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there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of... show more

Sudo su

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60,445 次观看 • 4 个月前 •via X (Twitter)

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I don't think Claude dashboards are meant for just making the traditional pretty dashboard/report. You can do that, but I think it's not where the value is. I see Claude dashboards more like a data agent that can really leverage visuals to explain complex data concepts and help you think about and use the data. I think it's the first tool I've used that can do this well, while also hiding the unnecessary "making of the code sausage" from business users and analysts, yet still making queries transparent. I almost wonder if it would've been better to call this Claude Data Analysis or Claude Data Agent so it doesn't carry the baggage of reporting/dashboarding before the AI era that kinda holds it back.

I don't think Claude dashboards are meant for just making the traditional pretty dashboard/report. You can do that, but I think it's not where the value is. I see Claude dashboards more like a data agent that can really leverage visuals to explain complex data concepts and help you think about and use the data. I think it's the first tool I've used that can do this well, while also hiding the unnecessary "making of the code sausage" from business users and analysts, yet still making queries transparent. I almost wonder if it would've been better to call this Claude Data Analysis or Claude Data Agent so it doesn't carry the baggage of reporting/dashboarding before the AI era that kinda holds it back.

Kurt Buhler

45,974 次观看 • 1 天前

So much of the complexity for multimodal/robotics data is just getting the right format that's both flexible and performant. I dig into it here in the context of 3d reconstruction. I'm hoping to build an opensource arkit like pipeline and explain the first bit for the data pipeline here! Heres a video of what the final data looks like in a registered catalog

So much of the complexity for multimodal/robotics data is just getting the right format that's both flexible and performant. I dig into it here in the context of 3d reconstruction. I'm hoping to build an opensource arkit like pipeline and explain the first bit for the data pipeline here! Heres a video of what the final data looks like in a registered catalog

Pablo Vela

10,804 次观看 • 2 个月前

the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below

the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below

John Doe

26,231 次观看 • 3 个月前

save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.

save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.

may.crypto {🦅}

26,174 次观看 • 1 个月前

Everyone is selling robotics data. Most of it isn't what you actually need. The right kind of data depends entirely on what you're training. And that's the question almost no one asks before the check gets cut.

Everyone is selling robotics data. Most of it isn't what you actually need. The right kind of data depends entirely on what you're training. And that's the question almost no one asks before the check gets cut.

PrismaX

13,318 次观看 • 4 个月前

Sitting here watching my Cybertruck charge with the mountains of Utah in the background, it hit me again… I really believe this is the most beautiful Tesla ever made. Every angle is different. It looks futuristic without trying too hard, and it fits just as well in the middle of a city as it does surrounded by nature like this. What makes this truck special is so much more than just the stainless steel or design. For me it’s how capable and practical it is. Long road trips, hauling gear, camping, tough weather, and FSD… it feels like THE one vehicle built to do almost everything. I’ve owned and driven many Teslas over the years, and every one has been amazing in their own unique way, but the Cybertruck is the first one that genuinely makes me stop and look back every time I park it. Driving across the U.S. in this beast has only reinforced that feeling. And to me, the Cybertruck is the best vehicle Tesla has ever built.

Sitting here watching my Cybertruck charge with the mountains of Utah in the background, it hit me again… I really believe this is the most beautiful Tesla ever made. Every angle is different. It looks futuristic without trying too hard, and it fits just as well in the middle of a city as it does surrounded by nature like this. What makes this truck special is so much more than just the stainless steel or design. For me it’s how capable and practical it is. Long road trips, hauling gear, camping, tough weather, and FSD… it feels like THE one vehicle built to do almost everything. I’ve owned and driven many Teslas over the years, and every one has been amazing in their own unique way, but the Cybertruck is the first one that genuinely makes me stop and look back every time I park it. Driving across the U.S. in this beast has only reinforced that feeling. And to me, the Cybertruck is the best vehicle Tesla has ever built.

Teslaconomics

27,065 次观看 • 3 个月前

$We were taught the derivative as a formula to memorise. A definition to recite. A rule to apply. Something that "gives you the slope." But nobody told us what the formula was actually saying. Every symbol is a sentence. Every fraction is a question. Every limit is a story about getting closer and closer to something you can never quite touch. The top of the fraction? That's a change. A difference. A before and after. The bottom? That's how long you waited to see it. The limit? That's you, zooming in, refusing to settle for an approximation - chasing the truth all the way down to an interval so small it almost disappears. Put it all together, and you get the most honest question in calculus: How fast is something changing - right now, in this exact instant? Not on average. Not over a minute. Not eventually. Right now. That's it. That's the derivative. It's not a trick. It's not a rule. It's a beautifully precise way of asking a very human question: what's happening, in this moment? We spent years solving these. Maybe it's time we actually understood them.$

We were taught the derivative as a formula to memorise. A definition to recite. A rule to apply. Something that "gives you the slope." But nobody told us what the formula was actually saying. Every symbol is a sentence. Every fraction is a question. Every limit is a story about getting closer and closer to something you can never quite touch. The top of the fraction? That's a change. A difference. A before and after. The bottom? That's how long you waited to see it. The limit? That's you, zooming in, refusing to settle for an approximation - chasing the truth all the way down to an interval so small it almost disappears. Put it all together, and you get the most honest question in calculus: How fast is something changing - right now, in this exact instant? Not on average. Not over a minute. Not eventually. Right now. That's it. That's the derivative. It's not a trick. It's not a rule. It's a beautifully precise way of asking a very human question: what's happening, in this moment? We spent years solving these. Maybe it's time we actually understood them.

The Math Flow

22,498 次观看 • 4 个月前

I think I found a planet nobody knew existed. It's been hiding in NASA telescope data for seven years. At the end of this thread, you can fly to it yourself.

I think I found a planet nobody knew existed. It's been hiding in NASA telescope data for seven years. At the end of this thread, you can fly to it yourself.

Pavel Rabtsevich

1,160,946 次观看 • 3 天前

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

Shanaka Anslem Perera ⚡

54,545 次观看 • 3 个月前

Quoting this because it deserves some attention. Trading is not a personality contest. It is not conviction. It is not being permanently bullish or permanently bearish and waiting to be right. It is adaptability. For the record: I am up over 200% this year. A year where retail is bleeding and most managers are probably underperforming a savings account. Not because I am smarter. Because I am fluid. I use data, I interpret it, and I act on it. When the data changes, I change. If you cannot do that, you will not survive this game. I will walk through the data in the thread below... and discuss why I believe we hit highs in early September. Then chop. Then face an October selloff... If the read changes, so will I. That is the entire point. 🧵

Quoting this because it deserves some attention. Trading is not a personality contest. It is not conviction. It is not being permanently bullish or permanently bearish and waiting to be right. It is adaptability. For the record: I am up over 200% this year. A year where retail is bleeding and most managers are probably underperforming a savings account. Not because I am smarter. Because I am fluid. I use data, I interpret it, and I act on it. When the data changes, I change. If you cannot do that, you will not survive this game. I will walk through the data in the thread below... and discuss why I believe we hit highs in early September. Then chop. Then face an October selloff... If the read changes, so will I. That is the entire point. 🧵

Deep Inference

11,453 次观看 • 1 个月前

As I sit here in DC this week, we are closer to something I was not sure I would ever see. I have been working in this industry since 2015. For most of those years, the defining feature of crypto in Washington was not policy. It was the absence of it. A gray zone where serious people built serious things under a constant cloud, never quite sure which rules applied or whether the ground would move beneath them. This week the CLARITY Act sits on the Senate calendar. A federal framework for digital asset market structure, the thing this industry has wanted for the better part of a decade, is closer than it has ever been. It is not law yet, and there are real hurdles left. But the distance between where we stood a few years ago and where we are sitting today is hard to put into words. I keep thinking about the work that got us here. Over the past year I watched Chainlink move from outside these conversations to inside them. Sergey at the White House for the signing of the GENIUS Act. The Department of Commerce putting government economic data onchain. Meetings with the SEC that became real interpretive guidance. Conversations with the lawmakers now writing the rules. None of that happens by accident. It happens because people keep showing up, year after year, and make the case in rooms where it is not yet obvious. And there is something fitting in it. The entire premise of what we build is verification. Making truth provable. Removing the question of what is real. The work here in DC is the same thing in a different form. Trading a decade of ambiguity for something the industry has never actually had. We are not at the finish line. But sitting here, it is hard not to feel the weight of it. The gray zone is ending. What comes next is something this industry has never had. Clarity.

As I sit here in DC this week, we are closer to something I was not sure I would ever see. I have been working in this industry since 2015. For most of those years, the defining feature of crypto in Washington was not policy. It was the absence of it. A gray zone where serious people built serious things under a constant cloud, never quite sure which rules applied or whether the ground would move beneath them. This week the CLARITY Act sits on the Senate calendar. A federal framework for digital asset market structure, the thing this industry has wanted for the better part of a decade, is closer than it has ever been. It is not law yet, and there are real hurdles left. But the distance between where we stood a few years ago and where we are sitting today is hard to put into words. I keep thinking about the work that got us here. Over the past year I watched Chainlink move from outside these conversations to inside them. Sergey at the White House for the signing of the GENIUS Act. The Department of Commerce putting government economic data onchain. Meetings with the SEC that became real interpretive guidance. Conversations with the lawmakers now writing the rules. None of that happens by accident. It happens because people keep showing up, year after year, and make the case in rooms where it is not yet obvious. And there is something fitting in it. The entire premise of what we build is verification. Making truth provable. Removing the question of what is real. The work here in DC is the same thing in a different form. Trading a decade of ambiguity for something the industry has never actually had. We are not at the finish line. But sitting here, it is hard not to feel the weight of it. The gray zone is ending. What comes next is something this industry has never had. Clarity.

Chris Barrett

14,798 次观看 • 3 个月前

This Huawei ad I just walked by at Munich airport hit me like a ton of bricks. Huawei watch? HUAWEI HEALTH? Delivering your intimate, real-time location, behavioral & physiological data (heart rate??) directly to the CCP is absolutely insane. I hadn't even considered how many of these watches must be on people hands right now, and how powerful a tool of espionage (the next-level invasive data gathering!) the CCP has at its disposal. Grok, what countries have the biggest usage per capita of huawei watch? Also, considering what I wrote earlier in this post, and given that Huawei is bound by Chinese law to share all data it has access to with the the Chinese government (and not disclose it is doing so), what impact is there for personal privacy and security for users of Huawei watch?

This Huawei ad I just walked by at Munich airport hit me like a ton of bricks. Huawei watch? HUAWEI HEALTH? Delivering your intimate, real-time location, behavioral & physiological data (heart rate??) directly to the CCP is absolutely insane. I hadn't even considered how many of these watches must be on people hands right now, and how powerful a tool of espionage (the next-level invasive data gathering!) the CCP has at its disposal. Grok, what countries have the biggest usage per capita of huawei watch? Also, considering what I wrote earlier in this post, and given that Huawei is bound by Chinese law to share all data it has access to with the the Chinese government (and not disclose it is doing so), what impact is there for personal privacy and security for users of Huawei watch?

Jan Jekielek

149,718 次观看 • 1 年前

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 次观看 • 2 个月前

Industrial data is no longer a byproduct. It's a strategic asset. Through the DMaaST initiative, Kamstrup is being onboarded to the Decentralized Knowledge Graph (DKG) — a foundation for reliable AI across the manufacturing value chain. The EU Data Act is writing the same shift into law. For smart manufacturing, that's an opening: → machine and product data put to work → transparent value chains → new services built on industrial data The factory of the future won't just make things. It will make insights. Trust the source.

Industrial data is no longer a byproduct. It's a strategic asset. Through the DMaaST initiative, Kamstrup is being onboarded to the Decentralized Knowledge Graph (DKG) — a foundation for reliable AI across the manufacturing value chain. The EU Data Act is writing the same shift into law. For smart manufacturing, that's an opening: → machine and product data put to work → transparent value chains → new services built on industrial data The factory of the future won't just make things. It will make insights. Trust the source.

OriginTrail

21,821 次观看 • 2 个月前

What goes into a perfect offensive possession? For me, I evaluate it with something called the "Basketball Bliss Point. I wrote about what that is and how you can use it to build offensive concepts at The Painted Possession. I made this one FREE for everyone to read, so I hope you check it out. Link is in the first reply 👇. Before you read, there is some required viewing--my favorite offensive possession in Europe last year:

What goes into a perfect offensive possession? For me, I evaluate it with something called the "Basketball Bliss Point. I wrote about what that is and how you can use it to build offensive concepts at The Painted Possession. I made this one FREE for everyone to read, so I hope you check it out. Link is in the first reply 👇. Before you read, there is some required viewing--my favorite offensive possession in Europe last year:

Eric Fawcett

17,051 次观看 • 24 天前

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 次观看 • 5 个月前

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 次观看 • 23 天前

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 次观看 • 1 年前

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

Chubby♨️

48,249 次观看 • 3 个月前