How to Get Ahead of 99% of Data Scientists with Streamlit (Tips from Tyler Richards)

Summary

In this Data Science podcast episode, Tyler Richards shares his journey from election science to becoming a data scientist at Snowflake. He discusses his experience at Meta, focusing on content integrity and the challenges of moderating vast amounts of user-generated content. Richards also delves into his passion for Streamlit, an open-source library that simplifies the creation of data apps. His enthusiasm led him to write a book on Streamlit, which he continually updates to reflect the evolving tool. The conversation highlights how Streamlit can enhance job applications and the potential for data scientists to monetize their work, with Richards' recent creation of the 'stpaywall' component, aiming to facilitate online monetization for Streamlit app creators.

Takeaways

• 😀 Tyler Richards, a data scientist at Snowflake, co-hosts the podcast and discusses his journey from election science to working at Meta on content integrity, and eventually joining Streamlit.
• 📚 Tyler authored a book about Streamlit, which he revised and updated after joining the company, aiming to create the resource he wished existed when he started learning Streamlit.
• 💡 Streamlit is positioned as an effective tool for data scientists to stand out in the job application process by creating shareable, interactive apps that demonstrate their skills and projects.
• 🔑 Tyler's experience at Meta involved working on complex machine learning models for content moderation, emphasizing the importance of balancing free speech with platform integrity.
• ✍️ Writing a book was a method for Tyler to consolidate his learning and create a comprehensive guide for others to learn Streamlit effectively, focusing on deep understanding rather than fragmented knowledge.
• 🤝 The value of mentorship and feedback is highlighted as crucial for Tyler's growth in programming and data science, suggesting that pair programming and code reviews are beneficial practices.
• 💼 Tyler's transition from Meta to Streamlit was driven by a desire to be part of an early-stage company and to work more closely with a product he was passionate about.
• 🌐 Streamlit's open-source nature allows it to be used across various platforms, not just within Snowflake, providing flexibility for developers to host their apps in various environments.
• 🛠️ Tyler introduced 'streamlit_paywall', a component to facilitate monetization of Streamlit apps, with the goal of enabling data scientists to generate income from their work online.
• 💡 The importance of curiosity and kindness is emphasized as core values that Tyler would advise his younger self to embody, suggesting these traits are fundamental to personal and professional development.

Q & A

What is Tyler Richards' background and how did he become a data scientist?

Tyler Richards has a background in election science, having worked in nonprofits and academia. He transitioned to data science and worked at Meta (formerly Facebook) focusing on integrity-related projects. His interest in data science was further fueled by his experience with the open-source library Streamlit, which he used extensively and eventually wrote a book about.

How did Tyler Richards and the host end up working at Streamlit?

Both Tyler and the host were initially internet friends who connected through their shared interest in data science. They both joined Streamlit after recognizing its potential and enjoying their experiences with the platform, transitioning from being just friends to co-workers.

What was Tyler Richards' role at Meta focused on?

At Meta, Tyler worked in the Integrity space, focusing on content moderation. His role involved developing machine learning models to efficiently identify and remove content that violated Meta's rules, balancing user experience, business interests, and free speech considerations.

What motivated Tyler Richards to write a book about Streamlit?

Tyler was inspired to write a book about Streamlit after publishers reached out to him due to his online writings about the library. As an avid reader, he saw it as an opportunity to create a resource that he would have wanted to read, focusing on practical applications and experiences with Streamlit.

How did Tyler Richards' experience at Meta influence his decision to join Streamlit?

Tyler's experience at Meta, where he worked on large-scale data science projects, made him appreciate the efficiency and capabilities of Streamlit. His desire to work in a more unpredictable and fast-paced environment, as well as his love for the product, led him to join Streamlit.

What is the significance of Streamlit in job applications according to Tyler Richards?

Tyler believes that creating Streamlit apps can significantly enhance a job application by demonstrating practical skills and creativity. By building an app tailored to a specific job or company, applicants can stand out and showcase their ability to create tangible, impressive projects quickly.

How did Tyler Richards learn Python and what was his initial programming language?

Tyler initially learned R during his stats classes and used it extensively. However, when he was offered a data science internship at Procter and Gamble, he had to quickly learn Python. He scrambled to learn Python in about three weeks and further honed his skills through pair programming with experienced engineers.

What advice does Tyler Richards have for finding a mentor?

Tyler suggests that mentorship should be a two-way street where both parties benefit. He emphasizes the importance of showing earnestness, intellectual curiosity, and the potential to reciprocate the help in the future. He also highlights the value of hands-on learning and feedback from those more experienced.

What is the purpose of the 'stpwall' component Tyler Richards created?

The 'stpwall' component is designed to simplify the process of monetizing Streamlit apps. It allows developers to easily implement a paywall in their apps, enabling them to charge users for access. Tyler's goal is to encourage more data scientists to create and sell their apps, potentially making a living from their work.

What advice would Tyler Richards give to his younger self?

Tyler would advise his younger self to be more curious and more kind. He believes that embracing curiosity and treating others with kindness are essential qualities that have helped him in his personal and professional life.

How can listeners reach Tyler Richards and learn more about his work?

Listeners can reach Tyler Richards through Twitter, where he is most active. His Twitter handle is Tyler J Richards, and his email can be found on his Twitter profile. He also mentions that the proceeds from his book are donated to charity, indicating his commitment to giving back.

Outlines

00:00

📈 Transition to Co-worker and Data Science Journey

The host welcomes Tyler Richards back to the data science podcast. Tyler introduces himself as a data scientist at Snowflake with a background in election science. He discusses his transition from internet friends to co-workers with the host after both joined Streamlit. Tyler shares his experience at Meta, focusing on content integrity and the challenges of content moderation at scale. He also talks about his journey of writing a book on Streamlit, which led to his current role as a data scientist at Streamlit.

05:01

🤖 Content Integrity at Meta and Machine Learning Models

Tyler delves deeper into his role at Meta, where he worked on content integrity, ensuring harmful content is removed from platforms like Facebook and Instagram. He explains the necessity of creating efficient machine learning models to automate content moderation due to the vast volume of uploads. Tyler also touches on the complexity of defining and moderating hate speech and the impact of these decisions on user experience and business.

10:02

🚀 The Appeal of Early-Stage Companies and Joining Streamlit

Tyler discusses his desire to join an early-stage company for the opportunity to work in a fast-paced and unpredictable environment. He was particularly drawn to Streamlit due to his love for the product and the chance to contribute to its growth. After writing a book on Streamlit and maintaining a relationship with the founders, he was invited to join the company as a data scientist, which he gladly accepted.

15:02

🔍 Leveraging Streamlit for Job Applications

The conversation shifts to how Streamlit can help job seekers stand out in the application process. Tyler shares his strategy of creating Streamlit apps tailored to specific job applications to impress recruiters. He suggests that creating an app for a potential employer demonstrates coding skills and initiative, setting candidates apart from the competition.

20:03

📚 Writing a Book on Streamlit and the Editing Process

Tyler describes his process of writing a book on Streamlit, emphasizing the importance of dedicated time and focus. He explains that the second edition of his book incorporated lessons learned from daily use of Streamlit, aiming to help readers avoid common mistakes. Tyler also discusses his learning style, preferring in-depth focus over short bursts of information.

25:03

🛠️ Streamlit for Work and Overcoming Deployment Challenges

The discussion moves to using Streamlit in a professional context, particularly after Streamlit's acquisition by Snowflake. Tyler outlines the benefits of Streamlit for Snowflake, which simplifies deployment, security, and data sharing. He also acknowledges that Streamlit remains an open-source project, free to use and deploy on various platforms.

30:06

👨‍🏫 Learning Python and the Value of Mentorship

Tyler shares his personal journey of learning Python, initially starting with R and then quickly adapting to Python for an internship. He highlights the importance of mentorship and receiving feedback from more experienced programmers, which significantly improved his coding skills.

35:09

💡 The Concept of STP Wall and Monetizing Data Science Projects

Tyler introduces STP Wall, a component he created to simplify the process of monetizing Streamlit apps. He expresses his belief in the potential for data scientists to generate income from their projects and hopes that STP Wall will encourage more developers to do so by making it easier to accept online payments.

40:11

💌 Advice to Younger Self: Curiosity and Kindness

In the final segment, Tyler reflects on the qualities he values most: curiosity and kindness. He advises his younger self to cultivate these traits and shares his belief that embracing curiosity through Streamlit and treating others with kindness are key to personal and professional growth.

Keywords

💡Data Science

Data Science is a multidisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. In the video, Tyler Richards, a data scientist, discusses his journey and work in this field, emphasizing its application in various domains such as election science, social media content moderation, and creating data products with Streamlit.

💡Streamlit

Streamlit is an open-source Python library used for quickly creating custom web applications for data science projects. Throughout the script, Tyler highlights his experience with Streamlit, both as a tool for building web apps and as a platform for sharing data science work, which eventually led him to work at Streamlit and author a book on the subject.

💡Election Science

Election Science refers to the application of scientific methods and data analysis to understand and improve electoral processes. Tyler mentions his background in election science, indicating his work in nonprofits and its foundational role in shaping his career in data science.

💡Meta (Facebook)

Meta, formerly known as Facebook, is a technology company that focuses on social media platforms. Tyler discusses his role at Meta, where he worked on Integrity-related projects, particularly in developing machine learning models for content moderation to ensure user experience and platform safety.

💡Content Moderation

Content Moderation is the process of monitoring and regulating user-generated content to prevent the spread of harmful or inappropriate material. In the context of the video, Tyler's work at Meta involved creating efficient machine learning models for content moderation, a critical aspect of maintaining a safe online environment.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Tyler's work at Meta heavily involved machine learning to develop models that could identify and remove rule-breaking content at scale.

💡Snowflake

Snowflake is a cloud-based data warehousing company that provides a platform for storing and analyzing large volumes of data. Tyler mentions working at Snowflake, where he leveraged Streamlit to create data applications within the platform, highlighting the integration of Streamlit with Snowflake's services.

💡Personal Projects

Personal Projects refer to individual endeavors outside of formal employment or academic requirements. Tyler talks about his personal projects with Streamlit, which led to him writing online about his experiences, attracting the attention of book publishers, and eventually authoring a book on Streamlit.

💡Monetization

Monetization is the process of generating revenue from a product or service. Tyler discusses his interest in enabling data scientists to monetize their work through Streamlit apps, as evidenced by his development of the 'stpaywall' component, which facilitates online payments for accessing Streamlit apps.

💡GitHub

GitHub is a platform for version control and collaboration that is widely used by developers. In the script, Tyler references GitHub in the context of creating apps that analyze GitHub history, demonstrating how Streamlit can be used to create personalized and engaging data applications.

💡Curiosity and Kindness

Curiosity and Kindness are personal values that Tyler emphasizes as important for personal and professional growth. He advises his younger self to foster these qualities, reflecting on their significance in his interactions and work within the data science community.

Highlights

Tyler Richards shares his journey from election science to data science at Meta and Snowflake, highlighting the transition from academia to industry.

He discusses the appeal of working with Streamlit, an open-source Python library for building custom web apps, and how it influenced his career move to Streamlit as a data scientist.

Tyler's experience in election science and content moderation at Meta provided a strong foundation for his work in data integrity.

The creation of Streamlit for Snowflake is detailed, emphasizing its ease of use for data scientists to share and deploy apps within a secure environment.

Anecdotes from Tyler's book writing process reveal insights into learning and teaching data science through Streamlit, and the value of focused study sessions.

Tyler's approach to job applications using Streamlit apps to stand out, showcasing his unique method of impressing recruiters with interactive projects.

He explains the importance of mentorship and receiving constructive feedback to improve programming skills, drawing from his experiences with pair programming.

Tyler's perspective on using Streamlit for work, beyond just deployment, includes its role in data analysis and the benefits of open-source accessibility.

The development of the 'sc-paywall' component by Tyler is explored, aiming to simplify online monetization for data scientists and content creators.

His vision for Streamlit as a platform for data scientists to create and sell apps, similar to the App Store model, is shared.

Tyler's advice for finding mentors involves creating win-win situations and showing intellectual curiosity and earnestness.

The importance of curiosity and kindness as core values in personal and professional development is emphasized.

His experience learning Python in a short time for a data science internship and the role of determination in skill acquisition.

Tyler's reflections on the power of feedback loops and the impact of working closely with more experienced programmers.

The role of Streamlit in democratizing data science by allowing anyone to create and share data apps without extensive coding knowledge.

Tyler's final advice to his younger self and the audience, focusing on the importance of continuous learning and maintaining a beginner's mindset.

Transcripts

so welcome back to another episode of

the data science podcast and today I

have the fortunate pleasure of having

Tyler Richards back on the podcast Tyler

would you like to introduce yourself

yeah yeah hey uh so good to be back um

yeah it's been it's been a hot minute

and I really enjoyed the the last time

that I was on here and then um basically

after after uh we recorded last time

both of us ended up joining streamlet so

now we're co-workers so before we were

just internet friends and now we're

co-workers and what a fun what a fun

transition that is so as you mentioned

I'm Tyler um I am a data scientist that

currently works at um at snowflake um my

background is in election science and I

did election science at nonprofits and

um at uh while I was at University a

little bit afterwards as well and then I

went over to meta to continue on uh

Integrity related work um as I um worked

more at meta I um came across open

source library streamlet and honestly

just really liked it I tried to bring it

inside of meta played around with it

with person personal projects and um

really just got enamored with the

library as a whole um as I was doing

that I started writing um online more

about my experience with uh streamlet

and then I uh a couple book publishers

got in contact with me and were like hey

uh your writing's a little bit about um

streamlet do you just want to run like

write a book and I was like okay that

that sounds great I'm a I'm a big reader

a pretty voracious reader and um so I

thought it was like yeah this is kind of

the I wanted to write the book that I

always um wanted to read and so I did I

did that I I published a first version

and then I went from after I published

the first version I joined stream as a

data scientist um and then I uh just

recently edited um and uh released a

second version uh of the book uh a few

weeks ago so that's the um the the gist

of it um uh yeah yeah that that that's

pretty much me that's a long

introduction but that's pretty much me

yeah awesome and and yeah definitely

check out the first episode uh of our

part podcast um if you haven't already

and yeah so I I guess you have already

provided um like your the the background

of who you are and then um your your

prior row over at me meta um could could

you maybe um go into a little bit depth

into like um what what did you do at

meta and then

and the thought process of actually

joining stret yeah So Meta I actually um

I have um nothing but positive things to

say about about my time at meta I know

that might be a little rare but I also

left right after um or right before uh

all of the kind of cycle that they've

been going through with um uh

optimization and cost optimization and

layoffs and um the stock price going

down and all this other like there's

definitely been a big Vibe shift in meta

um after I left but um I left long

before a lot of that was happening and

so I had a relatively um I had a really

good time I learned a lot about how to

work within a big organization a lot

about how to do data science how to work

with engineering partners and product

partners and um really learned a whole

lot there uh my actual role was in the

Integrity space and so lots of stuff

gets uploaded to Facebook and Instagram

um every single day um every single

second and and there is a very very

strong desire to get the worst of the

worst content off of um off of these

platforms um both because it's a bad

user experience and um it's also bad for

business as well like no Advertiser

wants to have their ad showing up right

next to hate speech or right next to

nudity or right next to um like

terrorist content or all this other all

these other related things um and so

there's huge teams at meta that are

frankly the best in the world at taking

um content down um that breaks meta's

rules and because there's so much

content you can't just pay people to to

um take down the content to like look at

it and take it down you have to create

these like very very efficient stable

machine learning models that will look

at a piece of content and take it down

um and then I'll also be able to have to

be able to say Hey you know we're 85%

sure that this is hate speech that this

post is hate speech why don't we go and

we send it to uh an expert that we know

is really good at detecting um if this

is hate speech or not and you know the

next piece of content might be oh we're

actually 92% sure that this is hate

speech we might actually take that one

down because we think that that it's

going to get 10,000 views in the next

you know in the next 30 minutes or

something so those are the sorts of like

very complicated questions because

they're they really were the

intersection of like social science and

the questions of well what does it mean

to have a rule um on Facebook like what

does it mean for us to preserve free

expression and Free Speech what does it

mean for the the business community on

Facebook what does it mean for different

populations um like what even what is

hate speech what is hate speech is a

really like hard question um and so my

main projects were focused on

measurement so are we doing a good job

at that at all that work or are we doing

a bad job um that like is really what it

came down to um that work was man I mean

the the pros and cons of that work are

are are definitely sharp I would say

everyone that I talked to while I was a

data scientist there had an opinion

about meta Integrity or this is when it

was called Facebook so this is Prem meta

but everyone had an opinion everyone was

like oh you guys are doing a blank job

at this you know either great or

terrible or they' be like oh you take

down too much content you know and then

I I would be in California right I'd be

in California people be like oh my God

you guys are not taking down nearly

enough content um you need to be taking

down blank blank blank whatever whatever

side that I don't agree with you know

you need to take down all their stuff

and then I would go back to and hang out

with my family in Florida and I would

you know see a bunch of you know friends

that I grew up with uh and then they

would be they would all say oh you guys

are taking down way too much content in

you know X Y and Z area and so there was

there no one was ever neutral about uh

how we were handling uh how we were

handling speech at meta so that was fun

because everyone has an opinion on your

job um and then annoying because I have

the same conversations about my job with

everyone uh and yeah so that that was my

my background at meta it was really

useful to be in election science

beforehand because I already had a lot

of background in um handling those

problems at a much smaller scale but I

mean with meta you just get like you're

like oh if I change the Precision on

this machine learning model by .1% that

literally over the course of the Year

might affect billions of posts um so

yeah wild wild place if you're looking

for scale there's no place like meta

I'll tell you what I mean Google Google

but but Google and meta are like

scale yeah sounds pretty amazing like

you know like the the work that you were

doing and yeah

hard the entire population of the world

it really does it's like basically

almost everyone I mean right now there

are more smartphones than there are

people on the face of you and and more

than half of people in the world use

Facebook every month I think that's

probably about right if I'm not exactly

right I'm close um or use a Facebook

product if you combine WhatsApp Facebook

and Instagram you're like you're

touching almost everyone outside of

mainland China that's pretty much it

it's wild yeah

definely and yeah could you walk us

through through your thought process of

you joining streamlet yeah yeah so as I

was at meta and kind of one of my one of

my conent there's like two sides to this

right where side number one is I had a

strong desire to join a very early stage

company because within me within meta I

had like I definitely felt free to do um

I felt free to you know work on

Integrity related problems and integrity

measurement related problems but if

there were like other problems that I

thought were really pressing I kind of

had to stay in my Lane which is good

like the whole point of being uh of a

very good large tech company and large

business is that you produce

organizations that are predictable like

I can predict what this organization is

going to produce in three to six months

because otherwise just makes it very

hard to run the business as a whole and

the point of a very small startup is to

do some things that are a little bit

unpredictable so I you want to you know

have these big step changes in

production you want to be able to move

people at a moment's notice into a more

critical area of the company um and so I

had to I I just wanted to be uh in a

space that was just a little bit faster

um and I wanted that that experience for

myself um in terms of streamlet

itself I mean I I join because I love

the product like I used the product and

I said this is awesome I don't want to

be creating data science work outside of

streamlet ever again so the number one

way to guarantee that no one will make

me use a tool that is other than

streamlet is to work at streamlet of

course I'm just gonna be creating stream

tools all the time um yeah so I uh I I

join because I I I love the product um

when after I I wrote the book um you

know some of the the founders reach out

I I would you know chat with Adrian you

know once a month or we had like a

bi-weekly call where we talked about

feedback and you know all all this other

um stuff and yeah so then uh when they

started hiring data scientists uh they

were like would you like to join and

yeah yeah yeah I would uh so that that

was the I wish it was like more

difficult or like a oh there was it was

super complicated and whatever else but

I mean I love the product I believed in

it a lot and this company was out there

wanting

to increase the number of streamlets

that were out there so it was like very

much a perfect fit for for the thing

that I was looking for so I'm just I'm

glad they said yes I'm glad glad you

know otherwise I would have just been

like all right well I guess I just like

stream from afar which would have been

okay but it was very good fit for me

awesome yeah uh like I I do remember um

uh I guess we join Str about

approximately the same time right yeah

yeah I guess I left Academia in November

and then end of November I joined

TriMet 2021 yeah yep yep exactly it was

like

very yeah I mean you're in exactly the

same boat right where you were like hey

I really like this product exactly I

want more people to be using this I want

to use this on a regular basis like it

totally Mak

sense same thing with a few other people

right like johannas is a great example

where he made a really cool app um the

the GitHub Stars app was excellent um

and that the machine learning generator

app yeah exactly machine learning

generator app that thing's amazing that

could have been a small business all on

its own um like so cool so for for

everyone listening he had these two apps

one of which um he finished in like a

few days over like break where it just

like looked through all of your GitHub

history and then it like ran a nice anal

is on your GitHub history um and then

showed it to you and then you could

tweet about it and that went that thing

went so viral like so fast I saw it

everywhere all over Twitter people were

like looking at you know entering their

own GitHub username and just going after

it it was awesome and he made this other

app that is like this machine learning

generator where you could kind of upload

a data set or upload um you know click a

bunch of different like questions and

then it would create all of this python

code that would train and then use and

then infer um a machine learning model

for you based on your data set so I was

like this is aw this is so sick yeah

definitely and yeah I mean we're we're

talking about like how we join strem Le

and then uh how we love strem um yeah so

I guess this brings us to the or our

next question is which is

um H in your opinion how can stre help

one find a job role yeah the easiest the

the way that I think about it now is

basically streamlet is an unbelievable

way to stand out in a job application

process or before a job application

process um so as I was like you know

working at meta every every you know six

months or something I would go out into

the job market and just kind of like see

what was out there and you know test the

waters and I found that and I don't know

if other people like have this same

feeling but I always felt like just

applying to a th000 jobs with a wor a

blanket

resume just like didn't work like my hit

rate is just low and I don't want to get

like just one of the Thousand jobs that

I'm applying for I I want a a set of

jobs that there's like five jobs that I

really care about and I would really

love to work at and so for me it made so

much more sense to spend more time on a

smaller number of applic that I cared

about rather than like almost no time on

a bunch of applications that I like most

of which I couldn't even tell you that

much about the company so um when I was

doing that I was like all right well how

do I stand out in front of a recruiter

okay I can have a cool GitHub but

recruiters can't code so if I show them

like oh there's this awesome python file

or Jupiter notebook there's no way

they're going to know if it's if it's

impressive or not right like they're

going to make three or four clicks get

into a jupter notebook and then wow they

used Panda really efficient like there's

no way they're going to be able to

assess if I'm good or not so then I'm

like all right well I went to a I went

to a US University which was definitely

a benefit but the University of Florida

where I went to wasn't it's not like a

you know one of the fancy private

schools it's not like a Harvard or a

Yale or something so I don't have that

brand name that would make everyone say

all right well we got to interview this

guy now because he went to Stanford um

so I didn't have that I'm like all right

well how do I like what and then I I

worked at Mena but even before I worked

at meta it was like I had worked at

Proctor and Gamble which is cool but no

one thinks of them as like wow they

produced the best data scientists in the

whole world like no one no one thinks

that so I'm like all right well how do I

stand out and I realize well if I can

create a data product that someone can

click on and see something cool really

fast

then then they'll be impressed and

they'll be like wow they created this

for me how did they create this whole

website just for me like it's an app and

I can mess around with it like that's

awesome um and with streamlet it's

honestly not that hard right where you

can create five apps that you know

whether it's you know some data science

project that you've done already where

it has some nice markdown some nice

explanations a cool map something that

people can click on like that is doable

for you know a small set of applications

um where if I was like before stream if

I was trying to show off something and

create a whole website that would be or

like you know oh I create an awesome API

or like ajango app or something it would

be like that's too much work for an

individual application so like the way

that I saw and my hit rate of like

applying for jobs

or once I was in the job application

process usually there's like a data

science project part of it just doing

that in streamlet and then sending them

the link um that that hit rate was

really high people were very impressed

by it um and so that's kind of just like

a little bit of alpha in a way that

hopefully so many people start doing

this that in the long term like it's not

uh you know every everyone expects to

people to create streamlets as part of

their like uh ja application process but

for now I think there's a lot of Alpha

and just

hey I'm going to create an app for you

and then like just imagine it from a

recruiter's perspective I I don't have

to see a lot of their code I know that

they can create something cool because

created something cool and here it is

like it's right here this person

definitely knows how to code um yeah so

that's that's the main like way that I

think about it at the moment so strongly

encourage people to just just try it out

like just try it with two two companies

right where find a way to make stre app

for them someone that you really want to

work for find someone of the company

that you think you're going to click on

your link email them or find them on

LinkedIn or something and say hey I saw

you had this job opening for United

Airlines data analyst um I pulled the uh

like flight history of the past thousand

flights and you know found this cool

model that predicted blank here it is or

something whatever it is I just did a

cool visualization that I um that I you

know that I thought was pretty and help

help me understand you know flight

patterns in the United States better or

something like whatever it is just

something like that is g to make you

like it's gonna make you so much better

than anyone else that's applying to

these jobs most people that are applying

to these jobs put no effort in they just

toss a resume in a pool and they leave

it so like you can be in the top 1% of

jobs very easily um of applications very

easy so I don't know I don't know if um

well I mean like I obviously haven't

gone around searching for jobs in in a

hot minute but um uh but yeah that

that's like how I started to do it and

and one thing that I talk about I have a

chapter in the book about like hey

here's an actual uh thing that I did

that helped me get an offer at a place

um yeah so that that's the uh the gist

bit yeah I mean that that's like a hack

that uh I think not a lot of people know

about it and you know like the listeners

now you know and yeah it's like it's

like it makes me think of kind of like a

cheat code you know like how you could

use that to leverage and you know up up

your profile and provide instant value

to the to the company that you're um

interested in applying for yeah I mean

that that's certainly a great tip

um yeah you you touched

upon writing your book um could you go

over the process of writing a book and

the thought process of you actually

writing the book and like the daily

routine and then finally maybe would you

consider writing another book yeah you

already have two books yeah so I would

say the process the process was actually

remarkably straightforward where you uh

at least for me what made sense for me

is um I designate time in my day and I

say okay the only thing I'm going to do

for this hour or this two hour block or

this 45 minute block is blank and then I

worked until that time was over and then

I left and then I did that I didn't just

like you know do this forever and so I

just had designated times throughout the

week where I'd be like all right this is

my writing time this is what I'm going

to do um the process of editing the book

um like for the for the second version a

lot of that was stuff that I'd been you

know thinking about and so to give you a

bit more background when I wrote the

first version I used streamlet as as

kind of a hobby where hey this isn't

this isn't something that I'm doing

every day all day but this is whenever

you know whatever I can whenever I can

find an excuse to make a streamlet app

I'm going to make it and so my number of

hours making streamlet apps was higher

than most people for sure um but then

after I joined streamlet

I

spend all of my time at stream is a

primary data product that we produce

like that's the only thing that I

produce these days it's like sometimes

I'll make a notebook but most of the

time I'm going to make a stream lip and

so for the second version my goal was

okay can I take all the things that I've

learned from making streamlets every

single day for the past year and a half

and can I like add that to the book in a

way that beginner and an intermediate

person can understand easily so that all

the mistakes that I've made they don't

have to make they can they can they just

they just yeah they don't have to make

these mistakes I can just figure it out

um yeah so that was the kind of like the

process of learning enough to be able to

have stuff to write about um and then

the actual writing experience is just

time so you just as long as you're

willing to dedicate time you could do it

um I've kind of

like I think that the reason I wrote it

is kind of what I said before which is I

I believe for me the best way for me to

learn is to sit down and focus on one

thing for an extended period of time I

do not learn very well from 20 minutes

of this don't touch it for two days 20

minutes of this don't touch it for six

days 20 minutes of this don't touch it

and and I don't I just don't really

learn well that way I know a lot of

other people that do and a lot of other

people learn well from audio or from

video um like think of the number of

people that you've helped with this

YouTube channel right where so many

people learn from video tutorials and

they love it and that helps them a whole

lot um I don't that wellest say um so I

I do a bit but I don't really that I

don't that much right so for me I was

like all right well I want my ideal

learning setup is long form it's focused

and it takes you a to z right that's how

I want to learn um and for streamlet

there was kind of no way of doing that

um and so that was the main reason and

so the reason for version two is really

stream was on

0.87 and um like the the book the first

version used 0.87 or 0.67 I for exactly

and now streamlet is on

1.26 1.27 maybe

1.28 um so there's been 50 updates since

since in that in that time um and so

yeah there's so many different features

that are just released and I have all

this new experience from using stream

life uh and learning about and and just

like using it every day so that's that's

the main like my main thought for for

writing the second version the question

of like whether or not to do it again is

really hard um for me I

think I'm very excited for lots of other

people to make really awesome streamlet

books and as lots of other people are

making really awesome streamlet books um

then I'm I'm excited to uh support those

stream books and not have to write like

more stream books uh I especially as a

data scientist I'm like okay well this

is this is great think like I enjoy

doing data science a little bit more

than I do writing um and so I I kind of

doubt that that I will like continue too

long in in the writing sphere um so I'm

not I'm not exactly sure but my my goal

is that I get to support lots of other

people writing really awesome streamlet

books and tell everyone go you know go

and look at those instead of mine you

know two years from now or

something cool yeah I mean certainly

your your book is amazing and it's a

great resource for anyone you know

looking to get started and um

yeah yeah I mean I was very fortunate to

be able to look at your book and read

the entirety of it and I do say it's

it's kind of like having a mentor you

know like sitting beside you because

like the the way that you you've written

it is it's not like a typical book it's

more like you're talking to a friend and

then you're guiding your friend like

step by step and then you're

incrementally U building up the app and

then you're you're you're taking us

along the thought process of here here

is a new feature and then why are we

adding it here and what's like the

caveat of that and how we could

incrementally um improve upon it yeah I

wonder when we were going to get to that

point when uh we revealed that you are

the technical editor of this book which

is so kind and so nice and you spent so

much time on it and did such a good job

like there are so many typos and edits

and like like like code stylistic things

and like the fundamentals of it are so

much better because of the work that you

did and so it's like I mean I just like

you know I try to thank you every every

time I can but I'll thank you to the

ends of the Earth I really appreciate it

it's so nice yeah you certainly my

pleasure and yeah I I get a new chance

to learn more about streamlet and you

know it's kind of interesting to you

know like when you already know about

something but then when when you're

relearning it um you get to see it at a

different angle and in doing that it it

kind of you know provides fresh

viewpoints to the existing technology

that you are already familiar with but

then not really familiar in some aspect

and yeah I guess it's it's a great way

to to learn in a holistic way yeah the

way that I've I've explained it in the

past of like my learning style and the

way that I try to like the way the way

that I try to approach this book is that

that there's a lot of like a lot of

people really like kind of a Swiss

cheese method of Lear where you have a

bunch of different slices of cheese and

there's holes in each one but then if

you lay them all on top of each other if

you lay enough slices you're going to

double learn you know there's going to

be sections where there's every section

has no holes like if you just like look

straight down on it because you know you

get a little bit from each one and a lot

of people love to learn that way I do

not I just want one slice of cheese that

has no holes in it so so the uh that

that's that's what I'm most interested

in so I tried to like write a book that

just gives you one slice of cheese no

holes not really the Swiss cheese method

um but I don't know if that makes any

sense as an analogy or not but that's

like how I try um I've tried to think

about it yeah love love the analogy um I

mean when when we're we're talking about

this it kind of lead me to think about

like um what are your advice on how one

could use TriMet for work

yeah yeah well so there's there's a

couple different things right so in s

since version one uh stream got acquired

by snowflake um and so that's why uh

both of us currently work for uh for

snowflake you've got a snowflake shirt

on perfect um yeah exactly perfect uh so

that we have this product inside

snowflake that we just called streamlet

for snowflake um and the what that does

is it essentially will handle all of the

the difficulties in hosting as well as

data connection and sharing and you know

all the annoying things um about like

deploying apps within work are mostly

related to can I get my it team to set

up an ec2 instance and then host it and

how do I do all the security around it

and how do I get access to my data and

make sure my data has access to this how

do I make sure I'm not actually

accidentally leaving some Port open and

then someone else can access it and I

didn't even know

um like all of that stuff is is kind of

difficult um and so snowflake just said

hey we'll figure it out for you like

we'll do all that annoying stuff so you

don't have to touch it um and you can

just go into your snowflake account and

start writing python code and there it's

your streamlit and you can press a

little share button and then great you

can share it with your colleagues your

product managers whoever else um and so

honestly that's the hardest part

um about like using streamlet for work

is all of the work rated security

hosting

things um that said uh that's probably

like the biggest the biggest pitch um

I'm also biased pitch person because uh

that's the product that I work on right

like that's that's what I've spent the

past like you know nine months uh

messing around with to try and get this

product state where like we're happy

with it and we think it's like the best

way to use streamlet at work that said

stream is an open source project stream

is going to remain an open source

project it is free to use you can grab

it you can go and host it on your own um

on your own instance you can host it on

a hugging face space hugging face has

hugging face for work too so you can

make streamlet apps inside of hugging

face you can do it inside of Heroku you

can do it right inside of AWS right if

you want to have all the knobs and be

able to you know make all of the little

intricate decisions yourself you

absolutely can do that and I think uh

there are lots of places online where um

you can uh like learn how to deploy an

app on AWS there's a section in my book

about how to deploy an app on Heroku um

and also for for hugging face as well so

the uh I'm not I'm not here to say oh

the only way that you can like use

stream letter it work is

um is is in is inside of snowflake like

no that's that's that's not what it is

and it's not the intent um uh the intent

either um so yeah I um

I would say that's the biggest uh the

biggest way to use TriMet for work is

like the the hardest thing is figuring

out deployment security sharing um you

know where does my data sit all of that

stuff right yes certainly and yeah so

yeah Str is like ubiquitous and it's not

only on on on you know like a specific

platform but everywhere

and yeah

um yeah so I guess it's now time to move

on to the next question uh which is if

you were going to learn stret again how

would you do it yeah well for I mean for

it it depends on it depends on

how

um let me think the the honestly the the

the the actual question the actual

answer to this is the first place I

would go is I would go to docs.

stream.io mhm at that's our where our

docs page is our docs page is amazing

it's really good um there's a bunch of

people there that do an excellent job

about creating and maintaining just like

a delightful documentation page um and I

would start there before I would even

think about going to all the different

ways um to to to consume streamlet

content and a learn streamlet so I would

pip install streamlet I would look at

the docs and I would try it out myself

and just see how it feels

um and I would you know go through the

different widgets and um maybe there's a

little getting started section in the

dogs that I think is really good that

just like oh here's how you pip install

something here's how you you know start

a you know create a new python file

here's how you run it as a streamlet app

like all that really basic stuff um and

so I would start there and then if I

felt oh I'm actually missing a lot of

the ecosystem I actually you know I I if

I really like to read and and read

reading was my best way that I wanted to

learn I would go and I I would you know

buy my book and and read it that way

right that that would be the the way

that I would go if I um if I really

liked uh like consuming video content um

I would go to to your channel and then I

would also go to Fino's Channel Fino's

channel is really good too um I think

both of your channels are like I've

definitely watched quite a few of the

videos and learned something new and I

think it's a great um a great and

entertaining way to learn as well um so

if I was doing video content I would

definitely go to those two places I've

seen some other YouTube channels I

forget exactly we can like link them

below as well but I think avra has

YouTube channel that is like quite good

um I I enjoy that um I've seen a couple

videos there and then I know there are

some other um some other videos that I'm

kind of forgetting right now but I like

I really like all of them um so I'm I'm

a fan so I'd start there and then my

main thing is I would find find a

project that I cared about and then just

tried to make it at streamless so

whatever it is just have a little

project and it like learning makes

learning is more fun when you're trying

to

learn to do something if you're just

trying to learn in a vacuum you're like

well I'm just learning to learn it's

kind of like well like why am I doing

this like I if I have something in front

of me that I want to create an app to

blank I want to analyze my good reads

history I want to I went to create an

app so that other people can analyze

their good reads history that was my

first project in stream life you know uh

what we were talking about with yanas

like he was like oh well I want to see

my GitHub history great I want to create

an app so other people can see their

GitHub history awesome those are perfect

that's exactly what you should be uh

where you should really be looking for

like find a project mess around with it

see if you can do it and uh do something

manageable and have fun with it that's

my that's about be my go-to yep and and

a pointy not is that the the good read

app that you've created I think is it

went viral and it's amongst one of the

top uh Pages uh for strs yeah yeah I

would say it's a probably a little

biased because it's on our

website exactly IO and so if uh so if

people click on that so I think that is

like a big source of the virality but

yes it it it went uh it went kind of

viral when I released it at first um

yeah that went that went well right yeah

think that people uh read these days so

a lot of people to analyze their

Goodreads history yeah or we analyze

their YouTube viewing history right yeah

or sptify oh yeah uh Tyler Simons

another data scientist that that works

on our team made a very nice Spotify um

app that I think is really cool we can

um link it in the comments as well um

but it's pretty it's it's a really

beautiful app all amazing yeah it's it's

kind of like when whenever we have IDI

wild ideas and then it's like you know

we could make a s that yeah yeah why not

I mean it's python right so like python

it's like what what can you do in Python

um pretty much anything pretty much

anything like as as long as it's you can

do it on a computer like it's turn

complate language like you're fine like

you could probably figure it

out yeah yeah speaking of which um how

did you learn python did you learn it in

school or did you so taught oh man I

learned python well all right so the the

actual answer is I started with r um and

I learned R and I did that because I was

in a lot of stats classes and those

stats classes use R um and so that was

like my main language my bread and

butter and then I went to Proctor and

Gamble and as I got to Proctor and

Gamble basically I was like uh I had

interviewed with them and they had

accepted me and I was originally going

to be a data analyst and I wanted to be

a data scientist but I was going to be a

data analyst and then um got this phone

call like a few weeks before my

internship they called me and it was

this guy named John his name is John

rool he was one of the data scientist

there and he called me and he was like

hey Tyler I'm gonna be your intern

manager um uh but you know we're trying

to figure out if uh we should have you

as a data science intern or we should

have you as a data analyst intern and I

was like okay uh and he like do you want

to be a data science intern or data

analyst insurance and I was like well I

want to be a data scientist so I think I

want to be a data science insurance he

was like okay do you know python on and

I was like uh not really and he was like

oh well then I don't know if that's

going to work and I was like oh but I

can learn I can learn I can figure it

out and it was like I was like I know R

like RS will be close and he was like

okay well if you can figure out python

then you can be a data science enter I

was like okay I'll figure out python

that was it and then I was like oh my

God I gotta figure out Python and then I

just scrambled around trying to Learn

Python for like three weeks um and then

so I just you know went online read all

the books that I could figure out like

kind of just like freaked out um and

scrambled and scrambled to figure it out

and then uh yeah then it was fine uh

Python's pretty close to R so it's okay

not that's not the biggest deal in the

world um and then so I I interned uh

with John for for two summers uh it

great he was such a great like

internship intern manager and then um as

I was doing that I started um like

Contracting for this nonprofit that's

called called protect democracy um they

did a lot of very cool very important

work and it was very related to a bunch

of research that I had done um as an

undergrad and so I like my python was

good but it wasn't like it was like a

data scientist python it was like not

like a z Engineers python um so

everything was like everything I wrote

was messy I like barely wrote any

functions it was just in Jupiter it was

like okay like is not like you're not

Building Systems like you're just

building like kind of scripts in Jupiter

or something

so then um uh Sam uh this guy named Sam

Royston um he uh was the kind of like

head engineer on um on protect democracy

and uh he basically spent like we would

just pair program together and he knew

python unbelievably well um yeah and so

then like I would just we would just sit

there and then I would like watch him

code and I'd be like wow this is amazing

look at thisy

this guy this guy's really coding like

that's a real thing um and then we

switch and I would have to code and i'

be like I don't know how to do anything

and You' be like you should do it this

way oh no you should you should name

your variable differently oh actually

python can do it this way oh well this

is you know what a Dunder is and like

all the other you just like sat there

and taught me for like a summer um and

so between all of that then that's how I

learned python I don't know that's too

long but it was I remember freaking out

over the phone because I was like I want

to be a data science I gotta Learn

Python I only know R and you did it in

only three weeks right

that's after three weeks

but yeah know kind of how to do some

stuff yeah very inspirational yeah yeah

yeah scary I mean yeah and you know like

and then and then you over the years you

mentioned about like your experience

doing the pair programming and how you

enhance your programming and then now

that you're you're working full-time as

a data scientist at meta at streamlet at

Snowflake and you know more and more

experience under your reps reps is a

great one and the other thing is that

keeping your feedback loops really tight

so like uh we have a a a data scientist

engineer on our team his name is Zachary

um and he's just really he's like a

really good programmer and so I get to

hang out with him a lot and he sees a

lot of my code and then he'll say oh I

think maybe you should do this a little

bit differently or maybe you should do

this a little bit differently nicest guy

and so it's just like super helpful to

have him reviewing the stuff that I'm

working on and getting that good

feedback from people that know more

about python than you um and so reps

plus feedback equal you're G to get

better at it like you're just definitely

going to get better at I don't think

there's any way that you get worse at

something by doing it more having

feedback from people that are better

than you like I cannot imagine getting

worse at something having those two

things whether that's at work or whether

that's online like posting more stuff

online and getting people to look at it

and review it or like however that is um

especially if you're starting out with

python almost everyone that codes in

Python is better than you so it's not

that hard to find someone that that will

at least look at it and be like oh is

this good or is this bad you know should

I do it this way should I do it another

way so that's kind of the the gist of it

and I think that's also like the thing I

should have sought as when when I was

younger is you know I I I stumbled into

to places where I got that good feedback

but like seeking out quality mentorship

from not like oh yeah I meet with this

Mentor once a month and he's this super

senior person at this company and

they're a mentor like that mentorship is

fine and dandy but like the mentorship

of like I know this person they're

willing to sit down with me right next

to me and we can code on something like

that is that's a mentorship that is like

really would have been useful I'm

getting a lot more of that um you know

when I was a bit younger yeah yeah super

amazing would you have any advice for

the listeners like how could one find a

mentor oh uh well I guess if

you're I've never asked someone to

Mentor me and it's gone

well um so I

think I don't know if that's the

greatest way of doing it and I seen

people that try to do it that way and I

don't see it really succeeding that well

I think a lot of it

is it's got to be two-way right like

these people helped me in part because

they knew I could help them eventually

too like I showed that I was Earnest I

was intellectually curious and I wanted

to do well at an internship or help them

with this job or something and so you

you want to find

like you you want to find win-win

situations with other people that are

more experienced with you and so I think

the only advice that I have is it's it's

probably not going to go super well if

you just email random people who you've

never talked to and just say will you be

my mentor please like I I just my only

advice is uh that hasn't worked out for

me but what it has worked out for me is

hey these people are willing to invest

in someone in part because they want to

do something good in part because they

also want to get something out of it um

and finding more of those situations is

um is is key at least it's been key for

me but that said like who knows like I

I'm a I'm an N of one um I don't know if

my if my experience is actually the best

way of doing things I don't know if my

experience is you know reflective of

other people I can just tell you you

know my story and if that helps um then

great but if it doesn't then

sorry yeah super super helpful uh indeed

and I think it will be very valuable for

the listeners um yeah

so I think we're we're bracing through

our questions um and yeah recently You

released this amazing tra component

called STP wall um could you tell us a

little bit about it and like your your

thought process of how you actually you

know came up with the idea and how did

you actually create the component yeah

so one thing that's kind of been in the

back of my head

for I mean at least a year if not longer

is I don't understand why there aren't a

bunch of data scientists that are making

money online with data science projects

like I see software Engineers do it

right like every every other week on

Twitter I'm sitting there and there's

some software engineer out there that is

like I have made two ,000 this week with

my cool little app or something and

they're like I just hit an MR of like

$5,000 that's awesome that's so great

why do I never see a data scientist

doing that like and so there's a few

options in my mind either data science

is not that valuable right and

possibility two um data science is only

valuable inside companies that spit off

lots of data and so doing it outside of

companies is kind of impossible um or

three there's like a skill issue um or a

capacity issue where it's like oh well

data scientists could be able to do this

but they're missing something um and so

I thought I and I still think that

streamlet is a unique and excellent

Avenue for this type of work um for a

cool like you make some cool large

language model prompt but you don't want

to sell that prompt or you just want to

sell access to it you know so if you can

do that in streamlet when I pay when I

charge people like you know 10 bucks a

month or say hey give me 20 bucks and

I'll give you access to this app so I

tried to I tried to do that in streamlet

and I realized oh actually that's pretty

hard that that's that's actually really

hard to do because you have to do

authentication right you have to do all

the connections with the different

payment providers you have to like

depending on how much you're charging

and what you're doing you might have to

set up like like an actual business you

know like with stripe or something like

use like stripe Atlas or or whatever

else like these things are it's not

trivial it's definitely not trivial so I

created SC payall as a hey let's just

see if

people like let's see if I can break

down some of these hypotheses and let's

see if I can make it a little bit easier

for people to collect money online for

their work um my goal is that I want to

see lots of streamlet developers that

are making either a full-time living or

a parttime living with the cool work

that they're doing right like I want it

to be like the App Store where you see

all these Indie developers that create a

cool little app like you remember those

those those initial iPhone apps when

like I didn't even have an iPhone I had

an ey touch right and they had the

little you know the the dumb apps like

there was some there was one app that

went viral that was like oh just buy

this app to prove to other people that

you're rich and they just charged like

$10,000 for your app for for the app or

something crazy or like $1,000 for the

app and they made so much money or

another silly little app that was like

oh this app will help you like it just

like is a fake beer drink and it just

had a nice cool animation when right

like you remember those things or like

or like all these other random like

iPhone apps like I want that for data

science where people can make cool

little silly things and then make some

money off it and then eventually not

silly things like uber Uber is on the

App Store like it is the app right so

like you need that your Facebook or

Airbnb or like other things like these

are apps first airb probably less so but

Uber yes Facebook yes Facebook exists on

the App Store um so I I like want and

that's like a very high aspiration

that's pretty ambitious for for what

what streamlet is but in order to do

that we kind of like I wanted it to make

it easier for people to accept money

online that's like suggest it um and so

SC pay wall is a way where you can like

uh basically use this Library add a

bunch um at these you know a couple

little calls that will um do Google

authentic

um and then the second thing that it'll

do is um it'll uh it'll check if the

user that uh you know signed in with

their Google account if they're one of

your subscribers onrie if they paid for

um paid for this product already and if

they're not then it prompts them to um

pay for it and if they don't pay for it

then they can't see it so that's kind of

the uh the G fit it's not the most

complicated thing in the whole world um

but yeah I I I test it out um I think it

works pretty well and it's just like

something that has been like scratching

at the back of my brain for like years

now so I know I'll have more um products

and more thoughts on this in the future

but this is kind of a first sitation of

it yeah super super cool um and yeah

definitely um I was also wondering um

like a year or two ago and then I think

I asked over on Twitter like does anyone

know how we could connect our stret app

is there any component available and at

the time there were none um and yeah

here we are today and it's it's super

exciting to see that and how you could

help to unlock you know new avenues of

content creators triet app creators to

monetized and maybe make a living out of

it right hopefully yeah or just make $10

like just making $10 online is addictive

like as soon as you make $10 you're like

all right I'm gonna want to make a

hundred like right that point be really

cool right right right definitely and

yeah so we've reached our last question

which is like

what are some advice that you would like

to give to your younger

self

um I think the so this is probably

outside of streamlet but the one thing

that I value in people a lot are two

things are curiosity and kindness and I

would tell myself to be more Curious and

be more kind um those are the two

biggest things and so streamlet is a way

where I embrace my curiosity about the

world about the people around me um and

if I do that in a kind way then I think

uh I think I think that's the advice

that I would give my younger

self uh yeah so it's it's a big pleasure

to have you on the podcast and and this

is not the first time right it's the

second time and yeah I do appreciate

this and hopefully we could have you uh

for you know subsequent times third time

fourth time fifth time who knows well

thanks for having me on I really

appreciate it it's always so fun um to

talk with you you know now here in the

podcast at work whatever so I'm sure on

Twitter who knows right right right

right it's thank you so much yeah and

we're amazing to you know in the last

time last iteration we were um you know

internet friends and now we're we're

actually you know like working in the

same company yeah right so uh yeah who

knows what's gonna happen before uh

before the third time on this podcast

something crazy I assume yeah cool yeah

and so yeah uh would you like to um tell

the listeners how you they could reach

you uh yeah so I'm just Tyler J Richards

on Twitter uh that's probably the

easiest place to to reach me um we'll

put uh links to all the stuff that we

talked about uh in the description or in

the comments or somewhere uh I'm not a

YouTuber I don't know like well we'll

figure it out and then uh yeah the best

place to find me is on on Twitter you

can find my email there um my email is

just my username on Twitter gmail.com

that's a great place to um if you want

to chat about anything and then there

are other social platforms but I'm I'm

mostly a Twitter guy

so that's AIT uh we'll link the book um

I felt kind of weird about uh you know

accepting money uh while while being for

for a book while being also a this is

just a weird thing about me not anything

else but we just going to donate the the

proceeds of the book to ladies um so

that's the the gist of that um yeah and

that's uh that's me that's uh that's all

the stuff here yeah the super amazing

and yeah I I we will provide the links

to all of the social platforms that

Tyler is on and also the link to his

book yeah so do definitely check it out

perfect thank you so much yeah my

pleasure