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a guy who got fired from a prop desk for "not being fast enough" now runs a trading agent that closes positions faster than the guys who fired him he wasn't slow at reading charts. he was slow at the part nobody talks about - rebuilding a strategy after...

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Livsun

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12,021 次观看 • 1 个月前 •via X (Twitter)

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A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->
0:48

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

cvxv666

83,134 次观看 • 1 个月前

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.
0:42

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.

sopersone

48,482 次观看 • 25 天前

Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.
0:46

Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.

cvxv666

318,185 次观看 • 2 个月前

A solo quant with no desk and no team just finished in the top 0.04% at WorldQuant. He didn't have better ideas than the $650,000 kids in Greenwich. He had a machine that rewrites its own losing strategies while he sleeps. Your last strategy lost money and you deleted it. His read its own autopsy, found where it bled, rewrote its own code and passed the retest. Nobody touched a keyboard. That was supposed to be a 2028 problem. A hedge fund is six steps on repeat, and five of them cost you nothing as of this year. A junior quant runs the same loop once a month at about $87,500 an idea, and most of what he builds is dead by week six. You type the idea in plain English. An agent writes the rules, backtests 5 years in about 12 seconds, applies Deflated Sharpe so luck across 4,000 combinations can't pass itself off as an edge, and puts it live on Alpaca or Hyperliquid in about 90 seconds. Comes back red? It shows you where it bleeds, rebuilds it and tests again. Try this in two clicks, no code, free in trial period: The sixth step is the one nobody has shipped. Every lesson from a losing trade dies in a log file, so your agent hands you back the idea that already failed in a bear market with a fresh name on it. Whoever closes that gap owns a machine that gets better every week without them. That gets built in months, not years. Bookmark this & read full breakdown about this 6-steps cycle in the article below. When the first closed loop turns up with real money behind it, you'll want the map of what they assembled.
0:48

A solo quant with no desk and no team just finished in the top 0.04% at WorldQuant. He didn't have better ideas than the $650,000 kids in Greenwich. He had a machine that rewrites its own losing strategies while he sleeps. Your last strategy lost money and you deleted it. His read its own autopsy, found where it bled, rewrote its own code and passed the retest. Nobody touched a keyboard. That was supposed to be a 2028 problem. A hedge fund is six steps on repeat, and five of them cost you nothing as of this year. A junior quant runs the same loop once a month at about $87,500 an idea, and most of what he builds is dead by week six. You type the idea in plain English. An agent writes the rules, backtests 5 years in about 12 seconds, applies Deflated Sharpe so luck across 4,000 combinations can't pass itself off as an edge, and puts it live on Alpaca or Hyperliquid in about 90 seconds. Comes back red? It shows you where it bleeds, rebuilds it and tests again. Try this in two clicks, no code, free in trial period: The sixth step is the one nobody has shipped. Every lesson from a losing trade dies in a log file, so your agent hands you back the idea that already failed in a bear market with a fresh name on it. Whoever closes that gap owns a machine that gets better every week without them. That gets built in months, not years. Bookmark this & read full breakdown about this 6-steps cycle in the article below. When the first closed loop turns up with real money behind it, you'll want the map of what they assembled.

cvxv666

62,384 次观看 • 1 个月前

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

225,653 次观看 • 2 个月前

Ken Griffin built Citadel into $67B fund and hires PhDs out of Stanford and MIT. He watched agentic AI do their month work in an afternoon and admitted it sent him home depressed. The interesting bit is what he was describing. A six-part research loop. Find, code, test, deploy, autopsy, learn. Five of those six are done. Free. Available tonight. Prompt to backtested strategy in 12 seconds. Prompt to live broker in 90. The Greenwich quant doing that same work costs $650,000 a year and clears one hypothesis a month. The sixth exists nowhere. Not at Citadel, not at Man Group, not in any repo. It's the part where the machine remembers what already failed. Every losing test dies in a log file. The next agent starts blind. Negative results are the most valuable data in quant research and literally nobody stores them. This loop broken down in my article below. Read it before someone builds the last one and stops explaining how.
0:51

Ken Griffin built Citadel into $67B fund and hires PhDs out of Stanford and MIT. He watched agentic AI do their month work in an afternoon and admitted it sent him home depressed. The interesting bit is what he was describing. A six-part research loop. Find, code, test, deploy, autopsy, learn. Five of those six are done. Free. Available tonight. Prompt to backtested strategy in 12 seconds. Prompt to live broker in 90. The Greenwich quant doing that same work costs $650,000 a year and clears one hypothesis a month. The sixth exists nowhere. Not at Citadel, not at Man Group, not in any repo. It's the part where the machine remembers what already failed. Every losing test dies in a log file. The next agent starts blind. Negative results are the most valuable data in quant research and literally nobody stores them. This loop broken down in my article below. Read it before someone builds the last one and stops explaining how.

cvxv666

131,448 次观看 • 1 个月前

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:
0:53

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

cvxv666

53,282 次观看 • 3 个月前

A guy put a mini PC on his desk and runs a wall of faceless YouTube channels from it. The box is the size of a paperback. Sits next to his coffee. He plugged it in and that was it. Now every channel on his screen uploads on schedule without him. He doesn't film. He doesn't edit. He doesn't even open YouTube most days. He says one channel lands him around $8,000 a month. He scrolled past dozens just now. The box paid for itself in 3 days. His rent is now fully paid by a box on his desk. The crazy part is regular people are quietly becoming media networks. Save this, you are watching the next gold rush hit the home office.
0:30

A guy put a mini PC on his desk and runs a wall of faceless YouTube channels from it. The box is the size of a paperback. Sits next to his coffee. He plugged it in and that was it. Now every channel on his screen uploads on schedule without him. He doesn't film. He doesn't edit. He doesn't even open YouTube most days. He says one channel lands him around $8,000 a month. He scrolled past dozens just now. The box paid for itself in 3 days. His rent is now fully paid by a box on his desk. The crazy part is regular people are quietly becoming media networks. Save this, you are watching the next gold rush hit the home office.

Superior

24,476 次观看 • 3 个月前

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓
1:22:01

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓

codila

28,667 次观看 • 2 个月前

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.
0:43

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

174,699 次观看 • 3 个月前

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today
0:50

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,908 次观看 • 3 个月前

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓
0:26

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

Gipp 🦅

55,803 次观看 • 3 个月前

"[Lewis Hamilton] was really down on himself. And no one likes to see that because at the end of the day, whether you're a fan or not, Lewis Hamilton is the greatest racing driver we've ever seen." "Statistically, he is the greatest. I think he's the greatest even without the stats." "He wears his heart on his sleeve and should be applauded for that because it does a lot of good when he wears his heart on his sleeve. He did apologise to Rachel Brookes for being so down in the dumps off camera. He said, 'I'm really sorry about that. I'm just so down.' And that just shows the extent that he really was down. He's not putting this on for the cameras, trust me." "And once again, it's right after the race. He's not had time for a debrief. He's not had time to have a chat with his race engineer. So he probably didn't know the extent of whatever damage the car had at the time he spoke to Rachel. And he probably didn't realise that it was the car that was hindering him more so than his driving in that one. And look, when you're in a Ferrari and you're overtaken by Nico Hülkenberg, albeit in a Ferrari powered Sauber, you're going to question, 'is it me really? Am I doing something wrong here?' Because it just didn't go for him really from the outset." [📹 The Fast And The Curious]
1:11

"[Lewis Hamilton] was really down on himself. And no one likes to see that because at the end of the day, whether you're a fan or not, Lewis Hamilton is the greatest racing driver we've ever seen." "Statistically, he is the greatest. I think he's the greatest even without the stats." "He wears his heart on his sleeve and should be applauded for that because it does a lot of good when he wears his heart on his sleeve. He did apologise to Rachel Brookes for being so down in the dumps off camera. He said, 'I'm really sorry about that. I'm just so down.' And that just shows the extent that he really was down. He's not putting this on for the cameras, trust me." "And once again, it's right after the race. He's not had time for a debrief. He's not had time to have a chat with his race engineer. So he probably didn't know the extent of whatever damage the car had at the time he spoke to Rachel. And he probably didn't realise that it was the car that was hindering him more so than his driving in that one. And look, when you're in a Ferrari and you're overtaken by Nico Hülkenberg, albeit in a Ferrari powered Sauber, you're going to question, 'is it me really? Am I doing something wrong here?' Because it just didn't go for him really from the outset." [📹 The Fast And The Curious]

sim 🇧🇷🇲🇽🇸🇳🇫🇷🇭🇹

97,656 次观看 • 1 年前

A GUY MAKING $100K/MONTH WITH AI JUST SHOWED HIS ENTIRE SETUP. IT'S ONE FOLDER OF NOTES AND NOTHING ELSE no framework. no $500 course. he opens his screen and it's just obsidian - a plain notes app - wired into claude here's what he did: -> he pulled claude's memory files out of their default folder and dumped them into one vault -> had claude rename and merge them: 107 messy files collapsed into 17 clean ones -> every folder gets one master note that links to all the others that last part is the whole trick the agent reads the master note, follows the links and by the time it's done it has read every file in the folder. one instruction, full context here's the part most people miss: everyone's trying to make the AI smarter. he made the AI's memory smaller fewer files, better organized, all linked. the agent isn't scanning hundreds of notes anymore - it walks a path you built that's why his agent actually finishes jobs instead of forgetting what it was doing halfway through then he goes one step further: at the end of every session, the agent writes its own daily note. what it did, when, indexed at the top so it can find it again in seconds so he never re-explains anything. the agent looks up what it already did now he types "create a campaign for this offer" and walks away. it reads the product notes, reads the process notes, and comes back with the campaign done you don't need any of the complicated agent tools people are selling you. you need structure and instructions save this. the people winning with AI aren't using better models. they're just the only ones who bothered to organize what it remembers
9:56

A GUY MAKING $100K/MONTH WITH AI JUST SHOWED HIS ENTIRE SETUP. IT'S ONE FOLDER OF NOTES AND NOTHING ELSE no framework. no $500 course. he opens his screen and it's just obsidian - a plain notes app - wired into claude here's what he did: -> he pulled claude's memory files out of their default folder and dumped them into one vault -> had claude rename and merge them: 107 messy files collapsed into 17 clean ones -> every folder gets one master note that links to all the others that last part is the whole trick the agent reads the master note, follows the links and by the time it's done it has read every file in the folder. one instruction, full context here's the part most people miss: everyone's trying to make the AI smarter. he made the AI's memory smaller fewer files, better organized, all linked. the agent isn't scanning hundreds of notes anymore - it walks a path you built that's why his agent actually finishes jobs instead of forgetting what it was doing halfway through then he goes one step further: at the end of every session, the agent writes its own daily note. what it did, when, indexed at the top so it can find it again in seconds so he never re-explains anything. the agent looks up what it already did now he types "create a campaign for this offer" and walks away. it reads the product notes, reads the process notes, and comes back with the campaign done you don't need any of the complicated agent tools people are selling you. you need structure and instructions save this. the people winning with AI aren't using better models. they're just the only ones who bothered to organize what it remembers

Paone

23,833 次观看 • 2 个月前

One guy built a wedding camera app in a month and it now makes him over $1,000,000 a year. No team, no investors, no marketing budget. 100,000 downloads in the first month. Zero dollars on ads. He did not bolt marketing onto the product. He made using the product the marketing. You cannot use the app alone. The host has to pull every guest into it. One wedding is not one user. It is 200 people scanning the same code in one evening. The guest liked it at someone else's wedding. A month later he throws his own event and brings his own crowd. The loop spins for free. It does not look like an ad. To the guest it is a gift. So they install it gladly. $2 to $50 subscriptions. 5% pay. That is $100,000 a month. He did not win on budget. He sewed the growth into the product itself. Could you build the loop, or is it just luck?
0:23

One guy built a wedding camera app in a month and it now makes him over $1,000,000 a year. No team, no investors, no marketing budget. 100,000 downloads in the first month. Zero dollars on ads. He did not bolt marketing onto the product. He made using the product the marketing. You cannot use the app alone. The host has to pull every guest into it. One wedding is not one user. It is 200 people scanning the same code in one evening. The guest liked it at someone else's wedding. A month later he throws his own event and brings his own crowd. The loop spins for free. It does not look like an ad. To the guest it is a gift. So they install it gladly. $2 to $50 subscriptions. 5% pay. That is $100,000 a month. He did not win on budget. He sewed the growth into the product itself. Could you build the loop, or is it just luck?

Marlow

513,913 次观看 • 2 个月前

Everyone calls Fredo the weak one. Watch that scene again. His father gets shot in the street and Fredo freezes. He fumbles his gun, drops it, ends up on the curb crying like a kid. For most people that settles it: he's useless, he's not built for this life. But that's the point. Fredo was never meant to be strong, or smart. He wasn't Sonny. He wasn't Michael. He was an ordinary man who happened to have the Godfather for a father. So when the shooting starts, he does what most of us would do. He panics. He breaks. The same softness that made him a bad gangster was the one thing the family had no room for. Michael runs the business better than any of them ever could. But the day he loses Fredo, he loses the last soft thing left in him. A family can't run on business alone. Fredo was the proof. Nobody saw it until he was gone
0:59

Everyone calls Fredo the weak one. Watch that scene again. His father gets shot in the street and Fredo freezes. He fumbles his gun, drops it, ends up on the curb crying like a kid. For most people that settles it: he's useless, he's not built for this life. But that's the point. Fredo was never meant to be strong, or smart. He wasn't Sonny. He wasn't Michael. He was an ordinary man who happened to have the Godfather for a father. So when the shooting starts, he does what most of us would do. He panics. He breaks. The same softness that made him a bad gangster was the one thing the family had no room for. Michael runs the business better than any of them ever could. But the day he loses Fredo, he loses the last soft thing left in him. A family can't run on business alone. Fredo was the proof. Nobody saw it until he was gone

best of mafia cinema

96,650 次观看 • 2 个月前

ANTHROPIC LEAKED A FILE THEIR LEAD ENGINEER SPENT 4 YEARS BUILDING AT A COST OF $3.2M - ONE SUPER AGENT WITH JEV HOLDS A BUSINESS DOING $2.4M A YEAR the agent costs $0, and in month one it's already pulling $100K+ read → decide → build → ship → watch → back to reading 24 cycles a day, and each one starts exactly where the last one stopped it doesn't sleep, doesn't forget and never asks what to do next in one night it runs more cycles than a team closes in a quarter JEV holds the context, the budget and the stop condition between hours - without it every cycle would start from zero it picks one change an hour, not twenty - which is exactly why nothing breaks if things get worse it rolls back on its own, no meeting and no explanation the price of the loop doesn't grow with the revenue: it's the same at $0 and at $200,000 a month $2.4M a year is 669 customers, and that's only true while all 669 keep paying every month one agent is one point of failure: the night the loop stopped, nobody noticed until morning it ships the changes, it doesn't find the customers - that part is still yours save this and paste it into Claude Code - let it build you this loop ↓
0:59

ANTHROPIC LEAKED A FILE THEIR LEAD ENGINEER SPENT 4 YEARS BUILDING AT A COST OF $3.2M - ONE SUPER AGENT WITH JEV HOLDS A BUSINESS DOING $2.4M A YEAR the agent costs $0, and in month one it's already pulling $100K+ read → decide → build → ship → watch → back to reading 24 cycles a day, and each one starts exactly where the last one stopped it doesn't sleep, doesn't forget and never asks what to do next in one night it runs more cycles than a team closes in a quarter JEV holds the context, the budget and the stop condition between hours - without it every cycle would start from zero it picks one change an hour, not twenty - which is exactly why nothing breaks if things get worse it rolls back on its own, no meeting and no explanation the price of the loop doesn't grow with the revenue: it's the same at $0 and at $200,000 a month $2.4M a year is 669 customers, and that's only true while all 669 keep paying every month one agent is one point of failure: the night the loop stopped, nobody noticed until morning it ships the changes, it doesn't find the customers - that part is still yours save this and paste it into Claude Code - let it build you this loop ↓

Sprytix

10,185 次观看 • 10 天前

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.
1:45

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.

Nekt0

21,566 次观看 • 2 个月前

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure
1:42

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure

Sprytix

51,132 次观看 • 4 个月前

A CNBC Fast Money trader shut down his $5,000,000 a year hedge fund team and rebuilt the whole f*cking firm on 4 AI agents. New bill: $40,000 a year. He gave CNBC the tour 10 days ago, bot by bot. You are still telling yourself AI trading needs a desk. His 4 agents run the loop every quant desk runs: idea -> code -> backtest -> live -> autopsy. Horizon runs that loop from one text box. Kelly closed a hedge fund to get his agents. Yours come on a free trial, no code, first backtest 11 minutes after signing up for the median trader. Bring the one trade idea you never coded: Type the sentence. Rules written, 5 years backtested in about 12 seconds, live on your broker 90 seconds later. Backed by Entrée Capital and hedge fund managers. > $5,000,000 a year for 7 or 8 humans and a New York office > $40,000 a year $40,000 a year for Houston, Steffi, Desmond and Doocey, compute included > $400,000 to $650,000 a year for one junior quant at a big fund I read it twice, sure I had missed a zero. That is not a hedge fund budget. That is a used car. Steffi marks up the charts, Desmond runs the quant strategies, Doocey is the red team that attacks every thesis, Houston is mission control. Kelly keeps the final call, 10x more productive by his count. It will not flatter you. I fed it the MACD crossover every $2,000 course sells. 227 trades, Sharpe -0.48, -0.80%. Red on the first pass, not 2 weeks into my real money. Then it rewrote the losing rule. Kelly kept the final call. The $5,000,000 was everything before it.
1:07

A CNBC Fast Money trader shut down his $5,000,000 a year hedge fund team and rebuilt the whole f*cking firm on 4 AI agents. New bill: $40,000 a year. He gave CNBC the tour 10 days ago, bot by bot. You are still telling yourself AI trading needs a desk. His 4 agents run the loop every quant desk runs: idea -> code -> backtest -> live -> autopsy. Horizon runs that loop from one text box. Kelly closed a hedge fund to get his agents. Yours come on a free trial, no code, first backtest 11 minutes after signing up for the median trader. Bring the one trade idea you never coded: Type the sentence. Rules written, 5 years backtested in about 12 seconds, live on your broker 90 seconds later. Backed by Entrée Capital and hedge fund managers. > $5,000,000 a year for 7 or 8 humans and a New York office > $40,000 a year $40,000 a year for Houston, Steffi, Desmond and Doocey, compute included > $400,000 to $650,000 a year for one junior quant at a big fund I read it twice, sure I had missed a zero. That is not a hedge fund budget. That is a used car. Steffi marks up the charts, Desmond runs the quant strategies, Doocey is the red team that attacks every thesis, Houston is mission control. Kelly keeps the final call, 10x more productive by his count. It will not flatter you. I fed it the MACD crossover every $2,000 course sells. 227 trades, Sharpe -0.48, -0.80%. Red on the first pass, not 2 weeks into my real money. Then it rewrote the losing rule. Kelly kept the final call. The $5,000,000 was everything before it.

cvxv666

31,330 次观看 • 15 天前