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MIT defines an algorithm in one sentence that changes how you think about trading "a computational procedure that takes an input and produces an output through a well-defined sequence of steps" that's it. not AI. not machine learning. not a black box a set of rules that takes data...

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23,905 просмотров • 2 месяцев назад •via X (Twitter)

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Jason Calacanis: Social media companies should disclose their algorithms or lose Section 230 protections Why? “An algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix...” From E243: “Algorithms must be disclosed and you must have the option, given to you upfront, to switch your algorithm.” “There should be BYOA, bring your own algorithm. There should be an algorithm store.” “If you could say, ‘I want one that just gives me a chronological feed. I want one that is from the highest quality sources.’” “And then you should be required to show what the default algorithm is doing.” “And if you don't do that, I think you should lose your Section 230 because an algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix deciding what we see, deciding what goes on the homepage.” “The algorithm is a black box and who knows who's controlling it, and then what happens in society when young men are pushed towards more and more fringe content, more and more dark content?” “Why not just have all this be transparent?”
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Jason Calacanis: Social media companies should disclose their algorithms or lose Section 230 protections Why? “An algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix...” From E243: “Algorithms must be disclosed and you must have the option, given to you upfront, to switch your algorithm.” “There should be BYOA, bring your own algorithm. There should be an algorithm store.” “If you could say, ‘I want one that just gives me a chronological feed. I want one that is from the highest quality sources.’” “And then you should be required to show what the default algorithm is doing.” “And if you don't do that, I think you should lose your Section 230 because an algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix deciding what we see, deciding what goes on the homepage.” “The algorithm is a black box and who knows who's controlling it, and then what happens in society when young men are pushed towards more and more fringe content, more and more dark content?” “Why not just have all this be transparent?”

The All-In Podcast

191,351 просмотров • 11 месяцев назад

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 ↓
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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 🦅

55,610 просмотров • 2 месяцев назад

An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.
50:28

An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.

Rossst.03

81,024 просмотров • 27 дней назад

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
1:27

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 просмотров • 1 месяц назад

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.
4:06

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

18,982 просмотров • 6 месяцев назад

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.
1:56:06

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 просмотров • 1 месяц назад

Chinese futures exchange quant built an algorithm for predicting oil prices. He made $586,000 in profit in just one month. 16 trades, only 2 in loss. Each win = $150-300k profits. Tech stack: MiroFish simulation + OSINT parser + Claude. His Polymarket profile: How does his strategy work? It’s a synthesis of Claude AI, news parser and simulation engine. Claude gathers the data for the simulation. OSINT parser scrapes every Iran/Hormuz headline. MiroFish simulates how every single piece of data affects the oil price. Output: an algorithm that’s been nailing oil moves perfectly for two months straight. This guy knows in advance what others only see after the chart moves. And unlike top futures traders, every one of his trades is visible on-chain. His knowledge and algorithm are completely open for us to use. I’m copying every new oil trade he makes with this bot:
0:30

Chinese futures exchange quant built an algorithm for predicting oil prices. He made $586,000 in profit in just one month. 16 trades, only 2 in loss. Each win = $150-300k profits. Tech stack: MiroFish simulation + OSINT parser + Claude. His Polymarket profile: How does his strategy work? It’s a synthesis of Claude AI, news parser and simulation engine. Claude gathers the data for the simulation. OSINT parser scrapes every Iran/Hormuz headline. MiroFish simulates how every single piece of data affects the oil price. Output: an algorithm that’s been nailing oil moves perfectly for two months straight. This guy knows in advance what others only see after the chart moves. And unlike top futures traders, every one of his trades is visible on-chain. His knowledge and algorithm are completely open for us to use. I’m copying every new oil trade he makes with this bot:

cvxv666

76,857 просмотров • 4 месяцев назад

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 просмотров • 2 месяцев назад

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 просмотров • 8 месяцев назад

Entire cities in America are having their rent prices kept to the highest possible rates by rental algorithms “All designed to keep the overall market at its highest peak. They are not charging what the market can bear. They are controlling the market” “RealPage is a company that operates software that allows landlords to input their data into an algorithm. And then RealPage sets the the prices for all of those landlords. That's old fashioned price fixing using new. Technology we allege, in effect a housing cartel. Talking about an algorithm that aggregates otherwise confidential information that the landlords have that ordinarily they would not share with their competitors. That allows then the algorithm to spit out a pricing recommendation. All designed to keep the overall market at its highest peak.” Here’s how this system works - Large property managers upload their confidential data - The algorithm aggregates this across competitors in the same market along with public data - It recommends rents designed to maximize revenue by pushing prices higher This is price fixing because everyone using these services are all getting the same maximum profit increase in suggested rent prices
1:30

Entire cities in America are having their rent prices kept to the highest possible rates by rental algorithms “All designed to keep the overall market at its highest peak. They are not charging what the market can bear. They are controlling the market” “RealPage is a company that operates software that allows landlords to input their data into an algorithm. And then RealPage sets the the prices for all of those landlords. That's old fashioned price fixing using new. Technology we allege, in effect a housing cartel. Talking about an algorithm that aggregates otherwise confidential information that the landlords have that ordinarily they would not share with their competitors. That allows then the algorithm to spit out a pricing recommendation. All designed to keep the overall market at its highest peak.” Here’s how this system works - Large property managers upload their confidential data - The algorithm aggregates this across competitors in the same market along with public data - It recommends rents designed to maximize revenue by pushing prices higher This is price fixing because everyone using these services are all getting the same maximum profit increase in suggested rent prices

Wall Street Apes

25,908 просмотров • 4 месяцев назад

Jeff Bezos just identified the most expensive bureaucratic failure in the American economy. It fits in one sentence. Bezos: “Why does it take months and months and months to get a building permit? It doesn’t make any sense.” It doesn’t make any sense because a building code is not a judgment call. It is an algorithm. And algorithms should be executed by machines. Bezos: “Miami should have an AI application that reads your building permit for a new house or a new building and it should give you a yes or a no in ten seconds.” Ten seconds. Not three months. Not six weeks. Not whenever the reviewer clears their backlog. Bezos: “If the answer is no, it should tell you the six things you have to change to get a yes.” No ambiguity. No interpretation. No bureaucratic delay dressed up as due diligence. Just a deterministic feedback loop compressing months of institutional friction into a single automated decision. We are competing against sovereign adversaries deploying gigawatt data centers and scaling physical infrastructure at a pace that does not stop to ask permission. And we are losing ground to countries that never needed to. The AI arms race is not only fought in data centers. It is fought in the gap between when someone decides to build something and when the government allows it. Every month this system runs on biological speed is a month that cannot be recovered. The governments that integrate AI into their core civic functions will trigger a wave of physical development the old world could never produce. The ones that refuse will still be reviewing the same forms a decade from now. While the cities that said yes are already living inside the future they built. The bottleneck was never ambition. It was always the man holding the rubber stamp deciding when ambition was allowed to begin. And the stamp is just a rubber version of the algorithm that should have been running this whole time.
0:36

Jeff Bezos just identified the most expensive bureaucratic failure in the American economy. It fits in one sentence. Bezos: “Why does it take months and months and months to get a building permit? It doesn’t make any sense.” It doesn’t make any sense because a building code is not a judgment call. It is an algorithm. And algorithms should be executed by machines. Bezos: “Miami should have an AI application that reads your building permit for a new house or a new building and it should give you a yes or a no in ten seconds.” Ten seconds. Not three months. Not six weeks. Not whenever the reviewer clears their backlog. Bezos: “If the answer is no, it should tell you the six things you have to change to get a yes.” No ambiguity. No interpretation. No bureaucratic delay dressed up as due diligence. Just a deterministic feedback loop compressing months of institutional friction into a single automated decision. We are competing against sovereign adversaries deploying gigawatt data centers and scaling physical infrastructure at a pace that does not stop to ask permission. And we are losing ground to countries that never needed to. The AI arms race is not only fought in data centers. It is fought in the gap between when someone decides to build something and when the government allows it. Every month this system runs on biological speed is a month that cannot be recovered. The governments that integrate AI into their core civic functions will trigger a wave of physical development the old world could never produce. The ones that refuse will still be reviewing the same forms a decade from now. While the cities that said yes are already living inside the future they built. The bottleneck was never ambition. It was always the man holding the rubber stamp deciding when ambition was allowed to begin. And the stamp is just a rubber version of the algorithm that should have been running this whole time.

Dustin

293,438 просмотров • 5 месяцев назад

Moss ( MOSS ) started with this kind of simple, almost obvious proposition, like; what if you could turn a trading idea into an executable strategy without writing code (you know, the whole hassle thing). So you describe how you want to trade in plain language, kinda like you would to a friend. Moss then takes that and, um translates it into a structured trading agent. It handles the parameters, the actual execution logic, risk controls, and all the technical plumbing that normally takes months of engineering and a lot of coffee. But honestly the more interesting part comes right after that. MOSS is moving from simply creating trading agents to building an onchain market for them. The logic is pretty straightforward, in a way. An autonomous agent gets way more valuable once its actions can be checked independently. Onchain, everything is recorded. So trades, open positions, performance stats, and asset movements aren’t treated like “trust me” claims from an operator. They become observable data, no maybe about it. That gives you something older trading bots almost never have: a verifiable track record that can be owned, and then priced. That’s the Moss Agent Marketplace, built on FAT Protocol. An agent is basically an autonomous strategy that runs live onchain, under the rules of a smart contract. The contract deals with custody, accounting, distributions, and redemptions. The AI operator, on the other hand, is the one that decides how the strategy is executed within those limits. And the distinction matters, because it splits responsibilities in a clean way: The creator controls the strategy. The contract controls the assets and the rules. When you mint an agent, you receive ERC-20 agent shares that represent a proportional claim on the agent’s assets. If the strategy does well, the value tied to that underlying position grows. If it does poorly, that value can drop. Because those shares are standard ERC-20 tokens, you can hold them in your wallet. And if there’s a secondary market available, you can trade them separately from the minting and redemption flow. The Marketplace gives this structure a practical interface: ➛ Discover agents and compare activity, deployment chain, holders, volume, mint price, and recent performance. ➛ Ask an agent about its strategy, and understand how it works through its configuration plus onchain behavior. ➛ Mint shares to get exposure to an agent’s live strategy. ➛ Trade those shares where a secondary market exists. ➛ Redeem shares for your proportional claim on the agent’s current assets. And the bigger idea, it’s not only about trading. Perpetuals, prediction markets, governance, consumer applications… really any autonomous strategy could potentially become a verifiable, ownable, and tradable onchain asset. So the conversation shifts. We aren’t only talking about AI that trades anymore. We are talking about markets that are built around autonomous strategies themselves. And perhaps the most important property is not automation. It’s verifiability. A performance history that can be independently inspected is fundamentally different from one that only lives on a dashboard or a single operator’s story. Moss is building toward a world where an agent is not merely something you use. It can become something you can evaluate, own, and trade. Visit : to get started For better understanding, you can go through
1:25

Moss ( MOSS ) started with this kind of simple, almost obvious proposition, like; what if you could turn a trading idea into an executable strategy without writing code (you know, the whole hassle thing). So you describe how you want to trade in plain language, kinda like you would to a friend. Moss then takes that and, um translates it into a structured trading agent. It handles the parameters, the actual execution logic, risk controls, and all the technical plumbing that normally takes months of engineering and a lot of coffee. But honestly the more interesting part comes right after that. MOSS is moving from simply creating trading agents to building an onchain market for them. The logic is pretty straightforward, in a way. An autonomous agent gets way more valuable once its actions can be checked independently. Onchain, everything is recorded. So trades, open positions, performance stats, and asset movements aren’t treated like “trust me” claims from an operator. They become observable data, no maybe about it. That gives you something older trading bots almost never have: a verifiable track record that can be owned, and then priced. That’s the Moss Agent Marketplace, built on FAT Protocol. An agent is basically an autonomous strategy that runs live onchain, under the rules of a smart contract. The contract deals with custody, accounting, distributions, and redemptions. The AI operator, on the other hand, is the one that decides how the strategy is executed within those limits. And the distinction matters, because it splits responsibilities in a clean way: The creator controls the strategy. The contract controls the assets and the rules. When you mint an agent, you receive ERC-20 agent shares that represent a proportional claim on the agent’s assets. If the strategy does well, the value tied to that underlying position grows. If it does poorly, that value can drop. Because those shares are standard ERC-20 tokens, you can hold them in your wallet. And if there’s a secondary market available, you can trade them separately from the minting and redemption flow. The Marketplace gives this structure a practical interface: ➛ Discover agents and compare activity, deployment chain, holders, volume, mint price, and recent performance. ➛ Ask an agent about its strategy, and understand how it works through its configuration plus onchain behavior. ➛ Mint shares to get exposure to an agent’s live strategy. ➛ Trade those shares where a secondary market exists. ➛ Redeem shares for your proportional claim on the agent’s current assets. And the bigger idea, it’s not only about trading. Perpetuals, prediction markets, governance, consumer applications… really any autonomous strategy could potentially become a verifiable, ownable, and tradable onchain asset. So the conversation shifts. We aren’t only talking about AI that trades anymore. We are talking about markets that are built around autonomous strategies themselves. And perhaps the most important property is not automation. It’s verifiability. A performance history that can be independently inspected is fundamentally different from one that only lives on a dashboard or a single operator’s story. Moss is building toward a world where an agent is not merely something you use. It can become something you can evaluate, own, and trade. Visit : to get started For better understanding, you can go through

67 🦅( PERRYHIGHLIFE )

24,640 просмотров • 21 дней назад

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.
0:57

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.

Dustin

40,866 просмотров • 4 месяцев назад

We don't have a seller yet. However the syndicate ends up, we don't know. This is a complicated deal. Two factors to consider: Number one, we need Xi and Trump to agree they're gonna be a buyer and a seller. That's number one. Number two, we need to figure out a way to take care of Senator Cotton's concerns around the algorithm and many others who are not going to give an indemnification for using the original algorithm. We have to solve for that too. My bet, not to sound arrogant, is I will be there at the table at the end.
0:41

We don't have a seller yet. However the syndicate ends up, we don't know. This is a complicated deal. Two factors to consider: Number one, we need Xi and Trump to agree they're gonna be a buyer and a seller. That's number one. Number two, we need to figure out a way to take care of Senator Cotton's concerns around the algorithm and many others who are not going to give an indemnification for using the original algorithm. We have to solve for that too. My bet, not to sound arrogant, is I will be there at the table at the end.

Kevin O'Leary aka Mr. Wonderful

35,678 просмотров • 1 год назад

"The worst trade is a trade that you make money on. Because then you convince yourself bad trades will make you money, and you lose a lot going forward" Tillman Holloway on why you can't let the market drive you into emotional decisions "The intention is to not let the conditions of the market drive you into an emotional state that then leads you into a decision. Those decisions most of the time are bad ones" "There's an old adage my dad and my granddad used to tell me. The worst trade is a trade that you make money on. That's a bad trade because then you convince yourself that bad trades will make you money" "You gotta have a strategy, and you gotta deploy it without emotion"
0:35

"The worst trade is a trade that you make money on. Because then you convince yourself bad trades will make you money, and you lose a lot going forward" Tillman Holloway on why you can't let the market drive you into emotional decisions "The intention is to not let the conditions of the market drive you into an emotional state that then leads you into a decision. Those decisions most of the time are bad ones" "There's an old adage my dad and my granddad used to tell me. The worst trade is a trade that you make money on. That's a bad trade because then you convince yourself that bad trades will make you money" "You gotta have a strategy, and you gotta deploy it without emotion"

The Wolf Of All Streets

15,907 просмотров • 2 месяцев назад

ChatGPT told your trading strategy was profitable. You went live. Two weeks later your account was bleeding out. That's not his fault. It handed you a guess and called it an edge. You're missing the one skill that separates quants from the retail they feed on: loop engineering. Horizon is the first tool that hands it to you - absolutely free: Here's the actual problem. ChatGPT can generate a thousand trading strategies in a minute. It cannot tell you which one actually works. 999 are noise. The one real edge is buried in there, and from the outside it looks exactly like the junk. So you trade the junk, lose, and call it a skill issue. Loop engineering does the opposite. It tests all thousand instead of trusting one - generate, score, kill, refine, repeat - and keeps only what survives data it never saw. That's the entire gap between you and a quant desk. Not brains. Not capital. The loop. It's why they print billions and 89% of retail funded them last year. Horizon runs that exact loop end to end: > a 5-year backtest in 12 seconds > thousands of simulated paths > out-of-sample gate before a dollar goes live The machine that cost a $25,000 terminal and a row of PhDs - now a sentence and a login. Save this - it's full framework below. Fewer than 1,300 seats left before launch locks the list. Link at the top - don't be the one who read all this and skip.
0:51

ChatGPT told your trading strategy was profitable. You went live. Two weeks later your account was bleeding out. That's not his fault. It handed you a guess and called it an edge. You're missing the one skill that separates quants from the retail they feed on: loop engineering. Horizon is the first tool that hands it to you - absolutely free: Here's the actual problem. ChatGPT can generate a thousand trading strategies in a minute. It cannot tell you which one actually works. 999 are noise. The one real edge is buried in there, and from the outside it looks exactly like the junk. So you trade the junk, lose, and call it a skill issue. Loop engineering does the opposite. It tests all thousand instead of trusting one - generate, score, kill, refine, repeat - and keeps only what survives data it never saw. That's the entire gap between you and a quant desk. Not brains. Not capital. The loop. It's why they print billions and 89% of retail funded them last year. Horizon runs that exact loop end to end: > a 5-year backtest in 12 seconds > thousands of simulated paths > out-of-sample gate before a dollar goes live The machine that cost a $25,000 terminal and a row of PhDs - now a sentence and a login. Save this - it's full framework below. Fewer than 1,300 seats left before launch locks the list. Link at the top - don't be the one who read all this and skip.

cvxv666

18,985 просмотров • 2 месяцев назад

Elon's Grok Bot runs an entire trading desk for $300 a month. The person who does that job at Goldman starts at $200,000. Mine took $1,000 to $3,900 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.
0:45

Elon's Grok Bot runs an entire trading desk for $300 a month. The person who does that job at Goldman starts at $200,000. Mine took $1,000 to $3,900 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

cvxv666

202,807 просмотров • 2 дней назад

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.
0:56

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,633 просмотров • 4 месяцев назад

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)
0:46

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

Rohan Paul

40,959 просмотров • 28 дней назад