쉐도잉 연습: Garry Tan: Own Your Intelligence - 영상으로 영어 말하기 배우기

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So, the internet calls me one of the most AI psychotic people online.

So, it's only right that I start my talk with a story about one of the most cancelled men in history.

His name was Baruch Spinoza.

And in case the philosophy elective wasn't your thing, here are the highlights you need to know.

In 1929, a New York rabbi challenged Einstein by telegram.

Do you believe in God?

Answer in 50 words.

Einstein answered in 25.

I believe in Spinoza's God who reveals himself in the lawful harmony of the world,

not in a God who concerns himself with the fate and doings of mankind.

The most famous scientist alive asked the biggest question there is, pointed at Spinoza.

But here's what Baruch Spinoza's own community did to him.

Amsterdam, July 27th, 1656.

Spinoza is 23 years old, a member of a tight-knit Sephardic Jewish community.

He stands in a synagogue while the elders excommunicate him with the most violent curse the community ever produced.

Cursed be he by day and cursed be he by night.

Cursed be he when he lies down and cursed be he

when he rises up nobody may speak to him nobody may

trade with him nobody may come within four cubits of him nobody may read anything he writes and this ban

uniquely among the roughly 40 bands spinoza's community issued that century has no repentance clause.

It has never been lifted.

It technically is still in force today.

Spinoza was 23.

His crime was evil opinions, expressing forbidden thoughts.

His punishment was complete deletion from the community.

Before his community cursed him, they tried to buy him a thousand guilders a year, Serious money.

All he had to do was show up at synagogue once in a while and keep his mouth shut.

Hear that in founder terms.

They've offered him a salary to stop building.

He said no. Not for $10,000, he said.

He wanted truth, not comfort.

Shortly before his excommunication, a fanatic came at him with a knife.

The blade tore through his cloak and missed him.

He kept that cloak, scar unmended, for the rest of his life.

He wanted to remember what ideas cost.

So what does the most canceled man of the 17th century do next?

He grinds lenses.

By day, he makes optical instruments, tools that let human beings see further than their eyes allow.

He makes them so well that the best scientists in Europe seek them out.

And by night, he writes a book so dangerous, he cannot publish it while he is alive.

When he dies at 44, lungs full of glass dust from making other people's lenses, the manuscript is locked in his writing desk.

His dying instruction?

Ship the desk by canal barge to his publisher in Amsterdam.

That manuscript became his posthumous works, which attracted immediate attention across Europe and inspired some of the most important philosophers of the Enlightened.

What does Spinoza have to do with startups, you might ask?

Well, here is a man, canceled by everyone he knew, offered a salary to stop,

nearly killed for shipping, and his response was to build precision tools by day

and write the most dangerous book in Europe by night alone, with no permission from anybody.

If you're going to start a startup, you could do well to learn from Spinoza.

He had a name for the engine that kept him going.

Kanatus.

It means your striving.

The drive in every living thing to keep going and to increase its power to act.

Not your resume.

Not your title.

Not your job.

The striving itself.

This talk is about the tools that amplify it.

So what did he actually say that was worth deleting a man over?

The gist of the heresy was that God is not a king on a throne.

God is spread through everything that exists.

God or nature, he wrote.

400 years later, we are making a similar mistake about intelligence.

Everyone is waiting for AGI as a singular event, a god in a data center,

some announcement, some threshold, some day when the sky changes color.

So now I'll say a version of Spinoza's heresy, updated 400 years later.

Everyone is watching the sky, and the thing they're watching for is already in the room.

It doesn't look like a god.

It looks like infrastructure, a terminal window, a folder of markdown files, a job that finishes while you sleep.

Spread through everything, which is exactly where Sminosa told you to look.

AGI isn't arriving as an event.

It's arriving diffused as your agent, running on your context, doing your work.

I call it personal AGI, not artificial general intelligence for everyone all at once, general intelligence for one person, you.

This was a dream of a great many people.

Vannevar Bush called it the memex, a machine that would be an extension of yourself and your brain.

And I want to be precise about what I mean, because the words personal AI has already been captured by marketing departments.

I do not mean a chatbot you pay $20 a month to.

I do not mean a slightly better autocomplete.

I do not mean an assistant that knows your calendar and nothing else.

That's just a subscription you rent.

It's a corporate AGI you don't own.

It resets when you close the tab.

It knows what everyone else already knows.

and when the company behind it pivots, your so-called assistant gets a lobotomy on someone else's schedule.

Personal AGI is a different animal.

An agent that runs on your infrastructure, reads from a memory you own, executes procedures you wrote, and compounds.

The corporate AGI that you don't own gets better only when the company ships something.

Your personal AGI gets better every single day you use it because every day it knows more of your life.

One of these is a product you consume.

The other is an asset you build.

Almost nobody in the world has this second thing yet.

And everyone in this arena could have it by Monday.

And I believe intelligence, intelligence of this kind should be owned, owned by you, not rented.

If you go forth and build this for yourself, if 2034 doesn't have to be like 1984.

You might ask why this personal AGI is happening only now.

Well, I think it's because of what agents can do, and nowhere is it more obvious than encoding agents.

In 2013, I was a YC partner building Bookface, our internal social network, at night.

I shipped maybe 14 useful lines of code a day, which, if you know the literature on programmer productivity, is dead on median.

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.

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I believe skill files are yours.

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Own your skills because if you don't, your job becomes a skill file.

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And this happened before.

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Craftsmen own own their tools.

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That's what made them free.

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The factory broke that.

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The loom belonged to the mill.

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The knowledge workers assumed we were safe because our tools lived in our heads where nobody could confiscate them.

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Skill files end that.

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For the first time in history, your cognition can be extracted, stored, versioned, and owned.

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The only question is, by whom?

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Do you remember the thousand guilders?

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That offer never went away.

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It got rebranded.

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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.

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And that's why you should start a startup, because this is how you can actually make those skill files work for you.

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Spinoza faced the upgraded version two.

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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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이 비디오는 복잡한 이야기 구조와 논리적 연결을 전달하는 능력을 키우는 데 중점을 둡니다. 구체적으로는 역사적 사례를 소개하고, 그와 주제(스타트업) 간의 연관성을 설명하는 과정을 통해 IELTS 스피킹에서 요구되는 '논리적 전개'와 '다양한 어휘 사용' 능력을 기르실 수 있습니다. 또한, 긴 문장을 자연스럽게 발음하고 강조할 부분을 구분하는 영어 쉐도잉 연습에 적합합니다.

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"the internet calls me..." – "인터넷에서는 나를...라고 부릅니다" (주관적인 평가를 소개할 때 사용)
"here are the highlights you need to know" – "알아야 할 핵심 내용은 다음과 같습니다" (정보를 요약할 때 유용)
"His crime was evil opinions" – "그의 죄는 악한 의견이었습니다" (행위의 원인을 명확히 할 때)
"the engine that kept him going" – "그를 계속 움직이게 한 원동력" (동기나 원인을 설명할 때)

약점을 보완하는 방법

이 비디오의 대화는 긴 문장과 다양한 접속사(but, so, well)가 많아 shadowspeak 연습에 적합합니다. 특히 "Shortly before his excommunication, a fanatic came at him with a knife."와 같은 문장은 '시간적 순서'를 나타내는 구와 동작을 연결하는 구조로, 발음 시 쉐도잉를 통해 리듬과 강세를 익힐 수 있습니다. 또한, "cursed be he by day and cursed be he by night"와 같은 반복 구조는 음성의 균형을 연습하는 데 도움이 됩니다. shadowspeaks 기법을 사용하여 여러 번 반복하면, 자연스러운 말하기 리듬과 어휘 선택 능력이 크게 향상될 것입니다.

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화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
“used to” used to + 동사 — 지금은 아닌 과거의 습관이나 상태	used to be · used to dread
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	has never been lifted · They've offered · has already been captured
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	been lifted · is locked · is spread
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	everything, which is · myself, which is · people who are

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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