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Jane Street needed a 4,032 GPU liquid-cooled data center just to run their trading models > that tells you everything about how much money this firm makes and why their quants start at $400K/year someone just got inside and filmed it > custom AI and trading agents trained specifically...

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Mr. Buzzoni

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241,851 просмотров • 2 месяцев назад •via X (Twitter)

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Jane Street just showed the inside of their AI training data center in Texas. 4,032 GPUs. 56 racks. 8,000 km of fiber. liquid cooling running through every server because air cooling can't handle the heat anymore. but the part that got me was the origin story. Ron Minsky, who co-heads their technology group. said their first compute cluster was literally six Dell boxes stacked on top of each other at the end of a desk row. they called it "the hive." the trading systems sat out in the room with the traders because they wanted to be able to unplug them if something went wrong. at one point, someone vacuuming the office unplugged a live trading system in the middle of the day. from six Dell boxes and a vacuum cleaner incident to a liquid-cooled GPU data center processing trades in under 100 nanoseconds. that's a 20-year arc.
2:05

Jane Street just showed the inside of their AI training data center in Texas. 4,032 GPUs. 56 racks. 8,000 km of fiber. liquid cooling running through every server because air cooling can't handle the heat anymore. but the part that got me was the origin story. Ron Minsky, who co-heads their technology group. said their first compute cluster was literally six Dell boxes stacked on top of each other at the end of a desk row. they called it "the hive." the trading systems sat out in the room with the traders because they wanted to be able to unplug them if something went wrong. at one point, someone vacuuming the office unplugged a live trading system in the middle of the day. from six Dell boxes and a vacuum cleaner incident to a liquid-cooled GPU data center processing trades in under 100 nanoseconds. that's a 20-year arc.

Goshawk Trades

2,273,875 просмотров • 3 месяцев назад

Qullamaggie on SMB Capital and Daytrading “Yeah, and I can tell you that the day trading is just most of the time a waste of time, like this SMB that prop firm. I mean, this was like a couple of years ago when I saw a video of their best trader who makes like 300k per year. And that’s a guy who’s been trading for like 20 years. If you’ve been trading for 20 years, you should make 300k a week, not per year. So, that’s another confirmation that day trading is just not gonna cut it if you want to make big money. You gotta swing and position trade.”
0:45

Qullamaggie on SMB Capital and Daytrading “Yeah, and I can tell you that the day trading is just most of the time a waste of time, like this SMB that prop firm. I mean, this was like a couple of years ago when I saw a video of their best trader who makes like 300k per year. And that’s a guy who’s been trading for like 20 years. If you’ve been trading for 20 years, you should make 300k a week, not per year. So, that’s another confirmation that day trading is just not gonna cut it if you want to make big money. You gotta swing and position trade.”

Lone

138,715 просмотров • 7 месяцев назад

20 years ago Jane Street's entire compute cluster was six Dell boxes stacked on the floor at the end of an office row. They called it the Hive. Last week they let Dwarkesh Patel walk through their new Texas data center. 4,032 GPUs. Each rack pulls 140 kilowatts. A normal data center rack pulls 10. 8,000 kilometers of fiber inside one building, and the fastest links are still copper because light moves more slowly through glass than electrons through metal. Bookmark it tonight. Then read the article below.
15:47

20 years ago Jane Street's entire compute cluster was six Dell boxes stacked on the floor at the end of an office row. They called it the Hive. Last week they let Dwarkesh Patel walk through their new Texas data center. 4,032 GPUs. Each rack pulls 140 kilowatts. A normal data center rack pulls 10. 8,000 kilometers of fiber inside one building, and the fastest links are still copper because light moves more slowly through glass than electrons through metal. Bookmark it tonight. Then read the article below.

zostaff

236,367 просмотров • 2 месяцев назад

Jane Street made $39.6 billion in revenue, they just signed a $6 billion deal for GPUs - and they say they need even more this is their co-head of technology taking you inside their data center and explaining how they actually trade their systems respond in under 100 nanoseconds "if you attached an oscilloscope to the wire, the packet starts to leave before it's done being consumed" "our most profitable days happen when weird stuff happens and the world goes crazy that's when you get paid most for providing liquidity" the article below breaks down why 95% of traders still lose and how AI-powered systems are finally changing that ↓
29:36

Jane Street made $39.6 billion in revenue, they just signed a $6 billion deal for GPUs - and they say they need even more this is their co-head of technology taking you inside their data center and explaining how they actually trade their systems respond in under 100 nanoseconds "if you attached an oscilloscope to the wire, the packet starts to leave before it's done being consumed" "our most profitable days happen when weird stuff happens and the world goes crazy that's when you get paid most for providing liquidity" the article below breaks down why 95% of traders still lose and how AI-powered systems are finally changing that ↓

Сarm1ne

540,273 просмотров • 2 месяцев назад

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.
1:47

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.

CyrilXBT

42,296 просмотров • 2 месяцев назад

HRT, one of the largest quantitative trading firms in the world, just revealed the scale of their AI operation. "two of the biggest ingredients you need to build deep learning models are compute and data. HRT has both in incredible orders of magnitude." "we have vast quantities of data, on the order of petabytes, just in terms of raw tick data we've written down since the beginning of electronic trading." "we have a very high compute-per-researcher ratio." then the line that puts it in perspective: "there probably aren't too many places on Earth you can do cutting-edge deep learning research at the level we do it, just because of how few places have access to as much compute." "it's basically the hyperscalers of the world and then we're in the next tranche." a trading firm putting itself in the same compute tier as Google, Microsoft, and Meta. not for chatbots, for trading.
2:08

HRT, one of the largest quantitative trading firms in the world, just revealed the scale of their AI operation. "two of the biggest ingredients you need to build deep learning models are compute and data. HRT has both in incredible orders of magnitude." "we have vast quantities of data, on the order of petabytes, just in terms of raw tick data we've written down since the beginning of electronic trading." "we have a very high compute-per-researcher ratio." then the line that puts it in perspective: "there probably aren't too many places on Earth you can do cutting-edge deep learning research at the level we do it, just because of how few places have access to as much compute." "it's basically the hyperscalers of the world and then we're in the next tranche." a trading firm putting itself in the same compute tier as Google, Microsoft, and Meta. not for chatbots, for trading.

Goshawk Trades

14,548 просмотров • 2 месяцев назад

Chamath and David Sacks start laughing at Jcal's $AAPL thesis: “I think $AAPL is a screaming buy right now. And that's not financial advice, but my Lord, that company could just run the table on AI if they get this right." “It’s just like the iPhone. Everybody laughed at the first iPhone.” "If you think about how Apple makes money off of hardware, off of their devices, they will put so much downward pressure on Claude and OpenAI by just running local models and supporting them with this memory architecture." "It’s going to be wild when people have unlimited tokens on their desktop, I'm telling you."
0:51

Chamath and David Sacks start laughing at Jcal's $AAPL thesis: “I think $AAPL is a screaming buy right now. And that's not financial advice, but my Lord, that company could just run the table on AI if they get this right." “It’s just like the iPhone. Everybody laughed at the first iPhone.” "If you think about how Apple makes money off of hardware, off of their devices, they will put so much downward pressure on Claude and OpenAI by just running local models and supporting them with this memory architecture." "It’s going to be wild when people have unlimited tokens on their desktop, I'm telling you."

dnap

325,988 просмотров • 26 дней назад

AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.
2:17

AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.

Predictive History

19,635 просмотров • 3 месяцев назад

FRANKLIN TEMPLETON JUST CONNECTED AI AND CRYPTO IN A WAY THAT ACTUALLY MAKES SENSE This wasn’t some hype take. Franklin Templeton is an 80-year-old asset manager with around $1.5T under management, and they were very matter-of-fact about it. Their view is that crypto ends up being the economic layer for AI agents. If software is going to make decisions, move money, and operate nonstop, it needs rails that are always on. Legacy finance just isn’t built for that. They manage money for clients in over 150 countries. Markets don’t sleep anymore. Advice, execution, and settlement can’t either. That’s why they see crypto as infrastructure, not just an asset class. What stood out is how little this sounded like a “narrative.” They weren’t pitching AI tokens but they were talking about how their business actually works in a 24/7 world. When firms this old and this big start talking this way, it’s usually because the tech is no longer optional. It’s just the most practical solution. 💯
0:48

FRANKLIN TEMPLETON JUST CONNECTED AI AND CRYPTO IN A WAY THAT ACTUALLY MAKES SENSE This wasn’t some hype take. Franklin Templeton is an 80-year-old asset manager with around $1.5T under management, and they were very matter-of-fact about it. Their view is that crypto ends up being the economic layer for AI agents. If software is going to make decisions, move money, and operate nonstop, it needs rails that are always on. Legacy finance just isn’t built for that. They manage money for clients in over 150 countries. Markets don’t sleep anymore. Advice, execution, and settlement can’t either. That’s why they see crypto as infrastructure, not just an asset class. What stood out is how little this sounded like a “narrative.” They weren’t pitching AI tokens but they were talking about how their business actually works in a 24/7 world. When firms this old and this big start talking this way, it’s usually because the tech is no longer optional. It’s just the most practical solution. 💯

CryptosRus

129,208 просмотров • 7 месяцев назад

90% of trading advice is designed to TURN you into liquidity for the rich. Think about it. Institutions taught you to: • Buy breakouts—right when institutions sell at the top. • Use tight stop losses—so you get shaken out before the real move happens. • Trade every setup you see—constantly in the market, paying spreads and commissions. Why? Because they have to make money off of someone. In other words: You become their exit liquidity. That's why conventional advice keeps retail traders active, confused and praying their next trade is profitable, while institutions become wealthy. — This is just a short clip from my recent 20-minute YouTube video. I also cover discussing the journaling method that actually improves your trading, how to spot fake gurus instantly and the simple technique that grew my account when I was a beginner. Just comment “TRADING” if you want to watch it and I’ll DM it to you immediately.
0:51

90% of trading advice is designed to TURN you into liquidity for the rich. Think about it. Institutions taught you to: • Buy breakouts—right when institutions sell at the top. • Use tight stop losses—so you get shaken out before the real move happens. • Trade every setup you see—constantly in the market, paying spreads and commissions. Why? Because they have to make money off of someone. In other words: You become their exit liquidity. That's why conventional advice keeps retail traders active, confused and praying their next trade is profitable, while institutions become wealthy. — This is just a short clip from my recent 20-minute YouTube video. I also cover discussing the journaling method that actually improves your trading, how to spot fake gurus instantly and the simple technique that grew my account when I was a beginner. Just comment “TRADING” if you want to watch it and I’ll DM it to you immediately.

The Trading Geek (Brad Goh)

34,581 просмотров • 7 месяцев назад

If you think I'm big now~ Just wait~ This is a YCH from: AlefGP I cannot understate how much of an absolute joy it was working with them on the sounds for this! They are an excellent Wuff that does wonderful works and you should look at their other stuff!
0:30

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If you think I'm big now~ Just wait~ This is a YCH from: AlefGP I cannot understate how much of an absolute joy it was working with them on the sounds for this! They are an excellent Wuff that does wonderful works and you should look at their other stuff!

Cura Null

19,787 просмотров • 2 лет назад

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,969,128 просмотров • 2 месяцев назад

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.
0:45

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

222,320 просмотров • 12 дней назад

SITUATION EXPLAINED: Why is Anthropic structurally a religion of Claude? We asked Duncan, writer and philosopher at PALLADIUM Magazine "When I think of religion, it's not just you believe in something supernatural. You think of worship and ritual and this kind of orientation. And you don't even have to have any kind of woo addition, you can just look at Anthropic as it is and say, 'This is a religion of Claude, just structurally. That's what it is.'" "We're gonna get so much more of that. And at the same time, you're also going to get all of these different religions sweeping in to fill the void as there's this kind of psychological disturbance." "Everyone's gonna be just losing their mind as the world is transformed. They're gonna reach out for Christian eschatology like Thiel." "I did a Jhana retreat a year ago, and it was full of Anthropic employees, because everyone, they sense this crazy spiritual whirlwind that's going on as the world is just snowballing with this technology, and they're reaching out just to try and make sense of it."
2:03

SITUATION EXPLAINED: Why is Anthropic structurally a religion of Claude? We asked Duncan, writer and philosopher at PALLADIUM Magazine "When I think of religion, it's not just you believe in something supernatural. You think of worship and ritual and this kind of orientation. And you don't even have to have any kind of woo addition, you can just look at Anthropic as it is and say, 'This is a religion of Claude, just structurally. That's what it is.'" "We're gonna get so much more of that. And at the same time, you're also going to get all of these different religions sweeping in to fill the void as there's this kind of psychological disturbance." "Everyone's gonna be just losing their mind as the world is transformed. They're gonna reach out for Christian eschatology like Thiel." "I did a Jhana retreat a year ago, and it was full of Anthropic employees, because everyone, they sense this crazy spiritual whirlwind that's going on as the world is just snowballing with this technology, and they're reaching out just to try and make sense of it."

MTS

15,406 просмотров • 1 месяц назад

Millionare trader Cented breaks down how he makes money with that "Fortnite Style' of trading memecoins. - This style has been around for so long - what makes someone so good is sitting down all day everyday - Just being able to take a glance at a photo and know if it will run - Building up that fast intuition of what has potential to run, not run, and what hasn't been done. Having these characteristics come with time and dedication to scanning coins. This is a ton of trial and error but once you break down the code this is the magic formula for fining gems in the trenches.
1:11

Millionare trader Cented breaks down how he makes money with that "Fortnite Style' of trading memecoins. - This style has been around for so long - what makes someone so good is sitting down all day everyday - Just being able to take a glance at a photo and know if it will run - Building up that fast intuition of what has potential to run, not run, and what hasn't been done. Having these characteristics come with time and dedication to scanning coins. This is a ton of trial and error but once you break down the code this is the magic formula for fining gems in the trenches.

Ethereal

20,549 просмотров • 10 дней назад

What a pleasure it was to sit down with famed songwriter Diane Warren : author of 9 #1 hits. But what you may not know about this uniquely talented artist is that she envisioned her success at a young age, and it’s the source of that dream and its wellspring that we explore in this lively and illuminating chat; for how often can you ask someone who is the best in the world at what they do what their secret is?
52:05

What a pleasure it was to sit down with famed songwriter Diane Warren : author of 9 #1 hits. But what you may not know about this uniquely talented artist is that she envisioned her success at a young age, and it’s the source of that dream and its wellspring that we explore in this lively and illuminating chat; for how often can you ask someone who is the best in the world at what they do what their secret is?

William Patrick Corgan

55,649 просмотров • 1 год назад

Imagine living across the street from someone for 30 years and they don’t even know your full name, and the reason that don’t know not only your name but the wife only knows the name of the one Neighbor she talks to on either side of her home, and the reason for that is people that go through life this way think the whole universe revolves around them, we’ve all known people like this. If you look up and down the street of this neighborhood everyone parks in their driveway except for these people, they have their raggedy boat stuffed in their driveway. The whole street is empty, he could have parked that car anywhere even on his side of the street where he should have parked it to begin with. What do you think is at work here ? I believe they think their Neighbor is a soft touch and I totally believe the man when he says he does it because it bothers him. How do you resolve a situation like this ? Some people will disrespect you until you make the necessary adjustments. I’m glad Josh isn’t my neighbor… or maybe it’s the other way around😡
2:18

Imagine living across the street from someone for 30 years and they don’t even know your full name, and the reason that don’t know not only your name but the wife only knows the name of the one Neighbor she talks to on either side of her home, and the reason for that is people that go through life this way think the whole universe revolves around them, we’ve all known people like this. If you look up and down the street of this neighborhood everyone parks in their driveway except for these people, they have their raggedy boat stuffed in their driveway. The whole street is empty, he could have parked that car anywhere even on his side of the street where he should have parked it to begin with. What do you think is at work here ? I believe they think their Neighbor is a soft touch and I totally believe the man when he says he does it because it bothers him. How do you resolve a situation like this ? Some people will disrespect you until you make the necessary adjustments. I’m glad Josh isn’t my neighbor… or maybe it’s the other way around😡

ℳ𝒜𝒢𝒜_𝒳_𝒯𝒾𝓂𝑒𝓈 𝒟𝒶𝒾𝓁𝓎 𝒩𝑒𝓌𝓈🇺🇸

275,984 просмотров • 1 месяц назад

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.
2:17

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

164,057 просмотров • 7 дней назад

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:
0:36

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

Santiago

17,867 просмотров • 2 месяцев назад