쉐도잉 연습: AX is the only Experience that Matters - Ivan Burazin - 영상으로 영어 말하기 배우기

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Welcome everyone.

So yes, a friend of mine said that we should talk about it in more extremities.

Obviously developer experience and whatnot still matters and humans do matter.

But we'll talk about why agent experience is probably the most important experience that you should focus on going forward. So basically,

the number of agents in the world as we progress will be the number of humans to the power of n.

And I say the power of n because we really don't know how many there will be,

but we do know that the speed at which people are creating software that are agents, and in each of these agents there are more agents,

is growing faster and faster and faster.

and it isn't hypothetical.

We don't have a lot of data, but I do have a little bit that I will share with you.

Namely, 25 % of YCs or Y Combinator, the most famous accelerator in the world, their last batch, not their current one,

we don't have the current data yet, 25 % of them, they said AI wrote 95 % of the code.

That part, put it there, not the most interesting.

To me, the most interesting part is is that 37 % were building autonomous agents.

So when you think of new SaaS offerings,

new startups, they are no longer deterministic web or mobile applications, they're actually agents.

And they are the new product.

And the most uncomfortable truth here is that most tools today break the moment you move a human from the loop.

So, if you have an agent that wants to go and do something, they're usually using human tools right now.

And so, we are entering a world where agents are not just assisting developers, because we're talking about developers here,

but they are the developers, right?

So, as my great MC said at the beginning, basically, if you're building DevTools or any tools for humans,

you essentially are building for the past.

And I say this again because the number of agents

that are coming online are going to be largely much more than humans.

So, what does it mean to build actually for the future?

It means building for agents.

And so before I get into why and how, I want to talk about what is an agent just so

that we have the underlining foundation and we're all on the same page.

When most people think about AI agents, they either think of something running on their machine like inside of Cursor or whatever,

or they think of OpenAI and Claude.

But as I mentioned earlier, more and more companies and products and services are actually agents.

So, you know, Cognition, Airtable became an agent, Lovable, Harvey, Manus, Cursor, the agent, they have the autonomous ones right now,

Decagon, which is a support engineer.

All of these services are AI agents, and essentially they spin up an AI agent for every single task that you as a human give them.

And how do we sort of define an agent?

So the way I look at it is it basically has three intersections, right?

You have the AI agent in the middle, which essentially is the piece of software, the mobile app, the web app, whatever it may be.

On one hand, you have the model.

I put LLM here, but it could be any model depending on what it's trying to achieve.

And that model gives it the intelligence.

And with that, it can sort of converse for you, with you, answer questions and whatnot.

On the second, it talks to the user, so it talks to you, the human.

You give it a prompt, a task, you ask it a question, it replies.

But the most important part, I think, for it to be able to get its job done is the tooling.

And so tooling can be anything outside of these two things.

Can it access your Google Drive?

Can you upload a file to it?

Can it use a browser?

Or can it actually use a computer?

In this case, we're talking about Daytona specifically.

So, an AI agent, when we talk about it, is something that runs on its own and intersects between a model, tools, and the user itself.

And so, going back to our question, what does it mean to build for the future?

Well, we need to focus on the agent experience or the agent's experience experience.

Matt Billman, who is the CEO and co -founder of Netlify, coined the term agent experience,

and it follows along the same idea of user experience and customer experience and developer experience.

The definition, I was looking for a definition for a while, and it wasn't actually Matt's, but it was his colleague Sean,

which I actually like the most of all the definitions of agent experience, and that is the following how easily agents can access,

understand, and operate within a digital environment to achieve user -defined goals. So think about that.

How easily can an agent get access to what it needs, how can it understand what it needs,

and how can it operate inside of that environment to achieve the goals that you, as a human, have set for it?

And I did a bit of research on some forward -thinking companies that are actually building out some agent experiences.

And before I get to that, I'm going to have a glass of water because my throat is really dry, so bear with me.

because I've been talking all day at this conference.

And so I have no affiliation with any of these companies, but I just like what they do.

So seamless authorization.

So that's a big problem.

If you have your agent and it has to go do something on your behalf, it'll probably have to log into your Gmail,

log into your booking .com, log into whatever.

It has to log in somewhere, right?

And so there's a company called Arcade.

I think their URL is arcade .dev.

And what they do is they're basically the authentication layer for agents.

So the agent doesn't actually get access to your credentials because you don't want your agent to have access to credentials.

But you can load your credentials in Arcade and then the agent can utilize that to access these services.

Moreover, if the agent doesn't have a login for whatever service, it will then basically roll back to you,

to the human, and say please log in through Arcade, again not giving the credentials to the agent, and then the agent can continue on, right?

Seamlessly continue their workflow.

Another great example, I mean a lot of people do this, but I picked on Stripe because Stripe does it very, very well.

It is agent readable documentation.

So some of the things that

Stripe does really well is if you take any URL from their docs, so docs .stripe .com slash whatever,

and you append a .md to that URL, it will render the markdown file.

No fluff, no UI, no graphics, no nothing.

All the stuff that gives an agent a headache and uses their context window, the agent can consume that much, much easier.

Or better yet, if you haven't done this in your documentation already, there's a standard called llms .txt.

And so llms .txt basically gives the agents one file for your entire documentation written in plain text

so the agent can easily read everything and comprehend what it needs.

My button doesn't work this far away, sorry.

And the third thing is API first designs.

I use the word API first, sometimes use the word machine native.

What we're talking about is, is there an interface where the agents can interact with your tool at a machine level, at an API level?

Like we don't want them to use a UI and whatnot.

And we can talk about MCP potentially, but MCP also sits on top of a API.

So just for the sake of this conversation I'll say

that everything that you your tool does should have an API

because it makes it much easier if your agent reads through your documentation

and sees there's an API endpoint it's super easy to do.

If it reads it through your documentation

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that's a login to a dashboard to click a green button after step three much much much harder

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and probably won't get it done. And so these

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These are like three nice examples that I pulled out of people thinking about the agent's experience.

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So, how do they log in?

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How do they learn about the documentation?

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And how do they access your tools?

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But is there more?

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Are there more things, right?

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And so, what is interesting is I think there's something really missing from Sean's definition originally

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and I'm going to go back to that.

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And so I added the word autonomously because I think that adds a little more emphasis to what we're trying to achieve.

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So how easily agents can autonomously access,

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so without a human, autonomously understand and autonomously operate within these environments to get their goals.

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Because it's a different scenario

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if it has to fall back to you as a human to do something

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or it can do it all on its own.

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And I think that currently when we think about agent experience, even some of the things that we mentioned here,

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helps agents do things faster, helps them get unstuck.

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But it's not like full -on autonomy.

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Still in a lot of places, everyone that is building these tools basically has to go back to the tool that they're building,

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to the drawing board, and ask one question is what if there are no humans around to click any buttons, right?

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To hit any keys, to debug errors, to type in a password.

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Like that is fundamentally what we're trying to solve.

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All of us here have made software for people and we've made it for us that interact through visual, a keyboard, a mouse and whatnot.

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And so if we're not here, how do agents actually solve that?

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And if you're not building...

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from the ground up, I feel that all you're doing is porting human experiences

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or developer experiences to the agent and not building out what the future is.

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A little bit of background on me, just like why am I talking all this stuff around here, just to give a little bit more context.

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So, I'm actually a bit old, so started companies in the early 2000s, building out data centers.

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So, we were actually screwing up like racks and IBM servers and Dells and whatnot, installing VMware and hypervisor and all that stuff.

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Second company was a company called Code Anywhere, the very first browser -based IDE in 2009.

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So, I was talking to someone earlier, like Replit before Replit, Codespaces before Codespaces.

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Sorry, GitHub people if you're in the crowd.

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We did this in 2009.

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We built everything from the IDE to the orchestration level to the isolation.

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We had to build it all on our own because none of these things actually existed.

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So what we learned in actually stacking servers, we sort of did it in a more virtual way, but for developers.

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After that, at Infobip, led the developer experience.

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Infobip, essentially a competitor to Twilio, which is a sponsor down here.

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One API for SMS voice, email, WhatsApp, and all those things.

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And lastly, Daytona.

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Interestingly enough, a lot of the team members in Daytona have worked with me throughout these companies as well.

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So it's like a really fun tight -knit group.

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And now what we do is we build agent -native runtimes, right?

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And my thing doesn't work again.

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Agent -native runtimes, right?

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So what are agent -native runtimes?

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First, runtimes are sandboxes or computers.

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So if an agent is equal to a digital human, what Daytona is, is a digital version of this MacBook right here.

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What makes it specific is that it's, and this is going to sound really weird, it is serverless, long -running,

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and stateful, which is kind of weird because that usually doesn't go with serverless.

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And I'll talk about it in a bit why.

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But basically, these things make it native for agents, giving the agents the ability to, you know, do what we do.

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So if an agent has to like clone a GitHub repository, where does it do that?

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It has to run the code.

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Where does it do that?

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If it has to do data analysis or visualization, if it has to use a computer or browser, where does it do that?

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It needs a machine.

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We actually have some agents playing video games, which is weird, but obviously they're trying to do that as well.

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So we have like some people playing

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Counter -Strike and teaching them how to do that on these runtimes.

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And you have vibe coding tools built on us that VibeCode 3 -day games.

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There is a lot of things.

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There's very few things.

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If I took everyone's laptop away today, how much work would you get done?

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And if you think of an agent as digital human, they also need that same workload.

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Slightly different, of course.

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Fine -tuned for them because the talk is agent experience.

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And so everything that we've learned around servers and working for developers and runtimes and IDEs, we've now put into Daytona,

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but with the agent Swiss.

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And so when we decided to build this, we had to think from first principles, what does a runtime look for an agent, right?

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And so if your agent, obviously we all like speed.

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Speed is really important to all of us.

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You don't want to wait.

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But spinning up your local dev environment or if it's running on EC2, it might take a minute or two minutes or 30 seconds,

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depending on how long it takes to build.

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But if your agent is, let's say you're talking to ChatGPT, and you have a coding task,

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you don't want to wait a minute for it to spin up an environment.

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It has to be really fast.

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So we created these runtimes that spin up in 90 milliseconds with latency.

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So that's including latency.

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It's a sub -30 milliseconds plus the network, and it gets about to 90.

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So that's pretty fast.

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You all blink.

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The time it takes you to blink is about 110 milliseconds, so faster than that.

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Secondly, it has to be stateful and long running.

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So, it spins up really fast, but it has to be a long running task.

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Why does it have to be long running?

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Well, it has to be long running because the agent is working on a task, and that task might last 15 minutes,

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might last an hour, might last two hours, might last five hours.

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You don't want it unlike serverless, which will die at some point in time depending on who you're using.

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You don't want your agent to be coding and then it kills the laptop.

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You want that to run for a very, very long time.

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And so the third thing is, which is probably the most important, is these machine -native interfaces.

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Again, we use keyboards, mouse, and screens, agents, ideally APIs, right?

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And I'll give you some examples of some of these machine interfaces that we've actually built in there.

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So the number one use case for these types of things still is coding.

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And even if it's not a coding agent, the agent does use code to get a lot of things done.

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So we built a bunch of tools inside, all of them headless.

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And just to take a step back, obviously, we have machine interfaces or API endpoints for the states of the machine.

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So the agent wants to turn on a machine, turn off a machine.

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It wants more CPU, more RAM, more disk, whatever, all that.

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But when it gets inside the machine, does it really want to parse the output of our human terminal?

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So we have, for example, a headless terminal.

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The agent has an endpoint, spin up a terminal, and then in a string just sends the command and gets the string back with the command.

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It doesn't have to figure anything out.

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We have a language server protocol.

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So most of the things that you have inside of an IDE, you have a Git client,

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so a full on Git client, and a file system.

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So create a file, remove file, find in files, all these things.

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All of these are headless tools that enable the agent to get the job done faster,

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use less context window, use less tokens, and get a higher quality outcome.

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But, and I have to keep walking back here, sorry.

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I have to stay on this side of this stage.

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But all these things that I mentioned are basically things that have existed.

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So you've had like serverless things that are really fast with APIs.

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You've had logging and running machines, although probably like an EC2 or something like that, so not the same.

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And we've had all these tools and we just added APIs on them, which is great.

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All of this together is a new thing, but what are some of the new primitives that we, talking to customers and users,

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building agents, had to build so the agent can autonomously do their jobs?

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And so one of the first examples was Daytona uses Docker as the default image for the runtime.

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So any Docker image that exists, it spins that up.

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The agent can pick that one, spin it up, and go.

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You can even connect your own container registry, and it can pick one, and it's fine.

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Obviously, if the agent spins up one of those and needs a new dependency, it can use the terminal,

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install that one dependency, you're good to go.

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But if the agent has to install 20 dependencies or 30 for multiple machines, that doesn't make sense.

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And the way that you would solve that usually is that a human would then create a new Docker image.

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It would publish that, and then the agent could use that one.

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But that calls for a human.

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And so what we want is the agent to do it end to end.

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And so we essentially created a declarative image builder.

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So now what the agent can do, and the agent can say, oh, like all these Docker images are like cool, but not what I need.

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I need a brand new one.

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And so basically through the API can say I need a new one and just list items.

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These are the dependencies I want.

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These are my environment variables.

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These are the commands that must execute.

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They don't build that on the fly, spins up the machine right after that,

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and that image is now a template and it can use it from here on in.

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So, a simple, like a little thing, but one more step where a human does not need to get into the loop.

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The second thing is you all use Docker.

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Docker is a great product.

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It's down there.

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And if you're working on your laptop and you have multiple containers and you have large data,

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you can share a drive or the files across them.

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It's actually called Docker Volumes.

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We didn't call it Daytona Volumes.

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We didn't think about it, but it ended up being quite similar and quite nice.

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Essentially, Docker Volumes enables you to share a file on your laptop and it works across all your containers.

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But if these agent runtimes are all isolated on their own, there's no sharing across any of this.

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yet you still have agents that have 100 gigabyte data sets.

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Literally a user, he says, I have 100 gigabytes that I need to use the same across multiple machines, but they're different run times,

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so I can't just snapshot it.

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I need to actually upload it 50 times or 100 times, which takes a lot of time, it's very expensive, not the way to do it.

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So we built Daytona volumes.

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The agent can say, oh, I need a new volume, it invokes it, essentially an S3 -esque type thing and then the agent can upload

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that once and attach it as a network drive to one or multiple sandboxes, all on its own.

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It can delete it after, it can use it after, it can do whatever it wants.

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The agent can solve its own problem and make the job easier.

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The last thing is parallel execution.

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As a human, I think that most people here work on one laptop at one time or one time at one time.

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Maybe if you're really dialed in on some stuff, you can maybe do one too.

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But essentially, you are working on a problem, trying to solve it.

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If it doesn't work, you go back and do it again and do it again and do it again.

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An agent can work like that, but an agent can also do it in multiple, right?

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So an agent can essentially say, at this point in time, I want to try five different things.

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There's five ways to solve this problem.

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And so Daytona basically, or these runtimes, enable the agent to fork the machine itself.

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Five times, 10 times, 50 times, 100 ,000 times, and try them all in parallel, all of them.

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And if they're all garbage, it can delete them and go back.

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If two of them, let's say number two and number seven are good, it can take those two,

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kill those, and it can actually fork from there again.

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So you get a bit of like this

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Marvel multiverse of things that the agent can try out, right?

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So it can do it all, all in parallel end to end.

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This is something that we as humans actually have no use for.

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We can't do that.

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Yet agents can and should use these types of things.

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Now, all these things that I've talked about today might not sound revolutionary,

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but the idea is to hopefully get you to think about how agents use the tools that you're building.

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That's what I'm trying to inspire you to get to.

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If you ask me what else is there, the hell if I know, I don't know what's out there.

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That's the honest answer and that is actually the point.

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The way you figure things out is you stay close to people building these agents,

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using your tools, and get a short feedback loop to figure out where it breaks.

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At what point is the agent not able to use your tool end -to -end?

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And so, how can you create it so that your agent actually never phones home?

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Sort of like if I'm watching ET, I'm old, yeah, ET phones home.

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Like, make sure that ET doesn't phone home.

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And so if you haven't tried that, if you haven't solved that, you're actually not building for agents, I would argue.

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You're just taking developer experience and you're porting that over with an API endpoint.

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And so we started this whole talk with this bold claim, agent experience is the only experience that matters.

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And I have to say it's not because we all are disappearing, hopefully.

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Humans are not there.

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But just that agents will outnumber us as humans and will they become the primary user.

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Most of them will be using that.

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And less and less will there be a human behind the screen, you know, reading the logs, clicking the buttons, typing into terminals.

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And just to leave you all with how we built these things, to accommodate the things that we did really shortly, we had to rethink everything.

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And as I mentioned, everything from the CLI to the control plane, to the infrastructure, to the orchestration, everything down to the bare metal machine,

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Daytona has now built out a cloud on bare metal servers, not on AWS, not on GCP,

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not anywhere else, because there was no other serverless, long -running, staple technology out there that we had to build it for ourselves.

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And we launched the product actually just two months ago, and I'm actually going to share some stats with you just

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to show you why agent experience is important also from a business perspective.

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So we launched it two months ago, two months and two weeks maybe ago number of people signing up to use this,

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these are actual numbers, 13 ,000, number of people that created an API key from those, this is our entire funnel,

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number of them that created a sandbox for a runtime, about 5 ,000, and 1 ,500 actually swiped their credit card already to use this.

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And because of these swipes of credit cards, in two months, Daytona has got to a million run rate,

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which makes it the fastest growing infrastructure company of all time.

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And I understand million is not the the biggest number yet

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and hopefully we'll be there still at 10 and 100 the fastest.

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But the point what I'm trying to show you is that infrastructure companies, and so here you have like

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Scale, Stripe, Vercel, Auth0, Twilio, Cloudfair, they usually take about 12 months to get there because you're not selling to a consumer,

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you're selling to someone building a product that has to decide to use you, build their product, integrate you,

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then market, launch the product, market the product, get users, and then that rolls back all the way to you.

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And why this is important is not that they don't have got to a million.

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You guys don't give a shit that we got to a million, which is fine, which is great.

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But what I'm trying to say is that the pull from the market, the number of people building tools,

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building agents, and need tools for these agents, it's insane.

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Like nothing of this magnitude has ever existed before.

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And And hopefully that inspires you, if you're building tools for humans, to think about agents as well as your user in your product.

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So with that, I just want to leave you with one thing,

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hopefully, one thought, is like if agents can't use your product in the future, probably nobody will.

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And so if you want to, that was like the clickbait slide at the end.

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And so if you want to learn more, we have this little booth in Hall 2 .2.

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The booth number is 225 slash 4, so it's really small somewhere by the stage.

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So thank you so much.

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이 말하기 레슨은 영상 “AX is the only Experience that Matters - Ivan Burazin”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: agent, experience, tool. 이 영상에는 섀도잉할 문장 327개와 단어 4429개가 있습니다. 말하는 구간의 길이는 25:06입니다. 화자는 분당 약 176단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 단어의 85%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
spin 동사	/spɪn/	회전하다
solve 동사	/sɒlv/	해결하다
server 명사	/ˈsɝvɚ/	서버, 봉사기
terminal 명사	/ˈtɚmɪnəl/	터미널, 공항 터미널
laptop 명사	/ˈlæp.tɑp/	노트북, 랩톱
interface 명사	/ˈɪn(t)ɚˌfeɪs/	인터페이스
arcade 명사	/ɑɹˈkeɪd/	아케이드
infrastructure 명사	/ˈɪnfɹəˌstɹʌkt͡ʃə/	기반시설, 기간시설
loop 명사	/luːp/	고리
parallel 형용사	/ˈpæ.ɹəˌlɛl/	평행의
install 동사	/ɪnˈstɔl/	설치하다, 깔다
container 명사	/kənˈteɪ.nəɹ/	그릇, 용기
upload 동사	/ˈʌpˌloʊd/	업로드하다, 올리다
string 명사	/stɹɪŋ/	끈
log 명사	/lɔɡ/	통나무

영상에 나오는 구동사

단어	뜻
go back 동사	돌아가다
turn off 동사	끄다, 턴오프하다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

Can you upload a file to it?
My button doesn't work this far away, sorry.
You don't want to wait.

이 영상의 문법

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

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have set · haven't done · have made
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	Decagon, which is · everyone that is · Twilio, which is

주의할 발음

화자는 you're, don't, doesn't 같은 축약형과 약화된 형태를 87번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: booth /buθ/, throat /ˈθɹoʊt/, authorization /ˌɔːθəɹaɪˈzeɪʃən/, hypothetical /ˌhaɪpəˈθɛtɪkəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/
“sh”와 “zh” 소리: essentially /ɪˈsɛnʃəli/, documentation /ˌdɑkjəmɛnˈteɪʃən/, shortly /ˈʃɔɹtli/, potentially /pəˈtɛnʃ(ə)li/, execution /ˌɛk.sɪˈkjuː.ʃən/
긴 단어 — 강세 위치에 주의: developer /dɪˈvɛləpɚ/, essentially /ɪˈsɛnʃəli/, documentation /ˌdɑkjəmɛnˈteɪʃən/, dependency /dɪˈpɛndənsi/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: interface /ˈɪn(t)ɚˌfeɪs/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, fork /fɔɹk/, founder /ˈfaʊ̯n.dəː/, behalf /bɪˈhæf/
/v/ — ㅂ(/b/)과 구별하기: developer /dɪˈvɛləpɚ/, solve /sɒlv/, server /ˈsɝvɚ/, virtual /ˈvɜɹt͡ʃuəl/, revolutionary /ˌɹɛvəˈluʃənɛɹi/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: browser /ˈbɹaʊ.zəɹ/, utilize /ˈjuː.tɪ.laɪz/, authorization /ˌɔːθəɹaɪˈzeɪʃən/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, spin, solve, server 같은 단어에 특히 주의합니다.

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.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: AX is the only Experience that Matters - Ivan Burazin - 영상으로 영어 말하기 배우기

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