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SITUATION EXPLAINED: How much are frontier labs actually spending on training data? .Sean Cai: "Frontier labs are spending about $10 to $15 billion per lab on data." "Really good long horizon tasks go up to $20,000 each. A complete browser-use version of SAP was rumored at $500,000." "Despite everybody... show more

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334,625 次观看 • 1 个月前 •via X (Twitter)

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Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber (in SF)

130,324 次观看 • 2 个月前

Andrej Karpathy had already talked about this possibility 1 month back, that he might join one of the big AI labs and why he may do so. "They (the AI labs) are kind of like at the edge of capability of what's possible. And they are working on what's coming down the line. And I think if you're outside of that frontier lab, your judgment fundamentally will start to drift because you're not part of what's coming down the line. And so I feel like my judgment will inevitably start to drift as well. And I won't actually have an understanding of how these systems actually work under the hood. It's a big system. I won't have a good understanding of how to develop and etc. And so I do think that in that sense, I agree and something I'm nervous about. I think it's worth basically being in touch with what's actually happening and actually being in the frontier lab. And if some of the frontier labs would have me come for some amount of time and do really good work for them, then I think that's maybe a good setup" --- From 'No Priors: AI, Machine Learning, Tech, & Startups" YT channel (link in comment)

Andrej Karpathy had already talked about this possibility 1 month back, that he might join one of the big AI labs and why he may do so. "They (the AI labs) are kind of like at the edge of capability of what's possible. And they are working on what's coming down the line. And I think if you're outside of that frontier lab, your judgment fundamentally will start to drift because you're not part of what's coming down the line. And so I feel like my judgment will inevitably start to drift as well. And I won't actually have an understanding of how these systems actually work under the hood. It's a big system. I won't have a good understanding of how to develop and etc. And so I do think that in that sense, I agree and something I'm nervous about. I think it's worth basically being in touch with what's actually happening and actually being in the frontier lab. And if some of the frontier labs would have me come for some amount of time and do really good work for them, then I think that's maybe a good setup" --- From 'No Priors: AI, Machine Learning, Tech, & Startups" YT channel (link in comment)

Rohan Paul

54,762 次观看 • 2 个月前

tom stallard on mclaren sharing all data between both drivers: “all of the data is available to both cars within mclaren and actually within our race engineering group we have a really good, and across the whole team as well [..] an incredibly collaborative relationship and it's very much like how do we use the data to make the team go faster. so, although some of it we're using to coach the drivers, i've not seen any kind of efforts from either driver to kind of hide things from their teammate or anything like that. actually, we very much use that to just push the next level up. so, as much as they want to beat each other, i think they both have quite good awareness that – like oscar wants to be the best version of lando”

tom stallard on mclaren sharing all data between both drivers: “all of the data is available to both cars within mclaren and actually within our race engineering group we have a really good, and across the whole team as well [..] an incredibly collaborative relationship and it's very much like how do we use the data to make the team go faster. so, although some of it we're using to coach the drivers, i've not seen any kind of efforts from either driver to kind of hide things from their teammate or anything like that. actually, we very much use that to just push the next level up. so, as much as they want to beat each other, i think they both have quite good awareness that – like oscar wants to be the best version of lando”

ray

169,554 次观看 • 8 个月前

Here is the full run of MicroFactory, that autonomously assembles a photo frame - a real product sold on Amazon. This is a good example of how to deal with complex tasks, while we do not yet have a large set of robotic data to train a big general ai model. Thread:

Here is the full run of MicroFactory, that autonomously assembles a photo frame - a real product sold on Amazon. This is a good example of how to deal with complex tasks, while we do not yet have a large set of robotic data to train a big general ai model. Thread:

Igor Kulakov

197,003 次观看 • 8 个月前

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 次观看 • 1 年前

On the heels of a weak August jobs report that fell well short of economists’ expectations, White House economic adviser Kevin Hassett is downplaying the jobs data, telling Margaret Brennan “the reason we are getting massive revisions and numbers that don't make sense is really that we have got to modernize the way we do the labor data.” Hassett goes on to add, “The thing I am most suspicious of in terms of data quality is the job number, in part, because there are other ways to do it.”

On the heels of a weak August jobs report that fell well short of economists’ expectations, White House economic adviser Kevin Hassett is downplaying the jobs data, telling Margaret Brennan “the reason we are getting massive revisions and numbers that don't make sense is really that we have got to modernize the way we do the labor data.” Hassett goes on to add, “The thing I am most suspicious of in terms of data quality is the job number, in part, because there are other ways to do it.”

Face The Nation

17,984 次观看 • 10 个月前

Author of Designing Data-Intensive Applications, Martin Kleppmann, on why engineers don't get to ignore ethics just because the work is technical — a theme he felt strongly enough about to address in both editions: “I felt that ethics had been quite ignored as a concern during my time in industry. Especially in startups, people were very focused on building a product that their customers would love and deprioritized ethical questions in the process. For example, with consumer facing products, it might be that the products are very much geared towards essentially data harvesting, collecting behavioural data, because that's what can be monetized in the form of advertising. There seemed to be very little reflection on what was good and bad about these sorts of things. I really wanted to encourage a bit of thinking there. I didn’t want to prescribe a particular approach, but to point out there is this thing such as data protection legislation now, which we do have to think about in the architecture of our data systems and there is an ethical responsibility.”

Author of Designing Data-Intensive Applications, Martin Kleppmann, on why engineers don't get to ignore ethics just because the work is technical — a theme he felt strongly enough about to address in both editions: “I felt that ethics had been quite ignored as a concern during my time in industry. Especially in startups, people were very focused on building a product that their customers would love and deprioritized ethical questions in the process. For example, with consumer facing products, it might be that the products are very much geared towards essentially data harvesting, collecting behavioural data, because that's what can be monetized in the form of advertising. There seemed to be very little reflection on what was good and bad about these sorts of things. I really wanted to encourage a bit of thinking there. I didn’t want to prescribe a particular approach, but to point out there is this thing such as data protection legislation now, which we do have to think about in the architecture of our data systems and there is an ethical responsibility.”

The Pragmatic Engineer

783,831 次观看 • 2 个月前

Dr. Larry Palevsky: “Vaccines are MADE to cause inflammation.” “Every child vaccinated is injured because inflammation has to happen.” “By definition, we cause inflammation when we give vaccines.” “How long does that inflammation go on?” “We have no data.” “Where does that inflammation go in the body?” “We have no data.” “When you look at the studies… there are no real understandings of what happens.” “We have so many kids with chronic inflammation.” “We are administering a medical procedure that causes inflammation without data to understand how long it lasts and where it goes in the body.” “We owe it to our children to find these answers before we keep causing inflammation.” MAHA Institute

Dr. Larry Palevsky: “Vaccines are MADE to cause inflammation.” “Every child vaccinated is injured because inflammation has to happen.” “By definition, we cause inflammation when we give vaccines.” “How long does that inflammation go on?” “We have no data.” “Where does that inflammation go in the body?” “We have no data.” “When you look at the studies… there are no real understandings of what happens.” “We have so many kids with chronic inflammation.” “We are administering a medical procedure that causes inflammation without data to understand how long it lasts and where it goes in the body.” “We owe it to our children to find these answers before we keep causing inflammation.” MAHA Institute

Children’s Health Defense

74,721 次观看 • 4 个月前

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

Carlow Weather

298,521 次观看 • 2 年前

SITUATION EXPLAINED: The anti-data center movement is trying to solve problems that don't exist. Adrian Dittmann: "There are a lot of misunderstandings around what exactly these things are, their environmental impact, why they even have an impact at all." "There's a neighborhood complaining about generator noise. But they actually requested the data center not connect to the local power infrastructure. So now the company has to run generators. The people complaining about the noise created the noise." "The water problem, people don't understand that these are self-contained systems and that these issues are largely 10 to 20 years old. Many of these issues do not even apply anymore." "You can even look this up yourself. The price for electricity because of data center construction has actually gone down because it has forced the energy companies to actually improve the infrastructure, improve the power grid." "Same thing happened with EVs. They said if there's gonna be a million EVs, there's going to be a collapse of the energy infrastructure. You have millions of EVs now. Nothing collapsed."

SITUATION EXPLAINED: The anti-data center movement is trying to solve problems that don't exist. Adrian Dittmann: "There are a lot of misunderstandings around what exactly these things are, their environmental impact, why they even have an impact at all." "There's a neighborhood complaining about generator noise. But they actually requested the data center not connect to the local power infrastructure. So now the company has to run generators. The people complaining about the noise created the noise." "The water problem, people don't understand that these are self-contained systems and that these issues are largely 10 to 20 years old. Many of these issues do not even apply anymore." "You can even look this up yourself. The price for electricity because of data center construction has actually gone down because it has forced the energy companies to actually improve the infrastructure, improve the power grid." "Same thing happened with EVs. They said if there's gonna be a million EVs, there's going to be a collapse of the energy infrastructure. You have millions of EVs now. Nothing collapsed."

MTS

13,833 次观看 • 1 个月前

🚨 RFK Jr Reveals Why CDC’s Fmr. Director of Immunization Was Really Fired “Early on during the measles outbreak, I promised Governor Abbott to send some badly needed money and help down to Texas. This individual blocked that money for a month … And then we tried to get the Vaccine Safety Data Link, which is the data that supposedly the CDC tries to use to make good decisions on whether vaccines are hurting people or not … For seven months, he stonewalled us so that we could not get that data.”

🚨 RFK Jr Reveals Why CDC’s Fmr. Director of Immunization Was Really Fired “Early on during the measles outbreak, I promised Governor Abbott to send some badly needed money and help down to Texas. This individual blocked that money for a month … And then we tried to get the Vaccine Safety Data Link, which is the data that supposedly the CDC tries to use to make good decisions on whether vaccines are hurting people or not … For seven months, he stonewalled us so that we could not get that data.”

Chief Nerd

339,973 次观看 • 10 个月前

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."

ANI

30,758 次观看 • 26 天前

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir

4,485,375 次观看 • 24 天前

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Boring_Business

75,612 次观看 • 3 个月前

Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.

Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.

Nic Cruz Patane

72,344 次观看 • 5 个月前

Famous Yellowstone Actor Mo Brings Plenty speaks at his city council meeting to oppose a proposed Data Center The proposed data center is the Piqua project in Ohio. It’s a hyperscale data center that is estimated to use up to 2 MILLION GALLONS OF WATER PER DAY There are also multiple more proposed data centers in the area, including one by Amazon Web Services Yellowstone Actor Mo Brings Plenty urges the city council to do the right thing and say no because of water concerns. He says he knows they’re in a tough position, but they ran for that position The amount of data centers being built in America is way out of control

Famous Yellowstone Actor Mo Brings Plenty speaks at his city council meeting to oppose a proposed Data Center The proposed data center is the Piqua project in Ohio. It’s a hyperscale data center that is estimated to use up to 2 MILLION GALLONS OF WATER PER DAY There are also multiple more proposed data centers in the area, including one by Amazon Web Services Yellowstone Actor Mo Brings Plenty urges the city council to do the right thing and say no because of water concerns. He says he knows they’re in a tough position, but they ran for that position The amount of data centers being built in America is way out of control

Wall Street Apes

340,648 次观看 • 4 天前

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,162 次观看 • 8 个月前

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,207 次观看 • 13 天前

PTI has come a long way. In past elections where we were victims of rigging, we demanded investigations. This time we took the data in our hands, studied that data for a week and finished investigation with authentic Form 45 data and manipulated Form 47 data. Good news is that we have 2/3 majority and we can prove it. Findings were presented brilliantly by ShandanaGulzarKhan at the #PTIPressConference today for national and international media. Hats off to all #Form45Avengers , you are always going to be my heroes. #MandateThieves have no place to hide now.

PTI has come a long way. In past elections where we were victims of rigging, we demanded investigations. This time we took the data in our hands, studied that data for a week and finished investigation with authentic Form 45 data and manipulated Form 47 data. Good news is that we have 2/3 majority and we can prove it. Findings were presented brilliantly by ShandanaGulzarKhan at the #PTIPressConference today for national and international media. Hats off to all #Form45Avengers , you are always going to be my heroes. #MandateThieves have no place to hide now.

Jibran Ilyas

93,055 次观看 • 2 年前

Bernie Sanders: "I think we need to be thinking seriously about a moratorium on these data centers. Frankly, I think you gotta slow this process down. It's not good enough for the oligarchs to tell us, 'It's coming, you adapt.' What are they talking about? Are they gonna guarantee healthcare to all people? What are they gonna do when people have no jobs? Make housing free?"

Bernie Sanders: "I think we need to be thinking seriously about a moratorium on these data centers. Frankly, I think you gotta slow this process down. It's not good enough for the oligarchs to tell us, 'It's coming, you adapt.' What are they talking about? Are they gonna guarantee healthcare to all people? What are they gonna do when people have no jobs? Make housing free?"

Aaron Rupar

980,684 次观看 • 6 个月前