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So, the internet calls me one of the most AI psychotic people online.
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So, it's only right that I start my talk with a story about one of the most cancelled men in history.
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His name was Baruch Spinoza.
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And in case the philosophy elective wasn't your thing, here are the highlights you need to know.
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In 1929, a New York rabbi challenged Einstein by telegram.
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Do you believe in God?
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Answer in 50 words.
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Einstein answered in 25.
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I believe in Spinoza's God who reveals himself in the lawful harmony of the world,
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not in a God who concerns himself with the fate and doings of mankind.
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The most famous scientist alive asked the biggest question there is, pointed at Spinoza.
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But here's what Baruch Spinoza's own community did to him.
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Amsterdam, July 27th, 1656.
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Spinoza is 23 years old, a member of a tight-knit Sephardic Jewish community.
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He stands in a synagogue while the elders excommunicate him with the most violent curse the community ever produced.
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Cursed be he by day and cursed be he by night.
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Cursed be he when he lies down and cursed be he
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when he rises up nobody may speak to him nobody may
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trade with him nobody may come within four cubits of him nobody may read anything he writes and this ban
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uniquely among the roughly 40 bands spinoza's community issued that century has no repentance clause.
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It has never been lifted.
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It technically is still in force today.
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Spinoza was 23.
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His crime was evil opinions, expressing forbidden thoughts.
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His punishment was complete deletion from the community.
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Before his community cursed him, they tried to buy him a thousand guilders a year, Serious money.
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All he had to do was show up at synagogue once in a while and keep his mouth shut.
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Hear that in founder terms.
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They've offered him a salary to stop building.
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He said no. Not for $10,000, he said.
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He wanted truth, not comfort.
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Shortly before his excommunication, a fanatic came at him with a knife.
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The blade tore through his cloak and missed him.
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He kept that cloak, scar unmended, for the rest of his life.
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He wanted to remember what ideas cost.
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So what does the most canceled man of the 17th century do next?
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He grinds lenses.
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By day, he makes optical instruments, tools that let human beings see further than their eyes allow.
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He makes them so well that the best scientists in Europe seek them out.
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And by night, he writes a book so dangerous, he cannot publish it while he is alive.
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When he dies at 44, lungs full of glass dust from making other people's lenses, the manuscript is locked in his writing desk.
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His dying instruction?
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Ship the desk by canal barge to his publisher in Amsterdam.
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That manuscript became his posthumous works, which attracted immediate attention across Europe and inspired some of the most important philosophers of the Enlightened.
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What does Spinoza have to do with startups, you might ask?
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Well, here is a man, canceled by everyone he knew, offered a salary to stop,
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nearly killed for shipping, and his response was to build precision tools by day
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and write the most dangerous book in Europe by night alone, with no permission from anybody.
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If you're going to start a startup, you could do well to learn from Spinoza.
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He had a name for the engine that kept him going.
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Kanatus.
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It means your striving.
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The drive in every living thing to keep going and to increase its power to act.
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Not your resume.
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Not your title.
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Not your job.
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The striving itself.
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This talk is about the tools that amplify it.
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So what did he actually say that was worth deleting a man over?
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The gist of the heresy was that God is not a king on a throne.
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God is spread through everything that exists.
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God or nature, he wrote.
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400 years later, we are making a similar mistake about intelligence.
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Everyone is waiting for AGI as a singular event, a god in a data center,
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some announcement, some threshold, some day when the sky changes color.
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So now I'll say a version of Spinoza's heresy, updated 400 years later.
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Everyone is watching the sky, and the thing they're watching for is already in the room.
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It doesn't look like a god.
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It looks like infrastructure, a terminal window, a folder of markdown files, a job that finishes while you sleep.
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Spread through everything, which is exactly where Sminosa told you to look.
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AGI isn't arriving as an event.
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It's arriving diffused as your agent, running on your context, doing your work.
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I call it personal AGI, not artificial general intelligence for everyone all at once, general intelligence for one person, you.
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This was a dream of a great many people.
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Vannevar Bush called it the memex, a machine that would be an extension of yourself and your brain.
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And I want to be precise about what I mean, because the words personal AI has already been captured by marketing departments.
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I do not mean a chatbot you pay $20 a month to.
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I do not mean a slightly better autocomplete.
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I do not mean an assistant that knows your calendar and nothing else.
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That's just a subscription you rent.
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It's a corporate AGI you don't own.
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It resets when you close the tab.
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It knows what everyone else already knows.
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and when the company behind it pivots, your so-called assistant gets a lobotomy on someone else's schedule.
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Personal AGI is a different animal.
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An agent that runs on your infrastructure, reads from a memory you own, executes procedures you wrote, and compounds.
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The corporate AGI that you don't own gets better only when the company ships something.
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Your personal AGI gets better every single day you use it because every day it knows more of your life.
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One of these is a product you consume.
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The other is an asset you build.
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Almost nobody in the world has this second thing yet.
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And everyone in this arena could have it by Monday.
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And I believe intelligence, intelligence of this kind should be owned, owned by you, not rented.
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If you go forth and build this for yourself, if 2034 doesn't have to be like 1984.
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You might ask why this personal AGI is happening only now.
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Well, I think it's because of what agents can do, and nowhere is it more obvious than encoding agents.
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In 2013, I was a YC partner building Bookface, our internal social network, at night.
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I shipped maybe 14 useful lines of code a day, which, if you know the literature on programmer productivity, is dead on median.
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That was me at full effort.
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This year, I run YC full-time, same brain, same hours, plus a five o'clock kid pickup.
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I did the math on my output, and I'm at about 400x what I did in 2013.
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Now, before the skeptic in row three deflates that number for me, let me deflate that for myself.
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You don't trust the raw lines of code?
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Fine.
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Apply the most pathological verbosity penalty you can stomach and assume the agent writes bloated code.
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assume half of it is scaffolding.
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Assume I'm flattering myself, which is always a live possibility.
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It's still 8x at the absolute floor and 10 times that in the middle of the range.
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The number is large no matter how you torture it.
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Now, this is just code, and if you're at the beginning of your career, you're in luck.
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This applies to design.
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This applies to product management.
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This applies to growth.
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This applies to every part of what you might want to do.
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The multiplier for coding is not just for coding.
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It's for every piece of knowledge work.
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And it's not just me.
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At YC, we get to watch this at portfolio scale.
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A year and a half ago in the winter 25 batch, a quarter of the companies had code bases that were 95% AI generated.
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Those companies use AI agents for everything now, not just code.
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And that batch is on track to becoming one of the fastest growing, most profitable batches in the history of YC.
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Now, I know what a correlation is, so let me say it carefully.
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I cannot prove that the AI generated code and everything else caused the growth.
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But what I can tell you is that the fastest growing founders we fund are not treating AI as autocomplete.
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They are treating it as a workforce.
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There are 2x people and there are 100x people who are using the same clod, same weights, same context window size, same API.
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But the leverage is not in the weights.
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It's in what context you give it, how relevant it is, and does it happen at the right step.
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We'll come back to this.
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Now, Spinoza has a definition I think about every single week.
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In ethics, he defines joy as the feeling of your power of acting increasing,
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which is why the first time an agent does a week of your work in an afternoon, it doesn't feel like a convenience.
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It feels like joy.
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And that's not me being poetic.
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That's the technical term.
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Your power of acting increased.
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Your canatus just got bigger.
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He defined the opposite to, by the way, sadness, the feeling of your power of acting decreasing.
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If your Sunday nights have a specific heaviness, like your ability to influence the world is receding, that you feel like you're quiet quitting,
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then this is what you feel and hold that thought
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because we're coming back to this in the second half of this talk and it gets political.
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So here's the equation for the next decade of your life, a frontier model, which is rented and a commodity and getting cheaper by the quarter,
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plus your context, which is owned by you and unique, and ideally nobody else on this earth has it,
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plus a harness that wires them together.
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That harness might be open claw, Hermes agent, clawed code, or codex.
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Add that up, and that gives you an agent that acts like a very fast version of you.
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Model quality is rented, but your brain is owned ideally by you.
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Marshall McLuhan said that technology is an extension of man.
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Steve Jobs called a computer a bicycle for the mind.
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And if you have what I'm describing here, then you have a self-driving rocket.
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Paul Graham taught every founder in this building two things, make something people want and do things that don't scale.
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Both still govern everything.
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What's new is the multiplier on the second one.
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Agents are how one founder now does unscalable things at scale.
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The advice didn't change, but the physics of all startups and of what you can do did.
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Spinoza ground lenses, instruments that let people see past the limits of their eyes.
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I want to spend the next 15 minutes showing you what grinding lenses for the mind looks like.
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This is the machinery I actually run my life on and every concept travels to whatever stack you use.
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Let's start with working memory because it explains everything.
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You and I as human beings hold about seven things in our head at once.
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Seven plus or minus two.
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It's the most famous paper in cognitive psychology.
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It's why local phone numbers are seven digits and why you forget the eighth item on a grocery list.
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That is the entire working memory of a human being and every institution humanity has ever built.
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Every checklist, every org chart, every filing cabinet, every stand-up meeting is a prosthetic for that limit.
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An AI agent, though, holds a million tokens.
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That's about a thousand pages.
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Three Harry Potter books sitting open on its head all at once,
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and it can find a needle in any of them and synthesize across all three in seconds.
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Three Harry Potter books versus seven digits.
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You could argue that's not quite AGI yet, but it is already a different operating regime.
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And almost everyone on Earth is still running their life on an org chart
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and a way of doing things designed for the seven-digit brain.
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Run that number in the other direction.
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A thousand pages is a lot, but it is also very little.
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Your life is not three books.
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Your life is a library.
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Every email you ever sent, every meeting, every decision, and every reason behind it, every conversation with every person you know.
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The question that determines whether your agent is a genius or a goldfish is this.
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Who decides or what decides which three books are open on the desk.
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And that's what a brain is.
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That's what G-brain is meant to be.
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The library plus the librarian.
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I've been building G-brain in the open.
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My personal open claw has a Karpathy-style knowledge wiki with about 220,000 markdown pages.
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25 years of my life, diarized.
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Every email, every meeting, my notes, my photos, my drafts, the things I got wrong.
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Compiled mostly by agents, curated by agents, searched for by agents, but I never re-ask a question I already answered.
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And the lived experience in the system is the point.
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A founder emails me about a crisis before I finish reading the email.
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My agent has already pulled every prior conversation I've had with that founder, three portfolio companies that hit the same wall, and what actually worked for them.
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When my agent does anything, it does knowing everything I know.
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And that's the difference between an assistant and a colleague.
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Let me walk you through an actual day because that matters more than an architecture diagram.
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While I slept last night, my agent processed my inbox.
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Not sorted it, processed it.
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It knows which emails are from founders in trouble, which are from people trying to sell me something, and which are from the 17 mailing lists I never quite unsubscribe from.
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The ones that matter are triaged with context pulled from the library, who this person is, my whole history with them, what they're really asking under what they wrote,
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and what that might mean for me.
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I wake up to a briefing, not a pile of emails.
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Before every meeting, a prep doc, who I'm meeting, what we said last time, what changed since, and what I should ask.
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Research I was curious about at midnight is finished by morning, and when something interesting happens in the world, my agent has usually read it, cross-referenced against what I care about,
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and filed it before I've had coffee.
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On top of this library sits my agentic coding framework, GStack, 123,000 stars now, which put it in the top 100 open source projects in the history of GitHub.
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and what's actually in the punchline of this full architecture, it's mostly skill files, plus a browser that the agents can drive,
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pages of English, and a way to act on the world.
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Markdown, not magic.
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Fat skills, thin harness.
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Let me show you what a skill file is
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because I keep saying this phrase and I want you to see how unmagical it is.
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Here's a real one, lightly redact is.
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It says, when a meeting recording lands from Circleback, transcribe it with speaker labels, pull out the commitment made, who made it, and the deadline.
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Cross-check every person named against the library and link their pages.
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File the summary here, full transcript there.
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If anything contradicts something we already believe, flag it.
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Don't overwrite it.
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That's it.
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That's a skill.
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It's a page of English.
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A smart intern, anyone really, who could read, could follow it.
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And that's the test, actually.
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If a smart intern could follow it, an agent can run it.
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Which means, actually, a kind of profound thing.
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I know I caught a lot of flack for talking about this, but I think it's more true than ever, especially now.
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Markdown is actually code.
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If you can write clear instructions in English, you're a programmer.
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The compiler is a language model.
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And that's why it's not just for engineers anymore.
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At YC, our media people, events staff, finance team, people who never open a terminal in their lives, are building skill files and scheduled jobs.
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One of our finance folks compiled about 100 Excel workbooks into a single app she built with an internal agent.
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She's not a programmer.
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She's a manager of agents now.
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Everyone is about to be.
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The most important question to ask here is, where is the computation happening?
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And there are exactly two answers, and confusing them causes every agent failure I've ever seen.
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Some computation belongs in latent space.
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Taste, judgment, reading what a human actually wants from a vague request, that lives in the model, and you steer it with a markdown file.
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And then some computation belongs in deterministic space, the arithmetic, the SQL query,
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For instance, the seating chart for what sessions you're going to go to today for your breakouts.
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All of that needs to be stored in a SQL database used by the markdown files.
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Being smart about this goes a long way.
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Ask an agent or human to seat five people around a table.
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That's easy.
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Do it in latent space.
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Ask it to make custom schedules for 6,000 people in an arena like we just did for you
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and your latent space agent needs to write some code to keep track of it.
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Your experience at this conference had to be markdown files calling code in exactly this way.
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And you couldn't do it without the code.
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The model fails where we fail.
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The fix is having the model compute the way humans compute.
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The latent and the deterministic markdown files calling databases and scripts.
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Simple, but it's what everything is actually built on.
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And I'll give you one more receipt my favorite one because you're sitting inside it right now.
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Five days ago, I decided this talk needed Spinoza, one of my favorite philosophers, especially because of how canceled he got.
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So my agent went and acquired three of the best biographies about the man, books by Nadler, Goldstein, and Stewart, about 1,500 pages.
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It read all three.
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It built me a synthesis, a dated chronology of his life, every place the three biographers disagree with each other, and the best verbatim quotes with chapter citations.
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And because it knows what I need, the 10 most tellable moments of his life ranked with delivery notes.
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The knife attack, the bribe, the desk.
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Every beat of our opening that might have given you some chills 20 minutes ago came out of that overnight run.
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1500 pages became a stage ready story that I could edit.
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I call it It's a compendium skill and I use it daily.
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It's a personal skill that is a mega, mega version of deep research, only deeper than anything the corporate AI products will give you.
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The spine of this talk you're watching was inspired by the machine we're describing now.
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And if you're wondering where mine actually started, it was not 220,000 pages.
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It was a folder.
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It was a few markdown files about the companies I was working with and the people I kept emailing.
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And the library got big the same way anything gets big.
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A little every day compounding with agents doing the filing.
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Nobody builds the warehouse first.
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First you build one shelf.
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When you sit down with an agent tonight, you're not coding.
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You're managing a workforce made of markdown.
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A skill file is an employee.
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It has one capability, one job written down clearly enough that someone new could execute it.
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A resolver is an org chart.
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A task comes in and it decides which markdown file or who handles it.
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Which means that before you ever incorporate anything, before you have a co-founder or a logo or a deck, you can already be running an organization.
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An organization of one plus your agents.
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You are the founder and the entire management layer of you incorporated
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and the headcount under you is now whatever you decide it is.
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This already produces companies that break the old math.
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Emergent out of our summer 24 batch went from public launch to nine figures of revenue in eight months.
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When they crossed $15 million in annualized revenue, they were 15 people.
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Retail, Winter 24, hit $60 million annualized with about 40.
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That revenue per person did not exist before.
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Not in software, not in oil, not in railroads.
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And these aren't freaks of nature.
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They're the first companies built natively on the new physics, and every one of them started as one or two people wired the way I just described.
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Now, picture our batch room in the dog patch.
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Hundreds of founders.
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Every single day, each one of them doing what used to be a person's entire year of work.
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That is not the future.
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That is the bar right now with this batch.
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If you're not doing it, your competitor is, and they will eat your lunch politely and thank you for it.
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It also changes what software even is.
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Software doesn't have to be precious anymore.
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You can build exactly the tool you need for the audience of one in a weekend.
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The old advice was scratch your own itch and hope it's the market.
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The new version is much better.
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Scratch your own itch because scratching itches is nearly free.
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And some of your tools for one will turn out to be entire companies.
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You'll know because other people start begging for them.
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And one honest caveat before the how-to because you catch me out in any way.
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A brain nobody curates is a garbage dump with great search.
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Retrieval will surface a stale fact with total confidence.
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A bad skill file encodes a bad process forever.
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So the primitive is memory plus hygiene, provenance on every fact, contradiction checks when new information collides with old,
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and a librarian whose actual job is pruning.
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Treat the brain like production infrastructure and it compounds.
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Treat it like a dumping ground and you get a very confident agent that is wrong in ways nobody can trace.
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Everything so far is philosophy and receipts.
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So let's get into some how-to.
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If you do what I describe in the next six minutes, you'll be ahead of 99% of people who watched this talk and just nodded.
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Step one tonight, pick a harness and run an agent on your own machine machine.
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I use OpenClaw and Hermes Agent with Gbrain.
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A hosted version of this is at gbrain.io.
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It's free.
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Gbrain itself is free and open source.
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I always recommend the Ferrari, but I'll be honest, the Honda is really good too.
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Codex, Claude Code, whatever.
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Any of them will do 99% of this, and the upside of not Ferrari is
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that it will also get you to your destination with a
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little less of less getting out to fix it on the side of the road.
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The concepts are the point, not any given repo or product.
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The intelligence is on tap and there are many paths.
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Step two this weekend, start your library, not a grand archive.
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One folder of markdown files, export your notes, export your email if you can.
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Write one page about each project you're working on, each person you work with, and on those pages write the things you actually know,
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what you're building together, what they care about what you owe them, what they said last time.
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That's stuff no model on earth has because it only exists in your head.
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And your head, as we established, only holds seven things.
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The first time an agent answers a question using your context instead of the internets, you'll feel the click and you won't go back.
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You are all sitting on five, ten years of your own history in one inbox or another.
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That's your moat just lying there, unindexed, doing nothing.
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The only gate between you and this entire architecture is probably 24 hours.
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Step three, write your first skill file.
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Picking it is easy.
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You know, what's the task you do every single week that you hate the most?
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Might be expense reports, meeting notes, the weekly status update, competitor research.
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Explain it to your agent.
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What do you want to do in plain English, the way you'd explain to a smart friend on their first day of a job, and then let it get it wrong.
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If it gets it wrong, correct it.
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Every rule, every exception, every O, and also put it in there, and it'll fix it.
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That page is now an employee.
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Run it.
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Step four, wire it up to be a recurring job.
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Maybe it's the job you just created in step three.
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Every morning at seven, do this.
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Every Friday, summarize that.
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The first time you wake up to work that finished while you slept, something shifts in your head permanently.
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That's the day that the day stops being the unit of work for you.
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It becomes what you can imagine, and it should be driven by what your goals are and what you want to create in the world.
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Step five, this is the discipline that separates the compounders from the dabblers.
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Never do one-off work.
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Most people run one operation with one agent and then throw the context away.
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They close the window, that's it.
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Don't.
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At the end of every task, ask the agent to skillify what it did.
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Skillify is a special skill you can find in Gbrain.
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You can point it at that repo and say, extract Skillify, learn how to do it.
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Turn it into a markdown file you can use and reuse forever.
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I'll say it the way I say it at YC.
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If you have to ask for something twice, you failed.
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The person who captures what they learn gets smarter every single day.
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The person who wakes up every morning with amnesia, well, that's a waste of your time.
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And it sort of doesn't matter how good the model gets if you can't turn it into real memory.
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Do those five things and I can tell you what your next 90 days look like.
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Week one, honestly, it's a toy.
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The library's thin.
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The skills are clumsy.
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You're fixing more than you're saving.
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Week four, the flywheel catches.
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The agent starts answering with your context.
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The morning job produces something you actually read.
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and you write your third and fourth skill because the first two worked.
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Week 12, you have a library that answers before you finish asking.
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A dozen skill files running the parts of your week you used to dread
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and one or two tools that other people keep asking to borrow, which in this room is called a startup.
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The curve is the same curve as any compounding thing you've ever seen.
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Flat, flat flat then not most people who try this will quit this in week two
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which is precisely why the ones who don't feel like they're
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cheating by week 12 now i need to tell you the part
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that isn't fun because everything i taught you just cuts both ways i told you spinoza's definition of sadness earlier.
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The feeling of your power acting, power of acting, decreasing.
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And I said it gets political.
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This is where.
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A skill file is not a document.
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It's a piece of your cognition, how you do the thing.
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Extracted from your head, written down, and executable.
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Every skill you teach an agent is you, externalized.
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And the exact same file is two opposite futures, depending on one variable who controls it.
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Take a fictional example of a support engineer.
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Let's call her Maya.
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Over two years, Maya teaches her agents 40 skills.
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How to triage a P0 at 2 in the morning.
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How to de-escalate the customer who's about to churn.
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How to write a post-mortem that actually prevents the next incident.
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40 files.
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That's her judgment, the thing that took her two years to build, sitting on a disk.
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Version 1, those files live in Maya's repo.
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She changes jobs, they go with her.
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Day one at a new company, she's operating with years of compounded judgment on tap.
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Every year she works, she compounds.
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That's ownership.
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And if she wanted to start a company that does this, it's her expertise.
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And it turns out she can.
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Entire startups these days will be Markdown files.
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Version two.
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Those files live in the company's repo.
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Under the company's IT policy, Maya leaves with nothing.
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The company keeps running her judgment without her.
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40 files executing forever and her name isn't even in the commit history.
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She didn't have a career.
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She had an extraction.
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Same files, same Maya, one variable.
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So this is the doctrine and I want you to be able to repeat it tomorrow.
418
I believe skill files are yours.
419
Own your skills because if you don't, your job becomes a skill file.
420
And this happened before.
421
Craftsmen own own their tools.
422
That's what made them free.
423
The factory broke that.
424
The loom belonged to the mill.
425
The knowledge workers assumed we were safe because our tools lived in our heads where nobody could confiscate them.
426
Skill files end that.
427
For the first time in history, your cognition can be extracted, stored, versioned, and owned.
428
The only question is, by whom?
429
Do you remember the thousand guilders?
430
That offer never went away.
431
It got rebranded.
432
Every comfortable arrangement where your judgment compounds in someone else's repo is a thousand guilders a year to show up, keep quiet, and stop building your own thing.
433
And that's why you should start a startup, because this is how you can actually make those skill files work for you.
434
Spinoza faced the upgraded version two.
435
In 1673, Heidelberg offered the cursed heretic a full professorship,
436
salary, legitimacy, a chair, and,
437
quote, freedom of philosophizing, provided he not disturbed the established religion.
438
His answer was, I do not know what the limits of that freedom of philosophizing might have to be.
439
He read the terms of service and he declined the acquisition.
440
He had a phrase for what he was protecting, under your own power, as opposed to under someone else's.
441
Your power of acting exists either way.
442
The political question in 1673 and in 2026 is who commands it.
443
Personal AI is about controlling your own cognitive abilities and protecting yourself.
444
That's the whole thesis of this talk in one sentence.
445
Personal AGI is how you stay under your own power in the age of agents.
446
So keep your brain
447
and your skills in a repo you control from day one before any platform or any acquirer has an opinion about it.
448
When Spinoza died, they inventoried the room.
449
Two pairs of pants, seven shirts, a lens lathe, 160 books.
450
And the ethics locked in a desk.
451
He owned almost nothing and nobody ever controlled his skill files.
452
The desk drawer was his repo.
453
Own yours like he owned his.
454
Now three objections and I can hear them from up here so let's just do them.
455
Objection one, the models are improving so fast that all this harness stuff will be obsolete.
456
Just wait for the next release.
457
This is the bitter lesson crowd, and I love them, but notice what actually happens in every model release.
458
The better the models get, the more the differentiator moves to context.
459
When everyone's engine is a thousand horsepower, the race is won on the driver and the map.
460
The weights are everyone's.
461
The library is yours.
462
At least I hope it is.
463
A better model makes your library worth more because a smarter reader extracts more from the same books.
464
I'm rooting for the labs as hard as anyone in this building, but every release they ship is a free upgrade to a workforce I already own
465
and a workforce I want you to own.
466
Objection two, is this just rag?
467
Sure, and Postgres is just bee trees.
468
Retrieval is the primitive, not the product.
469
The hard part is everything around it.
470
What gets written down in the first place, how it gets enriched and linked, What gets promoted to hot memory versus filed as cold reference?
471
Who arbitrates when two facts disagree?
472
Retrieval is easy.
473
Being worth retrieving from is the product.
474
Objection three, and it's the one that deserves the most respect.
475
You put your entire life in one system, your email, your meetings, your kids' schedules.
476
What happens when it leaks?
477
My answer is the same answer as the whole talk.
478
That's exactly why it has to be yours.
479
My brain runs on my own infra, in my own repo, under my own keys.
480
Compare that to the default, which is not privacy.
481
The default is your life is already scattered across 10 clouds owned by companies whose incentives are not yours, searchable by everyone except you.
482
I didn't create the risk by consolidating my context.
483
I took custody of it.
484
Custody is the security model.
485
And if you don't trust yourself to hold the keys, I promise you the answer isn't trusting someone else's terms of service more.
486
So why did I open source all of it?
487
The harness, the brain architecture, the skills, the whole personal operating system.
488
People ask me this because they seem like they think there must be a catch.
489
Well, the answer is because I can.
490
Because being at YC for me means I don't have to monetize my own infrastructure.
491
But because I can is also the answer to the wrong question.
492
The real question is why anyone should.
493
And the answer is that I believe tools of the powerful should be given away.
494
Every era has a private technology of leverage, a thing the powerful have and everyone else doesn't.
495
For a long time, it was literacy.
496
Then it was capital.
497
Right now, today, it's this, the harness, the library, the workforce made of Markdown.
498
The people who have it are quietly operating at a different scale than the people who don't.
499
And the gap is widening every month.
500
And that's what this whole conference is about.
501
to give you the power to be able to do it for yourself.
502
When something like that, that powerful stays private, you get a priesthood.
503
When it gets given away, you get a renaissance.
504
I know which one I want to live in, which means I get to do the thing that I actually believe in.
505
And I'll give it to you as a creed because it's the closest thing I have to one.
506
Say the things other people won't.
507
Fund the people other people won't.
508
Build the buildings other people won't.
509
Write and give away the code that other people won't.
510
Leave behind the institutions that other people won't.
511
And when you build in the open, you should know what's coming because Spinoza's story has one more chapter.
512
November 1676, Gottfried Liebnis.
513
The most glittering genius in Europe, silk stockings, a calculating machine in his luggage,
514
travels to The Hague to spend three days in an attic with the most hated man on the continent.
515
And then he spends the next 40 years lying about it.
516
Publicly, the visit was a few hours in passing.
517
Privately, his notes are crammed with obsessive commentary on Spinoza.
518
I love a small version of this weekly.
519
I say agents write most of my code now and the dunks arrive by lunch.
520
Then I look at what the loudest dunkers are actually shipping and it's agents all the way down.
521
So learn the pattern now because building in public guarantees you'll need it.
522
First they quote tweet you, then they git clone you.
523
The dunks are just the adoption curve announcing itself.
524
And I want to show you what this architecture looks like when it's pointed at the only thing that really matters.
525
I have a friend whose son has a rare form of epilepsy.
526
No lab, no grant, no permission.
527
He just went and you can just do things.
528
He built a repo of 80,000 markdown files, a brain for one small boy,
529
and pushed himself to the absolute edge of what humanity knows about his son's exact condition.
530
Every specialist visit, every paper, every seizure log, every drug interaction.
531
indexed and cross-linked and ready so that when a new doctor has an idea, he knows in minutes whether it's already been tried.
532
A father, a laptop, and a library.
533
That is personal AGI, not a benchmark, not a demo.
534
The entire architecture I've described tonight, the library, the librarian, the right three books open at the right moment,
535
aimed at the one thing one man loves the most in the world.
536
Nobody was coming to build that for him, so he built it.
537
And nobody is coming to build yours for you.
538
That's the good news.
539
Everything you were told you needed, the team, the funding, the permission, the credential, was a workaround for the fact
540
that one person could hold seven things in their head and work 16 hours a day.
541
That fact just expired.
542
you can fly now not metaphorically mechanically every problem where you
543
thought I wish I had this person I wish I could hire this person
544
but I can't get them you can every archive too big
545
to read every data set too gnarly to clean every ocean
546
you were told not to boil we can boil the ocean now I have a sentence I live by
547
and I want to leave it with you.
548
It's all made up, but you get to make it up.
549
Every institution in the world, including the one that read a curse over a 23-year-old in 1656, was made up by people no smarter than you.
550
The difference between you and every generation of founders before you is
551
that they had to recruit dozens of believers before they could build anything at all.
552
You need a laptop and a few years of your own history you're already sitting on.
553
There are about 7,000 people at this whole event, 7,000 kanadises, 7,000 strivings.
554
For most of history, almost all of that striving never got an audience.
555
It died waiting for funding, waiting for headcount waiting for permission, waiting for someone else to believe first.
556
The machinery I showed you tonight is the first technology I've ever seen that lets the striving go straight to work.
557
One person, no intermediaries, no permission.
558
I genuinely do not think the world understands yet what 7,000 people with
559
that kind of leverage walk out of a building and do.
560
Spinoza closed the ethics, the book that had to be smuggled out in a desk with nine words.
561
All things excellent are as difficult as they are rare.
562
The difficulty just collapsed.
563
The rarity is now up to you.
564
Go and build.
565
Thank you.
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Những Gì Bạn Sẽ Học Được
Khi xem video này, bạn sẽ phát triển được một số kỹ năng giao tiếp quan trọng. Đầu tiên, bạn sẽ học cách diễn đạt ý tưởng một cách rõ ràng và súc tích, điều này rất cần thiết khi tham gia vào các cuộc thảo luận hoặc trình bày. Thứ hai, video giới thiệu cách sử dụng ngôn ngữ để thể hiện quan điểm cá nhân mạnh mẽ, giúp bạn tự tin hơn khi giao tiếp bằng tiếng Anh. Cuối cùng, bạn sẽ cải thiện khả năng lắng nghe và hiểu ngữ cảnh, từ đó tăng cường khả năng phản ứng thích hợp trong các tình huống thực tế.
Nghe Những Âm Thanh Này
Trong đoạn hội thoại, có nhiều hiện tượng âm thanh thú vị bạn nên chú ý. Chẳng hạn, hiện tượng nối âm (connected speech) có thể được nghe thấy khi các từ được nối lại với nhau, tạo thành âm thanh liên tục và tự nhiên. Ngoài ra, hãy lưu ý đến việc giảm âm (reductions) trong tiếng Anh, ví dụ như khi người nói sử dụng “want to” thành “wanna”. Việc nhận diện và luyện nghe những âm thanh này sẽ giúp bạn cải thiện kỹ năng luyện nghe nói qua video hiệu quả hơn.
Nói Như Một Người Bản Xứ
Để bắt chước nhịp điệu và nhấn âm của người nói trong video, bạn cần chú ý đến cách họ nhấn mạnh các từ và câu. Cố gắng lặp lại những đoạn hội thoại ngắn theo cách họ nói để cải thiện luyện nói tiếng anh của bạn. Việc này không chỉ giúp bạn phát âm chuẩn hơn mà còn giúp bạn hiểu hơn về cách sử dụng nhấn âm để truyền tải cảm xúc và ý nghĩa trong giao tiếp. Nếu bạn sử dụng phần mềm shadowing, điều này sẽ trở nên dễ dàng hơn rất nhiều, vì bạn có thể luyện tập theo ngay tại thời điểm nghe.
Cuối cùng, hãy nhớ rằng việc luyện tập thường xuyên sẽ giúp bạn đạt được tiến bộ trong việc giao tiếp bằng tiếng Anh. Hãy kiên trì và thử nghiệm với các kỹ thuật khác nhau để tìm ra phương pháp phù hợp nhất cho bạn!
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| “Used to” used to + động từ — thói quen hoặc tình trạng trong quá khứ nay không còn | used to be · used to dread |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | has never been lifted · They've offered · has already been captured |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | been lifted · is locked · is spread |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | everything, which is · myself, which is · people who are |
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.