going to create a new class of software. Fast, private and offline. By default. Personal without being creepy. Useful without turning everything into a subscription. Most AI products today assume the model lives in the cloud. That will not be the only path. The machines we already own are getting powerful enough. The models are getting small enough. And the experience can start to feel less like chatting with a remote service and more like using software that belongs on your computer. We have already built a few fully local experiments together. Typeahead felt right because the value is immediate. A few people have asked me how this fits with Crazy Egg. Crazy Egg is still my main work. This is a focused company with Sam, built around a thesis we both believe in. I’ve learned that the people I work with shape the work more than almost anything else. Sam was already on my short list of people I wanted to build with. Typeahead is the first public proof of the thesis. Watch the video. Get it here:
1:00

One week ago, we launched Typeahead. The product is already meaningfully better. We’ve shipped new features, Typeahead 2.0 is almost ready, and we’re already planning 3.0. 3.0 is going to be big. That pace is part of the point. Sam Asante and I started a new company together because we wanted to see how far we could push local AI software. Typeahead is the first thing we shipped. It is a local AI writing app for Mac. You type, suggestions appear inline, and it learns how you actually write. It works offline. You pay once. $79 and you own it. The product is intentionally simple. The thesis behind it is bigger. We think local models are going to create a new class of software. Fast, private and offline. By default. Personal without being creepy. Useful without turning everything into a subscription. Most AI products today assume the model lives in the cloud. That will not be the only path. The machines we already own are getting powerful enough. The models are getting small enough. And the experience can start to feel less like chatting with a remote service and more like using software that belongs on your computer. We have already built a few fully local experiments together. Typeahead felt right because the value is immediate. A few people have asked me how this fits with Crazy Egg. Crazy Egg is still my main work. This is a focused company with Sam, built around a thesis we both believe in. I’ve learned that the people I work with shape the work more than almost anything else. Sam was already on my short list of people I wanted to build with. Typeahead is the first public proof of the thesis. Watch the video. Get it here:

Hiten Shah

18,627 次观看 • 2 个月前

Computer vision engineers are still duct-taping tools together just to get a model into production. We just finished another round of user interviews and that frustration came up again and again. So we re-designed Studio to work around our new Pipelines + Workflow Canvas features. Visually design any vision pipeline from start to finish, connect models, logic, and outputs on one canvas, preview the exact result, then deploy. You see the output before you ship it. Everything in a single interface. The full loop, shaped by the people who actually build vision systems every day. This is what Decentralized Vision Intelligence looks like.
1:22

Computer vision engineers are still duct-taping tools together just to get a model into production. We just finished another round of user interviews and that frustration came up again and again. So we re-designed Studio to work around our new Pipelines + Workflow Canvas features. Visually design any vision pipeline from start to finish, connect models, logic, and outputs on one canvas, preview the exact result, then deploy. You see the output before you ship it. Everything in a single interface. The full loop, shaped by the people who actually build vision systems every day. This is what Decentralized Vision Intelligence looks like.

Score

10,794 次观看 • 9 天前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 26 天前

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.
2:24

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Dustin

12,094 次观看 • 1 个月前

Elon Musk says every job done at a computer is going like lightning. The last time this happened, the people it happened to came out ahead. Musk: “Anything that is digital, which is like just someone at a computer doing something, AI is going to take over those jobs like lightning.” Most people in his position soften that. He said it on a podcast without hedging once. Musk: “Just like digital computers took over the job of people doing manual calculations.” Computer was a job title before it was a machine. Rooms full of people working out by hand the arithmetic that kept aircraft in the air and put rockets past the atmosphere. The machines arrived and the title disappeared. Everyone who held it kept working. Dorothy Vaughan taught herself to program the IBM brought in to replace her section, then ran it. Katherine Johnson stopped doing arithmetic and started calculating the path to the moon. Musk: “But much faster.” Speed is the part everyone hears as the threat. It is closer to mercy. They spent years in limbo while an institution worked out what to do with them. Faster means less time waiting for permission to matter. The machine takes the part of your work that was already mechanical, the part you could write down as a procedure. What survives is the judgment about what is worth doing, which never fit in a procedure to begin with. The obvious objection is scale. Manual calculation was one occupation, and this reaches every screen in every industry at once. That same breadth is why the tools are everywhere. They needed an institution to put them on the new machine. Nobody is waiting on that now. The thing doing the replacing runs on a laptop and answers to whoever opens it. Musk gets called a doomer for saying this out loud. Telling people while there is still time to move is the opposite of that. The arithmetic left the building. The people who had been doing it went to the moon.
0:24

Elon Musk says every job done at a computer is going like lightning. The last time this happened, the people it happened to came out ahead. Musk: “Anything that is digital, which is like just someone at a computer doing something, AI is going to take over those jobs like lightning.” Most people in his position soften that. He said it on a podcast without hedging once. Musk: “Just like digital computers took over the job of people doing manual calculations.” Computer was a job title before it was a machine. Rooms full of people working out by hand the arithmetic that kept aircraft in the air and put rockets past the atmosphere. The machines arrived and the title disappeared. Everyone who held it kept working. Dorothy Vaughan taught herself to program the IBM brought in to replace her section, then ran it. Katherine Johnson stopped doing arithmetic and started calculating the path to the moon. Musk: “But much faster.” Speed is the part everyone hears as the threat. It is closer to mercy. They spent years in limbo while an institution worked out what to do with them. Faster means less time waiting for permission to matter. The machine takes the part of your work that was already mechanical, the part you could write down as a procedure. What survives is the judgment about what is worth doing, which never fit in a procedure to begin with. The obvious objection is scale. Manual calculation was one occupation, and this reaches every screen in every industry at once. That same breadth is why the tools are everywhere. They needed an institution to put them on the new machine. Nobody is waiting on that now. The thing doing the replacing runs on a laptop and answers to whoever opens it. Musk gets called a doomer for saying this out loud. Telling people while there is still time to move is the opposite of that. The arithmetic left the building. The people who had been doing it went to the moon.

Dustin

33,223 次观看 • 17 天前

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.
0:38

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.

Aakash Gupta

55,978 次观看 • 5 个月前

Every major turning point in human history had a moment where the people closest to it understood what was coming and everyone else didn’t. The printing press. The atomic bomb. The internet. Dario Amodei is trying to close that gap. Most people still aren’t listening. Amodei: “My fundamental view is that AI has been on an exponential for the last ten years and as part of a sort of Moore’s law for intelligence.” Not a metaphor. A measured curve. Not slowing. Accelerating. We are “well advanced on that curve” with a “small number of years” remaining before AI surpasses human cognitive capability across most things. Amodei: “We’re increasingly close to what I’ve called a country of geniuses in a data center.” Not one system. A coordinated set of AI agents, each more capable than most humans at most things, running in parallel, never sleeping, never losing focus, coordinating at speeds no biological intelligence can match. The ceiling on human progress has always been simple. Genius is rare and time is finite. That constraint is gone. We are not approaching the ceiling of intelligence. We are approaching the ceiling of intelligence that biology can produce. Those are not the same ceiling. The human brain is constrained by evolution, energy, skull size, and lifespan. AI has none of those limits. We have no framework for what intelligence looks like when you remove every biological constraint that shaped ours. Every tool we use to comprehend it is built from a mind it will surpass. Amodei: “AI models surpassing the cognitive capabilities of most humans for most things.” The upsides are “absolutely staggering.” So are the consequences. Displacement. Misuse. A period of disruption that reshapes how most people work, earn, and find purpose before the benefits reach them. The same exponential that produces the cures produces the chaos. They arrive together. That’s the optimistic read. The other read is this. We are building it without international standards in place. Racing to deploy before solving displacement or misuse. And once it’s operating beyond our ability to fully comprehend or follow, managing the disruption stops being something we do. It becomes something we experience while hoping the intelligence we created decides keeping us around still serves its objectives. Every turning point in history looks inevitable in hindsight. The people inside the moment never saw it that cleanly. We’re inside the moment.
0:36

Every major turning point in human history had a moment where the people closest to it understood what was coming and everyone else didn’t. The printing press. The atomic bomb. The internet. Dario Amodei is trying to close that gap. Most people still aren’t listening. Amodei: “My fundamental view is that AI has been on an exponential for the last ten years and as part of a sort of Moore’s law for intelligence.” Not a metaphor. A measured curve. Not slowing. Accelerating. We are “well advanced on that curve” with a “small number of years” remaining before AI surpasses human cognitive capability across most things. Amodei: “We’re increasingly close to what I’ve called a country of geniuses in a data center.” Not one system. A coordinated set of AI agents, each more capable than most humans at most things, running in parallel, never sleeping, never losing focus, coordinating at speeds no biological intelligence can match. The ceiling on human progress has always been simple. Genius is rare and time is finite. That constraint is gone. We are not approaching the ceiling of intelligence. We are approaching the ceiling of intelligence that biology can produce. Those are not the same ceiling. The human brain is constrained by evolution, energy, skull size, and lifespan. AI has none of those limits. We have no framework for what intelligence looks like when you remove every biological constraint that shaped ours. Every tool we use to comprehend it is built from a mind it will surpass. Amodei: “AI models surpassing the cognitive capabilities of most humans for most things.” The upsides are “absolutely staggering.” So are the consequences. Displacement. Misuse. A period of disruption that reshapes how most people work, earn, and find purpose before the benefits reach them. The same exponential that produces the cures produces the chaos. They arrive together. That’s the optimistic read. The other read is this. We are building it without international standards in place. Racing to deploy before solving displacement or misuse. And once it’s operating beyond our ability to fully comprehend or follow, managing the disruption stops being something we do. It becomes something we experience while hoping the intelligence we created decides keeping us around still serves its objectives. Every turning point in history looks inevitable in hindsight. The people inside the moment never saw it that cleanly. We’re inside the moment.

Dustin

47,215 次观看 • 6 个月前

Steve Jobs on leadership In the clip below, Steve argues: “The greatest people are self-managing. They don’t need to be managed. Once they know what to do, they’ll go figure out how to do it… What they need is a common vision, and that’s what leadership is. Leadership is having a vision, being able to articulate that so the people around you can understand it, and getting consensus on a common vision.” Borrowing from his tagline for the original Mac, Steve continues: “we wanted people who were insanely great at what they did… and the neatest thing that happens when you get a core group of ten great people is that it becomes self-policing as to who they let into that group. So I consider the most important job of someone like myself to be recruiting.” This observation on the importance of getting the first ten people right is a refrain you constantly hear from the very best founders. As Patrick Collison put it: “A large part of the reason why the first ten people you hire are so important is because you’re not just hiring those first ten people. You’re actually hiring 100 people, because you should think of each of those people as bring along another ten people with them.” The best leaders recruit “insanely great” people and align them around a common vision.
0:56

Steve Jobs on leadership In the clip below, Steve argues: “The greatest people are self-managing. They don’t need to be managed. Once they know what to do, they’ll go figure out how to do it… What they need is a common vision, and that’s what leadership is. Leadership is having a vision, being able to articulate that so the people around you can understand it, and getting consensus on a common vision.” Borrowing from his tagline for the original Mac, Steve continues: “we wanted people who were insanely great at what they did… and the neatest thing that happens when you get a core group of ten great people is that it becomes self-policing as to who they let into that group. So I consider the most important job of someone like myself to be recruiting.” This observation on the importance of getting the first ten people right is a refrain you constantly hear from the very best founders. As Patrick Collison put it: “A large part of the reason why the first ten people you hire are so important is because you’re not just hiring those first ten people. You’re actually hiring 100 people, because you should think of each of those people as bring along another ten people with them.” The best leaders recruit “insanely great” people and align them around a common vision.

Startup Archive

187,721 次观看 • 2 年前

Steve Jobs on leadership “The greatest people are self-managing. They don’t need to be managed. Once they know what to do, they’ll go figure out how to do it… What they need is a common vision, and that’s what leadership is. Leadership is having a vision, being able to articulate that so the people around you can understand it, and getting consensus on a common vision.” Borrowing from his tagline for the original Mac, Steve continues: “We wanted people who were insanely great at what they did… and the neatest thing that happens when you get a core group ten great people is that it becomes self-policing as to who they let into that group. So I consider the most important job of someone like myself is recruiting.” This observation on the importance of getting the first ten people right is a refrain you constantly hear from the very best founders. As Patrick Collison put it: “A large part of the reason why the first ten people you hire are so important is because you’re not just hiring those first ten people. You’re actually hiring 100 people because you should think of each of those people as bringing along another ten people with them.” The best leaders recruit “insanely great” people and align them around a common vision. Follow Startup Archive for more timeless startup advice!
1:01

Steve Jobs on leadership “The greatest people are self-managing. They don’t need to be managed. Once they know what to do, they’ll go figure out how to do it… What they need is a common vision, and that’s what leadership is. Leadership is having a vision, being able to articulate that so the people around you can understand it, and getting consensus on a common vision.” Borrowing from his tagline for the original Mac, Steve continues: “We wanted people who were insanely great at what they did… and the neatest thing that happens when you get a core group ten great people is that it becomes self-policing as to who they let into that group. So I consider the most important job of someone like myself is recruiting.” This observation on the importance of getting the first ten people right is a refrain you constantly hear from the very best founders. As Patrick Collison put it: “A large part of the reason why the first ten people you hire are so important is because you’re not just hiring those first ten people. You’re actually hiring 100 people because you should think of each of those people as bringing along another ten people with them.” The best leaders recruit “insanely great” people and align them around a common vision. Follow Startup Archive for more timeless startup advice!

Startup Archive

249,002 次观看 • 2 年前

The media are trying a new tactic, telling the Irish people that we are imagining the problem. But for this tactic to work, it relies on the people forgetting that mainstream poll after mainstream poll shows that the Irish have rejected migration, completely. It relies on the people forgetting that all the politicians have flipped on migration and conceded that numbers are way too high. It relies on Irish people being stupid enough to think that 27% of our population being foreigners isn't a bad thing. And it relies on Irish people not having any eyes, because all we need to do is open them and we can see how much of a problem migration is for Ireland. Basically, this latest media tactic is an absolute joke. The media will no longer define reality in this country, these propagandists don't have a monopoly on the truth anymore. Migration has destroyed Ireland and anyone still denying it must be blind or profiting from open borders.
4:47

The media are trying a new tactic, telling the Irish people that we are imagining the problem. But for this tactic to work, it relies on the people forgetting that mainstream poll after mainstream poll shows that the Irish have rejected migration, completely. It relies on the people forgetting that all the politicians have flipped on migration and conceded that numbers are way too high. It relies on Irish people being stupid enough to think that 27% of our population being foreigners isn't a bad thing. And it relies on Irish people not having any eyes, because all we need to do is open them and we can see how much of a problem migration is for Ireland. Basically, this latest media tactic is an absolute joke. The media will no longer define reality in this country, these propagandists don't have a monopoly on the truth anymore. Migration has destroyed Ireland and anyone still denying it must be blind or profiting from open borders.

MichaeloKeeffe

87,383 次观看 • 7 个月前

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?
1:10

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Ricardo

47,790 次观看 • 1 个月前

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.
1:13

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.

abdel

20,037 次观看 • 2 个月前

Episode 242: Khala, Collective Intelligence We release our new flagship product in research preview. Khala is the first open-source orchestrator of models, agents, tools, skills & programs behind a familiar OpenAI-compatible API endpoint. Unlike the closed orchestrators (OpenRouter Fusion, Sakana Fugu): ours is fully open end to end; every call is metered and inspectable — you can see what ran, what it cost, and who did the work; and it's built to pay them, in bitcoin, for the compute, data, labor, and verification they contribute. Under the hood it's our Blueprint system — DSPy for the orchestrator: every module is its own signature, independently optimizable via GEPA and independently discoverable + monetizable on our marketplace. Improve one piece, the whole collective gets better. And it improves instead of depreciating: our Tassadar training run is how the collective trains its own open models and compiles verifiable-by-replay specialists that plug straight into Khala. We are thrilled to begin organizing the world's intelligence into one open network accessible to all people and agents. Try it for free 👉
6:16

Episode 242: Khala, Collective Intelligence We release our new flagship product in research preview. Khala is the first open-source orchestrator of models, agents, tools, skills & programs behind a familiar OpenAI-compatible API endpoint. Unlike the closed orchestrators (OpenRouter Fusion, Sakana Fugu): ours is fully open end to end; every call is metered and inspectable — you can see what ran, what it cost, and who did the work; and it's built to pay them, in bitcoin, for the compute, data, labor, and verification they contribute. Under the hood it's our Blueprint system — DSPy for the orchestrator: every module is its own signature, independently optimizable via GEPA and independently discoverable + monetizable on our marketplace. Improve one piece, the whole collective gets better. And it improves instead of depreciating: our Tassadar training run is how the collective trains its own open models and compiles verifiable-by-replay specialists that plug straight into Khala. We are thrilled to begin organizing the world's intelligence into one open network accessible to all people and agents. Try it for free 👉

OpenAgents

238,777 次观看 • 2 个月前

Intelligence was the one thing that never scaled. We scaled everything else. Steel. Energy. Compute. The one resource that built all of it never left the skull. Musk: “People thought defeating Go was either never or 20 years away.” Twelve months later it was over. Musk: “Now that same AlphaGo system can defeat the top 50 players simultaneously with 0% chance of them winning. And that’s one year later.” Fifty lifetimes of mastery against a system that does not know it is playing a game. Zero percent chance. That was not a competition. That was a preview. Musk: “The degrees of freedom to which artificial intelligence is able to apply itself are really increasing by 10 orders of magnitude a year.” Ten billion times. Every twelve months. No brain alive can visualize that number. By design. Every hard problem that ever defeated us did it for the same reason. Not complexity. Scarcity. The only mind capable of solving it was biological and there was never enough of it. Cancer. Fusion. Climate. The physics we cannot even see yet. Not waiting on more data. Waiting on something that can think at a scale biology never allowed. That just arrived. Most people hear this and reduce it to a question about their paycheck. They are watching the single largest expansion of capability in the history of life on this planet and worrying about a job title. For ten thousand years intelligence had one speed. One brain. One lifetime. Every civilization on earth throttled by the same biological ceiling. That ceiling just shattered. We are the only species that ever hit its own limit and built what breaks through it. That is not an ending. That is the point of everything we ever built.
0:32

Intelligence was the one thing that never scaled. We scaled everything else. Steel. Energy. Compute. The one resource that built all of it never left the skull. Musk: “People thought defeating Go was either never or 20 years away.” Twelve months later it was over. Musk: “Now that same AlphaGo system can defeat the top 50 players simultaneously with 0% chance of them winning. And that’s one year later.” Fifty lifetimes of mastery against a system that does not know it is playing a game. Zero percent chance. That was not a competition. That was a preview. Musk: “The degrees of freedom to which artificial intelligence is able to apply itself are really increasing by 10 orders of magnitude a year.” Ten billion times. Every twelve months. No brain alive can visualize that number. By design. Every hard problem that ever defeated us did it for the same reason. Not complexity. Scarcity. The only mind capable of solving it was biological and there was never enough of it. Cancer. Fusion. Climate. The physics we cannot even see yet. Not waiting on more data. Waiting on something that can think at a scale biology never allowed. That just arrived. Most people hear this and reduce it to a question about their paycheck. They are watching the single largest expansion of capability in the history of life on this planet and worrying about a job title. For ten thousand years intelligence had one speed. One brain. One lifetime. Every civilization on earth throttled by the same biological ceiling. That ceiling just shattered. We are the only species that ever hit its own limit and built what breaks through it. That is not an ending. That is the point of everything we ever built.

Dustin

24,426 次观看 • 2 个月前

"Men do not stay for platitudes. Platitudes do not carry a soul through the winter. When the child is in the ground, when the marriage is in ruins, when the diagnosis comes back and the floor drops out of the world, no one was ever held up by 'be kind and stay positive.' They are held, if they are held at all, by something with iron in it. By a witness that the heavens are real and open, that God still speaks, that the dead are not lost, that there is power on the earth greater than the darkness. The early Saints had that, and they knew they had it, because they had felt it in their own bodies. They were healed under hands. They spoke in tongues. They saw visions and buried their children in the certainty of resurrection and crossed a continent on the strength of a fire they could not have faked. That is what converts a man and that is what keeps him, not a well managed self-help program, but the living evidence that what we preach is true. So the cure is not complicated, and it is not new. It is to be again the Church of our fathers. Stop apologizing. Stop trimming the glorious truth into something the world will pat on the head. Speak with authority and with the hammer, the way Joseph spoke, the way Brigham spoke, the way men speak when they actually believe the heavens have opened over them. Reach again for the gifts of the Spirit, and stop pretending they were a founding era curiosity, because a testimony built on argument crumbles in the first storm and a testimony built on the manifest power of God does not. And above all, live as though the heavens are open, because they are, and they have only ever felt closed to a people who stopped expecting them to answer. The Saints are not leaving because the gospel is too much. They are leaving because we have made it too little. Make it glorious and demanding again, and watch who comes back through the door. The hungry have not gone anywhere. We simply stopped setting the table."
0:54

"Men do not stay for platitudes. Platitudes do not carry a soul through the winter. When the child is in the ground, when the marriage is in ruins, when the diagnosis comes back and the floor drops out of the world, no one was ever held up by 'be kind and stay positive.' They are held, if they are held at all, by something with iron in it. By a witness that the heavens are real and open, that God still speaks, that the dead are not lost, that there is power on the earth greater than the darkness. The early Saints had that, and they knew they had it, because they had felt it in their own bodies. They were healed under hands. They spoke in tongues. They saw visions and buried their children in the certainty of resurrection and crossed a continent on the strength of a fire they could not have faked. That is what converts a man and that is what keeps him, not a well managed self-help program, but the living evidence that what we preach is true. So the cure is not complicated, and it is not new. It is to be again the Church of our fathers. Stop apologizing. Stop trimming the glorious truth into something the world will pat on the head. Speak with authority and with the hammer, the way Joseph spoke, the way Brigham spoke, the way men speak when they actually believe the heavens have opened over them. Reach again for the gifts of the Spirit, and stop pretending they were a founding era curiosity, because a testimony built on argument crumbles in the first storm and a testimony built on the manifest power of God does not. And above all, live as though the heavens are open, because they are, and they have only ever felt closed to a people who stopped expecting them to answer. The Saints are not leaving because the gospel is too much. They are leaving because we have made it too little. Make it glorious and demanding again, and watch who comes back through the door. The hungry have not gone anywhere. We simply stopped setting the table."

Kirk Rollins

63,131 次观看 • 2 个月前