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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...

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John Turner

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21,029 просмотров • 11 дней назад •via X (Twitter)

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Why wouldn't the Mets want Pete Alonso? That is how the question always gets framed, and it is the wrong question. They wanted him. That was never the hard part. The decision was about years three, four, five. That is where a long deal for a first baseman in his thirties either holds up or turns into something you carry into every move you make after it. Everyone argues year one, because year one is the one you can see. The commitment lives in the back half, where nobody's forecast is any good.
0:17

Why wouldn't the Mets want Pete Alonso? That is how the question always gets framed, and it is the wrong question. They wanted him. That was never the hard part. The decision was about years three, four, five. That is where a long deal for a first baseman in his thirties either holds up or turns into something you carry into every move you make after it. Everyone argues year one, because year one is the one you can see. The commitment lives in the back half, where nobody's forecast is any good.

Zack Scott

31,399 просмотров • 1 месяц назад

Most people think their AI is not smart enough. It is smart enough. It just knows nothing about you. Every chat starts from zero. You paste the doc. You explain the project. You explain the same project again tomorrow. I finally fixed that part: 👇 I have been running Littlebird for a while now. The idea is simple. It is a desktop app for Mac and Windows that reads the text on your active window and sits in on your calls. So it already knows what you have been working on before you ask it anything. You do not brief it. You just ask. What that looks like in a normal day: 1/ Chat that already has the context No pasting. I ask what changed in a brief last week and it answers from the actual document that was open on my screen. 2/ Meeting Notes that write themselves It transcribes the call, then hands me the decisions and the action items. I get to stay in the conversation instead of typing through it. 3/ Routines that run on a schedule A morning briefing. A weekly summary of what I actually shipped. It shows up on its own. 4/ Hummingbird for the small stuff It appears right where you are working, so a quick question does not cost you a window switch and ten minutes of drift. The point is not the notes. Plenty of apps take notes. The point is that I stopped re-explaining my own work to a machine fifteen times a day. Free plan if you want to test the idea before deciding. Link is in the first comment, along with a discount for new users.
0:15

Most people think their AI is not smart enough. It is smart enough. It just knows nothing about you. Every chat starts from zero. You paste the doc. You explain the project. You explain the same project again tomorrow. I finally fixed that part: 👇 I have been running Littlebird for a while now. The idea is simple. It is a desktop app for Mac and Windows that reads the text on your active window and sits in on your calls. So it already knows what you have been working on before you ask it anything. You do not brief it. You just ask. What that looks like in a normal day: 1/ Chat that already has the context No pasting. I ask what changed in a brief last week and it answers from the actual document that was open on my screen. 2/ Meeting Notes that write themselves It transcribes the call, then hands me the decisions and the action items. I get to stay in the conversation instead of typing through it. 3/ Routines that run on a schedule A morning briefing. A weekly summary of what I actually shipped. It shows up on its own. 4/ Hummingbird for the small stuff It appears right where you are working, so a quick question does not cost you a window switch and ten minutes of drift. The point is not the notes. Plenty of apps take notes. The point is that I stopped re-explaining my own work to a machine fifteen times a day. Free plan if you want to test the idea before deciding. Link is in the first comment, along with a discount for new users.

Mushfiq Sajib

66,683 просмотров • 19 дней назад

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.
0:16

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Dustin

542,264 просмотров • 5 месяцев назад

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.
0:17

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 просмотров • 2 месяцев назад

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓
0:15

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 просмотров • 1 месяц назад

What the hell does Quant actually do? A model is trained in BF16, released in Q8, and we use Q4 because we're told 'its nearly lossless'. Fine, but I was curious about the 'nearly' part, what is actually being lost between BF16 all the way down to Q2? As always, its complicated. Needed a way to visualize the loss, so I had a Qwen 3.8 27B BF16 create a paragraph then had every quant below it do the same and marked the mutations. When a model writes a word, it assigns 'how sure' percentage to each. For example, 'The weather tomorrow is *windy*, a model is 90% sure all the words are correct except the windy part, since it can also be sunny, cold, hot, cloudy, etc. Any words a BF16 model was 90% sure of, every quant down to Q2 rarely changes it. On the other hand, any word that it was only 40~50% sure of, such as windy in our example, are the types of data that gets impacted by the quant process. Okay so what, some words changed, how does this impact anything? I set out to find out, and its all below if you are just as weird as I am about these things.
0:16

What the hell does Quant actually do? A model is trained in BF16, released in Q8, and we use Q4 because we're told 'its nearly lossless'. Fine, but I was curious about the 'nearly' part, what is actually being lost between BF16 all the way down to Q2? As always, its complicated. Needed a way to visualize the loss, so I had a Qwen 3.8 27B BF16 create a paragraph then had every quant below it do the same and marked the mutations. When a model writes a word, it assigns 'how sure' percentage to each. For example, 'The weather tomorrow is *windy*, a model is 90% sure all the words are correct except the windy part, since it can also be sunny, cold, hot, cloudy, etc. Any words a BF16 model was 90% sure of, every quant down to Q2 rarely changes it. On the other hand, any word that it was only 40~50% sure of, such as windy in our example, are the types of data that gets impacted by the quant process. Okay so what, some words changed, how does this impact anything? I set out to find out, and its all below if you are just as weird as I am about these things.

Killy

19,592 просмотров • 20 дней назад

Monday night on Jimmy Kimmel Live, Jelly Roll said he has lost 300 pounds. He also took a shot at President Trump’s weight. That’s the joke. The substance is the work. Losing 300 pounds is not a celebrity makeover. That is a man deciding he was done living half a life. Anyone who takes their health seriously deserves credit. Not because it is fashionable. Because it is hard. You change what you eat. You change how you move. You live with the discomfort instead of hiding in it. Most people never do that work. When one person actually does it, it gives other people permission. It shows the excuse was never as strong as they thought. Hollywood loves to preach. Fitness is one of the few sermons that is real. Get healthier and everything else in your life gets a better shot. He did it. More people should.
0:15

Monday night on Jimmy Kimmel Live, Jelly Roll said he has lost 300 pounds. He also took a shot at President Trump’s weight. That’s the joke. The substance is the work. Losing 300 pounds is not a celebrity makeover. That is a man deciding he was done living half a life. Anyone who takes their health seriously deserves credit. Not because it is fashionable. Because it is hard. You change what you eat. You change how you move. You live with the discomfort instead of hiding in it. Most people never do that work. When one person actually does it, it gives other people permission. It shows the excuse was never as strong as they thought. Hollywood loves to preach. Fitness is one of the few sermons that is real. Get healthier and everything else in your life gets a better shot. He did it. More people should.

Paul A. Szypula 🇺🇸

23,718 просмотров • 1 месяц назад

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.
0:13

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Armagan Amcalar

365,108 просмотров • 5 дней назад

They started with 50. Now they say they’re 18,000 In 1996 there were fewer than 50 of them. Today, according to the organizers, up to 18,000 walked through Copenhagen. From Dronning Louises Bro to the Imam Ali Mosque. Look at the curve. This is how it happens. First a handful. Then a few hundred. Then it fills a bridge, a district, a capital. A little at a time, until it is no longer a little. And let me be fair, because fairness is the point. There is nothing strange about them holding this mourning procession. They have done it as part of their faith for more than a thousand years. It is theirs, and they believe in it. There is nothing strange about that at all. What should stop us is the other half. There is nothing strange about Europe allowing it either, and that is exactly the problem. Europe allows it because Europe has forgotten who it is. A people that remembers what it stands for does not need to ban anything, it simply knows where its own line runs. We have lost that. And so the issue was never them. The issue is us. Now look at what actually moved through the streets. Men in front. Women in the second row. That is not a detail, that is the whole point. It is a view of women set into a system and marched out into the public square, in a city where generations fought for women and men to stand as equals. The real question is not whether people may believe what they want. They may. The question is why our capital should cultivate a political law-religion that commemorates a 7th-century power struggle by dividing people by sex on Nørrebrogade. One of the organizers is the Imam Ali Mosque, repeatedly described as the Iranian regime’s extended arm in Denmark. The same regime that hangs women and young men from cranes. We are not importing culture. We are importing a system. And we let it grow, not because they are strong, but because we forgot why we were. First a little. Then a lot. Then too late.
0:10

They started with 50. Now they say they’re 18,000 In 1996 there were fewer than 50 of them. Today, according to the organizers, up to 18,000 walked through Copenhagen. From Dronning Louises Bro to the Imam Ali Mosque. Look at the curve. This is how it happens. First a handful. Then a few hundred. Then it fills a bridge, a district, a capital. A little at a time, until it is no longer a little. And let me be fair, because fairness is the point. There is nothing strange about them holding this mourning procession. They have done it as part of their faith for more than a thousand years. It is theirs, and they believe in it. There is nothing strange about that at all. What should stop us is the other half. There is nothing strange about Europe allowing it either, and that is exactly the problem. Europe allows it because Europe has forgotten who it is. A people that remembers what it stands for does not need to ban anything, it simply knows where its own line runs. We have lost that. And so the issue was never them. The issue is us. Now look at what actually moved through the streets. Men in front. Women in the second row. That is not a detail, that is the whole point. It is a view of women set into a system and marched out into the public square, in a city where generations fought for women and men to stand as equals. The real question is not whether people may believe what they want. They may. The question is why our capital should cultivate a political law-religion that commemorates a 7th-century power struggle by dividing people by sex on Nørrebrogade. One of the organizers is the Imam Ali Mosque, repeatedly described as the Iranian regime’s extended arm in Denmark. The same regime that hangs women and young men from cranes. We are not importing culture. We are importing a system. And we let it grow, not because they are strong, but because we forgot why we were. First a little. Then a lot. Then too late.

Krisztina Maria

38,814 просмотров • 3 месяцев назад

every thought of yours creates a neural connection. you repeat it day after day, and you're already acting on autopilot, without choosing. every thought leaves a trace between the cells. one thought is a barely noticeable scratch, but return to it every day, at the stove, behind the wheel, and the scratch becomes a path, then a road, then a highway. the signal flies along it without asking you. from then on, you just drive where it's been laid out. the treacherous part: on this highway, the reaction happens before you arrive. the body tenses, the answer is given, and you only show up to sign it. it feels like your own because it's familiar. the brain takes the smoothest road, and its job is safety, not happiness. "i am just that kind of person" only means this is your most worn-out route. everything you say about yourself is a map of your most worn paths. "i am anxious", "i can't say no", "i get angry fast" are just reports on what you did most often. character is a thousand identical repetitions, and fate is that stretched over twenty years. you end up where the same reactions, multiplied by years, have been leading you. what is used lives on. the brain doesn't store the past, it only maintains what gets used: the connection nobody calls on weakens, the road not taken withers. character and fate rest on these routes, and every thought strengthens them. if only what's used survives, the old route needs feeding every day. you feed it: the same thought in the morning, the same answer in the evening, the same signature under every decision. the past holds only as long as you keep sweeping its path. you can't erase the old path, but stop sweeping it and it overgrows on its own. beside it you trample a new one, slower, less convenient, less convincing. it takes repetition, because understanding it a hundred times changes nothing. what the body lives through sinks deeper than any dry phrase from the head. so the work is small but real: don't make excuses once, state the amount and stay quiet, answer differently than always. each time is a meter of that new path. you're not a hostage of your character, you're its daily builder. the only question is where you're laying it today.
0:10

every thought of yours creates a neural connection. you repeat it day after day, and you're already acting on autopilot, without choosing. every thought leaves a trace between the cells. one thought is a barely noticeable scratch, but return to it every day, at the stove, behind the wheel, and the scratch becomes a path, then a road, then a highway. the signal flies along it without asking you. from then on, you just drive where it's been laid out. the treacherous part: on this highway, the reaction happens before you arrive. the body tenses, the answer is given, and you only show up to sign it. it feels like your own because it's familiar. the brain takes the smoothest road, and its job is safety, not happiness. "i am just that kind of person" only means this is your most worn-out route. everything you say about yourself is a map of your most worn paths. "i am anxious", "i can't say no", "i get angry fast" are just reports on what you did most often. character is a thousand identical repetitions, and fate is that stretched over twenty years. you end up where the same reactions, multiplied by years, have been leading you. what is used lives on. the brain doesn't store the past, it only maintains what gets used: the connection nobody calls on weakens, the road not taken withers. character and fate rest on these routes, and every thought strengthens them. if only what's used survives, the old route needs feeding every day. you feed it: the same thought in the morning, the same answer in the evening, the same signature under every decision. the past holds only as long as you keep sweeping its path. you can't erase the old path, but stop sweeping it and it overgrows on its own. beside it you trample a new one, slower, less convenient, less convincing. it takes repetition, because understanding it a hundred times changes nothing. what the body lives through sinks deeper than any dry phrase from the head. so the work is small but real: don't make excuses once, state the amount and stay quiet, answer differently than always. each time is a meter of that new path. you're not a hostage of your character, you're its daily builder. the only question is where you're laying it today.

alexei

388,071 просмотров • 7 дней назад

COPY is live on Robinhood Chain $COPY CA: 0xac79255f6f404eba14f316e8669d76573a2d7b1e Five walls read a setup before you copy it: how strong the source is, how deep the pool is, whether the market actually turns over, whether the move is still inside the band, and whether there is a fresh mark to trade against. EDGE, DEPTH, TURN, MOMO, PRICE. A wallet you follow buying is not a reason. Most setups die on the first wall, and you get the exact wall that refused it, every time. Every decision is printed. The terminal runs from one process, keeps local state, and half of it needs no key at all. It never predicts a price. It tells you what you would actually be copying. Open source, MIT, runs on your machine. No browser wallet, nothing signs.
0:20

COPY is live on Robinhood Chain $COPY CA: 0xac79255f6f404eba14f316e8669d76573a2d7b1e Five walls read a setup before you copy it: how strong the source is, how deep the pool is, whether the market actually turns over, whether the move is still inside the band, and whether there is a fresh mark to trade against. EDGE, DEPTH, TURN, MOMO, PRICE. A wallet you follow buying is not a reason. Most setups die on the first wall, and you get the exact wall that refused it, every time. Every decision is printed. The terminal runs from one process, keeps local state, and half of it needs no key at all. It never predicts a price. It tells you what you would actually be copying. Open source, MIT, runs on your machine. No browser wallet, nothing signs.

Lunar

112,261 просмотров • 18 дней назад

Watch These Three Videos. In the first, the Chief Minister of Assam calls a Congress politician a Gan*u. On camera. In public. Without apology. In the second, teenage girls dance to "Unki maa ka bhos*a" with hip thrusts that mime rape. In the third, a religious procession moves, thousands of men watching while women perform explicitly sexual acts while dancing on a truck (in the name of God). If you think these three things are unrelated, you haven't understood what Hindutva actually is. This is not crudeness. This is not a culture war about manners or etiquettes. This is a deliberate project, the systematic sexualization of Indian political and religious life, where the female body becomes spectacle and territory, where the language of violation becomes the language of faith and victory, where a Chief Minister and a religious procession share the same vocabulary. And it is thought out. I grew up in India, in a Hindu society. I never saw anything like this. This is not what Hinduism is. Whether you like it or not, Hinduism is part of every Indian's life. We grew up on Sunday morning Ramayan at 9 AM. The festivals of Diwali and Holi. The idea that good wins over evil. And so much more. It was simple. It was moral. It was universal. It belonged to all of us, even those of us who weren't Hindu. We had our own religions, our own prayers, but that moral code was shared. It was in the air we breathed. This is what is being hollowed out. Not just a religion, a common inheritance. That Sunday morning feeling. The idea that there is such a thing as good, and that it wins. Hindutva has taken that and replaced it with a truck, thousands of men, and women performing a sexual dance. This is Hindutva's deep perversion. It has taken a civilization's religious life and turned it into a mob spectacle. It has taken its political language and soaked it in rape. It has handed teenage girls a rape anthem and called it a dance. The slur is not a slip. The procession is not spontaneous. The choreography is not innocent. What is being built, across platforms, campaign rallies, and now religious processions is a political vocabulary where your opponent is not defeated but penetrated, where the enemy is not wrong but he is a Gan*u. When language and ritual are both sexualized to this degree, something specific happens to the people inside it: they stop seeing clearly. The violence stops feeling like violence. The degradation stops feeling like degradation. The opponent becomes pest. The procession becomes devotion. The rape joke becomes a rally cry. This is what the normalization is for. The filth is the mechanism. Run it long enough, from the Chief Minister's mouth to the temple truck to the religious procession, and an entire society quietly crosses a threshold it can never name, only feel. This is not Hinduism. This is Hindutva in it's ugliest form. And it's all connected.
0:05

Watch These Three Videos. In the first, the Chief Minister of Assam calls a Congress politician a Gan*u. On camera. In public. Without apology. In the second, teenage girls dance to "Unki maa ka bhos*a" with hip thrusts that mime rape. In the third, a religious procession moves, thousands of men watching while women perform explicitly sexual acts while dancing on a truck (in the name of God). If you think these three things are unrelated, you haven't understood what Hindutva actually is. This is not crudeness. This is not a culture war about manners or etiquettes. This is a deliberate project, the systematic sexualization of Indian political and religious life, where the female body becomes spectacle and territory, where the language of violation becomes the language of faith and victory, where a Chief Minister and a religious procession share the same vocabulary. And it is thought out. I grew up in India, in a Hindu society. I never saw anything like this. This is not what Hinduism is. Whether you like it or not, Hinduism is part of every Indian's life. We grew up on Sunday morning Ramayan at 9 AM. The festivals of Diwali and Holi. The idea that good wins over evil. And so much more. It was simple. It was moral. It was universal. It belonged to all of us, even those of us who weren't Hindu. We had our own religions, our own prayers, but that moral code was shared. It was in the air we breathed. This is what is being hollowed out. Not just a religion, a common inheritance. That Sunday morning feeling. The idea that there is such a thing as good, and that it wins. Hindutva has taken that and replaced it with a truck, thousands of men, and women performing a sexual dance. This is Hindutva's deep perversion. It has taken a civilization's religious life and turned it into a mob spectacle. It has taken its political language and soaked it in rape. It has handed teenage girls a rape anthem and called it a dance. The slur is not a slip. The procession is not spontaneous. The choreography is not innocent. What is being built, across platforms, campaign rallies, and now religious processions is a political vocabulary where your opponent is not defeated but penetrated, where the enemy is not wrong but he is a Gan*u. When language and ritual are both sexualized to this degree, something specific happens to the people inside it: they stop seeing clearly. The violence stops feeling like violence. The degradation stops feeling like degradation. The opponent becomes pest. The procession becomes devotion. The rape joke becomes a rally cry. This is what the normalization is for. The filth is the mechanism. Run it long enough, from the Chief Minister's mouth to the temple truck to the religious procession, and an entire society quietly crosses a threshold it can never name, only feel. This is not Hinduism. This is Hindutva in it's ugliest form. And it's all connected.

Darab Farooqui

37,260 просмотров • 5 месяцев назад

Every man lives two lives. The second begins when he realises that he can max out hypertrophy in 15 second sets. Four or five reps build muscle. The last four or five, where the bar slows on its own whether you like it or not. That is where the high-threshold motor units finally get called up. Where the cross-bridges are attached in maximum number at once. Where the fibre takes the tension it has been waiting all set for. Mechanical tension is the only language the muscle reads. It does not have a second one. Everything either side of that is filler, and somebody is paid to sell you filler. Tempo is filler. You are 30 to 50 per cent stronger lowering a weight than lifting it, so no descent on earth is slow enough to threaten you. Drag it out and all you have done is put a lighter weight on the half that counted. The pump is filler. Lactate infused straight into the blood grows nothing. Blood flow restricted on a resting muscle grows nothing. Swelling is a feeling and it gets no vote. The mind-muscle connection is filler. You cannot instruct a muscle in which fibres to use. You tell it to contract. The load decides the rest. So: a weight where the fourth rep slows by itself. Drive it. Stop one short. Rack it. Fifteen seconds. That set has already delivered everything your three sets of twelve spent forty minutes promising. Nobody has ever built a business on fifteen seconds of hard work, so they built one on everything you could bolt onto it.
0:16

Every man lives two lives. The second begins when he realises that he can max out hypertrophy in 15 second sets. Four or five reps build muscle. The last four or five, where the bar slows on its own whether you like it or not. That is where the high-threshold motor units finally get called up. Where the cross-bridges are attached in maximum number at once. Where the fibre takes the tension it has been waiting all set for. Mechanical tension is the only language the muscle reads. It does not have a second one. Everything either side of that is filler, and somebody is paid to sell you filler. Tempo is filler. You are 30 to 50 per cent stronger lowering a weight than lifting it, so no descent on earth is slow enough to threaten you. Drag it out and all you have done is put a lighter weight on the half that counted. The pump is filler. Lactate infused straight into the blood grows nothing. Blood flow restricted on a resting muscle grows nothing. Swelling is a feeling and it gets no vote. The mind-muscle connection is filler. You cannot instruct a muscle in which fibres to use. You tell it to contract. The load decides the rest. So: a weight where the fourth rep slows by itself. Drive it. Stop one short. Rack it. Fifteen seconds. That set has already delivered everything your three sets of twelve spent forty minutes promising. Nobody has ever built a business on fifteen seconds of hard work, so they built one on everything you could bolt onto it.

Sama Hoole

17,080 просмотров • 1 месяц назад

YOU DON'T NEED TO KNOW BLENDER TO MAKE THIS ANYMORE. GPT-5.6 drives it through MCP. you just describe the shot. a chrome figure carving a wave off an accretion disk. filaments of plasma raking across the frame. a car catching the same light on its way into the vortex. this is a viewport, not a render farm. the model never touched a menu. MCP hands it a socket into Blender, it writes python against bpy, it builds the emission shader, places the volumetrics, sets the camera roll. the hard part was never the picture. it was the ten thousand hotkeys standing between you and the picture. that toll booth just closed. it still can't see what it made unless you show it. you screenshot, you say the plasma's washing out the left third, it fixes, you screenshot again. the skill stopped being the software. the skill is now knowing what a good frame looks like and being able to say it out loud.
0:19

YOU DON'T NEED TO KNOW BLENDER TO MAKE THIS ANYMORE. GPT-5.6 drives it through MCP. you just describe the shot. a chrome figure carving a wave off an accretion disk. filaments of plasma raking across the frame. a car catching the same light on its way into the vortex. this is a viewport, not a render farm. the model never touched a menu. MCP hands it a socket into Blender, it writes python against bpy, it builds the emission shader, places the volumetrics, sets the camera roll. the hard part was never the picture. it was the ten thousand hotkeys standing between you and the picture. that toll booth just closed. it still can't see what it made unless you show it. you screenshot, you say the plasma's washing out the left third, it fixes, you screenshot again. the skill stopped being the software. the skill is now knowing what a good frame looks like and being able to say it out loud.

explos1ve

56,334 просмотров • 2 месяцев назад

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.
0:10

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.

Shanaka Anslem Perera ⚡

185,548 просмотров • 3 месяцев назад

clipping agencies rent offices now, $500,000 a year ten editors, a manager, a lease, and the only thing any of them is paid for is noticing noticing is 310 lines of python, and i gave it away it runs on Robinhood Chain, so the clip pays whoever cut it CUT never sees a single frame of the stream. it reads chat, because chat knows before the editor does, before the dashboard does, before the streamer does [what those 310 lines do] 1. measure the last 10 seconds against that stream's own 5 minute baseline 2. never a global number, a big channel idling still beats a small one peaking 3. count how many people just typed clip it 4. call the clip api in the same second, not after the room reacts that last one is the whole thing. a twitch clip only keeps the 90 seconds before you hit the button, so if you noticed the moment yourself, you already lost it no key to read chat. it cannot post for you. it runs on your machine, not mine one command replays three minutes of recorded chat offline and fires a real moment on your screen, so you can watch it work before you trust it
0:12

clipping agencies rent offices now, $500,000 a year ten editors, a manager, a lease, and the only thing any of them is paid for is noticing noticing is 310 lines of python, and i gave it away it runs on Robinhood Chain, so the clip pays whoever cut it CUT never sees a single frame of the stream. it reads chat, because chat knows before the editor does, before the dashboard does, before the streamer does [what those 310 lines do] 1. measure the last 10 seconds against that stream's own 5 minute baseline 2. never a global number, a big channel idling still beats a small one peaking 3. count how many people just typed clip it 4. call the clip api in the same second, not after the room reacts that last one is the whole thing. a twitch clip only keeps the 90 seconds before you hit the button, so if you noticed the moment yourself, you already lost it no key to read chat. it cannot post for you. it runs on your machine, not mine one command replays three minutes of recorded chat offline and fires a real moment on your screen, so you can watch it work before you trust it

Carver

281,824 просмотров • 21 дней назад

A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story
0:11

A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story

Valentin

600,828 просмотров • 5 дней назад

The most unsettling thing about training properly is how little it hurts. You do your handful of heavy sets. Each is genuinely hard for the few reps it lasts, and then it is over. You rack the weight, rest a full few minutes, do it again. Forty minutes later you are walking out breathing normally, barely damp, entirely intact. And the brain panics. That was too easy. Where was the suffering. Where was the two-hour grind, the pool of sweat, the crawl to the car, the soreness that used to reassure you the session counted. Surely something this comfortable cannot be working. This is the exact feeling that keeps people trapped in junk volume for years. It is rarely bad information that holds them there. It is the discomfort of a productive session that stubbornly refuses to feel productive. So they add sets to buy back the suffering, because the suffering felt like proof. The weights are the proof. They went up again this week, quietly, while you were busy missing the pain. The muscle does not grow from how hard the session felt. It has never once asked how you felt about it.
0:15

The most unsettling thing about training properly is how little it hurts. You do your handful of heavy sets. Each is genuinely hard for the few reps it lasts, and then it is over. You rack the weight, rest a full few minutes, do it again. Forty minutes later you are walking out breathing normally, barely damp, entirely intact. And the brain panics. That was too easy. Where was the suffering. Where was the two-hour grind, the pool of sweat, the crawl to the car, the soreness that used to reassure you the session counted. Surely something this comfortable cannot be working. This is the exact feeling that keeps people trapped in junk volume for years. It is rarely bad information that holds them there. It is the discomfort of a productive session that stubbornly refuses to feel productive. So they add sets to buy back the suffering, because the suffering felt like proof. The weights are the proof. They went up again this week, quietly, while you were busy missing the pain. The muscle does not grow from how hard the session felt. It has never once asked how you felt about it.

Sama Hoole

43,098 просмотров • 2 месяцев назад

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.
0:16

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.

Hasan Toor

175,108 просмотров • 26 дней назад

Alright this is starting to become a ridiculous problem. Second time in 24 hours on V14.3.7. This isn’t a native 14.3.7 problem, it has been happening to me (though rarely) since 14.2. I’m ok with nav thinking roads are closed and re-routing. 95% of the time it does this it is wrong, but that’s beside the point. It’s the fact that it does it at the very last minute when it literally already started a turning maneuver. It is so startling and tries to whip it out of a maneuver like it’s avoiding a crash. And the worst part is that the roads it’s trying to avoid are almost never actually closed. Please fix this Tesla AI
0:12

Alright this is starting to become a ridiculous problem. Second time in 24 hours on V14.3.7. This isn’t a native 14.3.7 problem, it has been happening to me (though rarely) since 14.2. I’m ok with nav thinking roads are closed and re-routing. 95% of the time it does this it is wrong, but that’s beside the point. It’s the fact that it does it at the very last minute when it literally already started a turning maneuver. It is so startling and tries to whip it out of a maneuver like it’s avoiding a crash. And the worst part is that the roads it’s trying to avoid are almost never actually closed. Please fix this Tesla AI

Mike P

38,425 просмотров • 1 месяц назад