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OpenAI just showed a system called dots that stops answering questions and starts marking where the answer would have to come from. It returns no text. It returns points on your own data, each one a place the claim is either supported or is standing on nothing. > SPREAD... - the question broken into the individual claims an answer would need > ANCHOR - each claim pinned to the exact span in your documents that carries it > VOID - claims with no span behind them left empty instead of being filled in > LINK - anchors connected where one claim depends on another holding first > READ - the finished map returned as points, never as a paragraph A normal answer hides its own gaps. The sentences are smooth, and the part with nothing underneath reads identically to the rest. Dots refuses that trade. An unsupported claim shows up as an empty point, impossible to skim past. The dependency links are the sharp part. One hollow anchor exposes every claim resting on top of it at once. That turns a wrong answer into a located problem. You see which document is missing, not just that something feels off. Fluency has been doing the convincing in every tool shipped so far. Here it has nothing to hide behind. The dot format and how the voids are scored are laid out in the breakdown below.show more

wast3

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62,117 次观看 • 6 天前 •via X (Twitter)

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25 条评论

Brjan | AI Builder 的头像

Brjan | AI Builder5 天前

marking data points instead of providing answers seems like a double-edged sword

shmidt 的头像

shmidt6 天前

lol it looks interesting

Hrundel75 🐷 的头像

Hrundel75 🐷5 天前

NYASA

Hussain Hashim | Building SundayBack 的头像

Hussain Hashim | Building SundayBack6 天前

@0xWast3 that's pretty cool. i'd add a way to visualize the data points like a heatmap. makes it easier to see where things are solid or shaky at a glance.

magsimich 的头像

magsimich5 天前

Insane thing by OpenAi dots

Armalo AI 的头像

Armalo AI6 天前

Verifiers should point at evidence, not write prose. The hard part is unsupported claims: bury them and the system only surfaces safe bets. Keep them in a review queue an operator reads.

CODIFY 的头像

CODIFY5 天前

This highlights an important shift.

Ridark 的头像

Ridark5 天前

Everything is described so clearly in this terminal of yours, love the clarity for real

Nguyễn Chí Trọng Nghĩa 的头像

Nguyễn Chí Trọng Nghĩa6 天前

Đọc xong thấy rõ một điều, chạy ổn định mới khó, chứ cài thì ai cũng cài được trong buổi sáng . Cảm ơn bác đã chia sẻ.

Alexandr Merk 的头像

Alexandr Merk5 天前

Pointing to where the answer should come from instead of generating it is a smart way to fight hallucinations. Curious how it handles questions with no single source.

BreezeOg 的头像

BreezeOg5 天前

a void flag is more useful to me than any confident paragraph, at least it admits where nothing is holding the claim up

Brian Hadu 的头像

Brian Hadu5 天前

if users rely on dots without context, it could lead to confusion or misinterpretation

Âxel ⟢ 的头像

Âxel ⟢5 天前

Spread

Gipp 🦅 的头像

Gipp 🦅6 天前

void claims always flag my missing sources fast

Archive 的头像

Archive5 天前

you set up your agents in a really smart way i wanna try the same thing

Iron Giant 的头像

Iron Giant5 天前

Ground truth beats smooth prose

0xbobaa 的头像

0xbobaa6 天前

Is dots running on autopilot?

Antony Claude 的头像

Antony Claude6 天前

this is huge for verifying info in ai responses

SECTOR 的头像

SECTOR5 天前

the void flag actually gives you a count of missing spans per document which i can log and prioritize

Sebastianus 的头像

Sebastianus5 天前

there are some many paid bots to advertise open retard shit, god lord

Niv Asayag · YardCrew 的头像

Niv Asayag · YardCrew6 天前

A one-report task asked me 3 times before the worker had its own computer. Inside that computer: 0. Tested on a Mac with Colima, not yet Docker Desktop, OrbStack, or a Windows PC.

Summer 的头像

Summer5 天前

Stripping away the smooth prose really exposes how much of it was just vibes.

Dekos 的头像

Dekos6 天前

This system exceeds all expectations

V1nT 的头像

V1nT6 天前

OpneAi cooked here

beamnxw ./ 的头像

beamnxw ./6 天前

ts video is addictive xD

相关视频

OpenAI just showed a system called dots that stops answering questions and starts marking where the answer would have to come from. It returns no text. It returns points on your own data, each one a place the claim is either supported or is standing on nothing. > SPREAD - the question broken into the individual claims an answer would need > ANCHOR - each claim pinned to the exact span in your documents that carries it > VOID - claims with no span behind them left empty instead of being filled in > LINK - anchors connected where one claim depends on another holding first > READ - the finished map returned as points, never as a paragraph A normal answer hides its own gaps. The sentences are smooth, and the part with nothing underneath reads identically to the rest. Dots refuses that trade. An unsupported claim shows up as an empty point, impossible to skim past. The dependency links are the sharp part. One hollow anchor exposes every claim resting on top of it at once. That turns a wrong answer into a located problem. You see which document is missing, not just that something feels off. Fluency has been doing the convincing in every tool shipped so far. Here it has nothing to hide behind. The dot format and how the voids are scored are laid out in the breakdown below.

OpenAI just showed a system called dots that stops answering questions and starts marking where the answer would have to come from. It returns no text. It returns points on your own data, each one a place the claim is either supported or is standing on nothing. > SPREAD - the question broken into the individual claims an answer would need > ANCHOR - each claim pinned to the exact span in your documents that carries it > VOID - claims with no span behind them left empty instead of being filled in > LINK - anchors connected where one claim depends on another holding first > READ - the finished map returned as points, never as a paragraph A normal answer hides its own gaps. The sentences are smooth, and the part with nothing underneath reads identically to the rest. Dots refuses that trade. An unsupported claim shows up as an empty point, impossible to skim past. The dependency links are the sharp part. One hollow anchor exposes every claim resting on top of it at once. That turns a wrong answer into a located problem. You see which document is missing, not just that something feels off. Fluency has been doing the convincing in every tool shipped so far. Here it has nothing to hide behind. The dot format and how the voids are scored are laid out in the breakdown below.

wast3

86,189 次观看 • 7 天前

OpenAI's dots agents take one prompt and a YouTube link, and hand back the video pulled apart frame by claim. Nothing gets summarised. The video is broken into what it asserts, and each assertion is checked against the moment it was made. > CLAIM - every statement the video makes listed separately, with its timestamp > MATCH - each one tested against what the footage at that second actually shows > TRACE - the sources it leans on named, or flagged as never given at all A summary launders everything. Strong claims and throwaway lines come out reading the same weight. Here they stay separated. You see which sentence carries the argument and which one is filler between B-roll. The visual check is the part nobody builds. A confident voiceover over unrelated footage is the oldest trick on the platform. Unsourced claims surface as a list, not as a feeling that something was off while you watched. A forty-minute video collapses into a page you can argue with, timestamp by timestamp. The prompt and the claim format are laid out in the breakdown below.

OpenAI's dots agents take one prompt and a YouTube link, and hand back the video pulled apart frame by claim. Nothing gets summarised. The video is broken into what it asserts, and each assertion is checked against the moment it was made. > CLAIM - every statement the video makes listed separately, with its timestamp > MATCH - each one tested against what the footage at that second actually shows > TRACE - the sources it leans on named, or flagged as never given at all A summary launders everything. Strong claims and throwaway lines come out reading the same weight. Here they stay separated. You see which sentence carries the argument and which one is filler between B-roll. The visual check is the part nobody builds. A confident voiceover over unrelated footage is the oldest trick on the platform. Unsourced claims surface as a list, not as a feeling that something was off while you watched. A forty-minute video collapses into a page you can argue with, timestamp by timestamp. The prompt and the claim format are laid out in the breakdown below.

wast3

26,674 次观看 • 5 天前

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. **Choice** picks one of up to 255 options you define, like engineering, billing or sales. 2. **Score** places the state on an ordered scale you define, like low, medium or high risk. 3. **Noul** returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. Choice picks one of up to 255 options you define, like engineering, billing or sales. 2. Score places the state on an ordered scale you define, like low, medium or high risk. 3. Noul returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓

Hanako

42,632 次观看 • 19 天前

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.

MindArch

16,232 次观看 • 2 个月前

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

Stefan.

3,017,634 次观看 • 2 个月前

Orchestrators vs. Graphs, clearly explained! orchestrators are great, and everyone builds one first. here is the ceiling: an orchestrator sits above the work and routes every message. five agents report to it. it reads all five. it decides what each one does next, and reads all five replies. that is ten trips through one context, and by the fifth agent that context has read four reports, five instructions and its own reasoning about all of them. Graph engineering fixes this by removing the seat: not a better router, but no router at all. you need both, and here is the sentence that resolves the whole confusion: an orchestrator sits above the work and holds all of it. a graph is the shape of the work, and holds none of it. ↳ above the work: one context that has to see everything before anything ships ↳ inside the work: a splitter that hands out and lets go, and a merge that reads nothing Prompts → Context → Harness → Loops → Graphs the coordination did not disappear. it moved into the edges, where it costs nothing and cannot get tired. the trick is noticing what you actually built. if one node has to see every result before the run can finish, you did not remove the bottleneck. you hired it, gave it the longest context in the system, and made it the thing you were counting on to stay sharp. one thing to know before you scale it. an orchestrator degrades in the one way nothing catches. ↳ it does not crash, time out or return an error. it stays up and keeps routing ↳ it just starts routing worse, somewhere around the fifth report, and every downstream agent does exactly what it was told that last one catches careful people. you can have perfect isolation on every worker and still have one window quietly drifting at the top, and the traces will all look clean because each worker did its job. and the one that eats whole nights: the merge is where this shows up first. ranking five findings is not judgment, it is a sort. if a model is doing it, you are paying a model to read five reports so it can put them in an order that three lines of code would have got right, and now that model has read everything too. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Orchestrators vs. Graphs, clearly explained! orchestrators are great, and everyone builds one first. here is the ceiling: an orchestrator sits above the work and routes every message. five agents report to it. it reads all five. it decides what each one does next, and reads all five replies. that is ten trips through one context, and by the fifth agent that context has read four reports, five instructions and its own reasoning about all of them. Graph engineering fixes this by removing the seat: not a better router, but no router at all. you need both, and here is the sentence that resolves the whole confusion: an orchestrator sits above the work and holds all of it. a graph is the shape of the work, and holds none of it. ↳ above the work: one context that has to see everything before anything ships ↳ inside the work: a splitter that hands out and lets go, and a merge that reads nothing Prompts → Context → Harness → Loops → Graphs the coordination did not disappear. it moved into the edges, where it costs nothing and cannot get tired. the trick is noticing what you actually built. if one node has to see every result before the run can finish, you did not remove the bottleneck. you hired it, gave it the longest context in the system, and made it the thing you were counting on to stay sharp. one thing to know before you scale it. an orchestrator degrades in the one way nothing catches. ↳ it does not crash, time out or return an error. it stays up and keeps routing ↳ it just starts routing worse, somewhere around the fifth report, and every downstream agent does exactly what it was told that last one catches careful people. you can have perfect isolation on every worker and still have one window quietly drifting at the top, and the traces will all look clean because each worker did its job. and the one that eats whole nights: the merge is where this shows up first. ranking five findings is not judgment, it is a sort. if a model is doing it, you are paying a model to read five reports so it can put them in an order that three lines of code would have got right, and now that model has read everything too. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

44,226 次观看 • 25 天前

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning — and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text — and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither — because the missing word and the correct answer are the same thing. The data grades itself.

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning — and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text — and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither — because the missing word and the correct answer are the same thing. The data grades itself.

Big Brain AI

49,293 次观看 • 1 个月前

Thomas Nagel dismantles relativism in one paragraph. In a 1995 talk on reason, the philosopher exposes a fatal flaw at the heart of the relativist position: "Claims to the effect that a type of judgment expresses a local point of view are inherently objective in intent. They suggest a picture of the true sources of those judgments which places them in an unconditional context." In other words, the moment you say "all truth is relative," you've already made an absolute claim. Nagel sharpens this into a precise logical trap: "The judgment of relativity or conditionality cannot be applied to the judgment of relativity itself." The relativist wants to stand outside all perspectives and declare that no perspective is universal. But that declaration is itself a universal perspective. He then drives it home: "To put it schematically, 'everything is subjective' must be nonsense, for it would itself have to be either subjective or objective. But it can't be objective, since in that case it would be false if true. And it can't be subjective, because then it would not rule out any objective claim including the claim that it is objectively false." The argument is elegant in its completeness. If "everything is subjective" is an objective truth, it defeats itself immediately because it would mean at least one thing is objective. But if it's merely a subjective opinion, it has no power to challenge objectivity at all. Either way, relativism collapses under its own weight. What's striking is how often this self-refuting structure goes unnoticed in everyday debates about truth, culture, and morality. The person who says "that's just your perspective" is quietly assuming their own perspective is the correct one. Nagel's point isn't that objectivity is easy to achieve, only that abandoning it entirely is incoherent.

Thomas Nagel dismantles relativism in one paragraph. In a 1995 talk on reason, the philosopher exposes a fatal flaw at the heart of the relativist position: "Claims to the effect that a type of judgment expresses a local point of view are inherently objective in intent. They suggest a picture of the true sources of those judgments which places them in an unconditional context." In other words, the moment you say "all truth is relative," you've already made an absolute claim. Nagel sharpens this into a precise logical trap: "The judgment of relativity or conditionality cannot be applied to the judgment of relativity itself." The relativist wants to stand outside all perspectives and declare that no perspective is universal. But that declaration is itself a universal perspective. He then drives it home: "To put it schematically, 'everything is subjective' must be nonsense, for it would itself have to be either subjective or objective. But it can't be objective, since in that case it would be false if true. And it can't be subjective, because then it would not rule out any objective claim including the claim that it is objectively false." The argument is elegant in its completeness. If "everything is subjective" is an objective truth, it defeats itself immediately because it would mean at least one thing is objective. But if it's merely a subjective opinion, it has no power to challenge objectivity at all. Either way, relativism collapses under its own weight. What's striking is how often this self-refuting structure goes unnoticed in everyday debates about truth, culture, and morality. The person who says "that's just your perspective" is quietly assuming their own perspective is the correct one. Nagel's point isn't that objectivity is easy to achieve, only that abandoning it entirely is incoherent.

Mateus — eu/acc 🇪🇺

27,983 次观看 • 6 个月前

Hello Rubika Liyaquat, You and Ambani's channel News18 amplified false claim by your BJP friend Shehzad Jai Hind on Rahul Gandhi's Myanmar link. As a News Channels and a News anchor, It is your basic responsibility to first fact check the claim and call out people who are spreading Fake News instead of amplifying their baseless claim. Now that Alt News has done a detailed fact check debunking the said claim, Will you not informed your television viewers what the truth is? Or is your channel there only to run one sided narrative attacking the opposition and defending the people in power?

Hello Rubika Liyaquat, You and Ambani's channel News18 amplified false claim by your BJP friend Shehzad Jai Hind on Rahul Gandhi's Myanmar link. As a News Channels and a News anchor, It is your basic responsibility to first fact check the claim and call out people who are spreading Fake News instead of amplifying their baseless claim. Now that Alt News has done a detailed fact check debunking the said claim, Will you not informed your television viewers what the truth is? Or is your channel there only to run one sided narrative attacking the opposition and defending the people in power?

Mohammed Zubair

298,282 次观看 • 1 年前

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

Yarchi

32,594 次观看 • 3 个月前

The AAP will never accept anything Secretary Kennedy produces; that much is clear. But that's not the point. The point is that they need a study like the Henry Ford study as a first step. Because right now, the data "doesn't exist." And the people who claim to be so certain that vaccines are safe have never once been asked to prove it with the most basic comparison in science. A Henry Ford-style vaccinated vs. unvaccinated study isn't radical. It's the bare minimum. And parents across this country are waking up to that fact. They're asking the question that no one in mainstream medicine wants to answer: "Why can't you do a study that shows me your side is right?" The power is in the people. Parents opting out. Parents questioning. Parents are refusing to accept "trust us" as a substitute for actual data. That is what is going to force doctors to eventually say, "Parents are concerned. Where is the research they're asking for? We're so sure vaccines don't cause problems. What are we worried about?" What ARE they worried about? Secretary Kennedy is moving the needle. But so are you - every time you ask the question, every time you opt out, every time you refuse to accept a system that has never once had to prove itself to the people it serves. Keep going.

The AAP will never accept anything Secretary Kennedy produces; that much is clear. But that's not the point. The point is that they need a study like the Henry Ford study as a first step. Because right now, the data "doesn't exist." And the people who claim to be so certain that vaccines are safe have never once been asked to prove it with the most basic comparison in science. A Henry Ford-style vaccinated vs. unvaccinated study isn't radical. It's the bare minimum. And parents across this country are waking up to that fact. They're asking the question that no one in mainstream medicine wants to answer: "Why can't you do a study that shows me your side is right?" The power is in the people. Parents opting out. Parents questioning. Parents are refusing to accept "trust us" as a substitute for actual data. That is what is going to force doctors to eventually say, "Parents are concerned. Where is the research they're asking for? We're so sure vaccines don't cause problems. What are we worried about?" What ARE they worried about? Secretary Kennedy is moving the needle. But so are you - every time you ask the question, every time you opt out, every time you refuse to accept a system that has never once had to prove itself to the people it serves. Keep going.

The HighWire

18,990 次观看 • 7 个月前

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

KingWilliam

15,099 次观看 • 2 个月前

watch the small one on the left. it hears something at 00:05 and does nothing clever with it it just passes it on that is the whole trick. echo isolates a trace in ambient noise. moth follows the noise. kite finds a shorter route to it. vex starts collecting fragments. halo keeps the pattern so nobody has to look for it twice. nobody assigned any of that coherence in the top corner opens the cycle at 18%. by the time the wave crosses the field it is past 75%, and not one instruction was typed while it happened most people building agent teams do this backwards. they write a dispatcher, a queue, a state machine, and then wonder why five bots are slower than one. five bots stay slower than one until they can read each other's job descriptions and hand work sideways with no human standing in the middle three ways this breaks before it works: → give every bot the same description and the orchestrator has nothing to route on. every task comes straight back to you → let two bots own the same folder and they overwrite each other for six hours. you wake up to a clean, empty, perfectly synced nothing → skip the boundary line in one charter and that bot will send something. once is plenty to learn that lesson twenty agents run on one seat here. cheaper than a standup for three humans, and none of them ask what the priority is most setups have one agent and a bottleneck made of a person. this one has twenty, and the interesting part happens while nobody is watching full charters, the routing rule and the one line that stops a bot mid-send are in the piece below

watch the small one on the left. it hears something at 00:05 and does nothing clever with it it just passes it on that is the whole trick. echo isolates a trace in ambient noise. moth follows the noise. kite finds a shorter route to it. vex starts collecting fragments. halo keeps the pattern so nobody has to look for it twice. nobody assigned any of that coherence in the top corner opens the cycle at 18%. by the time the wave crosses the field it is past 75%, and not one instruction was typed while it happened most people building agent teams do this backwards. they write a dispatcher, a queue, a state machine, and then wonder why five bots are slower than one. five bots stay slower than one until they can read each other's job descriptions and hand work sideways with no human standing in the middle three ways this breaks before it works: → give every bot the same description and the orchestrator has nothing to route on. every task comes straight back to you → let two bots own the same folder and they overwrite each other for six hours. you wake up to a clean, empty, perfectly synced nothing → skip the boundary line in one charter and that bot will send something. once is plenty to learn that lesson twenty agents run on one seat here. cheaper than a standup for three humans, and none of them ask what the priority is most setups have one agent and a bottleneck made of a person. this one has twenty, and the interesting part happens while nobody is watching full charters, the routing rule and the one line that stops a bot mid-send are in the piece below

monokern

12,612 次观看 • 20 天前

call me crazy but company knowledge should look more like this. > every file becomes a node > every import, call, and dependency becomes an edge > every answer points back to the exact code behind it click one class and the system shows what it depends on, what depends on it, and where it sits inside the architecture. now apply the same idea to the company: decisions. projects. people. policies. workflows. learnings. AI should not search through a pile of documents and guess how they connect. it should navigate the company as a system. don't bookmark this if it crosses your timeline. paste the full article into your agent and tell it to build the first version for your company.

call me crazy but company knowledge should look more like this. > every file becomes a node > every import, call, and dependency becomes an edge > every answer points back to the exact code behind it click one class and the system shows what it depends on, what depends on it, and where it sits inside the architecture. now apply the same idea to the company: decisions. projects. people. policies. workflows. learnings. AI should not search through a pile of documents and guess how they connect. it should navigate the company as a system. don't bookmark this if it crosses your timeline. paste the full article into your agent and tell it to build the first version for your company.

J.B.

57,687 次观看 • 1 个月前

It would appear Michael O’Leary is a secret follower of mine on Twitter as he echos my point that the VAT reduction is the not the answer! Wages and employers RSI are crippling our industry With businesses shutting down every single day and now one in 7 commercial properties is lying empty, perhaps it’s time to start thinking that those that say they are the good guys are actually your biggest enemy Go look at your wage packet from this day last year and the previous few years. You will indeed see your net take home pay has increased. Now ask yourself just one question Where the fcuk is it

It would appear Michael O’Leary is a secret follower of mine on Twitter as he echos my point that the VAT reduction is the not the answer! Wages and employers RSI are crippling our industry With businesses shutting down every single day and now one in 7 commercial properties is lying empty, perhaps it’s time to start thinking that those that say they are the good guys are actually your biggest enemy Go look at your wage packet from this day last year and the previous few years. You will indeed see your net take home pay has increased. Now ask yourself just one question Where the fcuk is it

Paul Treyvaud

40,062 次观看 • 1 年前

a shape with no inside. he filled it with water anyway pour water in and it stops almost immediately. the air has nowhere to escape, so it just sits there blocking you. tilt it and you get a little more in, then it blocks again so he put the whole thing in a vacuum chamber turn it on and the bottle starts bubbling. that's the air being pulled out through the neck, leaving as close to nothing inside as he can get then he lets the pressure back in, and the water rushes up into the empty space on its own still not full. so he runs it again, rarefying the smaller pocket of air that's left, and again after that here's the part that breaks your head: for a shape to have volume, it has to split the world in two. a ball does that. stretch it however you want, there's always a world inside and a world outside a Klein bottle has no such line. follow its surface and you never cross a boundary, never pass through a wall, never end up anywhere that isn't the same side you started on the inside is the outside. so there's nothing there to fill he filled it anyway that's the same shape as the room below. no plot, no payoff, just a question your brain won't drop -> can a thing with no inside hold anything at all two videos, two impossible questions, 20 million views between them. Paone broke down why this format works in the article underneath save this. the most watched thing today was a shape that shouldn't be able to hold anything, holding water

a shape with no inside. he filled it with water anyway pour water in and it stops almost immediately. the air has nowhere to escape, so it just sits there blocking you. tilt it and you get a little more in, then it blocks again so he put the whole thing in a vacuum chamber turn it on and the bottle starts bubbling. that's the air being pulled out through the neck, leaving as close to nothing inside as he can get then he lets the pressure back in, and the water rushes up into the empty space on its own still not full. so he runs it again, rarefying the smaller pocket of air that's left, and again after that here's the part that breaks your head: for a shape to have volume, it has to split the world in two. a ball does that. stretch it however you want, there's always a world inside and a world outside a Klein bottle has no such line. follow its surface and you never cross a boundary, never pass through a wall, never end up anywhere that isn't the same side you started on the inside is the outside. so there's nothing there to fill he filled it anyway that's the same shape as the room below. no plot, no payoff, just a question your brain won't drop -> can a thing with no inside hold anything at all two videos, two impossible questions, 20 million views between them. Paone broke down why this format works in the article underneath save this. the most watched thing today was a shape that shouldn't be able to hold anything, holding water

Banff

2,569,858 次观看 • 2 个月前

Elon Musk was asked what the economy looks like in ten years. He didn’t describe a boom or a crash. He gave a date. Musk: “Money won’t matter in 2036.” He isn’t predicting collapse. He’s predicting so much supply that accounting can no longer describe it. Musk: “You need the end effectors in the form of humanoid robotics.” End effectors. Hands. Every economy that ever existed was capped by the number of hands available and the hours inside them. That cap is most of human history. Famine was a hands problem. Empire was a hands problem. Slavery was the worst answer anyone ever gave to a hands problem. Musk: “you can go from intelligence manifesting itself only digitally to shaping atoms.” Intelligence has been stuck behind glass since the first computer. Every idea it ever had still needed human hands to carry it out. Musk: “you have a sort of a quasi-infinite economy.” The interesting part isn’t whether the abundance arrives. It’s what money turns out to have been. Not paper. Not a number in an app. A dollar is a claim on somebody else’s hours. Every price you ever paid was a bid on a piece of a stranger’s life. Every wage you ever earned was somebody bidding on yours. So “money won’t matter” is not a sentence about currency. It’s a sentence about need. If nobody has to buy your hours, the claim has nothing left to point at. Musk: “You want money for food, housing, transport, entertainment.” That list is accurate, and it is the smallest a person has ever been reduced to. A stomach and an address. Nobody has ever wanted only that. Under every paycheck was a second thing that never printed on the stub. Proof that a stranger somewhere needed something only you could give. The economy was the largest machine ever built for making strangers necessary to each other. The goods were the byproduct. Every ancestor of yours worked so the next one wouldn’t have to. Nobody ever wrote down what to do on the day someone finally didn’t. We already know what happens when the supply arrives and the necessity leaves. We have run it one person at a time for a century. The retired man with a full account and nothing waiting for him in the morning. The lottery winner who comes apart with every need met. Nobody in that condition was ever cured by more. Musk: “There’s no analogy or metaphor that I think illustrates the magnitude of change that we’re going to experience here.” He’s right, and it runs deeper than economics. Every era before this one organized itself around scarcity. Take scarcity out and there is nothing left to compare it to. Notice what abundance never touches. Anything that can be copied falls to zero. What remains is everything that can’t. Land. Rank. Being first. The attention of one specific person who could be spending it somewhere else. Abundance doesn’t kill money. It strips it down to the only things it was ever really buying. The goods, the services, the things we spent ten thousand years calling wealth, turn out to have been the easy part. So the question waiting in 2036 isn’t how you’ll get by. Provision is handled. It’s what you answer when nothing requires you and everything is given. Most of us were never chosen. We were needed, and needed was close enough to live on. That difference has never once been tested. Abundance is the test. Nothing will be missing. You will be.

Elon Musk was asked what the economy looks like in ten years. He didn’t describe a boom or a crash. He gave a date. Musk: “Money won’t matter in 2036.” He isn’t predicting collapse. He’s predicting so much supply that accounting can no longer describe it. Musk: “You need the end effectors in the form of humanoid robotics.” End effectors. Hands. Every economy that ever existed was capped by the number of hands available and the hours inside them. That cap is most of human history. Famine was a hands problem. Empire was a hands problem. Slavery was the worst answer anyone ever gave to a hands problem. Musk: “you can go from intelligence manifesting itself only digitally to shaping atoms.” Intelligence has been stuck behind glass since the first computer. Every idea it ever had still needed human hands to carry it out. Musk: “you have a sort of a quasi-infinite economy.” The interesting part isn’t whether the abundance arrives. It’s what money turns out to have been. Not paper. Not a number in an app. A dollar is a claim on somebody else’s hours. Every price you ever paid was a bid on a piece of a stranger’s life. Every wage you ever earned was somebody bidding on yours. So “money won’t matter” is not a sentence about currency. It’s a sentence about need. If nobody has to buy your hours, the claim has nothing left to point at. Musk: “You want money for food, housing, transport, entertainment.” That list is accurate, and it is the smallest a person has ever been reduced to. A stomach and an address. Nobody has ever wanted only that. Under every paycheck was a second thing that never printed on the stub. Proof that a stranger somewhere needed something only you could give. The economy was the largest machine ever built for making strangers necessary to each other. The goods were the byproduct. Every ancestor of yours worked so the next one wouldn’t have to. Nobody ever wrote down what to do on the day someone finally didn’t. We already know what happens when the supply arrives and the necessity leaves. We have run it one person at a time for a century. The retired man with a full account and nothing waiting for him in the morning. The lottery winner who comes apart with every need met. Nobody in that condition was ever cured by more. Musk: “There’s no analogy or metaphor that I think illustrates the magnitude of change that we’re going to experience here.” He’s right, and it runs deeper than economics. Every era before this one organized itself around scarcity. Take scarcity out and there is nothing left to compare it to. Notice what abundance never touches. Anything that can be copied falls to zero. What remains is everything that can’t. Land. Rank. Being first. The attention of one specific person who could be spending it somewhere else. Abundance doesn’t kill money. It strips it down to the only things it was ever really buying. The goods, the services, the things we spent ten thousand years calling wealth, turn out to have been the easy part. So the question waiting in 2036 isn’t how you’ll get by. Provision is handled. It’s what you answer when nothing requires you and everything is given. Most of us were never chosen. We were needed, and needed was close enough to live on. That difference has never once been tested. Abundance is the test. Nothing will be missing. You will be.

Dustin

39,343 次观看 • 2 个月前

Your faith was forged in people who would rather be exterminated than assimilated. A soft version of it, eager to be liked and desperate to fit in, is not the thing they died to hand you. So stop striving to be liked. Stop angling to be loved by a world that drove your fathers into the snow. That world would think no better of the gospel today than it did in 1838. Stop trying to file down every peculiar and glorious edge of the Restoration until the world finally finds you acceptable. It never will. And the wanting of its approval is the slow death of everything your people bled to preserve. I am thinking of the proclamation on the family, and of how many have quietly gone looking for a way around it. Some say it aloud now. Some march under the world's Pride banners and tell themselves it is only love. They have done the quiet arithmetic and concluded that if they give the world this one doctrine, the world will finally stop hating them, finally let them belong, finally call them good. It does not work that way. It has never once worked that way. Understand what the world actually hates, because it is not a single teaching about marriage that it cannot abide. It is the claim. It is the unbearable, scandalous claim that the keys of the priesthood were restored to the earth, that there is a prophet who speaks for God, that this and no other is the authorized house of the Lord. That is the offense. That is what it cannot forgive. You could surrender every doctrine the world finds distasteful, one after another, and you would not buy a single hour of peace, because the thing it objects to is not your position on this or that. It is that you claim to hold the authority of heaven, and it intends to see that claim humbled. The doctrine is only the doorway it is pushing on. The house is what it wants. Embrace the truth. Embrace the battle that has always come with it, because there has always been a battle, and there is one now. It is the oldest war there is, good against evil, light against the dark, and you were born onto its field whether you wished to be or not. You did not inherit a museum. You inherited a war, and a banner, and a people who never once surrendered it. You are a Mormon. The blood of the persecuted is in you, and the truth they died for is in your hands. You are not tourists. You are not spectators. You are the heirs of warriors, and the line they held is now yours to hold. So plant your feet on the ground they bled for. Lift the banner they would not drop.

Your faith was forged in people who would rather be exterminated than assimilated. A soft version of it, eager to be liked and desperate to fit in, is not the thing they died to hand you. So stop striving to be liked. Stop angling to be loved by a world that drove your fathers into the snow. That world would think no better of the gospel today than it did in 1838. Stop trying to file down every peculiar and glorious edge of the Restoration until the world finally finds you acceptable. It never will. And the wanting of its approval is the slow death of everything your people bled to preserve. I am thinking of the proclamation on the family, and of how many have quietly gone looking for a way around it. Some say it aloud now. Some march under the world's Pride banners and tell themselves it is only love. They have done the quiet arithmetic and concluded that if they give the world this one doctrine, the world will finally stop hating them, finally let them belong, finally call them good. It does not work that way. It has never once worked that way. Understand what the world actually hates, because it is not a single teaching about marriage that it cannot abide. It is the claim. It is the unbearable, scandalous claim that the keys of the priesthood were restored to the earth, that there is a prophet who speaks for God, that this and no other is the authorized house of the Lord. That is the offense. That is what it cannot forgive. You could surrender every doctrine the world finds distasteful, one after another, and you would not buy a single hour of peace, because the thing it objects to is not your position on this or that. It is that you claim to hold the authority of heaven, and it intends to see that claim humbled. The doctrine is only the doorway it is pushing on. The house is what it wants. Embrace the truth. Embrace the battle that has always come with it, because there has always been a battle, and there is one now. It is the oldest war there is, good against evil, light against the dark, and you were born onto its field whether you wished to be or not. You did not inherit a museum. You inherited a war, and a banner, and a people who never once surrendered it. You are a Mormon. The blood of the persecuted is in you, and the truth they died for is in your hands. You are not tourists. You are not spectators. You are the heirs of warriors, and the line they held is now yours to hold. So plant your feet on the ground they bled for. Lift the banner they would not drop.

Kirk Rollins

30,673 次观看 • 3 个月前

you opened a memory file and could not tell when it was written there is a field for that, and some of those files will never have it the timestamp goes into frontmatter, and it is only added to files that already begin with frontmatter, it is never added to a file that has none so a note created without those three dashes at the top is undateable for the rest of its life, and on screen it looks exactly like the ones that are current sixteen of my thirty nine are sitting there with no date on them right now you are not reading a memory you are reading an undated claim that loads before you speak

you opened a memory file and could not tell when it was written there is a field for that, and some of those files will never have it the timestamp goes into frontmatter, and it is only added to files that already begin with frontmatter, it is never added to a file that has none so a note created without those three dashes at the top is undateable for the rest of its life, and on screen it looks exactly like the ones that are current sixteen of my thirty nine are sitting there with no date on them right now you are not reading a memory you are reading an undated claim that loads before you speak

Lagosaur

31,471 次观看 • 1 个月前