SDS PODCAST

EPISODE 229:

DATA-DRIVEN APPROACH OF DOING

BUSINESS

Kirill Eremenko: This is episode number 229 with Co-Founder at

Cursor, Adam Weinstein.

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name

is Kirill Eremenko, Data Science Coach, and Lifestyle

Entrepreneur. Each week we bring you inspiring

people and ideas to help you build your successful

career in Data Science. Thanks for being here today,

and now let's make the complex simple.

Kirill Eremenko: Welcome back to the SuperDataScience Podcast ladies

and gentlemen. Super excited to have you back here

on the show. Today, we've got a very special guest

joining us for this episode, Adam Weinstein, who is a

Co-Founder at Cursor. Now, what you need to know

about Cursor is it's a company tool that helps organize

Data Science assets. So, if in your company you're

working on many different Data Science projects, you

have lots of different types of code, different

dashboards, different meta data, different teams

working on these projects. All that can be organized

with Cursor.

Kirill Eremenko: In this podcast, you will find out quite a lot of

interesting things. First of all, we'll talk about Adam's

own journey, his background. How he went from

working at Deloitte, all the way to working at LinkedIn,

and then founding his own company. So, if you're

interested in actually being an entrepreneur in the

space of Data Science, this podcast is definitely for

you. Plus, we'll talk about the concepts of Data

Literacy and Citizen Data Scientist, and you will find

out how Cursor can help you out in this journey. Of

course, in general what it means for an organization to

be data driven, data literate, and what Citizen Data

Scientists are.

Kirill Eremenko: So, if you are a founder of an organization or, if you

are an executive, this podcast is also for you. And, in

general, if you are interested in becoming more data

literate, and interested in the concept of Citizen Data

Scientist, whatever your level is in the organization,

once again this podcast is for you.

Kirill Eremenko: One more thing I wanted to mention before we get

started is that this podcast is available in a video

version. So, if you'd like to watch the video of us

chatting with Adam, head on over to

www.superdatascience.com/229. Then, you can enjoy

the video experience there. However, if you're listening

to an audio while you're running or in the car or

something else, then feel free to continue with the

audio because you will still get all the valuable insights

from here.

Kirill Eremenko: And now, without further ado, I bring to you Co-

Founder of Cursor, Adam Weinstein.

Kirill Eremenko: Welcome to the SuperDataScience Podcast ladies and

gentlemen. Today got a very exciting guest on the

show, Adam Weinstein. Adam, how are you doing

today?

Adam Weinstein: Doing great. Doing great. How about yourself?

Kirill Eremenko: Very good as well, thanks. We were just chatting

before how cool it is, the time difference. I'm in

Brisbane, Australia. It's almost 10:00 AM. What's the

time for you in San Francisco?

Adam Weinstein: Yeah, it's like a little before 4:00 PM the day before.

Kirill Eremenko: We were talking about it-

Adam Weinstein: [crosstalk 00:03:21] the day almost.

Kirill Eremenko: Yeah, I can tell you all about like in the morning you'll

have some rain. Then it'll get sunny. Whole bunch of

[inaudible 00:03:28] your day.

Adam Weinstein: We could use some rain here. There's about six years

of drought that we're trying to dig our way out of.

Kirill Eremenko: That is crazy. That is crazy. I heard about the fires

that were happening in California. Is that still going

on?

Adam Weinstein: Yeah. No, so they're luckily all ... they've all burned

out, I guess, at this point. Unfortunately, they finally

contained the fire about 12 hours before the rain

came. So, it was poor timing but, yeah. The impact has

been pretty massive. It's fascinating, Vice just did an

interesting special over the weekend on sort of what

does this mean in the long run, because we've had

now, two or even three years in a row, we've had tons

and tons of acreage burn, and houses burn. People

displaced, people killed, et cetera. Just because of this

wildfires that have started. Interesting to think if that's

not necessarily an anomaly anymore, right? Is it

becoming the normal?

Kirill Eremenko: Yeah.

Adam Weinstein: Maybe a topic for another time.

Kirill Eremenko: Yeah, I know what you mean. I originally saw a

visualization of they had ... what was it? I think they

had the 50 states of the US, and they had how often

abnormal weather events happened over the past like

60 years. And, all the same pictures, and it's

animated. You can see like, okay, if the start was here,

something popped up here, here, here, here, here.

Then, as you get into the 2000 it's like everything is

red every year like, something is popping up. It's crazy.

We might actually include that in the video version of

this podcast so people can see.

Adam Weinstein: Yeah, no it's interesting. I mean, the size of the area

that burned, I think, was roughly equivalent to almost

six San Francisco's or, you know ...

Kirill Eremenko: Wow.

Adam Weinstein: About 12 New York Cities, at least. So, if you imagine

like ... no grant that, these aren't densely populated

areas, but still, that ... if you've been to one of these

towns and you say, "Okay, that entire town burned.

Multiply that by 12 or six [inaudible 00:05:27]." It's a

huge amount of land that just totally been destroyed.

Kirill Eremenko: Wow, that's crazy. All right, well let's move on to Data

Science. Hopefully that situation will get better with

the fires. Data Science, so Adam, very excited to have

you on the show. You have an amazing journey

through Data Science with lots of highlights, LinkedIn,

Bright, and now your own company. I don't even know

where to start. Let's ...

Adam Weinstein: Yeah.

Kirill Eremenko: Let's maybe talk about if somebody were to ask you off

the street for the first time, and you were introducing

your ... how do you introduce yourself to people? Who

is Adam Weinstein, and what do you do?

Adam Weinstein: Yeah. I guess I'm a data geek, right? No. You know,

background is interesting, like you said. I started life

out of school like every undecided undergraduate. I

was a consultant for a couple of years. And, I always-

Kirill Eremenko: Sign me up for that as well. Yeah, same story.

Adam Weinstein: So, I'd always been interested in the hardware side of

technology. So, I actually got into infrastructure

consulting. We were helping really large companies

figure out how to deploy data centers around the

world. At the time, there was this big wave of

virtualization that was occurring, right? So, back in

the day you'd have one application on one server, and

even if it only ran a job for 10 minutes a day, it would

be an individual server that would be wasted for the

other 23 hours and 50 minutes.

Adam Weinstein: So, virtualization was like, okay, can you compact

multiple processes over the same box. Now, it's

containerization, or [Kubernetes 00:07:12] or

whatever. Fast forward 15 years. So, I happened to get

a little bit of a focus in data infrastructure. So, after I

had done my sort of tour in the consulting world I

joined a company, a start up actually, called Exact

Target. We had an office in Sydney ironically but,

never [inaudible 00:07:32].

Adam Weinstein: But, Exact Target was-

Kirill Eremenko: You worked in Sydney?

Adam Weinstein: Say what?

Kirill Eremenko: You worked in Sydney?

Adam Weinstein: No, we had an office in Sydney. I [inaudible 00:07:41]

in Sydney. Wish I worked there, but no. I worked in

Indianapolis, which is ... I lived in Chicago as a

consultant and moved back to Indianapolis where I

grew up after being a consultant for a while. Exact

Target was in the email marketing space. So, it's now

... it's currently Sales Force Marketing Cloud. Sales

Force bought the company about five years ago. It's

now the ... I think it's the largest email center in the

world still. Like, most large brands that send out any

quantity of email, whether it's Nike, or large banks, or

anything in between, right? If you're sending ... if you

need to send a few hundred emails, a few hundred

million emails in a few minutes, you tend to use

something like an Exact Target, or today's Sales Force

Marketing Cloud.

Adam Weinstein: But, they didn't really have a data team at the time.

So, that was a role I kind of jumped into shortly after

getting there. It was fascinating, right? We were

running the world's largest Microsoft sequel server,

which I'm not sure that's something you want to brag

about, but it was a fascinating time, right? The

company had gone from sending a few million emails

on behalf a few small businesses, to you know,

hundreds of millions of emails on behalf of Groupon,

and Nike, Bank of America.

Kirill Eremenko: How crazy is that, that like a company that's running

the world's biggest SEL server, and working with so

many users, and companies, they didn't have a data

team. Like, right now in this day and age, 10 years

later, it's unfathomable for a company [crosstalk

00:09:04] data team.

Adam Weinstein: Yeah, there was like an infrastructure team that kept

the lights on, right? They literally were hiring the

architects from Microsoft just to keep the thing

running. Like, [inaudible 00:09:14] Microsoft sequel

server because it was such a kind of a fire ... I'll refrain

from using other language, but yeah. It was a mess.

Adam Weinstein: But it's interesting, right? Even data companies

struggle sometimes I feel like, to step away from it and

say, "Okay, how can we look at the data that we have

and be more intelligent about how we use it?" So,

yeah. We were really a data driven company. We

helped companies identify, okay, you've got this list of

emails that you've accumulated from your website, or

from orders. There was a lot of retail at that time. How

do you market to them in a more cost effective

manner, as opposed to TV ads, or print mailers and

things like that. The company was growing through

the recession in 2008.

Adam Weinstein: So, I got there in 2007. Ironically, we probably went

public at the end of the year. Although, we ended up

pulling it because it was such a terrible time, but the

business did phenomenal through the recession,

mostly because it was a lost cost alternative. You

know, the cost of sending an email is a fraction of a

penny. The cost to send something in a mail or put a

TV ad is infinitely higher. So, yeah-

Kirill Eremenko: One of the first players there. One of the first

companies-

Adam Weinstein: Yeah, first data player at least there. So, we did

everything from how do you identify when a customer

is like, you know, high risk and looking to turn, to hey,

this customer just signed up yesterday and they've

already blown through their utilization so, why don't

we talk to them and find a better product. That kind of

thing. It was very early days of data, and I'm not even

sure it was called Data Science. It was probably more

business intelligence.

Kirill Eremenko: Yeah.

Adam Weinstein: But, it was a fun time. So, was there for a few years.

We got to about 1200 employees and I decided I

wanted to go do something small again. I actually took

kind of a brief hiatus from data. So, I was always a

sarcastic greeting card sender. I used to send cards to

family and friends. I had this crazy idea that the

challenge with cards is you can go down the street and

you can buy some cards, but if they're not the style

that you like or in the language that you need you're

kind of ... you have no options, right? It's not like you

can just go find another store. Card stores were kind

of dying too.

Adam Weinstein: So, I came up with this idea like, okay, if you just print

everything on demand you can have a selection of an

infinite number of cards. Someone could come online

in Australia, order a card, have it printed and mailed

in New York today and get to the person tomorrow.

You know, if you just have this distributive print

infrastructure. So, that was a company called Engreet.

We were a small team. Grew it, never raised any

money ironically, but debated to moving it out west to

do so. Coincidentally it was bought by the printer, so

the printer that was doing all this was like, hey we

want to get in that business. We talked over drinks

and they decided, okay, well what if we just brought

you on board. So, ended up selling them Engreet, and

now it's called The Greeting Card Shop. So, it still

exist. It's funny, I still get their emails every holiday

season.

Adam Weinstein: Then, I moved out west, and this is probably were my

real start in data kind of occurs, at least modern day

data. To work for a company called Bright. Bright was

a company in the machine learning space that helped

match jobs to people. So, if you think about the job

search process going back a few years, you'd go to a

career builder on Monster and Indeed. You'd type in a

job title and say, hey, I want to be a software engineer

or a product manager, or marketing coordinator or

whatever it may be. Then, they'd show you all the lists

of jobs that had those titles. But, companies were

getting a little creative with titles. I think the joke we

used to use was, what if the job was called ninja.

Nowadays, I don't think, hopefully, anybody uses that

title, but maybe they do.

Adam Weinstein: So, we built an algorithm that would basically parse a

resume and a job description. Calculate the

normalized skills that were basically being used, right?

So, Oracle, and Facebook, and SAP, and Microsoft and

Google. They all recruit people that can write Java, but

how they describe it is very different. So, we would

come up with a way to normalize the skills being

represented on both the job description and the

resume. Then, score the fit between the two of them.

Adam Weinstein: So, instead of searching for a job title you would

actually just [inaudible 00:13:21] show you all the jobs

you were qualified for. So, that was a fun couple of

years.

Kirill Eremenko: Sorry, just kind of like a recommender engine, right?

Adam Weinstein: Yeah.

Kirill Eremenko: Like you upload your resume and then instantly you

get jobs that you're ... is that still used online when I

go on Indeed or Glassdoor, and stuff like that?

Adam Weinstein: Yeah. So, LinkedIn bought Bright in 2014. So, when

you do ... when you perform a job search on LinkedIn

that scoring algorithm is actually being used behind

the scenes to [inaudible 00:13:53] a job. So, you still

do search for a title but, there's sort of a marriage of

the title, and then that score that help recommend

what jobs to see.

Kirill Eremenko: Oh okay.

Adam Weinstein: And then, [inaudible 00:14:03] LinkedIn emails on job

recommendations. I'm sure there are still some

mistakes in the algorithm but yeah, those

recommendations are being informed by the same

score.

Kirill Eremenko: Interesting.

Adam Weinstein: [crosstalk 00:14:14] owned by LinkedIn, but yeah.

Kirill Eremenko: So, it uses the information on your profile when you

search for stuff? That's so cool. That's so cool. It's

important to get your profile right, not just for other

people to see it, but also for your searches also be

most relevant to you.

Adam Weinstein: Right. And, increasingly, so LinkedIn's core business is

this recruiter product that if you're a recruiter inside of

a large corporation you pay a substantial fee to be able

to search across the entire LinkedIn network. You

can't ... you know, you can then send an email to

anybody. Increasingly, that search process is

converting to a recommendation process. So, instead

of the recruiter saying, "Hey, I'm looking for software

engineers that have three years of job experience and

have worked at these five companies." LinkedIn is

trying to push candidates to those recruiters. That's

being informed by the same recommendation

algorithms. I think, you know, the downside of the

hey, you have to put all your sort of life's work into

this profile, but the more you put there the better off

people that might be looking to hire you will be.

Although, as a Data Science you're probably not

hurting for inbound interest in being hired.

Kirill Eremenko: Yeah. [crosstalk 00:15:22]. And, just maybe a year or

two ago LinkedIn started ... when somebody endorses

somebody for their skills, now it's not as easy as

before. Just like click, click, click. Now you have to

explain. How do you know the person, what level of

endorsement and so on. I think that's probably has to

do with that whole system as well.

Adam Weinstein: Yeah, it has to do with how knowledgeable, or what's

the quality of that recommendation. So, before

anybody could go and endorse anybody for anything. I

could endorse somebody for material science

engineering and I don't know the first thing about

material science, and vise versa. Somebody could

endorse me for something I might good or might not be

good at but, even if they don't know anything.

Adam Weinstein: There was actually a time, maybe embarrassing length

in history, where you could create your own skills. We

famously endorsed people for very inappropriate things

like, you know, I don't know what a good example

would be. Like dropping things on the floor, or tripping

in public places, or things like this.

Kirill Eremenko: Yeah.

Adam Weinstein: You know, not skills that anybody would want in their

profile but, you could just endorse anybody for

anything. So, why not?

Kirill Eremenko: You'd endorse them ... you'd create a skill for them,

right? You endorse them for something they don't even

have on the profile.

Adam Weinstein: Exactly. Exactly.

Kirill Eremenko: Good times, yeah.

Adam Weinstein: So, no. Yeah, they're trying to get the quality aspect

figured out because it's ... that really is what tells you

whether somebody is good at something. If somebody

that's in a domain can endorse somebody else in that

same domain for something, that should be a very

valuable endorsement, and that's what they're trying

to get to with skills.

Kirill Eremenko: Yeah, totally. Totally gotcha. What happened after

LinkedIn? You were there ... you were at Brights until

they got acquired, then, you were for what? Two years.

Then, at LinkedIn you worked for another three years?

Adam Weinstein: Yeah, exactly. So, here I was, this sort of start up data

guy at Bright, right? We had a small team but it wasn't

LinkedIn size. When I got to LinkedIn, they actually ...

the joke was they didn't necessarily know what to do

with me. They're okay, here's a guy that knows how to

build data teams in small organizations. We've already

got 200 of those people. What do you want to do?

Adam Weinstein: But, it turned out that LinkedIn had just built a ...

sorry. An office in China, so in Beijing. And, the way

doing business in China works it was technically a

subsidiary. So, we had a couple of folks there but we

were building it out as if it were an independent

company. It had to be autonomous, right? LinkedIn in

California didn't know the first thing about succeeding

in China. We hired a team that had been really

successful previously, a couple from Google, from

Apple, and elsewhere. We just wanted to give them the

autonomy to do so.

Adam Weinstein: So, I became the data ops guy that was sent there to

help build out all the tooling that they needed to be

successful against most of the LinkedIn data that

already existed. But, the recommendation was to don't

necessarily use what we already have. Think of things

as if you were doing it from the ground up. So, I was

there like two weeks when they asked me to do this.

Adam Weinstein: I started getting on a plane, going back and forth every

six weeks. My first question was okay, how are things

done around here? What are the metrics we care

about? Where is the data? Show me models that are

relevant. How do I get an understanding of this

business that was about 5000 employees at the time.

The challenge I found was that there was no one place

to go. So, I had about 200 or so, it was like 180

coffees. Ironically I don't drink coffee, but 180

meetings over the course of about 18 months where I

just met leaders in different domains. And said, "Okay,

you're the marketer for this product. How do you

measure new customers? What is the definition of a

customer? What's the definition of a customer at risk?

What's the definition of a successful customer?" Like,

all these things that were not captured in one place.

They were just in individuals peoples' heads or on

their local machines.

Kirill Eremenko: Sorry, this isn't the main LinkedIn? You're getting that

information on the main LinkedIn [crosstalk 00:19:17]-

Adam Weinstein: Then, I would take that, go to China and say, "Okay,

here's how we do it in the US. You could use this if

you want." Or, I should say the rest of the world. We

were supporting a global business, kind of sort of

being as one carve out. Yeah, that was sort of my 18

month window of life. Where literally, come back, I'd

pick a new domain, a new product, new area, go learn

as much as I could about it. Then, fly sort of like ...

show the team what I'd figure out. Then, go do the

same thing all over again.

Adam Weinstein: What I ended up developing was this great corpus of

knowledge of, hey, here's data inside LinkedIn.

Knowing every time I interviewed someone like

chances are three weeks later it would change. But,

you know, it was relatively up to date. A bunch of

people around the business started using it as well,

right? So, it was a collection of code, of terminology,

nomenclature, like data definitions. A little bit of like,

okay here's were we keep ... we had a bunch of

different reporting systems because we were a software

company. So, we built them every time we needed

them.

Adam Weinstein: So, here's the reporting system for this metric. Here's

the reporting system for that metric. Sometimes it was

Tableau, sometimes it was homegrown. Sometimes it

was something that had been there for 13 years that

we didn't know why it was there. Yeah, it was a

fascinating journey, but I think it taught me that even

in a really innovative company it can be hard to keep

your arms around what's going on where, and how to

find answers to sort of even the most basic data

questions, which I think as a Data Scientist is ...

before you can have fun with data as a Data Scientist,

you have to know where things are. You have to know

what it means. Can you trust the data? Is it of high

quality? Is it being refreshed? Is this the source of

truth, if you will, right?

Adam Weinstein: So, that drove me to start Cursor, which I can talk

about but, that was sort of my journey at LinkedIn. I

left last March officially to ... you know, decided that a

paycheck was no longer worth it.

Kirill Eremenko: Did you already know when you were leaving that you

have this idea for Cursor, or did you leave and then,

come up with the idea for Cursor later on?

Adam Weinstein: Yeah, so I had a good understanding of what I wanted

to do. It wasn't ... I don't think it was perfectly nailed

down. What I wanted to do was spend a couple of

months talking to other companies, particularly

outside of the area of technology. Silicon Valley can be

a little bit of a bubble in terms of how we look at

problems, and how we solve them. So, I wanted to talk

to banks and industrial companies, and retailers and

understand what is data inside their organization and

how do people interact with it.

Adam Weinstein: I had a good sense though of what I wanted to build in

terms of somewhat of a data catalog but something

that was more interactive for the average business

user. So, that was the premise. I think we honed it for

a few months before we actually started the company,

and raised money and that kind of thing. I had a

strong sense of what it was we were going build at

maybe [inaudible 00:22:14].

Kirill Eremenko: Okay. Awesome. So, that leads us to Cursor.

Adam Weinstein: Yeah.

Kirill Eremenko: First of all, why the name? Why Cursor?

Adam Weinstein: Yeah. So, I've liked the name Cursor since long before I

came up with the idea. You know, I guess you could

say the concept to me is like, in a knowledge

management kind of problem, which I guess you could

generically call Cursor a knowledge management

solution, although it's not generic knowledge

management. It's specific to data.

Adam Weinstein: I think that the notion of a cursor helping you seek, or

find something is really I think powerful. Then, I think,

you know, there's also a database Cursor concept,

although we talked to a DVA about a database Cursor

although they'll run you out of the room because

they're not very conferment. But, I think the sort of

marriage of those two, right, that it's steeped in data.

At least the concept of a cursor, or even code right? I

mean, cursors have existed in [inaudible 00:23:11] for

a long time.

Adam Weinstein: Then, the notion of like, okay, people can relate to a

cursor helping to find something. Whether it's on the

screen, or potentially buried in a data link somewhere,

right? Like, I've always liked the name. So, it just so

happened I found the dot com was available.

Kirill Eremenko: Oh wow.

Adam Weinstein: Between the two of them, I was like okay, well this is

the right name. So, we had the name before we had

the company and the idea.

Kirill Eremenko: Yeah. Gotcha. As in Cursor like the cursor you have

on the screen, that type?

Adam Weinstein: Yeah, exactly.

Kirill Eremenko: That's so cool. Rarely those domain names are

available, short ones like that.

Adam Weinstein: Yeah. It'd be interesting to see if domain names last.

You know, they're sort of a real estate gold rush for

domain names now that you've got so many other

TLDs, right? Dot IO, dot APT. You know? Other things,

right, is the dot com is valuable? I guess we'll find-

Kirill Eremenko: Yeah, we'll see. As soon as Google updates their search

algorithms. Right now I think [crosstalk 00:24:10].

Adam Weinstein: Yeah, exactly. Exactly.

Kirill Eremenko: Easiest to find. Okay, well Cursor. Tell us about the

company. What does it do? We've heard your story.

Obviously, you've built up a lot of experience,

knowledge and data, and then some pressing issues

that you actually saw first hand. How does Cursor go

about solving? And just in general. Give us an

overview.

Adam Weinstein: Yeah. So Cursor, the challenge if I had to sort of boil it

down that we had at LinkedIn is that we had a bunch

of users across the organization that were creating

content, right? Could be Ad Hawk Sequel code, could

be dashboards in Tableau. Could be an Excel

spreadsheet. Could be a Python model, right? There

was no one place to go find all of that. Mostly because

everybody was using their own set of tools. So, you

had people that had locally installed sequel editors.

Tableau, I guess if you were looking for a Tableau

dashboard, you could search if it had been published

to the server. But, there was a lot of work that was

being done on the local machine. Even Jupiter

notebooks, right, for the most part were installed in

local environments.

Adam Weinstein: So, Cursor is a tool that a user can start with, or a

team, that if they work inside the product it has a built

in sequel editor, it has a built in Python environment.

It connects to all these places where data lives, and to

BI solutions like the Tableau and any database that

you might use. It basically curates in sort of an

intelligent way all the data that it's seen. Or, I should

say meta data that it's seeing. It's actually not looking

at the raw data itself.

Adam Weinstein: So, if you connected to three databases, you've written

some sequel, you've written some Python, you've

connected to Tableau, it helps build a single corpus of

knowledge that any user in that business can come

search. And, helps ... and the goal of defining things

that have already been done, or answers that may

already exist. So, an example might be I'm an analyst

and I'm trying to figure out how many products have

we sold today? Generically speaking. If somebody else

has done that work, how do I find them? If they

haven't, how do I find what table has the product data

in it? If I do find that table, how do I know that table is

the right table? So, we help built a place where people

can come find what they're looking ... you know, find

an answer to a data question. Make use of data if they

don't necessarily know what the answer may be, and

then understand what they're seeing.

Adam Weinstein: So, it's you know, simply speaking you can think of it

like an Evernote or Dropbox for an analyst, or for a

data user. It could be also for a Data Scientist, but it's

designed to scale as wide as need be. So, data we

know is siloed, right? As are the teams that use it. So,

the solution's kind of designed to fit that. You can

start with one team. You can let another team come on

later. This was a challenge that we saw on LinkedIn.

We looked for a solution in the market to try and solve

it for the business, and the problem was we couldn't

get everybody to agree. The perfect prevented was the

enemy of the good, right? You can't have a solution for

everybody, nobody had anything.

Adam Weinstein: So, this is designed to solve that challenge with hey,

one team can start using Cursor. They can at least

start sharing with themselves, and then, typically what

you'll see is okay, another user gets jealous of this

corpus of knowledge. They'll come on, and that brings

their team with them, and it kind of grows from there.

Kirill Eremenko: Wow. That is such a cool idea. It's like, and I'm already

hooked because I think of myself as a very organized

person, and what you described it sounds like a tool to

organize Data Science assets. You know, whether it's

code, whether it's data, whether it's like anything to do

with the Data Science projects. Very cool.

Kirill Eremenko: So, basically I can not only search ... as I understand

it you are combining, first of all the tools. Sort of like if

something was done Python or in R, or in Tableau, I

don't know, right? I might only know Tableau or might

only know Python. I don't know what other people

have done in other tools. Or, even I've worked in many

tools. I can actually put those entries into Cursor, and

that way I will know what I've done across different

tools like keep track of it across different platforms.

Adam Weinstein: [crosstalk 00:28:25] right? We don't want to have to

have you pull in everything manually. So, in many

cases we've built connectors. Like, if you've got

Tableau already, you can just plug in your credentials

once. We'll automatically suck everything in. We'll pull

all the queries behind every dashboard, make it

searchable, and same thing goes for other

environments too. The goal, again, it's like we don't

want to replace every tool. We just want to bring them

all together into one sort of searchable interface.

Kirill Eremenko: Gotcha. So, that's tools. Then, on the other hand you

also organize across people and departments, right?

So, in a bigger organization, or even if it's like ... even

if it's a small organization, but decentralized. Like, our

business is across different countries. So, if somebody

has worked on a project and I don't know if they

worked. So, again, you want to reduce double work,

right?

Adam Weinstein: Yep. That's exactly right. So, what we separate is that

to know that somebody has worked on something

versus, being able to see the results. So, we have

teams where, let's just say sales and finance. There

may be certain things that finance produces that

they're comfortable knowing, like okay they worked on

a quarterly sales pipeline. But, they may not be

comfortable sharing the results of it. So, what that

separates is like, okay the sequel query or the Python

code that's been written, you can see that but then,

the only way you can actually see the results is if you

have the credentials to actually execute it. So, we allow

you to sort of separate those two, because the model or

the code is often times less sensitive than the actual

results.

Adam Weinstein: That's a challenge we see time and time again, that

you know, why have somebody start from scratch

when they can reuse 80% of something that someone

else produced just because you know, you're on a

different team.

Kirill Eremenko: Gotcha. Yeah, that's a really cool idea. I'm surprised

nobody has done it before. Were you shocked that it

didn't exist?

Adam Weinstein: Yeah. I think it's ... I think it would have been difficult

to do it too many years ago. The reason being, like if

you look at how fragmented ... I mean, I would say it's

becoming more fragmented, but if you look at how

fragmented the tool space was even just a few years

ago and how few of those were web accessible. So, it's

really easy to build an integration to Tableau because

they have rich APIs that you can connect to, and it

allows you to extract a lot of the relevant information

you want to add into some sort of search interface.

But, if you go back to the world of SAP and Oracle,

where that was commonly what you would see in big

enterprise, there weren't rich APIs. There weren't great

ways to stitch things together.

Adam Weinstein: So, it would have been harder to build a solution that

was trying to do what we're doing. To be fair, we get

asked to plug into things and depending on the

product, we can do good sometimes and less good

others. It is a ... it is something in this web era where

things are built with, I don't know if it's collaboration,

but certainly accessibility in mind, and the ability to

come from third party platforms. You know, it's getting

easier to do.

Adam Weinstein: But yeah, I was surprised that there was nothing

focused on this search problem. There were data

catalogs, and data catalogs was sort of like a V1 of this

problem set, which is like how do you at least provide

just a dictionary. Think of like a telephone directory of

data inside of a business. But, the problem I saw with

those ... we looked to deploy one at LinkedIn too. The

problem I saw with those is that everybody has to go

upload the dictionary manually, and by the time you're

done uploading it and surveying the entire business,

it's already out of date. Like, ingrained in the person's

workflow. So, if they're not using it on a daily basis,

and they have to take time to separately go document

something, just like documentation in general, right?

It's not going to get done.

Adam Weinstein: So, we tried to build something thoughtfully that was

part of a user's daily workflow. That's why I hope we

can succeed.

Kirill Eremenko: Gotcha. What kind of integrations do you have at the

moment? You mentioned Python, Tableau, R. Can you

give us a quick overview?

Adam Weinstein: Yeah, yeah. So, we've sort of focused on three areas.

Any data store that you'd want to plug into from ... so,

big data, like a Hive or a Spark, to you know,

traditional data stores like an Oracle, or Terra Data or

Microsoft Sequel, right? Any database we want to be

able to plug into on the BI front. So, we think of that

as sort of layered to you know, that there's Tableau,

there's Click, there's Looker, there's Power BI. We

started with those just because they're sort of the

larger, more popular ones on the market.

Adam Weinstein: Then, you're right, on the language front we have

Python. R is in process. Not there just yet, but it's on

the horizon. And, sequel, lots of sequel and various

flavors of sequel. If you're writing P sequel, Microsoft

world, they support that of course. Then, support a

number of different operating systems. So, we have a

Mac client, a Windows client, and a Linux client too if

you want that.

Adam Weinstein: The product is ... you know, it's cloud based in the

sense that when you share something, like if you write

some code and we're on the same team, and you want

to share it with me, that's shared via the cloud. But,

there is a client aspect to deal with the certain

networks if you layer in between. It's like often times

inside big companies, you know all the places where

data live are not accessible to the clouds. We couldn't

directly connect to it from our cloud layer. We'd have

some sort place, or some place internal to be able to

get into that.

Adam Weinstein: So, you can use the client as a means of doing that, or

you can actually deploy it on a server internally if you

want. It's up to you. It's much like an R server, or a

Jupiter notebook environment, right? You need some

place internally for it to live in order to connect to

data.

Kirill Eremenko: Okay, gotcha. Let's talk about actually people using

this. How has it been received? Have you had people

and companies try it out, because I can imagine it

actually solves a lot of pain points, and for some of our

listeners listening in, they're probably already seeing

this at the company they work in, or maybe have

experienced this in the past, or maybe it's their own

business so they're seeing it. So, tell us about how

others have perceived this, and what kind of benefits

has this been able to deliver?

Adam Weinstein: Yeah. So, I think it depends on the audience, right?

So, there's probably three or four audiences that have

crept up. I don't know if they were intentional or not.

In no particular order, right? So, there's an

engineering audience that like more traditional

software engineering. They may support a data

organization, or an analytics team, but they'll often

times have queries that they want people just to be

able to see. They could be health checks, they could be

just actually like business insight type information.

Like hey, here's a metric that we look at that we

monitor. They've used the tool as a way to democratize

that, make it easy for other people to come find it. You

know, if they want to go on vacation not have to worry

about they're going to get a phone call just to get a

snippet of code. You know, like get ...

Adam Weinstein: Our tools like that do a great job of documenting code,

and sort of version control, but they may not have the

business context. So, they'll use our product as a

means of sharing that. That's sort of software

engineering.

Adam Weinstein: On the Data Science front, I think it's probably more

in collaboration with a BI team or an analytics team,

where too much of Data Science has become data

prep. How do you get dirty data or the right data in a

format that you can then actually start performing

machine learning on, or for that matter, even just

modeling. So, where Data Science teams, and BI teams

have come out of the platform like, if the BI team

comes first, which has been a common trend, they'll

get all their code in there. Then, a Data Scientist, they

might want to go look at A, is there something

predictive in this data set that we could use, or we

could monetize? They'll at least know, okay, I'll pick

the code the BI guy uploaded. I'll get the result set,

and then I can just go and I don't have to waste time

finding the data, prepping it, getting it ready for

whatever I'm trying to do to it. So, that's probably

audience number two is this sort of joint BI/Data

Science audience.

Adam Weinstein: Then, audience number three, coincidentally is like a

business user. So, somebody who spends all day

looking for a report or an answer to a question. They

don't know whether it's in sales force, or in Tableau, or

they just need to ask the analyst sitting next to them.

They're looking for a quicker way to not bug people

over email or slack or whatever it may be. So, they're

using the product sort of asking the team like, hey,

can you start using something like this so that I can

not bug you as much. That's sort of one of our selling

points. It's like, hey, if you're a business leader and

you're constantly bugging someone for answers to

questions, for your sake and theirs, put it all in one

place so that you can come find it.

Kirill Eremenko: Yeah, yeah. So, it's kind of like you're benefiting from

this network effect. Yeah, it's classic Silicon Valley

start ups.

Adam Weinstein: Yeah, exactly. We didn't invent that, right? Same thing

as [inaudible 00:37:31], same thing ... you know, even

self service BI, the Tableau's [inaudible 00:37:36].

Same thing, like hey, if you've got a dashboard come

find it. Right? But, not everything is in a dashboard,

and for that matter, not every dashboard is accurate.

Kirill Eremenko: Not every dashboard is Tableau.

Adam Weinstein: But yeah, those are probably the three audiences.

Engineers, analyst and business leaders that use the

project, or that are driving to push the adoption of the

product.

Kirill Eremenko: You mentioned four audiences, no?

Adam Weinstein: Do what?

Kirill Eremenko: You said four.

Adam Weinstein: Data Scientist and business analyst.

Kirill Eremenko: Oh okay. Gotcha. Very cool. Very cool. I actually want

to talk a bit more about the business audience, right?

So, the way I see it is it's not just like business data is

for sure, executives and directors, but also I think this

could be useful for really anybody in the business.

Like, as an organization, and the world is moving on to

[inaudible 00:38:26] more kind of data driven type of

environment approach of doing business. Every

business is starting to try to become data driven. You

actually, you talk about this concept. The whole

notion. Maybe it's a good time to talk about this. The

Citizen Data Scientist, right? So, let's talk about that

for a bit.

Adam Weinstein: Yeah. Data Science is fascinating right? I think it

almost feels to me like the early days of BI, or I should

I say self service BI. Self service BI, I think the sales

pitch was like oh, you build this cube, which was what

it used to be, right? Excuse me. Then, anybody can

come to this system and ask a question and it'll give

you the answer. How many [inaudible 00:39:09] did we

sell yesterday? How many employees do we have in

this country? How many of them graduated from this

college? You can always come up with a question that

a self service BI system may or may not be able to

answer, right?

Adam Weinstein: Data Science sort of feels like a similar problem set in

the sense that there are really hard Data Science

problems that require someone with extensive

statistical understanding, and math capabilities, and

the ability to code, and all that. But, there's also a set

of Data Science problems that should be approachable

to what I call like a technical business analyst or a

Citizen Data Scientist. So, you know, I think helping

those folks feel comfortable exploring data, and playing

with it, and using tools, whether it's Cursor or there's

sort of even a growing auto ML set of solutions, right?

How do you automatically model ... throw a number of

different models against the data set and figure out

what's predicted, right? Someone should be able to feel

comfortable using that if they're comfortable writing

sequel.

Kirill Eremenko: Something like Data Robot you mean.

Adam Weinstein: Yeah, Data Robot, or there's a number of different ... I

mean, Acer has one that fits in the [inaudible

00:40:20]. Amazon is in the process of making one. I

think that ... there's an audience for that type of use

case where like maybe 60% of the ML problems might

be solvable by that audience in the next five years. Not

today, but at some point soon. Maybe it's more than

60, I don't know.

Adam Weinstein: I think that the challenge is sort of like how do you

help breed these folks that they may be stuck in their

current day job, and how do you help sort of

encourage that type of exploration, and

understanding? So, I think that's a little bit of what

Cursor can hopefully help with, but it's not just

Cursor, right? It's how do you encourage people to

take that leap. So, we saw that a lot at LinkedIn where

somebody that was a technical analyst would just

start playing with Python. They'd take a course, and

sometimes on Udemy right? They'd figure out, hey,

there's something more than just pulling data that I

can do that might be more valuable to the business,

and just understanding that that opportunity is out

there is ... it's the only thing stopping them.

Adam Weinstein: I don't know if that answer is where you're getting at,

but yeah, there's a growing audience there and I see it.

It's probably going to be the sequel user of today that's

the Citizen Data Scientist of tomorrow.

Kirill Eremenko: Yeah, yeah. And, to your point, I recently read a ... I

think it was like a study somewhere. It was not

recently obviously, I'd remember it better. But, it was a

while ago, and it ... what they did ... a bit of a different

situation but, to illustrate the same concept, that they

were developing certain, I think it was certain drugs, to

fight some kind of diseases. With drugs, you need to

put the chemical formula together in order to you

know ... and, they had the modeled environment

prepared, so basically there's this environment where

all the tests can be run. But now, it's just about

iterating and trying out these millions variations of the

chemical compounds and formula.

Kirill Eremenko: So, instead of doing it internally or running brute force

through it, and running simulations, what they did

was they opened up a online place where people,

anybody, could go, and just try it out for themselves.

So, people, random people from all around the world,

would log in. Not even log in, just go there and drag

and drop these chemical compounds and, click run

and see what comes up. In the end, they came up with

the most non standard, and they solved all the

problem. They found all the right composite they

needed. So, that just shows that even people who don't

understand chemicals and drugs-

Adam Weinstein: Sure.

Kirill Eremenko: Bacteria and all these diseases, and stuff, they still

have creativity, right? People can still ... you just

provide a self serve drag and drop type of environment.

They can solve probably like half, or like you say, 60%

of your business problems can be solved by people just

in their spare time. Like, oh you know, let me try this

machine learning algorithm and things like that.

Kirill Eremenko: I think what you're doing in Cursor is like a massive

step towards that. I think that with time, businesses

not only need to leverage their data more, but also the

creativity of the people that work there in general.

Adam Weinstein: Yeah, no that's a really good point. I think there's ... if

you open the newspaper every morning, and you look

at headline of okay, this company had this much of a

data breech, and the sort of repercussions, right?

There's sort of this desire to just crawl into a shell. We

used to joke. The last role I was in in LinkedIn I

actually helped work on the security side of the house.

It was interesting. We'd walk into meeting and

sometimes you've have some pessimist or, there'd be a

negative tone to it. I say, "Well, okay. You can just turn

off all the servers and go home. Then, there's no

security risks." No business either, but you know.

Adam Weinstein: So, I think there needs to be a comfortable way to

allow people, like you said, experiment, explore, learn

because your employees are your biggest advocates. I

mean, generally speaking. There's always going to be

bad actors, but you know, rarely are they internal. So,

this balance of like, okay, how do you trust but then,

excuse me, also have some security around how you

do it is an important one to strike.

Adam Weinstein: So, yeah. I couldn't agree more. How do you open

things up as much as you can without putting yourself

at risk? That's a question I think people are grappling

with, and even Cursor, we often live in a hybrid

environment. So, companies have some data in the

cloud, and some on prim. I don't see that mix

changing. I don't think it's going to go 100% cloud any

time soon. Yet, if it did, it would open up so many

different opportunities from an infrastructure

perspective, or a tool perspective and what they could

use, and how it would actually benefit the company.

But, because of this security fear they have data is

probably one of the last things to go to the cloud

unfortunately.

Kirill Eremenko: Mm-hmm (affirmative). Yeah, gotcha. Also, big

companies, like a lot of these large corporations have

so much momentum that it's going to take years

before things change there. Okay. Obviously Cursor is

solving a very interesting problem, and looking very

forward, [inaudible 00:45:55] tool. What would you say

to those listening who are ... they see the value of

Cursor, but they're not ready to go ahead yet. They

want to build a data driven culture with Citizen Data

Scientists but, not yet there that to invest in a tool like

Cursor. What would you ... any advise for business

leaders, or even people in organizations that are of that

mindset?

Adam Weinstein: Yeah. I mean, I think the key is just to always

experiment. So, you know, whether it's to try open

source tools, and I know there's some apprehension in

large corporations around open source. Not because of

cost, but because of support, and security and that

kind of thing. But, I think if you're a company that's

not always experimenting and looking for ways to use

data to drive efficiency or productivity or even, you

know, if you want to use the phrase like, monetary

gain, right? Not doing that, then your competitors are.

Adam Weinstein: So, you know, we were joking the other day. It's like

okay, what companies have been displaced in the last

10 years by the Amazon's, the Uber/Lyft's, the ... what

industries had been turned upside down, and

[inaudible 00:47:12] turned upside down. And, it's all

just data, right? Uber and Lyft are still using the same

cars, but you know, they may reduce the number of

needed cars on the road in the next 10 years because

of, whether it's self driving or just data and being able

to put cars at the right place at the right time. Same

thing with Amazon, right? They're not selling any

different products than all the retailers down the

street. They're just delivering it in a better fashion.

Adam Weinstein: It's fascinating, I think to me, that companies would be

afraid to experiment. I think, you know, often times I

see that coming from ... this is actually something I've

seen with Cursor and before Cursor as a consultant.

You know, not listening to people that are actually in

the trenches on a daily basis is usually where that sort

of mindset with set in, and people that are actually

interacting with and, you know, there are plenty of

people in the world that are still looking at an Excel

spreadsheet every day, spending hours a day manually

cleaning data. Not helping them find a solution to get

out of that is like, you're wasting a very valuable and

productive person's time doing something that can be

automated in an instant.

Adam Weinstein: So, you're not helping anybody. The company,

yourself, that person. What's more likely to happen is

that person will quit and then, go find a better job and

your company will have to suffer the pain and

consequences, right? But yeah. I think just always

experiment, and find time to do it. Carve out 20% of

the quarter, or the year to just ... maybe it's less.

Maybe it's 5%, who knows? Whatever it is, but some

amount of time to look at ways to do things better.

Kirill Eremenko: Gotcha. So, experiment. Very valuable advise. What

about spreading Data Literacy? Any thoughts on that?

How does an executive inspire people in the company

to become more ... to want to become more Data

Literate?

Adam Weinstein: Yeah. I think there's always going to be a crowd that is

literate, right? It may be a small analytics team. It

maybe a CIO's organization or whatever. But, I think

making ... I don't know if it's a requirement, but

inspiring them to teach the rest of the organization.

So, we had these brown bags constantly at LinkedIn

where we would invite almost anybody to come listen

to a talk on a data topic. It was a big deal for the

author to put together the content, and to be able to

actually articulate it, and document it in a way that

was easy to understand. But, it was also really exciting

to go listen to it if it wasn't a domain that you were a

part of.

Adam Weinstein: So, having that kind of a conversation and, giving it a

forum, I think is one way to start increasing Data

Literacy. That's not even doing it in a systematic way,

right? That's just hey, how do you have sort of a

conversation about it. You know two, I think is, teams

that work with data finding a way for them to share

with those that might care what [inaudible 00:50:07].

You know, we made it a basic goal every quarter for all

the teams to send out an email update of all the work

they were doing. It's like, what are the priorities? What

got done this quarter? What's going on next quarter?

We actually emailed it to basically the entire company,

even in sales you would get updates from data

infrastructure that would say, "Hey, we're adding

10,000 Hadoop notes and, here's what that's going to

do for us."

Adam Weinstein: You know, they may not care, but the ones that do

care you'll quickly identify because they'll raise their

hand and say, "Hey, I want to know more." And they

want to help. So, that's a great way to, I think, get

started around literacy, and certainly collaboration

products. Products that could help. It doesn't have to

be ours, right? There's tons of tools in the market,

whether it's Jira, or Slack, or something like that,

right? Just allowing people to have a conversation

helps create empathy, and ultimately helps, I think,

solve problems.

Kirill Eremenko: Yeah, gotcha. Why did you call them brown bags? I

didn't quite understand that reference.

Adam Weinstein: Oh, bring a lunch. Brown bag, like a brown paper bag.

Kirill Eremenko: Oh okay, gotcha.

Adam Weinstein: Yeah, people literally didn't [inaudible 00:51:10]. We

had a cafeteria. We were spoiled. But, in the older

days, right, you'd bring a brown paper bag lunch. So,

that was ... yeah, you'd have your sandwich and your

soda, and your chips, and that's what you'd-

Kirill Eremenko: So, you're enjoying lunchtime. I remember we had

those at Deloitte as well. That was really ...

Adam Weinstein: Yeah. It was back to a different time.

Kirill Eremenko: Yeah. I gotcha. So, experiment. Don't be afraid to

experiment, and empower people by good

conversations about Data Literacy because, you're

right, that's where the world is going. Organizations

are going to be doing more and more of that, and

people want that. That's what I find. People are so

fascinated with data these days that surprisingly a

very large segment of employees who actually want to

be more involved in this business because they see the

value and they see this as something that ... inevitably

it's part of our lives more and more. Like, with social

media and stuff. So, they're like, oh cool, I can do this

in business. Something exciting and interesting.

Adam Weinstein: There's not a person in the organization, whether

you're hanging up the phone and realizing that okay,

we need a prompt for people that have this question,

or you know, making lunch and realizing oh, I got to

refresh this paper food more often because ... there's

always ... everyone has a thought on data. So, giving

them a forum to do that and ... or an executive, right?

That's wondering why can't I get a quicker answer to

this question to take six weeks. There's always

someone that needs help, and yeah. I think it makes

sense that making it easier to get to would be positive

for everybody.

Kirill Eremenko: Yeah. Adam, I wanted to ask you another thing. I know

you guys have, for Cursor, you have like a free version.

How does that work? Because I understand, you would

need ... like an executive would need to approve it and

install into the business. That's a long process. How

does the free version work?

Adam Weinstein: Yeah. So, the free version is actually pretty good. It

does quite a few things, I think, out of the box. So, the

free version uses our cloud. You would download a

client on a local machine, much like you would a

Python editor, or a sequel editor. And, just like you use

other sort of cloud based tools, like Dropbox, or

Evernote, or that kind of thing, the work that you do

gets shared to the cloud, right? You can determine

how you want that shared. You can determine if you

want it visible to your team, or just to you. But, the

idea of being that like the data never leaves your

network. So, if you're running code the data lives in

your local machine, but the code and the meta data,

like hey, you worked with this table, or this was some

query that you wrote, and this is what actually-

Kirill Eremenko: The columns and the rows of the table, that kind of

stuff.

Adam Weinstein: Yeah, the names or the columns, that kind of thing.

That gets shared to the cloud. So, if you wrote

something that says, okay, how many laptops did we

sell in Brisbane this year? There's a guy that's in New

York that wants to know that same question. They can

discover that code. They still need the credentials to be

able to actually run it, it doesn't share that. But, it

does share anything that's being done. So, they would

be able to see the database you connected to. Be able

to see the table names that you used. But again, if

they don't have the credentials to that database they

can't actually do anything with it.

Adam Weinstein: So, it's sort of a light weight way to get started, and the

idea is ... you know, what we've seen is that even

though often times IT or legal or security may need to

get involved. Most companies will have a way or a user

that'll try it on their side time at home to be able to

play with something, and if they see that hey, this is

great, this is useful. It makes the process of getting it

in the enterprise version a little bit easier. So, it's a

pretty fully featured ... we call it the Cursor Core

Product, which is just sort of like the lighter weight

version of it, but it doesn't have every integration. It

doesn't have every language, but it has most.

Adam Weinstein: So, you should be able to get a decent amount of value

out of it.

Kirill Eremenko: That's cool. That's very nice of you to share that as

well, because you know especially the start ups that

don't really have ... like data is not being shared. So,

they don't really care about their intellectual property

at this stage. They could use that, especially [inaudible

00:55:31] maybe I'll sign up and use them for our

company now.

Adam Weinstein: You should try it.

Kirill Eremenko: Because we're decentralized and we have that problem

a lot. Everybody is all over the world, and it's different

time zones. It's so hard to get to the bottom of things

sometimes. So yeah, the free version would work there

as well.

Adam Weinstein: Yeah.

Kirill Eremenko: Yeah, very cool. Well, Adam, thanks so much. It's been

a pleasure. We're coming close to the hour mark.

Before I do let you go, I wanted to ask you what are

some of the best ways that our listeners can get in

touch, follow you, your career, or maybe get in contact

to learn more about Cursor?

Adam Weinstein: Yeah. So, certainly feel free to reach out to me directly.

I mean, my email is just adam@cursor.com. Our

website is Cursor.com. Check it out. Feel free to

download the product. Follow us on twitter, Cursor

Data. But yeah. We'd love to chat and hear what

people think, right? Good, bad or indifferent.

Kirill Eremenko: Awesome. Awesome. Okay for people to connect with

you on LinkedIn as well?

Adam Weinstein: Sure. Always.

Kirill Eremenko: Fantastic. Okay, Adam, thanks so much for coming on

the show. It's been a massive pleasure for having you.

Adam Weinstein: Thanks for having me. It's really been awesome talking

to you as well.

Kirill Eremenko: So there you have it. That is Adam Weinstein, Co-

Founder of Cursor. Hope you enjoyed this podcast. My

personal favorite part was the whole notion of

organizing Data Science assets. I'm very surprised that

no company in the world has been doing this as

actively as Cursor, and I think it's a very APT problem

that needs to be solved because more and more

companies will want to become Data Literate, data

driven, and will want to introduce Citizen Data

Scientist kind of tool like that can really help out with

that.

Kirill Eremenko: So, on that note, if you'd like to get the show notes for

this episode, head on over to

SuperDataScience.com/229. You'll find all the

materials that we mentioned in this podcast, plus the

URL to connect with Adam, and of course, the URL to

Cursor, which is Cursor.com. If you are interested in

building a Data Literate organization and, helping

organize your data size assets then check out

Cursor.com. Check out their product and see if it can

help you. So, they have, as you know, they have the

Core of Cursor, which is a paid product. It might be

interesting to larger organizations that are ready to

make the jump.

Kirill Eremenko: If you are not there yet, then they have a free version,

which you can try out in the cloud and see how that

works for you. On that note, thank you so much for

being here, and spending this hour with us. Can't wait

to see you back here next time. Until then, happy

analyzing.