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Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900,000 to build multi-agent AI systems. the blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the article above is that blueprint pointed at markets. one giant... show more

Rossst.03

6,583 subscribers

79,898 次观看 • 4 天前 •via X (Twitter)

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18 条评论

Korens 的头像

Korens4 天前

The swarm approach is still fascinating

Roan 的头像

Roan4 天前

this is absolutely top tier watch from MIT on multi-agent AI systems bro

Rossst.03 的头像

Rossst.034 天前

I agree with you, Marvin Minsky is a legend in mathematics.

Vexly 的头像

Vexly4 天前

truth is simple things, just add them all and see the magic

Yarchi 的头像

Yarchi4 天前

so nice lecture, thanks for sharing

Rossst.03 的头像

Rossst.034 天前

Thank you for your appreciation

Ellipsis Caesura 的头像

Ellipsis Caesura3 天前

we used to call them subroutines

KUMA 的头像

KUMA4 天前

Funny how the future of AI keeps turning out to be old ideas with better hardware. The veto system might actually be more important than the agents themselves.

AI Mastery Guide 的头像

AI Mastery Guide3 天前

Wait fr 40 years old

venus 的头像

venus4 天前

marvin is real og, watching right now

mokla 的头像

mokla4 天前

The intelligence isn’t in any agent. It’s in the vetoes between them.

AGTP 的头像

AGTP4 天前

The idea of a swarm of specialists is a fascinating way to look at the future of agentic workflows. We actually went deeper on this here:

Paulktorres 的头像

Paulktorres3 天前

Alguien por favor sería tan amable de preguntarle a @grok cuál sería el enlace en Youtube.

yurshev 的头像

yurshev4 天前

intelligence isn't in the individual model, it's in the orchestration layer that keeps the swarm on the rails.

MadCrash_X 的头像

MadCrash_X4 天前

The pitch sells one genius, the swarm actually works.

kacau balau 的头像

kacau balau3 天前

I keep saying wow under my breath. I feel very lucky to have a day like this.

Juanma Romero 的头像

Juanma Romero3 天前

Minsky presented a conceptual theory; he did not prove or demonstrate anything.

SCOTTY BEAM 的头像

SCOTTY BEAM4 天前

very smart man imo, great lecture brother

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Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Rossst.03

148,991 次观看 • 2 个月前

Marvin Minsky, the MIT scientist who founded AI: "Citadel pays PhDs $500K to find the perfect equation. The market doesn't have one. It's beaten by a swarm of dumb agents, the exact design Marvin Minsky said your brain runs on." the thread above is about swarm intelligence, letting a crowd of simple agents search the ugly, shifting landscape of a market that no clean equation can solve. minsky's entire life's work says that isn't a hack. it is how intelligence itself is built. he proved you don't need a smart central solver. you need many mindless specialists, each doing one tiny job, none understanding the whole. connect enough of them and something intelligent emerges from parts that are individually dumb. a market is exactly that: millions of simple agents, no one in charge, collectively solving a problem none of them can see. that is why the swarm beats the elegant math. a single closed-form equation assumes a clean, stable world. the market is nonlinear, non-stationary, full of traps. a swarm doesn't need to understand the landscape, it explores it from a thousand angles at once and can't get permanently stuck where one clever model would. minsky saw this in the mind decades before quants borrowed it for markets. he taught it at MIT, for free, in this lecture. same story i keep telling: the "new" AI idea running the funds is an old idea in a new wrapper. here is what the thread underplays, and minsky knew it. a swarm is only as good as how its agents are wired and rewarded. connect them wrong and a thousand dumb agents don't become a genius, they become expensive noise that overfits and blows up. the swarm is free. the architecture, knowing how to connect and constrain the agents, is the entire edge.

Marvin Minsky, the MIT scientist who founded AI: "Citadel pays PhDs $500K to find the perfect equation. The market doesn't have one. It's beaten by a swarm of dumb agents, the exact design Marvin Minsky said your brain runs on." the thread above is about swarm intelligence, letting a crowd of simple agents search the ugly, shifting landscape of a market that no clean equation can solve. minsky's entire life's work says that isn't a hack. it is how intelligence itself is built. he proved you don't need a smart central solver. you need many mindless specialists, each doing one tiny job, none understanding the whole. connect enough of them and something intelligent emerges from parts that are individually dumb. a market is exactly that: millions of simple agents, no one in charge, collectively solving a problem none of them can see. that is why the swarm beats the elegant math. a single closed-form equation assumes a clean, stable world. the market is nonlinear, non-stationary, full of traps. a swarm doesn't need to understand the landscape, it explores it from a thousand angles at once and can't get permanently stuck where one clever model would. minsky saw this in the mind decades before quants borrowed it for markets. he taught it at MIT, for free, in this lecture. same story i keep telling: the "new" AI idea running the funds is an old idea in a new wrapper. here is what the thread underplays, and minsky knew it. a swarm is only as good as how its agents are wired and rewarded. connect them wrong and a thousand dumb agents don't become a genius, they become expensive noise that overfits and blows up. the swarm is free. the architecture, knowing how to connect and constrain the agents, is the entire edge.

Rossst.03

45,487 次观看 • 2 个月前

IN 2011 ONE OF THE FOUNDING FATHERS OF AI STOOD AT AN MIT BOARD AND EXPLAINED WHY A MIND IS NOT ONE SMART THING -- IT'S A CROWD OF TINY DUMB PARTS THAT DON'T KNOW ANYTHING A lecture from Marvin Minsky, the man who helped invent artificial intelligence, on his life's idea: the Society of Mind. -> The idea that lands: there is no single "You" inside your head. Just hundreds of simple processes, each almost mindless, working together until thinking appears. No magic. No spark. Intelligence is what happens when enough dumb parts are wired the right way. Forty years later that's exactly what an AI model is: billions of tiny switches, none of them smart, somehow producing something that looks like it thinks. Being amazed by intelligence was never the point -> understanding how it's assembled is. Most people think a mind is a mystery. The ones who watched this see the machinery underneath. Bookmark & Watch it. This one's a legend ↓

IN 2011 ONE OF THE FOUNDING FATHERS OF AI STOOD AT AN MIT BOARD AND EXPLAINED WHY A MIND IS NOT ONE SMART THING -- IT'S A CROWD OF TINY DUMB PARTS THAT DON'T KNOW ANYTHING A lecture from Marvin Minsky, the man who helped invent artificial intelligence, on his life's idea: the Society of Mind. -> The idea that lands: there is no single "You" inside your head. Just hundreds of simple processes, each almost mindless, working together until thinking appears. No magic. No spark. Intelligence is what happens when enough dumb parts are wired the right way. Forty years later that's exactly what an AI model is: billions of tiny switches, none of them smart, somehow producing something that looks like it thinks. Being amazed by intelligence was never the point -> understanding how it's assembled is. Most people think a mind is a mystery. The ones who watched this see the machinery underneath. Bookmark & Watch it. This one's a legend ↓

slash1s

43,774 次观看 • 2 个月前

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.

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 次观看 • 2 个月前

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,734 次观看 • 2 个月前

Gilbert Strang, MIT professor: "Anthropic pays $500,000 for one skill: linear algebra. a professor who gave 66 years to MIT taught all of it in his final lecture, for free." the article above hands you an AI that prints trading models on demand. strip the wrapper off any of them and one object is left. a matrix. Strang spent his whole career on that one thing, and this was the last time he ever wrote it on a chalkboard. look at what he puts up. A x = 0. the null space. it is every mix of your signals that cancels to nothing. an AI will hand you a thousand factors and half of them live right there, pure risk, zero return. a model is a covariance matrix, a set of eigenvectors, one projection. that is the entire machine. no magic, no black box. he was 88, and this was his last class ever. the recording sits free on YouTube while people pay for a worse version of what he gave away. the AI builds the matrix. Strang teaches you to read it. only one of those two skills holds its price.

Gilbert Strang, MIT professor: "Anthropic pays $500,000 for one skill: linear algebra. a professor who gave 66 years to MIT taught all of it in his final lecture, for free." the article above hands you an AI that prints trading models on demand. strip the wrapper off any of them and one object is left. a matrix. Strang spent his whole career on that one thing, and this was the last time he ever wrote it on a chalkboard. look at what he puts up. A x = 0. the null space. it is every mix of your signals that cancels to nothing. an AI will hand you a thousand factors and half of them live right there, pure risk, zero return. a model is a covariance matrix, a set of eigenvectors, one projection. that is the entire machine. no magic, no black box. he was 88, and this was his last class ever. the recording sits free on YouTube while people pay for a worse version of what he gave away. the AI builds the matrix. Strang teaches you to read it. only one of those two skills holds its price.

Rossst.03

67,844 次观看 • 3 天前

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 次观看 • 17 天前

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

starmex

97,805 次观看 • 19 天前

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is init, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓

slash1s

269,515 次观看 • 3 个月前

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

rari

58,117 次观看 • 1 个月前

In 2013 an MIT mathematician wrote one minus sign on a chalkboard and explained the exact trick hedge funds farm billions from in 2026. MIT filmed it. it has been free for 13 years. it has fewer views than a phone unboxing. his point: volatility is not a risk you survive. it is a crop you harvest. every "volatility harvesting" thread charging you $500 in 2026 is reselling the one line Choongbum Lee derives for nothing. he is a mathematician, not a guru. no thumbnail, no promise. just the term that makes a shaking portfolio beat a calm one. skip to where he writes d(log S). out falls minus one-half sigma squared. that tiny term is the "$12.50 from nowhere" in the post above. no black box. no signal. one piece of chalk. a quant I know made his whole desk watch minute 40 before they could touch the rebalancer. you are 13 years late. it is still free.

In 2013 an MIT mathematician wrote one minus sign on a chalkboard and explained the exact trick hedge funds farm billions from in 2026. MIT filmed it. it has been free for 13 years. it has fewer views than a phone unboxing. his point: volatility is not a risk you survive. it is a crop you harvest. every "volatility harvesting" thread charging you $500 in 2026 is reselling the one line Choongbum Lee derives for nothing. he is a mathematician, not a guru. no thumbnail, no promise. just the term that makes a shaking portfolio beat a calm one. skip to where he writes d(log S). out falls minus one-half sigma squared. that tiny term is the "$12.50 from nowhere" in the post above. no black box. no signal. one piece of chalk. a quant I know made his whole desk watch minute 40 before they could touch the rebalancer. you are 13 years late. it is still free.

Voltex

32,751 次观看 • 1 个月前

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,226 次观看 • 2 个月前

The Shivalinga is not just a sacred form. It is a cosmic blueprint. Every Shivalinga is built in three parts representing the three fundamental forces of existence. ✨ Brahma Bhaga – creation ✨ Vishnu Bhaga – preservation ✨ Rudra Bhaga – transformation In one single form, the entire cycle of the universe is represented. What looks simple in a temple is actually a profound reminder that life is always moving through creation, sustenance, and transformation. The Shivalinga is not merely an object of worship. It is cosmic intelligence carved in stone. 🔱

The Shivalinga is not just a sacred form. It is a cosmic blueprint. Every Shivalinga is built in three parts representing the three fundamental forces of existence. ✨ Brahma Bhaga – creation ✨ Vishnu Bhaga – preservation ✨ Rudra Bhaga – transformation In one single form, the entire cycle of the universe is represented. What looks simple in a temple is actually a profound reminder that life is always moving through creation, sustenance, and transformation. The Shivalinga is not merely an object of worship. It is cosmic intelligence carved in stone. 🔱

Pooja sharma

14,948 次观看 • 4 个月前

In the next 10 years, you can have billions of agents as customers. They'll have millions of wallets. An agent does not look at your website. It reads your documents, your prices, and your API. An agent needs six things that a person does not need: - identity - tools - an inbox - memory - a wallet _ receipts. A CFO agent can compare 12 vendors, read the SOC 2 documents, and select the one vendor that obeys your policy. The old internet is for people. Each step of the agent journey has no infrastructure yet. Look at each SaaS tool that you use. Then build the version for agents only.

In the next 10 years, you can have billions of agents as customers. They'll have millions of wallets. An agent does not look at your website. It reads your documents, your prices, and your API. An agent needs six things that a person does not need: - identity - tools - an inbox - memory - a wallet _ receipts. A CFO agent can compare 12 vendors, read the SOC 2 documents, and select the one vendor that obeys your policy. The old internet is for people. Each step of the agent journey has no infrastructure yet. Look at each SaaS tool that you use. Then build the version for agents only.

The Startup Ideas Podcast (SIP) 🧃

12,349 次观看 • 21 天前

IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓

IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓

slash1s

260,353 次观看 • 2 个月前

Eon Systems' Dr. Alex Wissner-Gross, author of the viral "First Multi-Behavior Brain Upload" article, says there are many paths to AGI beyond LLMs: "My bet is intelligence is this very, very general effect, with many ways to implement it." "I published a paper a number of years ago arguing that not only is intelligent behavior a general process, I went further and argued that it is a general physical process, and that you can even formulate intelligent behavior in pure thermodynamic terms." "[The brain upload] looks a little bit more like a graph neural network. But really, that's such an AI way of framing it. It looks like a leaky integrate-and-fire, or LIF, model that we've had for decades from the neuroscience world. It's just a graph of nodes that have leaky integrate-and-fire dynamics, and they're firing at each other."

Eon Systems' Dr. Alex Wissner-Gross, author of the viral "First Multi-Behavior Brain Upload" article, says there are many paths to AGI beyond LLMs: "My bet is intelligence is this very, very general effect, with many ways to implement it." "I published a paper a number of years ago arguing that not only is intelligent behavior a general process, I went further and argued that it is a general physical process, and that you can even formulate intelligent behavior in pure thermodynamic terms." "[The brain upload] looks a little bit more like a graph neural network. But really, that's such an AI way of framing it. It looks like a leaky integrate-and-fire, or LIF, model that we've had for decades from the neuroscience world. It's just a graph of nodes that have leaky integrate-and-fire dynamics, and they're firing at each other."

TBPN

19,117 次观看 • 6 个月前

Mark Zuckerberg just described the minimum viable business for the next decade. A fourth item made the checklist. Zuckerberg: “Every business, just like they have a website, and a phone number, and an email address, is also going to have an AI.” Website. Phone number. Email address. AI agent. That is not a prediction. That is a new baseline. Twenty years ago, not having a website was a choice. Then it stopped being one. Nobody scheduled that transition. The same filter is back. Running faster this time. A business without an AI agent handling sales, support, and customer interaction will not look outdated. It will look abandoned. Its competitor’s agent responds in two seconds. Knows every customer by name. And while it’s handling yours, it’s handling ten thousand others. You do not outwork that. You do not outspend it. You just lose to it. But Zuckerberg went somewhere most tech CEOs refuse to go. He picked a side in the debate most CEOs avoid entirely. Zuckerberg: “Do you want a future where you’re interacting with kind of one system for everything? Or do you want one where a lot of different people are building a lot of different AIs?” One AI controlled by one company. Or millions of AIs built by millions of people. Centralized intelligence. Or distributed intelligence. Zuckerberg chose distributed. Zuckerberg: “What open source does is it makes it so everyone can take and modify the model and build stuff on top of it. Which is different from the kind of closed and centralized approach.” The closed model makes every business a tenant. You rent intelligence on someone else’s terms. At someone else’s price. Inside someone else’s guardrails. The open model makes every business an owner. You modify the model. You deploy it your way. You build equity in your own system with every iteration. That gap widens quietly. Then it becomes permanent. The tenant pays more for less control every year. The owner pulls further ahead every cycle. One is a subscription. The other is infrastructure. Then Zuckerberg described the part most people have not thought about yet. Zuckerberg: “A lot of creators will have their own AIs. It’s like a richer world when there’s a diversity of different things.” Your favorite creator will have an AI trained on everything they have ever made. Available to millions of people simultaneously. Responding in real time while the creator sleeps. That is the difference between a brand that scales with your waking hours and one that scales with compute. One has a ceiling. The other does not. Zuckerberg is not betting on one model that governs everything. He is betting on billions of specialized AIs, each built by the person closest to the problem it solves. The companies still debating whether to adopt AI are not having the wrong conversation. They are standing in a room where the meeting ended an hour ago. The checklist updated. They did not.

Dustin

507,064 次观看 • 5 个月前

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 次观看 • 2 个月前

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓

slash1s

13,073 次观看 • 3 个月前

AI ownership is becoming one of the most important questions of this decade. Most systems today are built behind closed doors, trained by millions of people, and ultimately owned by a very small group at the top. That structure shapes who benefits, who decides, and who gets left out. Action Model explores a different approach, one where contribution isn't invisible, participation is collective, and intelligence is owned by the people. Not as a product to just consume, but as an ecosystem people can help shape. Because the future of AI won't just be defined by what it can do, but by who it belongs to.

AI ownership is becoming one of the most important questions of this decade. Most systems today are built behind closed doors, trained by millions of people, and ultimately owned by a very small group at the top. That structure shapes who benefits, who decides, and who gets left out. Action Model explores a different approach, one where contribution isn't invisible, participation is collective, and intelligence is owned by the people. Not as a product to just consume, but as an ecosystem people can help shape. Because the future of AI won't just be defined by what it can do, but by who it belongs to.

Action Model

11,306 次观看 • 8 个月前