– English Lyrics

Lyrics & Translation

[English]

We realized we were really inherently

excited about the future of coding. And

I think we took a step back and realized

that if we were being really consistent

with our beliefs, there was going to be

an opportunity for all of coding to

change in the next 5 years and for all

of software development to flow through

models. It felt like no one working on

the space at the time was really taking

that seriously. It felt like they had

great products and they were making them

a bit better, but they weren't really

aiming for a world where, you know, all

of coding as we know it today gets

automated and building software ends up

looking very, very different. Then with

that in mind, we set out to to work on

that.

Let's start this talk with sort of the

origin story of your journey as a

founder. You kind of have to go way back

to middle school when you were reading

the essays from PJ, right?

So early on I think uh you know had been

interested in in starting a company for

a long time. Had been interested in a

bunch of a bunch of other things too. I

think actually originally got into

programming being interested in in

starting something kind of commercial

where uh the first time that I ever saw

code. It was over some winter break and

my brother and I we wanted to create a

hit mobile game. We didn't really know

how to do that. We looked on Google how

do you create a game? We heard that you

need to download this application called

Xcode. Uh we did that and uh we were hit

with these weird colorful esoteric

symbols which were Objective C which you

know uh is still around but maybe a

little bit less popular than it was then

for good reasons and um stared at uh

this kind of impenetrable wall of

Objective C and my brother promptly

ejected. Didn't move on with

programming. He now is on a very

different career path. He's kind of

trying to paint or something like that.

Um but um I yeah kept going and uh

bought a book on Objective C and then

eventually uh started working on on

mobile games. That was the genesis of me

getting into programming and then along

the way also yes was a big fan of uh

PG's essays and uh Sam's essays too also

and a bunch of the folks in YC and that

was definitely a big inspiration even

from the very early stages of of high

school. I think the wildest thing about

cursor is that right now you're just 24

and build this monster of a company in a

really short amount of time. To a lot of

people it could seem that it's a bit of

a out of out of nowhere, but this was

really in the making for more than a

decade. You've been working and shipping

a lot of different projects, right? And

you were working in AI even when you

were in high school, right? Tell us a

bit about the projects and how you got

started with that.

Was lucky enough to find programming

early on. was also lucky enough to be

interested in AI early on uh and have

some great collaborators to work on AI

projects with soon after kind of the fay

into mobile games which also turned into

uh I wasn't very good at mobile games so

one of the things that I built and

actually one of the things that got most

popular which was kind of the

technically easiest thing to build which

was maybe a lesson in startups of you

know the code isn't everything uh was uh

this mobile game or this mobile app

where you could spoof high scores in

things like piano tiles and flappy

and then send them to your friends. And

that was kind of the thing that went

viral. It wasn't the, you know,

painstakingly handcrafting the game

engine yourself thing. But uh yeah, no,

soon after that uh got interested with a

friend in the idea of building a robotic

dog where we thought it would be really

great to have a robot that you could

teach to do things without programming

it. Um instead you could give it

positive and negative feedback like you

give a dog. So you could give it a treat

if it does some, you know, quote treat

if it does something good. Uh you could

say bad if it does something bad. and

then maybe it would you know you could

teach it to play fetch and things like

that. Uh that idea really animated us.

But we had no idea how to build it. And

so again you know started the place

where one would start which is Google

and kind of went down a lot of rabbit

holes and you know took us into a place

of uh learning about genetic algorithms

and maybe that was going to be helpful

for building this robot dog that we

wanted to build. And then we eventually

learned about you know this neural

network stuff because some people were

playing with taking genetic algorithms

and using them to evolve neural networks

at the time you know with work like

neat. And then uh eventually it took us

to RL reinforcement learning um which

was you know even back in 2015 uh people

had been working on it for a long time.

In the end my friend and I we did

eventually build a couple of robots. We

did we didn't do any sort of substantial

work that really lasted but we did work

that was interesting at the time in

taking reinforcement learning algorithms

and making them more data efficient. you

know, making them better at learning

from very, very few data points, you

know, order of tens of data points and

also from noisy data, you know, data

that a human's giving. It wasn't exactly

a dog, but we built a couple of robots

where one of them was this many axis

robot arm that could kind of swing a

paddle and play ping pong and if you put

the right sensor on it and then you gave

it the right sort of positive and

negative feedback, you could teach it to

swing when it sees a ball. And then uh

we had this Kiwi drive robot that we

would teach to follow a line. to do

that. It was actually kind of this great

education in ML. Um, partially because

of our dumb naive where we didn't really

know that there were things like Torch

and TensorFlow and kind of other you

know lots of building blocks we could

use from maybe we weren't good enough at

Googling.

So you like implemented your own neural

network from scratch.

Yeah. So

when you were like I don't know 16 17

the constraints of the problem were we

were dealing with uh robots and so we

were dealing with microcontrollers

um and so microcontrollers have very

little memory and they couldn't really

fit any of the normal standard ML

libraries. So as part of our bike

shedding trying to build a robot dog, uh

we implemented our own tiny neural

network library and I have memories of

us not really understanding any of the

internals of how these things worked or

not really understanding calculus but

kind of fumbling our way through

re-implementing um some important ideas

from neural networks. I you know I think

it it taught us a lot. I think there

were a lot of gaps of the fundamentals

that it took many years to fill in

later.

Then fast forward to the founding of

anyphere. It's a interesting name

because cursor is not what it is. When

you guys started, you had just um

graduated MIT, right? That was back in

2022.

What were the first idea that all four

of you started working on back in 2022?

Yeah. So, the the genesis of cursor was

in 2021. Uh my co-founders and I, we had

been interested in AI for a long time.

Each of us kind of had our own little

robot dog moment where one of my

co-founders he worked on uh trying to

build a competitor at Google actually uh

using LMS in in 2021 and and training

his own um and training his own

contrastive models. Uh one of my

co-founders uh worked on computer vision

in academia and you know some of us also

worked on recommendation systems at at

companies like Google. But uh we were

really interested in AI. In 2021 we were

trying to figure out what we do with

that interest. Do we go and work on AI

in academia or you know do we go join

you know a big existing AI effort or do

we start our own thing and there were

two moments that really got us excited.

One was seeing the first AI product

start to come out uh you know GitHub

copilot was really the canonical example

for us. The other was seeing work about

how it looked like AI was going to

predictably get better in the future as

you scale up these models. At the very

beginning of 2022, uh me and my

co-founders, we went on a like a

month-long hackathon basically and we

started hacking on ideas related to kind

of picking an area of knowledge work and

building what it looks like as AI gets

uh more and more mature. Um

you guys have collected a lot of data

for that first idea, right?

Yeah. So the first real idea that we

worked on for a long time was in me

mechanical engineering. was trying to

build a a co-pilot for mechanical

engineers and trying to train models to

kind of predict what you would do in a

CAD system like Solid Works or Fusion

360 um which is where uh Mecky's model

out parts in 3D on a computer. We picked

it because we thought it would be boring

and sleepy and uncompetitive and uh we

were kind of doing an armchair MBA thing

even though it was a horrible choice

from the get-go because none of us were

really mechanical engineers and also the

science wasn't really ready for that

area.

But you guys kept working at it for a

number of months, right? And you crawled

and got all these CAD files and actually

got something working with autocomp

completion, right? That was like the

first version of it working.

Yes, we a bunch of the work was in data

scraping. Honestly, it was trying to get

all the CAD CAD models in the internet.

There also all these different file

formats and trying to convert them all

into something that's canonical because

CAD is this weird software market where

there are all these different systems

that are pretty popular. It's very

fragmented. Uh there are also cloud CAD

systems that don't have easily

exportable files and they don't want you

to scrape their stuff and so there was a

bunch of work there. Also the training

infrastructure for doing any kind of

modeling work back then was pretty

rudimentary and so there was a lot of

work on the on the infra side there and

just a lot of experimenting with models

and a lot of experimenting with how you

even jerryrig an extension into these

CAD systems because they're the you know

we were building an extension these

applications aren't really extensible at

all. There were actually also other

projects that we were working on at the

time. So um two of my co-founders they

were working on an endtoend encrypted

messaging system um because one of them

has a background in security research

and the idea there was apps like Signal

and WhatsApp um they encrypt the body of

the messages but they don't hide who's

talking to who at what time which is

actually really crucial information if

you don't want to trust the messaging

app provider. So, you know, if a

journalist is talking to, you know,

talking to some informant in the

government, just knowing that they're

communicating at all is a is actually,

you know, a really big piece of

information.

So, that was in the middle of uh 2022.

So, you guys were working for about good

6 months on this idea.

Yes.

And how many users were you getting at

did you get at that point? So, you

shipped the product.

All all of these projects were elevated

and it had basically no users.

At what point did you realize that the

idea was not working? It's like, oh no,

we we're all working on this. We we're

trying to do the startup. It's not

working. And

what was that moment like?

I think it was a bit different for each

of the projects. I think for the

messaging system that uh two of my

co-founders worked on. It was really

technically impressive, but it had these

bad trade-offs where it wasn't very

scalable. And I think they tried to give

it to people and it didn't really work.

And then they tried to sell it B2B and

then it didn't really work. And I think

it was after a couple of months of

trying to get traction for the CAD

ideas. It was yeah many months of trying

to get the models to really be useful

um for end users and then also reckoning

around are we really interested in these

areas or is there something else that

we're inherently much more excited

about.

So there was a moment that you decided

okay these ideas are not working we have

to pivot again.

Yes. you you you turned through a three

ideas, three, four, five ideas before

landing into into uh code completion.

Yeah, I think that um so we had been

inspired by um tools like copilot really

early on um and we had avoided working

on um AI and coding because we thought

it was too competitive competitive then

still is competitive now. So

2022 GitHub copilot was making already

about 100 million revenue

I think or more

potentially more. Yeah.

And you guys are like, "Oh, we could

still do a better job than GitHub

Copilot because people thought the game

was done." It's like, "Hey, GitHub."

Well, I mean, we we didn't think we

could at at the start. And then I think,

you know, it was the desperation of

having worked on ideas for a while and

not really being excited about them

after a while and them not really

working out. And that kind of shapes, I

think, what you care about and what

you're aiming for. And we realized we

were really inherently excited about the

future of coding. I think also we got to

see how some of the other people in the

space were, you know, working on their

products. We got to see how the tech was

developing. And I think we took a step

back and realized that if we were being

really consistent with our beliefs, you

know, there was going to be an

opportunity for all of coding to change

in the next 5 years and for all of

software development to flow through

models. And it felt like no one working

on the space at the time was really

taking that seriously. like it felt like

they had great products and they were

making them a bit better but they

weren't really aiming for a world where

you know all of coding as we know it

today gets automated and um building

software ends up looking very very

different. Then with that in mind we set

out to to work on that.

That was a bold move because you said

okay we're going to stop working on all

these other ideas that we didn't have as

much of a background and you were

excited about programming even though

you had this big Goliath in the room

with GitHub Copilot. you decided to go

and let's just solve this problem.

It didn't really feel bold or like a

move at the time because it's like you

know bunch of people sitting around in

their living room like on laptops. It's

not like you know like pivoting some

giant company but uh yeah no we did and

uh you know initially we kind of waited

into it where we were thinking well you

know maybe we do this kind of very a

niche tool for basically security

reviews you know trying to detect future

CVES in your code or maybe we build

something that's just for this one niche

area of software. um you know we we

thought about building for quants and

actually um kind of prototypes and

things just for quantitative

researchers. But yeah, in in doing that

we were just brimming with ideas for

what cursor could be if it were just

about trying to be the best way to code

with AI uh in general. And then I think

that we just we had a ton of conviction

about that and we had a ton of

excitement about that and so at some

point we just decided to to go for it.

Yeah.

And that was end of uh 2022, right? when

you decided to make that move and how

quickly did you ship the first product

and what did the first product look like

and that was around you shipped it a

couple weeks later and what was what was

that look like?

Um it did take us a little bit of time

to ship something publicly.

Mhm.

It took us roughly I think three months

from first line of code to open it up

and G it. Originally what we did is we

built our own editor quote unquote from

scratch.

Oh my god.

Still it was still using a bunch of open

source building blocks. There are a lot

of great primitives like code mirror and

you know the language servers and

there's a lot of open source tech that

can that can help you build an editor

but uh yeah no it was called together

from scratch and there was our own

version of remote SSH or own copilot

integration at the time because we

didn't have anything like autocomplete

you know you have to build you know your

own peen system you have to build all

your own language server integrations

there's just a lot that ends up going

into um something as developed as you

know the code editor market you know

making something that can actually be

competitive there and service someone's

daily driver but it was I think it was

four weeks until we built something that

we could use as our daily driver. It was

maybe four weeks later where we gave it

to the first beta testers and then there

was another four weeks and then we g it

and it was still very very crude at the

time. It didn't feel like a big thing to

just open it up to the public.

What did you learn in that first

version? Because you you built a code

editor from scratch. You guys haven't

done the whole forking yet.

Yeah. And we had the fear of God in us.

I mean we had

people hadn't hadn't really liked some

of the things we built for a while. So I

think that you know we were kind of all

in on it and very focused. But what did

we learn from that? Um, I think that we

learned kind of the first initial set of

AI features where, you know, when we

started, I think that there was just one

key command and it pulled up this like

universal remote in the editor and then

you asked it to do something and then

entirely the AI would just figure out,

oh, do you what what what exactly do you

want it to do? um you know, do you want

something back that's like a chat

response or do you want um like a code

suggestion that you can then take or do

you want it to go search around your

codebase and answer a question or do you

want it to go spin for a really long

time or a short time and there wasn't a

lot of control and I think that we

learned you know given the tech of the

time um at the end of 2022 that you

actually it has to the form factor has

to look a bit different and so we

learned kind of the first early AI

features that then became part of the

core of cursor from iterating both for

ourselves and also giving it to people.

I think another thing we learned was,

you know, we were very rapidly building

a feature version of what we want in a

normal code editor plus then some AI

stuff that we thought was great. But

then, you know, a feature complete code

editor for the world, um, is going to be

a way, way, way longer road. We thought

that, you know, FS code had been

developed over the course of 12 years,

was one of the earliest TypeScript

projects, um, had lots of people on it.

Thought, oh yeah, of course, you can

kind of spin something up that's just

equivalent for the world in in a few

months. And I think that we learned very

rapidly that that wasn't the reality and

our time was going to be best spent just

focused on the AI stuff. And so similar

to how browsers often based themselves

off of Chromium's rendering engine, we

then switched to being based off of VS

Code.

The other thing is you guys had also

implemented your own models too. Like

back then you got a lot of inspiration

from uh codecs, right?

Mhm. Yes. So when we were setting out to

work on uh you know our our first idea

that we really spent a bunch of time on

which was trying to help mechanical

engineers be more productive using AI uh

one of the things when we raised our

first round of funding because we we

actually kind of needed money from the

get-go to do a little bit of model

training because you couldn't bootstrap

it with the models that existed off the

shelves. They weren't good enough at

that task. One of the papers that we

would tout around is actually the

original Codex paper u because by our

calculations codeex which was the first

this was the first autocomplete model

behind GitHub copilot it didn't really

cost that much money to train even

though even back then at kind of the

beginning and middle of 2022 people were

talking about how expensive AI models

were to train I think it cost uh my math

might be wrong but I think it was about

100k in training costs and then you know

during this fay into mechanical

engineering we had done our our own

training and then uh when we set off on

cursor. I think we were a little bit

burned by that. And so we wanted to be

as pragmatic as possible, not to

reinvent the wheel. And so we started by

doing none of that. And then over the

course of 2023, you know, in dialing in

the product, that ended up being a

really important product lever,

especially as we got to scale and we got

a bunch of people using the product. And

then that also gives you the ability to

use product data to make the product

better. And so that actually has been a

really important uh muscle to build in

the company. YC's next batch is now

taking applications. Got a startup in

you? Apply at y combinator.com/apply.

It's never too early and filling out the

app will level up your idea. Okay, back

to the video.

What happened then in 2023 was when you

were still not sure about whether cursor

was going to be a thing, right? You were

still debating with your co-founders

whether you should still pivot. It's

like, oh, is this idea still going to

work? And you were still trying to grow

it, right? Because it took a long time

to to get to revenue, right?

Yeah. I think that over 2023 it uh it

was growing the numbers were kind of

small and I think that also we were

working on something where there wasn't

always a clear next step. Um I think

that there are probably some markets

where you're really well served by

going immediately talking to a bunch of

people listing down their problems

really rigorously or you know really

kind of systematically and exhaustively

thinking through each problem what would

kind of be the direct solution and then

prioritizing them and then going from

there. Um, but I think that we were and

are in a space that's that's a bit bit

different than that. You know, I we're

this end user application that doesn't

have much of a complexity budget. We are

trying to uh build the best way to code

with AI. And so a lot of that is

figuring out, you know, given the tools

that you have today, what can you

actually do? There's a lot of things

that you could write down that that

would be useful if you could build them,

but then, you know, figuring out how to

build them and all the details. It's not

entirely clear how to move forward on

that. And so yeah, there were a lot of

times over the course of 2023 and then

you know actually also to add to this of

our early user base if you just kind of

followed the gradient of exactly what

they wanted you would get pulled in

slightly different directions than we

ended up in. You know we had a really

loud segment of users that didn't know

how to code at all and we talked about

you know should we focus on those folks.

We had a really loud segment of users

that wanted us to do things that were

very techstack specific. Um, you know,

just building for one technology and

making it much less of a horizontal tool

and we resisted doing that too. There

was a lot of early prototyping and uh

kind of wandering the desert in 2023 and

then you know figuring out things around

you know where does it make sense to not

just build the software but also build

our own models to improve the API models

or or to replace them in places like you

know for instance with our our tab you

know our next edit prediction and then

how exactly to do that.

You went from zero to 1 million around

2023 right and it was uh it took it took

a lot to get there right? Um, yeah, it

was a a bit more than that, but sort of

roughly that that

And then 2024 was a crazy year. You guys

went from one to 100 million

in one year. Tell us about this uh loss

of uh compounding power because you kept

that growing 10% week over week. How did

that happen?

So, the numbers felt small early on,

then the compounding kind of kept going.

I think that there were a couple of

things that really drove our growth. Um,

we're in this market where if you make

the product better, you kind of see it

in the numbers immediately where, you

know, things start to grow more. And so

we felt it around, you know, when we

first started to make cursor codebase

aware, when we first started to, you

know, be able to predict your next

action, when we made that then more

accurate, then when we made that faster,

then when we made that more ambitious,

um, you know, it could predict sequences

of changes, and then when we let the AI

model start to take more action within

your codebase and then do that really

fast, you know, speeding that up. And so

all along the way, um, you know, I we

kind of just focused on making the

product better. the compounding

continued. Um, and I don't think that

this is true of all markets, but I think

we're we're in a market where end user

preferences matter a lot. And if you

make the best thing, people hear about

it and talk about it. And that kept

going for, you know, a long time. I

think one of the funny things that a lot

of that that's happened around that time

we did see a big shift in the YC

companies as they were going through the

batch because we would ask what what

kind of tech stack you use to build your

applications and it was night and day

from one batch to the other. I remember

in 2023 I think it was maybe single

digit percentage of the batch would use

cursor then 2024 it was like 80%. It

just like spread like wild wildfires

like the best builders were using you

got onto their Twitter feed. Yeah.

The Twitter feed. Was that where a lot

of adoption? How how did all the growth

came from? So the the very early stages

when we were first launching the editor

we uh tried to kind of evangelize it on

on social networks and actually my uh

one of my co-founders when kind of the

the dopamine hit keeping him going in

2022 when we were working on some of

these ill- fated ideas started posting

on the internet and kind of explicitly

set out to try to gain a lot of

followers not by doing kind of normal

social media things but by talking about

AI actually it was kind of surprising

you know, the degree to which someone

could actually just read kind of all the

papers, think kind of deeply about what

was going on at the time, talk about

that publicly and then get recognized by

influential people in the space. And so

there was like this particular um open-

source model, Flynn T5 at the time that

multiple

uh AI efforts that ended up using that

model. They found out about, you know,

kind of the benefits of that model

directly from my co-founder just because

he was posting on Twitter and doing that

kind of consistently. But he became like

sort of niche, very niche, like sort of

niche niche niche of SF micro celebrity.

He would actually kind of evangelize the

product early on. And so we had this

kind of very movie magic demo. Um, you

know, when we when we first launched and

when we first did a wait list to just

get our initial batch of users. I think

that that was helpful getting the, you

know, us kick started. But then after

that, we kind of stepped away from that

and we kind of lived like monks 2023 and

just focused on the product. And it it

really just spread from word of mouth. I

remember there were a couple of times

during that year where there were

members of the team that would say

things like, "Guys, the product's

already good enough. Like, let's put it

aside. Let's just focus on growth

engineering." And um then there would be

like a two-month sprint on, you know,

doing some version of that. uh and it

just never it kind of washed away

compared to the to the other stuff that

we worked on that year.

And by that time um in 2024, how big was

Kurser? How many how big was the company

at that point?

Uh it was pretty small in 2023 where my

co-founders are are fantastic engineers

and so and there were four of us and so

we could go uh pretty far without hiring

anyone. We also had our own, you know,

set of missteps and figuring out the

first set of people to hire and how

exactly to do that. And um so we're both

very patient early on and also, you

know, focused on hiring a lot less than

we probably should have early on. Um I

think we ended

2023

at only single digits people. Like we

were less than 10 still.

Yeah.

Amazing. So, so

no, I guess one cu curious uh now

shifting gears a little bit about what

are your thoughts in terms of how the

future's going to look with uh coding.

We were kind of this this maybe middle

road bet from the start where when we

set out to work on the company and we

were kind of hiring our our first

people, we would get these weird looks

around, you know, why are you I mean at

the end of 2022 it wasn't really like

this, right? because kind of catchy

happened and then the whole world woke

up to things, you know, beginning of

2023. But especially during 2022, uh

when we were working on the CAD stuff

and then the early code stuff, um people

thought working on AI uh was it was kind

of weird to do. People were not entirely

convinced that it was a good use of time

and that there were going to be lots of

great applications to fall out of AI.

And then even the people who were

interested in AI,

there was I in our space, you know, a

bunch of people that were just focused

on optimizing kind of the form factor

that existed already um and just making

those products a little bit better. And

then at the same time, you know, in our

social circles and professional circles,

there's a bunch of people that, you

know, we're thinking, oh, why would you

work on anything other than AGI? And,

you know, all of the work that you're

doing right now in one or two years, you

know, circa 2022 is going to go away.

And yeah, I think that we've always had

this view that there's going to be lots

and lots of um incredibly valuable

things to build over the next couple

decades. AI is going to be this

transformative technology. Uh maybe more

so than you know any revolution in

recent technological revolution in

recent centuries, but it's going to take

a couple of decades. And it's going to

be this industrywide effort where there

are all of these independent

capabilities that each need to fall to

really get to you know a place where you

can entirely get to the end state of uh

transforming building software on

computers or kind of the other areas of

knowledge work that might be transformed

by AI. And yeah, I think concretely kind

of in the near term

um we think that for professional

engineers, which is the end user we

serve, uh the market that we serve, um

you know, code is still really important

and uh there will be this long messy

middle where um you will be working with

the AI more and more it will become like

a colleague more and more. It may also

become like a you know a very advanced

compiler that can start to hide some of

the code for you. You're going to have

to read the logic and um yeah, and

review it and and edit it. So, and

what do you think are the skills that

are still going to matter? What should

everyone still be studying or stop

studying?

I mean, I think that programming like

math is kind of just a good general

education.

Um, and I don't think that that goes

away. And I think that there's also lots

of practical skills that comes from

studying computer science right now. I

mean, often when people are kind of

entering dynamic industries, the like

specific stuff that they they study in

school uh isn't super crucial. It's more

the kind of learning that they get along

the way. I don't think that's changed

with AI. What advice do you have for the

audience? If you have like a young

Michael Trello, maybe not just three

years ago, they want to be like you

three years ago before they start

cursor. What should they be doing right

now?

I think just working on things that

you're interested in and

uh doing it with people both that you

enjoy being around but that you respect

a ton. Uh and taking that really

seriously. Yeah, I think that for a lot

of people that are in school, it there's

so many things that pulls you toward um

more checking boxes and less, you know,

uh focusing on building something up

over time uh and really focusing on on

something that you're that you're

interested in.

All right, let's give it a round of

applause to Michael.

Thank you so much.

Yeah, of course. Thank you for having

me.

Key Vocabulary

Vocabulary	Meanings
coding /ˈkoʊdɪŋ/ A2	noun - the process of writing computer programs verb - to write instructions for a computer or other device
software /ˈsɔːftwer/ A1	noun - the programs and other operating information used by a computer.
company /ˈkʌmpəni/ A1	noun - a commercial business
mobile /ˈmoʊbaɪl/ A2	adjective - able to move or be moved freely or easily
game /ɡeɪm/ A1	noun - a form of play or sport, especially a competitive one played according to rules and decided by skill, strength, or chance.
build /bɪld/ A1	verb - to construct (something) by putting parts or materials together noun - the form or manner in which something is constructed.
mobile /ˈmoʊbaɪl/ A2	adjective - able to move or be moved freely or easily
robot /ˈroʊbɑːt/ A2	noun - a machine capable of carrying out a complex series of actions automatically
engineering /ˌendʒɪˈnɪrɪŋ/ B1	noun - the branch of science and technology concerned with the design, building, and use of engines, machines, and structures.
models /ˈmɑːdlz/ B1	noun - A representation, typically on a smaller scale, of a person, thing, or concept.
product /ˈprɑːdʌkt/ A1	noun - an article or substance that is manufactured or refined for sale.
editor /ˈedɪtər/ B1	noun - a person who is in charge of the content of a newspaper, magazine, etc. noun - a program allowing users to create and change computer files.
AI /ˌeɪˈaɪ/ B2	noun - artificial intelligence
data /ˈdeɪtə/ A2	noun - facts and statistics collected together for reference or analysis
users /ˈjuːzərz/ A1	noun - a person who uses a computer or network.
improve /ɪmˈpruːv/ A2	verb - to make or become better.