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Welcome to the Mongo DB Podcast.
I'm Shane McAllister. 

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I'm one of the leads on our 
developer relations team and 

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it's great to have you join us 
today. 

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So in a world when AI can write 
your code, well, at least some 

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of the time, software 
development and data careers 

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need rethinking. 
How you work and how you learn 

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are utterly changing. 
Do you vibe code everything and 

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hope for the best, as we've seen
many, many articles recently? 

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Do you spend your evenings and 
weekends pouring over all the 

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latest tools and frameworks? 
Well, today's guest, we're going

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to have a great discussion and 
dive into the controversial area

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of keeping up with the vibe 
coders. 

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And today, I'm really, really 
happy to welcome Richie Cotton, 

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a senior data evangelist, Pardon
me, at data camp to ruminate the

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changing relations chip between 
engineering teams and data teams

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and modern developer workflows 
and everything else that we can 

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decide to get on to on today's 
topic. 

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Richie, you're very welcome to 
the Monetary podcast. 

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How are you? 
Life is good, Shane. 

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Really pleased to be here. 
I'm based in New York City. 

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We're having a heat wave today. 
It's got a message from the 

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building saying they are turning
the air conditioning down in 

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order to save energy. 
If I start sweating throughout 

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the show, that's what's 
happening. 

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Hey, this is a live stream, 
anything can happen and I'm not 

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usually for regular viewers, I'm
not usually in my normal place. 

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I'm in Amsterdam at the moment 
doing a, what we call a 

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developer day at Mongo DB where 
we get some of our clients into 

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a room for a day or two and we 
run through some are going to be

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fundamentals and also some 
deeper topics with them as well 

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too. 
So if the Wi-Fi goes or anything

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else happens or somebody comes 
in and knocks on the door, I do 

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apologize. 
So if you pass out from heat 

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exhaustion, Richie or I get 
moved out of this room. 

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Everyone knows that this, this 
chat is live. 

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I'd love to start with most 
guests to kind of dig back a 

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little bit your career path to 
date. 

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Richie, where did you start? 
What roles have you gone through

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all the way up to your current 
role as evangelists in Data 

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camp? 
Absolutely, yeah. 

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Uh, my background is in data 
science, so, uh, I started well 

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before it was called data 
science. 

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Really. 
So this is back in 2005, uh, I 

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was working on data for chemical
health and safety. 

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So that was the start of my 
career. 

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I've also done data science in 
other industries. 

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So I moved from chemical health 
and safety to debt collection. 

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And then I moved from debt 
collection to proteomics. 

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And there it was like it was 
trying to work out what's going 

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on with like proteins. 
I hadn't done any biology since 

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I was 16. 
I started asking stupid 

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questions about, you know, 
what's that thing in a, in a 

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cell again? 
But yeah, industry shifts are 

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very difficult, but the data has
been sort of persistent 

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throughout. 
I joined data camp in 2016. 

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Since then I've been teaching 
things around data and more 

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recently, AI. 
OK, excellent. 

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And and you're I'm always super 
impressed when I get published 

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authors on the show as well too,
because I totally I see the work

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that's required into creating 
content like that. 

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Tell us about the the two books 
that you put together as well. 

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Sure. 
Yeah. 

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So I wrote two books on our 
programmes. 

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I suppose it was called Learning
R and then I wrote Testing R 

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code. 
So I really enjoyed the process 

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of writing. 
Actually, that was how I kind of

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ended up working in education. 
You know, I enjoy teaching 

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people. 
It's kind of sad to me that R is

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sort of declining as a language.
I think it's a great language 

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for working with data. 
Python second or everything. 

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I like Python as well. 
But yeah, 2 great books now 

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obsolete. 
And these days, yeah, I, I do 

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all my teaching via, well, as 
you say, evangelism. 

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So that means I host our, the 
Data for the Data Camp podcast 

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called Data framed over in our 
webinar program. 

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I also organize virtual 
conferences. 

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Actually the next one is on 
Thursday if you want to sign up 

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for Data Camp Radar. 
I don't think I have a link for 

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you, but yeah, Internet search 
for data on radar, that's 

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happening on Thursday. 
Excellent. 

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So what's it like to be the 
guest instead of the host? 

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So it's going to be tricky if I 
start asking you questions, 

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that's usually for some habits. 
That's fair enough. 

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That's fair enough. 
So like I was familiar with data

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Camp, I know Mongo DB has done 
stuff and has lots in the future

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with data Camp coming. 
But for our viewers who wouldn't

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be familiar, give us a little 
audit history of Data Camp and 

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and where it's at today. 
Sure. 

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Yeah. 
So Data Camp is an education 

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company. 
So we do online courses for data

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and AI skills and as well as 
courses we also do projects. 

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We have certifications for 
testing your skills. 

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And then we have productivity 
platforms. 

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Basically, it's a hosted tube to
notebook platform called Data 

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Labs, designed to make it easy 
to get started and easy to 

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collaborate with others. 
OK. 

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And you've been there since 
2016, as you said, obviously 

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massive changes right in the, in
those nine years or so in this 

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space, more particularly 
certainly in the last, well, 

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publicly in the last two, 2 1/2 
years in the AI world. 

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How has that changed Datacamp's 
approach to, to learning? 

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And if I could slot in a second 
question there longer to be 

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redesigned its university 
platform about that probably 

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about 18 months ago to make them
shorter, more succinct courses. 

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How does Data cap appeal to that
as I would call them the TikTok 

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generation Richie? 
Oh yeah, yeah. 

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So things have changed widely. 
Like when I started data camp in

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2016, there were eighteen of us.
There's more than 200 now. 

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So obviously like it's it's a 
growing space, a lot of people 

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wanting to learn about things. 
And I think our focus to begin 

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with was very much on just data 
scientists and data analysts. 

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And since then, everyone wants 
to know about data. 

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And the last couple of years, 
everyone wants to know about AI.

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So we've created a lot more 
slightly less technical context 

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or more introductory stuff for 
people who like, OK, maybe your 

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background is like marketing or 
HR, but you need to understand 

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things about like the basics 
about data and AI. 

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So we have a lot more basic 
content. 

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And then I'll just say like the 
the rise of the AI, the AI 

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engineers with the big story in 
the last couple of years. 

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So we have content for that 
crowd as well. 

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But yeah, you mentioned the 
TikTok generation. 

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So when we started all our 
courses, they were they were 

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four to five hours long. 
And this is like, OK, we need to

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go faster because before that it
was like, you know, you go on 

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courser and you've got to take 
an 8 week course. 

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No one has time for that. 
So we're like, OK, we can do 

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fast content and yeah, we are 
getting increasing pressure for 

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one hour courses and shorter 
format content. 

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Yeah, yeah, I think so. 
Listen as the as the father of 

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three kids who are teenagers and
upwards, yeah, their attention 

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spans are just non existent 
Richie. 

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So those damn. 
Kids. 

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I just sounded like an old 
curmudgeon now. 

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But anyway, when we have people 
with the data science background

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on the podcast, there are pains 
to point out that AI is nothing 

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new. 
Machine learning has been around

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for a long time. 
The principles behind AI, I've 

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been there for for, you know, 
decades at this point in time. 

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But we've obviously seen this 
huge impact. 

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Certainly as I said earlier, in 
the last two, 2 1/2 years, the 

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impact of AI on coding practices
and obviously copilot, one of 

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the first at the gate there with
we can be your peer programmer, 

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we can sit beside you, we can 
help you. 

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How has, in your view, AI 
changed the day-to-day work of 

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developers? 
Yeah. 

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I mean, I think this is 
interesting because developers 

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tend to adopt new technology a 
lot faster than data scientists.

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It's just a fight for life. 
Like all the tooling gets built 

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for developers first because 
it's developers creating stuff 

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for themselves and it's 
obviously a much bigger target 

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audience than than the smaller 
sort of data realm. 

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So yeah, I think things like 
Copilot and more recently you 

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got like a person wins up all 
these kind of newfangled ID ES 

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that are very much AI powered, 
so these target developers 

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first. 
So I think, yeah, you hit the 

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nail on the head. 
The big difference this is AI is

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actually getting quite good at 
writing your code for you. 

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And that means you need to think
less about, well, what's the 

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syntax and more about what do I 
actually want to build? 

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And so these kind of these 
product skills are deserving, 

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like what do I need to do for to
solve a user problem? 

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That's becoming increasingly 
important to think about also I 

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think because it doesn't work 
all the time. 

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Like you've got to spend more 
time just looking at like code 

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smells like what's gone wrong 
with this code or what's weird. 

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So looking, looking at for 
weirdness, identifying things 

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that might be going wrong. 
That's that's seems to be a 

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great proportion of people's 
time compared to just trying to 

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remember syntax. 
Yeah, I think and at Mongo DB we

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spent quite a while probably, 
but about two years ago we first

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started, uh, been concerned a 
little bit about coding 

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assistance in, you know, 
obviously there's a different 

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discussion about the value and 
the use case and is it a junior 

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developer, should they be using 
it and they don't know what 

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they're doing versus senior 
developer, which is reducing the

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mundane and the boilerplate. 
But we were, I suppose 

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preoccupied in Mongo DBS, 
particularly in trying to 

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understand how good these code 
assistants were with Mongo DB 

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operations, making sure that 
they had the ground truths and 

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the best practices, etcetera. 
So we did a lot of projects with

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many code assistant providers to
make those better and to good 

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effect. 
It was it opened up a different 

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area for us as well too. 
But when we were preparing for 

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the live stream, we deliberately
chose the Kick Bailey title with

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invite. 
Cody. 

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Talk to me a little bit about 
where we are there now, Richie. 

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Is it a viable strategy or 
incredibly risky shortcut? 

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Yeah, I mean, a lot of it 
depends on what the consequences

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of writing bad code are. 
So, you know, you're developing 

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something for like a satellite 
that's going into space. 

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It's like you want every single 
line of code to be thoroughly 

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checked and optimized, because 
if you get anything wrong, the 

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satellite crashes, you've lost 
10s of hundreds of millions of 

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dollars. 
On the other hand, for if you're

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a data analyst, you're doing 
exploratory data analysis. 

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If the code's terrible, it's 
like, OK, I've drawn a plot that

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doesn't give me any value. 
You've just wasted like 2 

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seconds looking at a stupid 
plot. 

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It's like it doesn't matter. 
So I think actually in data use 

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cases, vibe coding has got a lot
of value. 

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You don't want to care so much 
about the code you like, 

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particularly for exploratory 
stuff, you just want to generate

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lots of things fast. 
So it works really well there. 

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Yeah. 
So it's all about the trade-offs

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of like, what happens if things 
go wrong? 

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Hmm, I think so. 
And I suppose that look, that's 

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what I was trying to allude to 
with the the junior developers 

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using using code assistance. 
Yes, they can get started super 

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quickly in an area that they 
they mightn't have the skills or

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expertise to potentially debug 
further down the tracks. 

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And and you know, I think, and I
know the developers that I know 

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using the code assistance to 
great effect are, yeah, it's 

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replacing the boilerplate. 
It's replacing the mundane. 

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They're still a little skeptical
on the business logic going to 

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the coding assistance per SE, 
but they're using it as a 

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canvas. 
In other words, they're almost 

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like a a writer getting a draft 
going. 

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I want to build this. 
Can you start some of it? 

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And I'm going to tweak and shape
it. 

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How do you feel in that as well 
too? 

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Have you seen that? 
Yeah, definitely. 

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So I like the idea of like, 
coming up with ideas using AI. 

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Like even for you, you start 
writing code, it's like, well, 

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what do I want to do? 
What should the structure of 

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this be like? 
Yeah, generative AI is great for

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that, but don't you point about,
like, junior developers? 

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That's a tricky one because 
you're right that like, 

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understanding your code is 
becoming incredibly important. 

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But if you've only ever been 
using AI assistance to develop 

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stuff, it's like, as a hiring 
manager, do I really want to 

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hire someone who can generate 
code really well, but they can't

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understand it? 
And it's a tricky one, but it's 

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not a clear answer. 
If for example, if you're a 

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marketing analyst and you only 
write code like 5% of your job, 

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it's like maybe that's fine that
you can only use AI assisted 

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tools to build something because
most of it you just your 

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marketing knowledge is the most 
important thing. 

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If you're a software developer, 
that's a very different 

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proposition. 
Like I think I would only want 

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to hire software developers who 
can write code themselves or at 

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least understand code without 
having the AI explain it to 

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them. 
True. 

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I, I hear you. 
And I think on, on other shows 

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I've done on similar topics, the
notion of checks and balances. 

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There's the notion of, yeah, by 
all means use AI to write some 

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of your code, but go review that
or get some peer review of that 

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and, you know, put those checks 
and balances in place. 

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And we've had Red Monk who are 
basically, you know, consultants

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with regard to the developer 
space and, and then and on the 

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show as well too, talking about 
this very topic. 

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And they were discussing the 
risk of skills atrofying if 

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you're not used to this. 
And if you're just relying on 

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the code assistance over time. 
And the more you rely on it, the

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more that kind of fundamental 
first principle skills are going

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to disappear. 
And I suppose the more 

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potential, as you might have 
said, upstream problems you 

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could encounter if you can't 
debug that code. 

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Yeah. 
And so I like the idea of having

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00:13:59,800 --> 00:14:02,200
backup plans. 
So one of my hobbies, I go 

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backpacking and there it's like,
well, it's fine to have 

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technology until you don't have 
it. 

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00:14:08,240 --> 00:14:11,240
So for example, you can take 
Agps with you and then you can 

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see like where you are at all 
times. 

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00:14:13,520 --> 00:14:16,160
And it's great till you drop 
your phone in a Creek. 

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00:14:16,160 --> 00:14:20,120
And it's like, OK, fine. 
Do you want a backup plan? 

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00:14:20,120 --> 00:14:23,120
Like it's nice to have a map, 
preferably washproof like a 

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00:14:23,120 --> 00:14:25,000
paper map. 
And then you need to have some 

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00:14:25,000 --> 00:14:26,320
map reading skills to find out 
where you are. 

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00:14:26,880 --> 00:14:29,560
And suppose the map blows away. 
Well, it's actually also quite 

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useful. 
Felt like a vague sense of where

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00:14:31,480 --> 00:14:34,040
you are in the countryside. 
So having these backup plans for

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like when stuff fails is really 
useful. 

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00:14:37,280 --> 00:14:38,920
I think the same is true in 
coding. 

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So it's like, OK, great to use 
AI assistants, but when they 

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ate, the AI API goes down. 
Whatever you want to still be 

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productive. 
Yeah, you need a backup plan for

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00:14:50,440 --> 00:14:53,640
when stuff goes wrong. 
Yeah, I think so. 

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00:14:53,640 --> 00:14:56,400
I, I, I was laughing there 
because I wrote, I, I did 

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00:14:56,400 --> 00:14:59,520
electronic engineering in 
college, which was basically 

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00:14:59,520 --> 00:15:02,840
four years of maths, pure maths.
And I remember giving out to our

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00:15:02,840 --> 00:15:05,400
professor lecturers that, you 
know, I thought I'd be building 

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electronic circuits and cool 
stuff. 

284
00:15:07,280 --> 00:15:10,480
And he goes here, we Teach First
principles so that you will be 

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00:15:10,480 --> 00:15:13,240
able to do those, or at least 
know how they should work. 

286
00:15:13,480 --> 00:15:16,400
But you're not a technician, you
are the person who designs how 

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00:15:16,440 --> 00:15:19,560
they should work. 
And I suppose, you know, using 

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00:15:19,560 --> 00:15:22,720
any tool you need to have a 
fundamental knowledge of kind of

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00:15:22,720 --> 00:15:24,960
the grounding in it etcetera as 
well too. 

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00:15:25,280 --> 00:15:28,920
You mentioned, you know, people 
playing with AI and play, you 

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00:15:28,920 --> 00:15:31,280
know, who wouldn't be coders 
etcetera as well too. 

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00:15:32,240 --> 00:15:36,480
Do you think we're narrowing the
gap between people who wants to 

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00:15:36,480 --> 00:15:40,040
dabble and, you know, have an 
idea or have a concept or 

294
00:15:40,040 --> 00:15:43,200
something going back to this 
vibe coding and being actually 

295
00:15:43,200 --> 00:15:45,360
able to see whether that's 
viable or not? 

296
00:15:47,240 --> 00:15:48,880
Absolutely. 
I mean, this has sort of long 

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00:15:48,880 --> 00:15:54,040
been the dream that everyone can
do technical things. 

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00:15:54,240 --> 00:15:57,800
So like, for example, in the 
business analytic space, the big

299
00:15:57,800 --> 00:16:01,280
story the last decade has been 
around self-service analytics. 

300
00:16:01,320 --> 00:16:03,880
And what you really want is like
all your sales team, your 

301
00:16:03,880 --> 00:16:06,720
marketing team to be able to 
answer their own damn questions 

302
00:16:06,720 --> 00:16:09,080
about data rather than to get 
the data team involved every 

303
00:16:09,080 --> 00:16:12,480
time. 
And I think the same is true in 

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00:16:12,480 --> 00:16:15,240
terms of coding. 
It's like you, you want to build

305
00:16:15,240 --> 00:16:17,200
an app. 
It's like you shouldn't have to 

306
00:16:17,200 --> 00:16:19,960
rely on someone technical to 
build something simple. 

307
00:16:20,280 --> 00:16:25,000
And I'm hoping, I'm really 
hoping that all code generation 

308
00:16:25,200 --> 00:16:30,080
and I guess no code tools as 
well as as just AI just allow 

309
00:16:30,080 --> 00:16:32,320
more people to be able to build 
stuff that they want to build 

310
00:16:32,320 --> 00:16:34,680
without having to rely on the 
extra technical person. 

311
00:16:35,920 --> 00:16:39,520
Yeah, and I I'm going to kind of
have dig down into the change in

312
00:16:39,520 --> 00:16:43,480
relationship, but I see a 
question in from Rahm and I 

313
00:16:43,480 --> 00:16:45,240
don't know the answer to this. 
I hadn't. 

314
00:16:45,480 --> 00:16:47,720
Have you heard about the Super 
Bass paradox? 

315
00:16:47,760 --> 00:16:49,400
Richie is that? 
Familiar to you? 

316
00:16:49,440 --> 00:16:51,480
Not heard of the Super Bass 
paradox? 

317
00:16:52,760 --> 00:16:56,840
Do you want to give us a a bit 
more explanation and then then 

318
00:16:56,840 --> 00:17:00,720
we can argue about it? 
Yeah, and I said at the 

319
00:17:00,720 --> 00:17:03,920
beginning, obviously we streamed
live on LinkedIn and YouTube. 

320
00:17:03,920 --> 00:17:07,319
I've just got some DMS 
internally here going. 

321
00:17:07,319 --> 00:17:09,400
It doesn't seem to be on 
LinkedIn at the moment. 

322
00:17:09,400 --> 00:17:12,960
So I apologize if there's less 
interactivity or you're trying 

323
00:17:12,960 --> 00:17:17,280
to get this to work on LinkedIn.
It doesn't seem to be, I don't 

324
00:17:17,280 --> 00:17:19,560
know, some connection somewhere 
has gone. 

325
00:17:19,560 --> 00:17:22,440
So I think we're solo on YouTube
at the moment, Richie. 

326
00:17:22,440 --> 00:17:24,560
So that's, that's our audience 
anyway. 

327
00:17:24,560 --> 00:17:28,359
The, you mentioned there the, 
you know, the business 

328
00:17:28,359 --> 00:17:32,400
intelligence and needing data 
teams and data engineers to get 

329
00:17:32,400 --> 00:17:34,800
involved with the marketers and 
the people looking for the 

330
00:17:34,800 --> 00:17:37,600
information. 
And that relationship having 

331
00:17:37,600 --> 00:17:40,880
changed because AI is bridging 
that gap. 

332
00:17:41,080 --> 00:17:44,360
Umm, how is that changing how 
those teams work together? 

333
00:17:44,360 --> 00:17:46,680
How's that changing how those 
teams collaborate together and 

334
00:17:46,680 --> 00:17:50,320
how does and the evolvement of, 
you know, how that relationship 

335
00:17:50,320 --> 00:17:53,360
will be? 
Yeah, so actually, umm, this had

336
00:17:53,360 --> 00:17:56,560
a big effect on where data 
people are placed within 

337
00:17:56,560 --> 00:17:59,880
organizations. 
So one thing we've been seeing 

338
00:17:59,880 --> 00:18:04,760
is that the data analyst role 
itself has been kind of dying 

339
00:18:04,760 --> 00:18:09,040
out as a job title in favour of 
more commercially focused 

340
00:18:10,360 --> 00:18:12,240
alternatives. 
So for example, rather than just

341
00:18:12,240 --> 00:18:15,120
being a data person in a data 
team, you are now a marketing 

342
00:18:15,120 --> 00:18:17,200
analyst or you're a sales 
analyst or you're a product 

343
00:18:17,200 --> 00:18:19,400
analyst and you're embedded 
within one of these commercial 

344
00:18:19,400 --> 00:18:21,280
teams. 
So there's been that move of 

345
00:18:21,280 --> 00:18:25,360
like you need data skills plus 
some other like secondary 

346
00:18:25,360 --> 00:18:28,120
business skill. 
But on the other side of things,

347
00:18:28,120 --> 00:18:32,440
you get data engineers and they 
are becoming more centralized. 

348
00:18:32,440 --> 00:18:35,760
So if the law, the further away 
from customers you are, the more

349
00:18:35,760 --> 00:18:37,720
likely you are to be in a 
central team. 

350
00:18:38,040 --> 00:18:41,080
And the closer your work is to 
customers, the more likely you 

351
00:18:41,080 --> 00:18:43,080
are to be in one of these sub 
commercial teams. 

352
00:18:43,440 --> 00:18:46,480
So those job roles have been 
changing a bit. 

353
00:18:47,680 --> 00:18:51,160
And I think as you maybe throw 
this back to you, have you seen 

354
00:18:51,160 --> 00:18:53,760
like a difference in who's 
working with Mongo DB? 

355
00:18:53,760 --> 00:18:55,880
Are there different job titles 
interacting with it? 

356
00:18:57,440 --> 00:19:01,200
I suppose there is. 
And I look, we obviously, uh, to

357
00:19:01,200 --> 00:19:03,560
use that American expression, 
eat our own dog food. 

358
00:19:03,560 --> 00:19:06,000
We're using AI in some of our 
own tools. 

359
00:19:06,360 --> 00:19:10,560
And we started off putting an AI
chat pod in our own docs so that

360
00:19:10,560 --> 00:19:13,320
you couldn't, didn't have to 
search through the docs per SE, 

361
00:19:13,320 --> 00:19:15,840
but you could kind of just 
describe what you were looking 

362
00:19:15,840 --> 00:19:18,240
for and it would surface up the 
most appropriate results. 

363
00:19:18,600 --> 00:19:22,320
And we've taken that across the 
board into one of the areas that

364
00:19:22,800 --> 00:19:25,440
we used to actually do some 
training on quite a lot was 

365
00:19:25,440 --> 00:19:28,600
aggregation pipelines in, in the
data context. 

366
00:19:29,000 --> 00:19:32,800
And now we have a natural 
language to aggregation pipeline

367
00:19:32,800 --> 00:19:35,080
generator built into Mongo DB 
Compass. 

368
00:19:35,520 --> 00:19:38,000
You can tell it what you wanted 
to build and it will output the 

369
00:19:38,000 --> 00:19:41,400
code for you. 
So we're seeing, I suppose, uh, 

370
00:19:41,640 --> 00:19:46,480
and more recently, certainly in 
the last 6-7 weeks, we actually,

371
00:19:46,880 --> 00:19:51,520
for those who are familiar with 
them, MCP servers, MongoDB Atlas

372
00:19:51,520 --> 00:19:55,480
is now an MCP server. 
So in essence, you can have, you

373
00:19:55,480 --> 00:20:00,080
know, direct access to your data
back on Mongo DB without really 

374
00:20:00,080 --> 00:20:03,680
knowing how to query that data 
on Mongo DB and that's for 

375
00:20:03,680 --> 00:20:06,400
yourself or that's through 
agentic systems or anything as 

376
00:20:06,400 --> 00:20:08,760
well too. 
So we're seeing a big change in 

377
00:20:08,760 --> 00:20:11,200
how people are working directly 
with the data. 

378
00:20:11,560 --> 00:20:14,880
In the past, Richie, we were 
obviously, you know, Mongo DB is

379
00:20:14,880 --> 00:20:18,960
very idiomatic it, you know, 
we're using drivers to directly 

380
00:20:18,960 --> 00:20:21,880
interact with the data. 
Now a lot of that has been 

381
00:20:21,880 --> 00:20:24,120
extracted away. 
Yes, under the hood that. 

382
00:20:24,200 --> 00:20:28,160
What's going on in essence, but 
the, the requirements to really 

383
00:20:28,160 --> 00:20:31,600
understand about, you know, 
collections and documents and 

384
00:20:31,600 --> 00:20:35,920
sharding and, and you know, how 
we manage all of that, that's 

385
00:20:35,920 --> 00:20:40,280
just seems to be not, not 
disappearing, but less of an 

386
00:20:40,280 --> 00:20:43,160
onus as to I just need to work 
with this data. 

387
00:20:44,200 --> 00:20:46,120
And so big change on our side as
well too. 

388
00:20:46,680 --> 00:20:49,960
Definitely have to say, like, 
I'm sure that like the theory of

389
00:20:49,960 --> 00:20:52,320
sharding is very cool. 
It's not something I want to 

390
00:20:52,320 --> 00:20:55,560
worry about like on a day-to-day
basis because it's just not 

391
00:20:55,560 --> 00:20:58,760
directly adding value to my 
role. 

392
00:20:59,000 --> 00:21:01,240
I think for a lot of people it's
like, well, you know, how does 

393
00:21:01,240 --> 00:21:05,440
it make me more money? 
It's like, well, yeah, it's 

394
00:21:05,440 --> 00:21:07,720
better if it's someone else's 
problem if you can't answer that

395
00:21:07,720 --> 00:21:10,440
immediately. 
But Shardim was one of those I 

396
00:21:10,440 --> 00:21:12,760
started among going to be about 
5 1/2 years ago. 

397
00:21:12,760 --> 00:21:16,600
And back then you had to very 
carefully choose your Shard key.

398
00:21:16,600 --> 00:21:19,000
In other words, what were you 
deciding where, how and where 

399
00:21:19,000 --> 00:21:23,640
your data was split up amongst 
essentially your, your primaries

400
00:21:23,640 --> 00:21:26,360
and secondaries and all of your 
servers, etcetera. 

401
00:21:26,840 --> 00:21:30,480
Now and a while ago you could 
change your Shard key on the 

402
00:21:30,480 --> 00:21:32,960
fly. 
So this this whole, you have to 

403
00:21:32,960 --> 00:21:34,960
think really long and hard 
because you're never able to 

404
00:21:34,960 --> 00:21:38,880
change this went to you can 
change it on the fly to exactly 

405
00:21:38,880 --> 00:21:41,040
what you're saying. 
I don't really need to worry 

406
00:21:41,040 --> 00:21:42,920
about it. 
I'm going to use the tools and 

407
00:21:42,920 --> 00:21:45,840
we've got performance monitors 
baked into Atlas now. 

408
00:21:45,840 --> 00:21:49,880
So you can go in there and say, 
yes, I can use, they're using AI

409
00:21:49,880 --> 00:21:53,800
tools themselves to show you how
you might be better able to 

410
00:21:53,800 --> 00:21:57,080
structure your data, change your
scheme and where most of your 

411
00:21:57,080 --> 00:21:59,880
queries are coming from, what 
part of your data collection 

412
00:21:59,880 --> 00:22:02,720
most of those queries are 
hitting and therefore how you 

413
00:22:02,720 --> 00:22:06,720
get more performance, more speed
as well to Yeah. 

414
00:22:06,720 --> 00:22:09,760
So it's it's a yeah, an ever 
changing space. 

415
00:22:09,760 --> 00:22:13,800
And on that note, obviously in 
data camp, you're creating this 

416
00:22:13,800 --> 00:22:19,240
content, creating these courses 
with that rapid changing of the 

417
00:22:19,360 --> 00:22:22,920
ecosystem that we operate in. 
What are your kind of top 

418
00:22:22,920 --> 00:22:26,200
strategies, Richie, for, you 
know, continuous learning data 

419
00:22:26,200 --> 00:22:28,200
camp aside, because that's what 
we're going to get to, we're 

420
00:22:28,200 --> 00:22:30,520
going to have a demo. 
But you know, how do you keep 

421
00:22:30,520 --> 00:22:32,440
ahead of things as well too in 
this space? 

422
00:22:33,480 --> 00:22:35,720
Yeah, sure. 
I mean, there's a definite 

423
00:22:35,720 --> 00:22:37,600
problem in that. 
I mean, particularly with AI, 

424
00:22:37,600 --> 00:22:40,080
there's just so many companies, 
it's impossible to keep up with 

425
00:22:40,080 --> 00:22:43,120
everything. 
And there's half life of the 

426
00:22:43,120 --> 00:22:45,000
skills seems to be getting 
shorter and shorter. 

427
00:22:45,000 --> 00:22:48,120
Like you learn stuff and then it
goes out of date like maybe a 

428
00:22:48,120 --> 00:22:52,680
year or two later at most. 
So yeah, you just have to spend 

429
00:22:52,680 --> 00:22:55,400
more time learning in order to 
keep up with stuff that there's 

430
00:22:55,400 --> 00:22:58,480
no way around. 
But I do think there are a lot 

431
00:22:58,480 --> 00:23:02,040
more sources of information. 
So I mean, you've got sort of 

432
00:23:02,040 --> 00:23:04,640
formal learning through courses,
but you've also got, I mean, the

433
00:23:05,080 --> 00:23:07,800
my LinkedIn feed is full of 
people just blathering about 

434
00:23:08,080 --> 00:23:11,560
the, the latest tool and there's
tons of influencers like willing

435
00:23:11,560 --> 00:23:14,040
to tell you like what the latest
cool tool is. 

436
00:23:14,640 --> 00:23:16,160
There are tons of reports going 
on. 

437
00:23:16,520 --> 00:23:19,880
I really like the reports from 
McKinsey Quantum Black. 

438
00:23:20,960 --> 00:23:24,240
That's their AI arm of McKinsey.
They do some very good research,

439
00:23:24,840 --> 00:23:28,040
but there's there's tons of 
stuff around. 

440
00:23:28,040 --> 00:23:31,680
So I mean, it really is just a 
case of like keep keep listening

441
00:23:31,680 --> 00:23:33,080
to what other people are trying 
to tell you. 

442
00:23:33,080 --> 00:23:37,880
I mean, of course, podcasts and 
webinars are also our race on 

443
00:23:37,880 --> 00:23:42,880
Tetra, both of us. 
So yeah, keep keep attending 

444
00:23:42,880 --> 00:23:48,120
those sorts of events as well. 
Yeah, we had Eguazio, which is a

445
00:23:48,120 --> 00:23:51,640
company acquired by McKinsey and
Quantum Black. 

446
00:23:51,640 --> 00:23:54,160
They're on the live stream there
last week as well too. 

447
00:23:54,160 --> 00:23:57,120
So we had a, we had a great 
conversation about that as well 

448
00:23:57,160 --> 00:24:01,920
and, and how they are bringing 
AI to, you know, that's a space 

449
00:24:01,920 --> 00:24:03,360
that they've been in a long 
time. 

450
00:24:03,680 --> 00:24:08,560
And I think in the learning 
context then, Richie, how do 

451
00:24:08,560 --> 00:24:13,800
identify, how do developers 
identify kind of the new tools 

452
00:24:13,800 --> 00:24:16,400
and the frameworks that are 
worth their time? 

453
00:24:16,760 --> 00:24:18,800
It's changing, it's moving so 
quickly. 

454
00:24:19,160 --> 00:24:22,080
You don't want to bet your stack
on something that might be gone 

455
00:24:22,080 --> 00:24:24,640
in six or eight months. 
You know, what's your advice 

456
00:24:24,640 --> 00:24:27,840
there? 
Yeah, I mean it is tricky. 

457
00:24:27,840 --> 00:24:33,120
So I think just checking the the
financial viability of some of 

458
00:24:33,120 --> 00:24:35,360
these AI startups is kind of 
worthwhile at the moment. 

459
00:24:35,360 --> 00:24:40,360
Like there's been a lot of like 
cash thrown at AI companies in 

460
00:24:40,360 --> 00:24:41,920
the last sort of couple of 
years. 

461
00:24:42,320 --> 00:24:43,920
It's not clear how much longer 
that's going to last. 

462
00:24:43,920 --> 00:24:48,280
So we might be looking at like 
sort of 2027 like big crash 

463
00:24:48,280 --> 00:24:49,840
where half of these things 
disappear again. 

464
00:24:50,720 --> 00:24:53,520
One thing you'll see like I'll. 
Hold you to that, Richie. 

465
00:24:53,520 --> 00:24:55,600
I'll get you back. 
Absolutely. 

466
00:24:56,840 --> 00:25:00,880
I mean, we've seen a few big 
failures already, but like I 

467
00:25:00,880 --> 00:25:04,360
think edge of the ecosystems 
fairly fairly cash flush still. 

468
00:25:04,880 --> 00:25:07,080
But. 
In terms of getting started, I 

469
00:25:07,080 --> 00:25:11,200
always think like just making 
use of some of the APIs around 

470
00:25:12,080 --> 00:25:14,320
like work on the large language 
models and some of their data 

471
00:25:14,320 --> 00:25:16,600
storage tools is a good place to
start. 

472
00:25:18,280 --> 00:25:21,400
LLM frameworks are also like a 
very useful thing. 

473
00:25:21,400 --> 00:25:23,240
Other than both, there's only 
like 3 of them. 

474
00:25:23,240 --> 00:25:26,080
It seems to be standardized 
around like Lang chain, LAMA 

475
00:25:26,080 --> 00:25:30,240
Index, and Haystacks are one of 
those three seems to be useful. 

476
00:25:30,520 --> 00:25:34,640
And then beyond that, yeah, it's
the data storage tools that are 

477
00:25:35,000 --> 00:25:36,120
that are very important to 
learn. 

478
00:25:37,520 --> 00:25:40,880
And I suppose going back to your
earlier commented that it's easy

479
00:25:40,880 --> 00:25:43,280
to get started, it's easy to 
experiment. 

480
00:25:43,600 --> 00:25:47,560
How important are, you know, is 
that a level of experimentation?

481
00:25:47,560 --> 00:25:51,240
I meet a lot of developers when 
we're doing Mongo DB events, et 

482
00:25:51,240 --> 00:25:55,960
cetera, who may not be using AI 
in the day job, but they're 

483
00:25:55,960 --> 00:25:57,520
doing it in their personal 
projects. 

484
00:25:57,520 --> 00:26:00,760
They are experimenting, they're 
jumping onto platforms like 

485
00:26:00,760 --> 00:26:03,720
Lovable to build something 
quickly, those sort of things. 

486
00:26:03,880 --> 00:26:08,440
How important are those kind of 
personal projects to a 

487
00:26:08,440 --> 00:26:11,760
developer's career pathway and 
progression? 

488
00:26:12,960 --> 00:26:15,640
Oh yeah. 
So having a portfolio is pretty 

489
00:26:15,640 --> 00:26:18,080
essential. 
So thinking about this from a 

490
00:26:18,080 --> 00:26:20,360
hiring manager's point of view, 
whenever I've like trying to 

491
00:26:20,360 --> 00:26:23,480
hire people, it's like you start
off, you like at the moment, 

492
00:26:23,480 --> 00:26:26,440
like, you know, you put a job 
out there, you might be getting 

493
00:26:26,440 --> 00:26:29,320
500 applications just because, 
you know, things are tight in a 

494
00:26:29,320 --> 00:26:33,640
lot of industries. 
So then you look at their resume

495
00:26:33,680 --> 00:26:37,400
or CV and you filter out, OK, 
well, you know, let's get rid of

496
00:26:37,400 --> 00:26:40,560
like 400 people who obviously 
don't have the qualifications is

497
00:26:40,560 --> 00:26:43,240
like sent in, but know what the 
job was. 

498
00:26:43,640 --> 00:26:45,680
And then you read the, the 
covering letter. 

499
00:26:45,680 --> 00:26:48,000
It's like, OK, we'll weed out 
anyone who can't string a 

500
00:26:48,000 --> 00:26:49,720
sentence together because 
they're going to be hard to 

501
00:26:49,760 --> 00:26:52,480
communicate with the work. 
And that leaves you with more 

502
00:26:52,480 --> 00:26:55,560
people than you can hire still. 
So then you're looking through, 

503
00:26:55,600 --> 00:26:58,720
you want to find reasons for 
like, well, which people are 

504
00:26:58,720 --> 00:27:02,840
going to get rid of. 
And having a portfolio is just a

505
00:27:02,840 --> 00:27:06,680
sanity check to make sure the 
person has the skills that they 

506
00:27:06,680 --> 00:27:12,560
claim to have in their resume. 
So that means, yeah, if I'm 

507
00:27:12,680 --> 00:27:15,120
hiring manager, I'm probably 
going to only like spend a 

508
00:27:15,120 --> 00:27:17,960
minute or two looking at each 
thing in the portfolio. 

509
00:27:18,120 --> 00:27:20,440
But I'm just checking. 
Do you actually have the skills 

510
00:27:20,440 --> 00:27:22,960
that you say you have? 
So really you want to have 

511
00:27:22,960 --> 00:27:26,520
simple projects that are easy to
understand in under a minute or 

512
00:27:26,520 --> 00:27:27,840
two. 
It does not be something really 

513
00:27:27,840 --> 00:27:30,800
complex, it just has to be 
something that demonstrates that

514
00:27:30,800 --> 00:27:34,520
you have the skills that are 
relevant to the job that you are

515
00:27:34,520 --> 00:27:39,000
applying for. 
OK, And I suppose something new 

516
00:27:39,000 --> 00:27:42,320
that we did in Mongo DB recently
as we produced skills by just 

517
00:27:42,320 --> 00:27:45,160
going back to the TikTok 
generation that I was talking 

518
00:27:45,160 --> 00:27:48,560
about. 
These are small, really, really 

519
00:27:48,560 --> 00:27:50,960
bite size. 
Pondering our pieces of learning

520
00:27:50,960 --> 00:27:54,640
that you can do and get a badge 
that you can display on your 

521
00:27:55,480 --> 00:27:57,600
profile on LinkedIn, for 
example. 

522
00:27:58,480 --> 00:28:01,000
Do you have something similar 
inside a data camp? 

523
00:28:01,000 --> 00:28:03,400
You know the level of courses 
that people have taken or go 

524
00:28:03,400 --> 00:28:05,440
through? 
Is that something that would add

525
00:28:05,440 --> 00:28:09,040
to their CV or resume? 
Yeah, sure. 

526
00:28:09,040 --> 00:28:11,400
So there's sort of two 
alternatives here. 

527
00:28:11,400 --> 00:28:16,080
So first of all, if you complete
a course, you get a certificate.

528
00:28:16,080 --> 00:28:19,360
So this is just a statement of 
completion and this just proves 

529
00:28:19,360 --> 00:28:21,240
that you've put in some effort 
into learning. 

530
00:28:21,520 --> 00:28:24,960
We also have certifications 
which are basically you have to 

531
00:28:25,200 --> 00:28:28,720
pass exams in order to say that 
you've got qualifications. 

532
00:28:28,960 --> 00:28:34,480
And so some of these 
certifications are with existing

533
00:28:34,720 --> 00:28:37,800
companies. 
So for example, like Microsoft, 

534
00:28:37,800 --> 00:28:40,960
Amazon, whatever, they have the 
these big long standing training

535
00:28:40,960 --> 00:28:43,720
programs like for all the sort 
of cloud technologies. 

536
00:28:44,000 --> 00:28:46,240
So you take data camp courses 
and then you go and pass a 

537
00:28:46,240 --> 00:28:49,480
Microsoft certification. 
But for things like data 

538
00:28:49,480 --> 00:28:51,840
science, where there's no sort 
of standard certification, we 

539
00:28:51,840 --> 00:28:54,400
provide our own. 
OK. 

540
00:28:54,400 --> 00:28:57,520
And for that data science 
certification, how long is that 

541
00:28:57,520 --> 00:28:59,920
course pathway, Richie? 
How long does that take to go 

542
00:28:59,920 --> 00:29:02,720
through? 
So it depends whether you've got

543
00:29:02,720 --> 00:29:06,000
a background or I think from 
scratches it's about 50 hours of

544
00:29:06,000 --> 00:29:08,680
learning, then you might have to
do a bit of practicing as well 

545
00:29:08,680 --> 00:29:10,560
on top of that. 
OK. 

546
00:29:10,680 --> 00:29:13,720
It's probably similar to our 
larger accredited Mongo DB 

547
00:29:13,720 --> 00:29:17,080
certification then that we have 
it's yeah, it's a similarly 

548
00:29:17,080 --> 00:29:20,600
decent chunk of learning in 
order to send over the badge or 

549
00:29:20,600 --> 00:29:23,280
the certification that you get 
at the end of that as well too. 

550
00:29:25,040 --> 00:29:29,200
Have you seen obviously the type
of courses that Datacamp are 

551
00:29:29,440 --> 00:29:32,520
pulling together and putting out
their change in the last two 

552
00:29:32,520 --> 00:29:36,800
years or so with the onset of AI
and all the tools in that space?

553
00:29:38,440 --> 00:29:43,840
Yeah, definitely. 
Umm, so umm, interestingly, like

554
00:29:43,960 --> 00:29:47,480
from a corporate point of view, 
because we do subscriptions for 

555
00:29:47,480 --> 00:29:50,520
individuals, but we also do a 
lot of corporate training as 

556
00:29:50,520 --> 00:29:52,280
well. 
And from a corporal point of 

557
00:29:52,280 --> 00:29:54,800
view, the most popular AI 
courses is all about like 

558
00:29:55,120 --> 00:29:56,480
getting started with gender of 
AI. 

559
00:29:56,480 --> 00:29:58,920
How do I do prompt engineering? 
How do I do the basics 

560
00:29:58,960 --> 00:30:01,160
understanding like what's 
possible with AI on saying 

561
00:30:01,160 --> 00:30:02,400
what's not? 
And what we're? 

562
00:30:02,400 --> 00:30:05,440
Finding is that there are so 
many people who previously never

563
00:30:05,440 --> 00:30:08,520
had any interest in the space 
whatsoever, they now suddenly 

564
00:30:08,520 --> 00:30:10,480
realize that they need to learn 
this part of the job because 

565
00:30:10,480 --> 00:30:14,160
you've got like every CEO going.
We are now an AI first company. 

566
00:30:14,240 --> 00:30:15,680
We're going to put AI 
everywhere. 

567
00:30:15,880 --> 00:30:19,680
And so regardless of whether you
think your job needs AI, you do 

568
00:30:19,680 --> 00:30:22,600
need to have some AI skills 
because your boss or your boss's

569
00:30:22,600 --> 00:30:23,960
boss is going to demand 
interview. 

570
00:30:26,080 --> 00:30:30,600
So those skills are obviously 
augmented by, you know, whatever

571
00:30:30,720 --> 00:30:34,720
ID the developer is using the 
code assistants that can either 

572
00:30:34,720 --> 00:30:37,920
be natively in there, that can 
be put through as extensions or 

573
00:30:37,920 --> 00:30:43,320
plugins, etcetera as well too. 
But you still see the the need 

574
00:30:43,320 --> 00:30:47,960
for proper pathway of learning 
as opposed to just as we said 

575
00:30:47,960 --> 00:30:49,560
earlier, vibe your way through 
it. 

576
00:30:51,640 --> 00:30:53,760
Yeah, definitely. 
I think every hiring manager 

577
00:30:53,760 --> 00:30:56,880
wants people who do have skills,
so there's definitely a need for

578
00:30:56,880 --> 00:30:59,440
learning. 
I would say please don't 

579
00:30:59,440 --> 00:31:01,400
outsource your entire brain to 
AI. 

580
00:31:01,400 --> 00:31:03,880
It's not going to do you any 
good in the long term, actually.

581
00:31:03,920 --> 00:31:08,360
Oh, so there was an MIT paper 
that went viral recently. 

582
00:31:08,600 --> 00:31:12,040
So it's all about like they did 
like brain scans of people 

583
00:31:12,120 --> 00:31:15,840
writing essays, both with that 
with AI assistance and without 

584
00:31:15,840 --> 00:31:18,440
AI assistance. 
And unsurprisingly, the people 

585
00:31:18,440 --> 00:31:21,120
who had to like, think about 
what they were writing were 

586
00:31:21,120 --> 00:31:24,280
thinking more than those who 
were just like typing a quick 

587
00:31:24,280 --> 00:31:27,560
prompt and saying generate this.
So kind of an obvious result. 

588
00:31:27,560 --> 00:31:30,320
But it was cool they had the the
sort of brain scans and in 

589
00:31:30,320 --> 00:31:31,800
general, this is a good life 
lesson. 

590
00:31:31,800 --> 00:31:34,880
It's like it's cool to be more 
productive with AI assistants. 

591
00:31:34,960 --> 00:31:37,360
But actually, yeah, occasionally
you do want to engage your own 

592
00:31:37,360 --> 00:31:39,240
brain. 
Critical thinking is going to 

593
00:31:39,280 --> 00:31:43,920
like become like a highly 
commodified skill or highly 

594
00:31:43,920 --> 00:31:47,040
valued skill. 
A highly valued skill such as 

595
00:31:47,040 --> 00:31:50,400
common sense has become a highly
valued skill as well too. 

596
00:31:51,320 --> 00:31:52,640
A rare thing. 
A rare thing, yeah. 

597
00:31:52,880 --> 00:31:56,480
A rare, a rare thing. 
Umm, I know we've been doing 

598
00:31:56,480 --> 00:31:59,920
some work with, with our data 
camp of doing some Mongo DB work

599
00:31:59,920 --> 00:32:02,600
and we've got more in the future
as well with yourselves. 

600
00:32:02,600 --> 00:32:05,840
Tell us a little bit about that.
What's what's been done already?

601
00:32:05,840 --> 00:32:07,360
What's coming down the tracks? 
Richie, uh. 

602
00:32:08,400 --> 00:32:11,320
Yeah. 
So, uh, we are currently in the 

603
00:32:11,320 --> 00:32:14,800
middle of remaking our course, 
just introduction to MongoDB in 

604
00:32:14,800 --> 00:32:16,400
Python. 
So this is like how to use 

605
00:32:16,400 --> 00:32:19,560
MongoDB Python tools. 
Uh, this is going to be 

606
00:32:19,560 --> 00:32:25,520
launched, uh, I think late July 
and then in, in August, we're of

607
00:32:25,520 --> 00:32:29,200
course called building AI agents
with Landgraf and Mongo DB. 

608
00:32:29,200 --> 00:32:31,720
So that's really sort of getting
cutting edge. 

609
00:32:31,920 --> 00:32:33,760
I'm hoping they're going to be 
more courses. 

610
00:32:33,920 --> 00:32:37,760
So this is just so data 
campaign, Mongo DB recently 

611
00:32:37,760 --> 00:32:41,240
signed a formal partnership. 
So this is just the start of 

612
00:32:41,240 --> 00:32:42,520
things. 
I'm hoping they're going to be a

613
00:32:42,520 --> 00:32:45,280
lot more courses around Mongo DB
coming soon. 

614
00:32:46,560 --> 00:32:49,760
Excellent and and am I correct 
in thinking we were the first 

615
00:32:49,760 --> 00:32:53,760
non relational database data 
camp had a course on back in the

616
00:32:53,760 --> 00:32:54,880
day? 
Yeah. 

617
00:32:54,880 --> 00:32:58,760
So actually this intro to Mongo 
DB in Python course, it's a 

618
00:32:58,760 --> 00:33:01,280
remake of a course that was 
built I think back in 

619
00:33:01,280 --> 00:33:03,400
20/17/2018. 
So yeah, you would. 

620
00:33:03,960 --> 00:33:07,840
Mongo DB is definitely the first
no sequel base course around, 

621
00:33:08,200 --> 00:33:11,440
and I think pretty much the only
one for many many years. 

622
00:33:12,720 --> 00:33:17,760
Yeah, okay, that's great. 
And I suppose obviously MongoDB 

623
00:33:17,800 --> 00:33:21,680
gets to participate in this AI 
space primarily through to the 

624
00:33:21,880 --> 00:33:24,760
fact that two years ago we 
launched vector search on 

625
00:33:24,760 --> 00:33:28,040
MongoDB, which has been an 
enabler for us. 

626
00:33:28,040 --> 00:33:31,080
It's meant that, you know, 
regardless of what large 

627
00:33:31,080 --> 00:33:33,360
language models you use, 
regardless of what embedding 

628
00:33:33,360 --> 00:33:36,600
models, if you store the 
invectors out of those embedding

629
00:33:36,600 --> 00:33:41,120
models in MongoDB, the beauty is
they're stored in the same 

630
00:33:41,120 --> 00:33:44,640
document as the original data, 
which has a lot of advantages as

631
00:33:44,640 --> 00:33:47,200
well too. 
Are you seeing that that 

632
00:33:47,200 --> 00:33:49,920
resonates with with when 
building these forces and 

633
00:33:49,920 --> 00:33:52,880
putting together the AI agents 
with land graph, are you seeing 

634
00:33:52,880 --> 00:33:56,080
that that resonates as well too,
making it easier for people to 

635
00:33:56,520 --> 00:33:59,120
have their data and the 
embeddings associated with that 

636
00:33:59,120 --> 00:34:01,800
data side by side? 
Yeah, absolutely. 

637
00:34:01,800 --> 00:34:05,120
So I think one thing for people 
who've been in the data space 

638
00:34:05,120 --> 00:34:09,080
for a long time, we get used to 
the idea data is mostly about 

639
00:34:09,080 --> 00:34:12,239
like numbers and categorical 
data. 

640
00:34:12,440 --> 00:34:13,800
But actually that's no longer 
true. 

641
00:34:13,800 --> 00:34:18,080
It's like images of data now, 
words of data now, video is data

642
00:34:18,080 --> 00:34:21,120
now, audio is data now. 
Basically anything you think of,

643
00:34:21,159 --> 00:34:24,520
it's data now. 
And so unstructured data is just

644
00:34:24,520 --> 00:34:28,120
so important. 
And there's a lot of people 

645
00:34:28,120 --> 00:34:29,560
who've been around in data for a
long time. 

646
00:34:29,560 --> 00:34:32,360
It's time to like retrain their 
thinking to be like, OK, you 

647
00:34:32,360 --> 00:34:33,960
need to think about unstructured
data. 

648
00:34:34,280 --> 00:34:36,520
And I think this is where Mongo 
DB shines. 

649
00:34:37,120 --> 00:34:41,360
And the fact that you've got 
the, the sort of see traditional

650
00:34:41,360 --> 00:34:43,520
no sequel, I mean, it's not been
around that long, but it's been 

651
00:34:43,520 --> 00:34:45,080
what, like less than two 
decades? 

652
00:34:45,560 --> 00:34:48,000
It's been getting old again. 
So yeah, you've got no sequel 

653
00:34:48,000 --> 00:34:50,840
stuff and you've got voted 
database stuff together in one 

654
00:34:50,840 --> 00:34:52,760
place. 
I think that just makes things 

655
00:34:52,840 --> 00:34:56,080
so much nicer. 
It's yeah, not having to learn 

656
00:34:56,080 --> 00:35:00,160
different systems or different 
tools and different things. 

657
00:35:00,160 --> 00:35:03,360
Is is definitely. 
It streamlines your workflow. 

658
00:35:04,440 --> 00:35:06,640
Yeah, yeah. 
No, I couldn't have plugged 

659
00:35:06,640 --> 00:35:09,320
Mongo DB better. 
Thank you for that. 

660
00:35:09,320 --> 00:35:12,760
The other thing too is that 
Datacamp actually use Mongo DB 

661
00:35:12,920 --> 00:35:15,840
underlying your product and 
platform as well too. 

662
00:35:15,840 --> 00:35:18,000
Tell us a little bit about that.
Sure. 

663
00:35:18,000 --> 00:35:20,560
So I can't give you too many 
details on the engineering team,

664
00:35:20,560 --> 00:35:24,360
but recently in our Slack Wind 
Channels, Windows channel, 

665
00:35:24,360 --> 00:35:27,880
they'll talk about how they've 
just done an upgrade on how we 

666
00:35:27,880 --> 00:35:32,080
store experience point data. 
So Datacap has a gamification 

667
00:35:32,080 --> 00:35:33,720
systems. 
Every time you click complete 

668
00:35:34,120 --> 00:35:38,160
and exercise, you get some XP. 
All this XP data is stored using

669
00:35:38,160 --> 00:35:41,000
Mongo DB. 
There was recently an upgrade to

670
00:35:41,000 --> 00:35:44,280
using Mongo DB 8. 
The engineering team got very 

671
00:35:44,280 --> 00:35:48,480
excited because the performance 
improvements from Mongo DB 8, 

672
00:35:48,480 --> 00:35:51,040
that stuff goes faster and 
they're actually it was more 

673
00:35:51,040 --> 00:35:52,880
efficient. 
So they actually managed to 

674
00:35:53,160 --> 00:35:56,160
start running or storing all 
this on a slightly lower powered

675
00:35:56,160 --> 00:35:58,760
server, save some money. 
So basically it's going faster 

676
00:35:58,760 --> 00:36:01,120
and saving money, which two good
things. 

677
00:36:01,160 --> 00:36:02,920
Getting both at once is is very 
nice. 

678
00:36:03,840 --> 00:36:06,600
Yeah, and that was one of the 
things with Mongo DB 8 at the 

679
00:36:06,600 --> 00:36:09,720
time when we released it, it 
wasn't chock a block with a ton 

680
00:36:09,720 --> 00:36:13,200
of new features, but the main 
messaging was around speed and 

681
00:36:13,200 --> 00:36:15,640
performance and costs as well 
too. 

682
00:36:15,640 --> 00:36:17,760
So it's great to see that in 
real life. 

683
00:36:18,200 --> 00:36:21,000
It's probably prudent maybe to 
have a, you know, we talked 

684
00:36:21,000 --> 00:36:24,320
about data camp a lot. 
Maybe if you can do a bit of a 

685
00:36:24,320 --> 00:36:27,160
screen share, Richie, so that we
can, you know, for those that 

686
00:36:27,160 --> 00:36:30,040
aren't familiar, have a bit of a
look around a bit under the hood

687
00:36:30,040 --> 00:36:33,160
as well to and to show us how it
works. 

688
00:36:33,160 --> 00:36:36,400
The platform. 
You know that XP data as you 

689
00:36:36,400 --> 00:36:39,080
said, that gets gathered as 
people complete things as well 

690
00:36:39,080 --> 00:36:41,560
too perhaps? 
Sure, absolutely. 

691
00:36:41,760 --> 00:36:47,720
Let me share my screen now. 
OK so this is the the home page 

692
00:36:47,720 --> 00:36:54,000
for data camp and. 
So dating up is very simple from

693
00:36:54,760 --> 00:36:58,480
a platform point of view. 
It is basically Netflix for data

694
00:36:58,480 --> 00:37:01,920
and AI education. 
So basically you just choose the

695
00:37:01,920 --> 00:37:03,840
courses you want, you click on 
them, go and take them. 

696
00:37:03,840 --> 00:37:06,640
So from a navigation point of 
view, very, very 

697
00:37:06,640 --> 00:37:10,400
straightforward. 
So one thing I would like to 

698
00:37:10,400 --> 00:37:13,040
show you though is our tracks. 
I'm just going to go to the 

699
00:37:13,120 --> 00:37:15,680
learning. 
So tracks are series of courses 

700
00:37:15,680 --> 00:37:19,160
that make sense in order. 
We have got 24 career tracks. 

701
00:37:19,160 --> 00:37:21,000
So depending on what job you 
want, whether you want to be 

702
00:37:21,000 --> 00:37:24,600
data, data analyst, whatever 
data engineer, we've got a track

703
00:37:24,600 --> 00:37:26,240
for you. 
I'm going to click on the AI 

704
00:37:26,240 --> 00:37:28,640
Engineering 1. 
So we've got two of these. 

705
00:37:29,880 --> 00:37:31,720
So. 
One's a series of courses for if

706
00:37:31,720 --> 00:37:34,160
you have a background in data 
and you really want to get into 

707
00:37:34,240 --> 00:37:37,720
AI engineering. 1 is for if 
you've got a background in 

708
00:37:37,720 --> 00:37:39,880
software development, you want 
to get into AI engineering. 

709
00:37:41,480 --> 00:37:46,560
So these have got, yeah, 
basically this one's 15 courses.

710
00:37:46,560 --> 00:37:48,880
So there's about 60 hours of 
content and there's a 

711
00:37:48,880 --> 00:37:50,360
certification available at the 
end. 

712
00:37:50,360 --> 00:37:52,800
So if you want to prove that you
have the skills, you can take 

713
00:37:52,800 --> 00:37:55,720
that certification. 
There is a certification coming 

714
00:37:55,720 --> 00:37:59,360
from the developer track 
sometime in Q3. 

715
00:37:59,400 --> 00:38:02,120
So if I click through to this, 
you can see an example of what 

716
00:38:02,120 --> 00:38:05,720
is going on. 
So this one's slightly shorter. 

717
00:38:05,720 --> 00:38:07,600
So it takes about 26 hours to 
complete. 

718
00:38:08,480 --> 00:38:12,000
And the first version of this, 
so it's focused on the open 

719
00:38:12,000 --> 00:38:14,720
AIAPI. 
There's some hugging space stuff

720
00:38:14,720 --> 00:38:18,680
in there and then you learn 
about Pine Cone and Lang Chain. 

721
00:38:19,040 --> 00:38:21,880
One thing we're hoping to do 
with sort of future versions of 

722
00:38:21,880 --> 00:38:26,280
this, we want you to have a 
choice of model. 

723
00:38:26,280 --> 00:38:28,720
So it doesn't have to be open 
AI. 

724
00:38:28,720 --> 00:38:34,400
It could be the Anthropic Cloud 
API, could be Google Gemini API.

725
00:38:35,040 --> 00:38:39,320
Same with the the the data 
storage. 

726
00:38:39,320 --> 00:38:40,800
So safe. 
We're just starting a 

727
00:38:40,800 --> 00:38:43,840
partnership with Mongo DB, so 
maybe you want to swap out Panko

728
00:38:43,840 --> 00:38:44,720
and have Mongo? 
DB in. 

729
00:38:44,720 --> 00:38:46,800
There, of course. 
Maybe, of course. 

730
00:38:47,200 --> 00:38:49,440
Of course you. 
Is we'll fight the Panko people.

731
00:38:50,400 --> 00:38:53,360
And then yeah, if you maybe 
don't want Lang Jane, maybe 

732
00:38:53,800 --> 00:38:55,720
maybe you want to use Llama 
index, maybe you want to use 

733
00:38:55,720 --> 00:38:57,560
Haystack. 
So we're going to have a bit 

734
00:38:57,560 --> 00:38:59,520
more flexibility in those. 
But. 

735
00:39:00,720 --> 00:39:03,360
So that's the idea. 
You basically care about like 

736
00:39:03,360 --> 00:39:07,960
APIs, data storage and LM 
development frameworks. 

737
00:39:07,960 --> 00:39:11,920
Those are like the three most 
important sort of broad skills. 

738
00:39:12,160 --> 00:39:16,440
So I want to show you an example
of an exercise. 

739
00:39:16,440 --> 00:39:20,400
So within this course you can 
see sort of split into smaller 

740
00:39:20,400 --> 00:39:23,000
chapters. 
And within that, if you have a 

741
00:39:23,000 --> 00:39:27,080
look at the flows, these play 
things, that's an example of a 

742
00:39:27,080 --> 00:39:29,440
video. 
So we do short videos, like 3 or

743
00:39:29,440 --> 00:39:31,440
4 minutes and then the rest of 
the course. 

744
00:39:31,440 --> 00:39:35,360
So at least 75% of the course is
going to be hands on learning. 

745
00:39:35,760 --> 00:39:38,800
So let me show you just an 
example of this. 

746
00:39:39,040 --> 00:39:43,880
So we've got a short video and 
basically, yeah, you can see 

747
00:39:43,880 --> 00:39:46,880
it's what it's like 4 minutes 
long, nice and pretty soft. 

748
00:39:46,880 --> 00:39:50,280
You get a transcript once you do
watch that. 

749
00:39:50,800 --> 00:39:54,080
Let me just reset this. 
You have a coding exercise, so 

750
00:39:54,080 --> 00:39:55,880
all the coding is done in a 
browser. 

751
00:39:55,880 --> 00:39:59,480
You don't need to install 
anything in this case, it's just

752
00:39:59,480 --> 00:40:03,400
asking you to. 
So no external ID you needed, 

753
00:40:03,400 --> 00:40:05,120
you can do it all straight on 
the platform. 

754
00:40:05,640 --> 00:40:07,480
Exactly. 
So yeah, it's basically like 

755
00:40:07,480 --> 00:40:09,840
built in IDE. 
It's called complete stuff. 

756
00:40:09,840 --> 00:40:13,040
So in this case, it's like 
sending a message to the open AI

757
00:40:13,040 --> 00:40:15,560
client. 
So let me just type that. 

758
00:40:15,560 --> 00:40:20,440
So you got to create an open AI 
client and then you've got to 

759
00:40:20,720 --> 00:40:24,560
create a chat completion and 
then submit that to see if 

760
00:40:24,560 --> 00:40:29,640
that's right. 
And yeah, it's, it's, it's 

761
00:40:29,640 --> 00:40:31,000
generated stuff. 
So you don't need to worry about

762
00:40:31,000 --> 00:40:34,000
like API tokens, you don't need 
to worry about setting 

763
00:40:34,000 --> 00:40:35,960
everything up. 
You get a nice success message. 

764
00:40:36,160 --> 00:40:39,400
Actually, I can show you if you 
do something we've got for next 

765
00:40:39,400 --> 00:40:41,360
exercise. 
I'll, I'll, I'll fill this one 

766
00:40:41,360 --> 00:40:46,080
out wrong. 
So maybe we do the wrong model 

767
00:40:46,080 --> 00:40:49,880
there. 
So if you do something wrong, 

768
00:40:49,880 --> 00:40:51,240
it's going to tell you you've 
got like. 

769
00:40:51,320 --> 00:40:52,640
Auto. 
Graded feedback. 

770
00:40:53,080 --> 00:40:55,960
There's an error in your code. 
You can see what the error 

771
00:40:55,960 --> 00:40:58,120
message is. 
There's an AI assistant which 

772
00:40:58,120 --> 00:41:01,120
will explain what went wrong. 
That's horrible. 

773
00:41:01,240 --> 00:41:05,080
Brilliant. 
So yeah, so helps you learn 

774
00:41:05,080 --> 00:41:07,320
faster, basically. 
And if you get stuck, you can 

775
00:41:07,320 --> 00:41:09,560
also take hints about what's 
going wrong. 

776
00:41:10,360 --> 00:41:13,600
And then, yeah, you can see if 
you get stuff right, you get XP.

777
00:41:14,040 --> 00:41:15,560
You've got to pay XP to. 
Take the hints. 

778
00:41:15,800 --> 00:41:18,280
So all very straightforward. 
You lose your. 

779
00:41:18,320 --> 00:41:21,440
XP, Yeah, Yeah, exactly. 
Yeah, you'll ask for the answer,

780
00:41:21,440 --> 00:41:23,440
but you don't get any XP there, 
so. 

781
00:41:23,520 --> 00:41:25,920
You ask for the gamification, 
then I suppose there. 

782
00:41:26,200 --> 00:41:28,560
Yes, I have to say, 
gamifications been one of the 

783
00:41:28,560 --> 00:41:31,720
things you've been working on a 
lot over the last few months. 

784
00:41:32,000 --> 00:41:35,600
So I kind of realized that I'm 
actually better at keeping my 

785
00:41:35,600 --> 00:41:38,360
Duolingo streak than my data 
camp streak, even though data 

786
00:41:38,360 --> 00:41:39,960
skills are more important to me 
than Spanish. 

787
00:41:39,960 --> 00:41:43,000
It's just because there's like 
stupid OWL giving me messages 

788
00:41:43,000 --> 00:41:44,800
like 3 * a day saying please 
practice. 

789
00:41:45,080 --> 00:41:47,560
So we're pushing a bit harder on
gamification just to give you 

790
00:41:47,560 --> 00:41:50,320
that extra motivation in order 
to keep learning. 

791
00:41:51,480 --> 00:41:53,800
OK, good. 
So that's something coming down 

792
00:41:53,800 --> 00:41:55,880
the tracks from Data Camp then 
as well too. 

793
00:41:57,120 --> 00:42:00,600
And I put up a link there. 
I'll post it in the comments as 

794
00:42:00,600 --> 00:42:03,200
well too. 
But for anybody interested in, 

795
00:42:03,600 --> 00:42:06,440
you know, taking some of the 
Data Camp courses, you've 

796
00:42:06,560 --> 00:42:09,040
generously given a 50% discount 
there. 

797
00:42:09,040 --> 00:42:12,400
So appreciate that, Richie. 
I'll make sure to copy that and 

798
00:42:12,400 --> 00:42:13,960
get it into the comments as 
well. 

799
00:42:15,840 --> 00:42:16,800
Is there some? 
Is there? 

800
00:42:16,880 --> 00:42:19,000
Can they get started for free as
well too? 

801
00:42:19,000 --> 00:42:20,400
Is there trials? 
How does it? 

802
00:42:20,400 --> 00:42:23,120
How does it generally work? 
Yeah, so you can register for 

803
00:42:23,120 --> 00:42:25,680
free. 
You get to take a few exercises 

804
00:42:25,760 --> 00:42:27,840
and there are a few costs that 
are entirely free. 

805
00:42:28,240 --> 00:42:31,440
So yeah, there are things you 
can do without. 

806
00:42:31,680 --> 00:42:35,360
Before you pay money, you want 
to kind of try the platform and 

807
00:42:35,360 --> 00:42:39,280
learn a few bits and pieces. 
But yeah, if you, if you want 

808
00:42:39,280 --> 00:42:42,640
the full experience, access to 
the modern 500 courses, then 

809
00:42:42,640 --> 00:42:44,240
yeah, yeah, you've got to sign 
up. 

810
00:42:44,960 --> 00:42:49,720
Yeah, half price. 
So yeah, of course, one thing I 

811
00:42:49,720 --> 00:42:52,360
find even better than buying 
data camp yourself, we also have

812
00:42:52,360 --> 00:42:54,600
corporate plans. 
So if you want to get your boss 

813
00:42:54,600 --> 00:42:58,320
to pay for data camp, get them 
to speak to the sales team. 

814
00:42:59,760 --> 00:43:02,200
That's certainly the best, the 
best way to go about it. 

815
00:43:02,680 --> 00:43:04,760
I'm just scanning through some 
of the comments. 

816
00:43:04,760 --> 00:43:07,480
As I said earlier, I think we 
had a breakdown in the LinkedIn 

817
00:43:07,480 --> 00:43:09,520
connection, then I see it's 
picked up again. 

818
00:43:09,520 --> 00:43:12,320
So something has fixed itself in
the meantime. 

819
00:43:12,320 --> 00:43:16,280
So thank you for everybody who's
joined us on that as well too. 

820
00:43:17,600 --> 00:43:20,920
There was one in about seeing 
the boundary between the data 

821
00:43:20,920 --> 00:43:23,480
engineers and software engineers
shifting. 

822
00:43:23,480 --> 00:43:26,000
We did touch on that a little 
bit earlier obviously. 

823
00:43:26,600 --> 00:43:31,000
Do you want to maybe if if that 
was comment was missed by this 

824
00:43:33,480 --> 00:43:36,240
person joining us from LinkedIn,
maybe touch on that a little bit

825
00:43:36,240 --> 00:43:37,840
again in terms of that 
shrinking? 

826
00:43:39,160 --> 00:43:42,760
Yeah. 
So I think there is a bit of 

827
00:43:42,760 --> 00:43:44,880
blur between those as 
increasing. 

828
00:43:44,880 --> 00:43:48,640
So one thing I'll say is that 
traditionally dated teams have 

829
00:43:48,640 --> 00:43:51,240
been bit scrappy and I think 
there's been increasing 

830
00:43:52,200 --> 00:43:54,600
requirements about them becoming
more professional in terms of 

831
00:43:54,600 --> 00:43:57,360
software development. 
So software development like 

832
00:43:57,360 --> 00:44:02,720
processes have been pretty well 
defined for decades now. 

833
00:44:02,760 --> 00:44:04,120
Yeah. 
I mean, you got like the agile 

834
00:44:04,120 --> 00:44:06,080
process around me. 
You've got a lot lots of kind of

835
00:44:06,080 --> 00:44:08,760
tools around like quality 
control that haven't been 

836
00:44:08,760 --> 00:44:11,320
present as much in the data 
space. 

837
00:44:11,520 --> 00:44:14,680
I think data engineers are being
treated as though they're a type

838
00:44:14,680 --> 00:44:17,320
of software engineer. 
So there's a lot of more, so a 

839
00:44:17,320 --> 00:44:20,920
lot of professionalism around 
software quality there. 

840
00:44:22,160 --> 00:44:24,280
OK. 
And on the other side of things,

841
00:44:24,280 --> 00:44:28,840
I think software engineers are 
requiring, are requiring more 

842
00:44:28,840 --> 00:44:30,320
data skills. 
Like there's a lot more 

843
00:44:30,320 --> 00:44:32,440
requirements around 
observability and just being 

844
00:44:32,440 --> 00:44:36,200
able to understand, oh, wait, is
this an anomaly And, and what's 

845
00:44:36,200 --> 00:44:39,160
going on? 
Just being able to like predict 

846
00:44:39,640 --> 00:44:42,040
what's going to happen? 
I think so things like time 

847
00:44:42,040 --> 00:44:44,400
series forecasting are 
increasingly useful. 

848
00:44:45,440 --> 00:44:47,280
I think on the on the on the 
product side as well. 

849
00:44:47,360 --> 00:44:51,600
AB testing has been around 
forever, but well, certainly in 

850
00:44:51,600 --> 00:44:53,240
the last decade he's been 
incredibly popular. 

851
00:44:53,240 --> 00:44:55,760
And maybe software engineers 
need to understand like what the

852
00:44:55,760 --> 00:44:58,080
product manager's doing with 
with those AB tests. 

853
00:44:58,320 --> 00:45:00,800
So that's definitely an 
important skill to learn. 

854
00:45:01,680 --> 00:45:04,240
OK. 
All the time when we speak about

855
00:45:04,240 --> 00:45:06,960
data, there's always questions 
generally come up around 

856
00:45:07,600 --> 00:45:11,800
authorization, security, 
encryption, access. 

857
00:45:11,920 --> 00:45:14,320
Is there those sort of courses 
on data camp as well too, 

858
00:45:14,320 --> 00:45:16,360
Richard? 
Yes, we have of course around 

859
00:45:16,360 --> 00:45:19,520
data privacy, data quality, data
security, data governance, all 

860
00:45:19,520 --> 00:45:21,040
these sort of things. 
Most of them are conceptual 

861
00:45:21,040 --> 00:45:24,120
courses, because it's not just 
technical people that need to 

862
00:45:24,120 --> 00:45:27,080
understand these is everyone in 
your organization really. 

863
00:45:27,320 --> 00:45:30,120
So yeah, all that's covered. 
Yeah. 

864
00:45:31,440 --> 00:45:33,920
Yeah, I, I, I think these are 
kind of becoming increasingly 

865
00:45:33,920 --> 00:45:36,680
universal skills. 
Like you need to at least have 

866
00:45:36,680 --> 00:45:38,160
the basics. 
Like, oh, well, maybe we 

867
00:45:38,160 --> 00:45:42,480
shouldn't put our sensitive 
commercial data out out into the

868
00:45:42,480 --> 00:45:44,600
public. 
Same with personally 

869
00:45:44,600 --> 00:45:47,120
identifiable identification. 
It's worth understanding what 

870
00:45:47,120 --> 00:45:48,920
those things are and how you 
should treat them. 

871
00:45:50,600 --> 00:45:54,560
And in a similar vein, I suppose
when it comes to authorization 

872
00:45:54,560 --> 00:45:58,960
and security and all of that 
with the rise of AI agentic 

873
00:45:58,960 --> 00:46:01,720
systems. 
So AI that's due in a series of 

874
00:46:01,720 --> 00:46:05,480
tasks on your behalf before 
surfacing back with the results.

875
00:46:05,480 --> 00:46:08,960
Two things, how does is that 
addressed maybe in some of the 

876
00:46:08,960 --> 00:46:11,720
content you have there. 
But I suppose more secondly, 

877
00:46:11,720 --> 00:46:15,520
more importantly for me is kind 
of your thoughts on that agentic

878
00:46:15,520 --> 00:46:16,880
space. 
We discussed AI. 

879
00:46:16,880 --> 00:46:20,480
We didn't discuss the kind of AI
doing its own thing until it 

880
00:46:20,480 --> 00:46:22,800
comes back to you with a viable 
results. 

881
00:46:22,880 --> 00:46:24,800
Thoughts on that, Richie? 
Yeah. 

882
00:46:24,800 --> 00:46:28,960
So AI agents, it's a very broad 
definition. 

883
00:46:28,960 --> 00:46:32,480
People can disagree on exactly 
what constitutes an agent. 

884
00:46:32,480 --> 00:46:36,240
So I think that there are two 
fields of thought. 

885
00:46:36,240 --> 00:46:40,960
So 1 is that the AI agents you 
should build should just be 

886
00:46:40,960 --> 00:46:44,000
basically business processes 
encoded in the software. 

887
00:46:44,000 --> 00:46:46,240
And maybe it calls an LLM 
somewhere, some really, really 

888
00:46:46,240 --> 00:46:48,720
simple agents just to make 
things automated and more 

889
00:46:48,720 --> 00:46:52,640
efficient. 
So a good example of this, when 

890
00:46:52,640 --> 00:46:55,920
our sales team make a call, 
they're supposed to write down 

891
00:46:56,120 --> 00:46:59,480
what was said in the call 
according to it's called the Med

892
00:46:59,480 --> 00:47:01,080
Pick framework. 
I can't remember what the 

893
00:47:01,080 --> 00:47:03,720
acronym is, is something sales, 
but it's like basically about 

894
00:47:03,720 --> 00:47:06,880
the state of the deal and sales 
people hate doing this because 

895
00:47:06,880 --> 00:47:09,800
it's really tedious and it was 
thinking and it's taking away 

896
00:47:09,800 --> 00:47:13,320
time from them actually like 
doing something there's a it's 

897
00:47:13,320 --> 00:47:15,760
going to help their targets. 
So they do it badly. 

898
00:47:15,840 --> 00:47:20,240
So we have a very simple agent 
which takes the call transcript 

899
00:47:20,480 --> 00:47:23,400
and then it categorizes it 
according to this framework and 

900
00:47:23,400 --> 00:47:27,040
then dumps the the results into 
sales force or wherever they're 

901
00:47:27,040 --> 00:47:29,440
supposed to be in order to do 
this. 

902
00:47:29,440 --> 00:47:33,920
So really, really simple agent 
really, really useful because it

903
00:47:33,920 --> 00:47:36,160
makes sales team happy because 
they don't have to do something 

904
00:47:36,160 --> 00:47:38,080
they hate that's tedious and 
boring. 

905
00:47:38,360 --> 00:47:42,280
And this is a great use case. 
On the other hand, you've got 

906
00:47:42,960 --> 00:47:46,960
some companies trying to build 
really, really advanced employee

907
00:47:46,960 --> 00:47:50,360
replacement agents. 
So you've got some Cognition 

908
00:47:50,360 --> 00:47:53,080
Labs has Devin it's like an AI 
software agent. 

909
00:47:53,080 --> 00:47:56,640
You've got Julius AI creating 
like AI data scientists. 

910
00:47:56,880 --> 00:48:00,520
You've got called Emos trying to
create like a universal AI 

911
00:48:00,520 --> 00:48:02,840
employee, which sounds way too 
ambitious. 

912
00:48:02,920 --> 00:48:05,000
I think it's mostly customer 
service Asians at the moment, 

913
00:48:05,000 --> 00:48:06,760
but like trying to get real 
people. 

914
00:48:07,080 --> 00:48:09,840
And I feel like the sweet spot 
is kind of halfway in between 

915
00:48:10,560 --> 00:48:12,520
that. 
Like if you have a lot of 

916
00:48:12,520 --> 00:48:16,440
repetitive processes, then yeah,
OK, do very simple agents. 

917
00:48:16,680 --> 00:48:19,960
But the modern sort of reasoning
AIS mean you can actually do 

918
00:48:19,960 --> 00:48:23,840
something a little bit more 
advanced there that and have 

919
00:48:24,480 --> 00:48:27,760
slightly more flexible processes
being dealt with using agents. 

920
00:48:28,520 --> 00:48:32,640
And this broach is on the usual 
elephant in the room topic when 

921
00:48:32,640 --> 00:48:36,080
it comes to AI. 
It's AI is going to do everybody

922
00:48:36,080 --> 00:48:39,200
out of a job. 
And I think my personal take on 

923
00:48:39,200 --> 00:48:43,400
that is, you know, from a 
developer perspective, yeah, who

924
00:48:43,400 --> 00:48:45,880
doesn't want, like, I don't want
to write boilerplate code. 

925
00:48:45,880 --> 00:48:47,360
I don't want to write the 
mundane code. 

926
00:48:47,360 --> 00:48:50,520
I want to do the work that, you 
know, gives me a key 

927
00:48:50,520 --> 00:48:54,840
differentiator. 
And I think that the types of 

928
00:48:54,840 --> 00:48:59,720
jobs that developers will do 
will change the areas that get 

929
00:48:59,720 --> 00:49:02,240
involved in a change. 
What's your thoughts on that? 

930
00:49:02,240 --> 00:49:04,480
AIS come in to take all our 
jobs, Richie. 

931
00:49:06,080 --> 00:49:09,800
Yeah, I mean, it just very much 
depend on your job. 

932
00:49:09,800 --> 00:49:13,720
So for example, like earlier 
this year there was the Duolingo

933
00:49:13,720 --> 00:49:17,600
CEO talking about Duolingo's 
going AI 1st and no more 

934
00:49:17,600 --> 00:49:19,360
contractors is going to be a 
hiring freeze. 

935
00:49:19,360 --> 00:49:23,320
Everyone has to use AI and it's 
going to be your performance 

936
00:49:23,320 --> 00:49:25,240
reviews. 
So pretty extreme take. 

937
00:49:25,480 --> 00:49:27,760
And if you're a Duolingo 
contractor, then obviously, 

938
00:49:27,760 --> 00:49:29,600
yeah, your job is being taken by
AI. 

939
00:49:29,880 --> 00:49:34,200
But I think for developers at 
the moment, there is there are 

940
00:49:34,200 --> 00:49:38,800
more development tasks to be 
done that there are developers 

941
00:49:38,800 --> 00:49:40,560
in the world. 
There's like there's definitely 

942
00:49:40,560 --> 00:49:42,600
a shortage of people with enough
technical skills. 

943
00:49:42,880 --> 00:49:46,640
So I don't think we're going to 
see developers losing jobs 

944
00:49:48,080 --> 00:49:49,960
rapidly. 
I think may there may be some 

945
00:49:49,960 --> 00:49:52,960
hiring freezes at some companies
at least in the short term. 

946
00:49:53,320 --> 00:49:58,840
But I think like being able to 
build stuff is just incredibly 

947
00:49:58,840 --> 00:50:02,320
valuable. 
Like there is no shortage of 

948
00:50:02,320 --> 00:50:06,760
problems where a bit of software
skills, it's not going to come 

949
00:50:06,760 --> 00:50:08,520
in handy. 
Like I feel like the world has 

950
00:50:08,520 --> 00:50:12,080
so many problems we need all the
technology help we can get to 

951
00:50:12,080 --> 00:50:15,280
solve them. 
Software is eating the world, 

952
00:50:15,280 --> 00:50:19,040
Richie, right? 
I think from our perspective 

953
00:50:19,040 --> 00:50:21,000
that most definitely. 
Software is eating the world. 

954
00:50:21,080 --> 00:50:26,520
AI is eating software I guess. 
I got a question in from Sri, 

955
00:50:26,520 --> 00:50:29,440
which I'll answer because it's 
more of a Mongo DB question than

956
00:50:29,440 --> 00:50:34,640
a data cow question. 
You know, we, we have recently 

957
00:50:34,640 --> 00:50:36,960
introduced A Django longer to be
back in. 

958
00:50:36,960 --> 00:50:39,840
So Sri go check that out. 
If you just search for that, 

959
00:50:40,440 --> 00:50:43,240
you'll find our developer 
article around that. 

960
00:50:43,240 --> 00:50:45,960
I think you'll find some 
examples as well too. 

961
00:50:46,600 --> 00:50:49,040
We have a lot of Django 
developers. 

962
00:50:49,600 --> 00:50:51,600
It's kind of we've been at the 
Django cons. 

963
00:50:51,600 --> 00:50:54,480
I think we're going to the US 
one in another couple of months 

964
00:50:54,480 --> 00:50:57,600
as well too. 
So it's a key, it's a key key 

965
00:50:57,600 --> 00:51:01,560
area for us in terms of Django 
as yourselves on Datacamp. 

966
00:51:01,560 --> 00:51:03,440
Do you have much course in 
content around that? 

967
00:51:04,760 --> 00:51:07,640
No. 
So Django is very much for web 

968
00:51:07,640 --> 00:51:08,960
developers. 
It's slightly out of our 

969
00:51:08,960 --> 00:51:11,440
wheelhouse at the moment, so we 
don't have any content on that. 

970
00:51:13,320 --> 00:51:18,760
I did use Django briefing about 
2010 with it in the last 15 

971
00:51:18,760 --> 00:51:22,640
years. 
Well, I look, we have a lot of 

972
00:51:23,120 --> 00:51:25,480
Django proponents in here in 
Mongo DB. 

973
00:51:25,480 --> 00:51:28,200
So it's something that we 
generally keep alive. 

974
00:51:28,200 --> 00:51:32,080
And as I said that Mongo DB back
in when public preview about 6-8

975
00:51:32,080 --> 00:51:35,040
weeks ago I think. 
So it was something new from us 

976
00:51:35,040 --> 00:51:39,320
and it's certainly an area you 
mentioned obviously or your 

977
00:51:39,320 --> 00:51:42,680
books, the language and you're 
kind of go, where's that gone? 

978
00:51:42,680 --> 00:51:45,360
Obviously Python has has a 
resurgence with the AI. 

979
00:51:45,360 --> 00:51:48,400
You've seen a lot of that 
yourselves as well, too. 

980
00:51:50,360 --> 00:51:55,360
Yeah, so certainly, I mean, one 
thing I say like Python is sort 

981
00:51:55,360 --> 00:51:57,680
of eating everything. 
Like my background I mentioned 

982
00:51:57,680 --> 00:52:00,040
like I came for the our 
community, I wrote books on our 

983
00:52:00,040 --> 00:52:07,000
and yeah, that's all gone away. 
So I think, yeah, it seems like 

984
00:52:07,000 --> 00:52:09,360
the AI community is sort of 
settled on like Python And 

985
00:52:09,360 --> 00:52:11,120
JavaScript being the two main 
languages. 

986
00:52:11,640 --> 00:52:13,440
Yeah. 
So that's it's incredibly 

987
00:52:13,440 --> 00:52:16,560
popular. 
I don't think the developer can 

988
00:52:16,560 --> 00:52:19,760
we will ever settle on anything 
being the two main languages at 

989
00:52:19,840 --> 00:52:22,440
all. 
This is this is a battle that's 

990
00:52:22,440 --> 00:52:25,240
always going to happen. 
If it was, you know, there's, 

991
00:52:25,280 --> 00:52:28,600
there's, you know, we know the 
Ruby on Rails, the Ross, you 

992
00:52:28,600 --> 00:52:31,440
know, there's a whole host of 
languages that were the next big

993
00:52:31,520 --> 00:52:34,040
thing, right? 
Yeah, actually one thing I've 

994
00:52:34,480 --> 00:52:36,560
still been waiting for. 
Like Julia was supposed to be 

995
00:52:36,560 --> 00:52:39,320
the next big language in data 
has never quite happened. 

996
00:52:39,320 --> 00:52:41,920
Like maybe, maybe one day it 
will. 

997
00:52:41,920 --> 00:52:46,520
But yeah, not just yet. 
Great. 

998
00:52:46,520 --> 00:52:48,240
Well, look, this has been 
superb. 

999
00:52:48,240 --> 00:52:50,600
I kind of think we're getting 
towards the end. 

1000
00:52:50,600 --> 00:52:54,560
Anyone got any questions in the 
comments, please put them there 

1001
00:52:54,560 --> 00:52:58,040
before Richie and I disappear. 
We touched a little bit early on

1002
00:52:58,040 --> 00:53:02,040
how to keep up to date and data 
camp aside, obviously you you 

1003
00:53:02,040 --> 00:53:04,480
need to prioritize that over 
your Duolingo, Richie. 

1004
00:53:04,480 --> 00:53:07,920
But how else do you consume the 
new stuff? 

1005
00:53:08,600 --> 00:53:11,360
Is it blogs? 
Is it videos, podcasts? 

1006
00:53:11,680 --> 00:53:15,560
How do you like to absorb, I 
suppose, your information to to 

1007
00:53:15,560 --> 00:53:19,000
keep it on top of things? 
All of these things actually, I 

1008
00:53:19,000 --> 00:53:21,400
have to say, if I want to learn 
about something new and then we 

1009
00:53:21,400 --> 00:53:25,480
find an expert and I get them to
come on on the webinar podcast 

1010
00:53:25,720 --> 00:53:27,960
and I just get to interview them
for an hour and that's that's 

1011
00:53:27,960 --> 00:53:30,200
how I find out about stuff. 
So yeah. 

1012
00:53:31,600 --> 00:53:34,120
That's something that I need to 
do as well too. 

1013
00:53:34,120 --> 00:53:36,040
I do get dropped in it from time
to time. 

1014
00:53:36,040 --> 00:53:39,000
Totally the deep end doing these
podcasts as well to go ahead. 

1015
00:53:39,000 --> 00:53:40,600
I know nothing. 
I've got to do my homework 

1016
00:53:40,600 --> 00:53:44,960
before this guest joins me. 
So it's certainly a good way to 

1017
00:53:44,960 --> 00:53:46,560
go. 
That's that's what you on on 

1018
00:53:46,560 --> 00:53:48,320
age, right? 
You're going to say right? 

1019
00:53:48,440 --> 00:53:51,120
And I want to learn, and here's 
the person who's going to teach 

1020
00:53:51,120 --> 00:53:52,840
it to me. 
Exactly. 

1021
00:53:52,840 --> 00:53:54,400
Yeah. 
So I have to say, quite often I 

1022
00:53:54,400 --> 00:53:56,480
do end up like having 
conversations about topics I 

1023
00:53:56,480 --> 00:53:59,240
have no idea about. 
The hardest 1 though, is the 

1024
00:53:59,240 --> 00:54:03,680
sports analytics things because 
I'm, I'm not really like a huge 

1025
00:54:03,680 --> 00:54:06,000
sports fan. 
We did an episode of the of data

1026
00:54:06,000 --> 00:54:11,760
framed on like how data is 
changing like the NBA, and I had

1027
00:54:11,760 --> 00:54:15,240
to study so much. 
Just basic basketball technology

1028
00:54:15,240 --> 00:54:17,160
just did not sound like an idiot
in front of everyone. 

1029
00:54:17,440 --> 00:54:20,400
So yeah, AI still fine. 
You can get away with a bit of 

1030
00:54:20,400 --> 00:54:23,360
black there because not that 
many people understand 

1031
00:54:23,360 --> 00:54:26,120
absolutely everything. 
Yeah, sports is hard to get away

1032
00:54:26,120 --> 00:54:28,200
with. 
Yeah, sport. 

1033
00:54:28,200 --> 00:54:30,440
Well, at least basketball has a 
lot of action. 

1034
00:54:30,440 --> 00:54:32,840
I find there's sports that have 
lots of downtime. 

1035
00:54:33,160 --> 00:54:36,880
Baseball, American football, 
cricket, they are the ones that 

1036
00:54:36,880 --> 00:54:39,240
have lots of data associated 
with them because that's where 

1037
00:54:39,240 --> 00:54:42,840
you keep the interest up in 
between the downtimes of action 

1038
00:54:42,880 --> 00:54:46,040
on the pitch of the field or or 
wherever it might be. 

1039
00:54:46,040 --> 00:54:50,400
I'm always amazed and I know 
Moneyball, you know, that is at 

1040
00:54:50,560 --> 00:54:54,760
stats.com is the company that 
the Moneyball guys found. 

1041
00:54:54,760 --> 00:54:56,640
It's still going very, very 
strong. 

1042
00:54:56,640 --> 00:55:00,160
So it's a super interesting area
and most definitely. 

1043
00:55:00,640 --> 00:55:03,960
Yeah, and I have to say, just 
learning about the the data 

1044
00:55:03,960 --> 00:55:06,760
analytics behind things has 
given me a greater appreciation 

1045
00:55:06,760 --> 00:55:09,960
for the sports because, yeah, 
it's knowing like how people are

1046
00:55:09,960 --> 00:55:12,200
thinking about stuff and like 
what actually works well with 

1047
00:55:12,200 --> 00:55:13,680
data. 
It's like, Oh yeah, kind of 

1048
00:55:13,680 --> 00:55:16,800
makes sense. 
Yeah, no, it certainly does. 

1049
00:55:16,880 --> 00:55:18,280
It's certainly churning out a 
lot. 

1050
00:55:18,560 --> 00:55:22,960
And I suppose obviously we put 
up the code for Datacamp 

1051
00:55:22,960 --> 00:55:25,040
earlier. 
It's in the comments there as 

1052
00:55:25,040 --> 00:55:26,960
well too. 
You can grab that so people can 

1053
00:55:26,960 --> 00:55:29,320
jump across there and get 
started. 

1054
00:55:29,320 --> 00:55:32,240
Any final thoughts for our 
viewers, Richie on? 

1055
00:55:32,680 --> 00:55:36,000
You know, somebody's saying and 
I think we'd one of the we'd one

1056
00:55:36,000 --> 00:55:39,200
comment come in if I can just 
find it is, you know, I want to 

1057
00:55:39,200 --> 00:55:40,680
learn but I have some 
difficulty. 

1058
00:55:40,680 --> 00:55:44,400
How can I start with app dev? 
You know, obviously data camp, 

1059
00:55:44,400 --> 00:55:48,440
go Start learning some stuff 
there, Go other frameworks and 

1060
00:55:48,440 --> 00:55:51,520
use their courses that Mongo DB 
has a university as well too. 

1061
00:55:51,840 --> 00:55:56,400
But any other final thoughts for
people who I think AI is one of 

1062
00:55:56,400 --> 00:56:00,000
the areas that I think people 
are happy to experiment in my 

1063
00:56:00,000 --> 00:56:03,440
view. 
In other words, it's around, you

1064
00:56:03,440 --> 00:56:06,800
know, relatively new that there 
isn't, OK, People will always 

1065
00:56:06,800 --> 00:56:09,400
say they're experts, right? 
But there isn't anybody who's an

1066
00:56:09,400 --> 00:56:12,680
expert in this space per SE, 
because it's so new. 

1067
00:56:12,920 --> 00:56:15,640
And there's a lots of experiment
that you can just do and get up 

1068
00:56:15,640 --> 00:56:18,320
and running yourself. 
Yeah, absolutely. 

1069
00:56:19,320 --> 00:56:24,440
And so 11 tip I'll have is like 
it's always hard to find time to

1070
00:56:24,440 --> 00:56:26,680
learn if you have a day job and 
like there's always this 

1071
00:56:26,680 --> 00:56:28,280
competing pressure. 
Well, I've got a deadline 

1072
00:56:28,280 --> 00:56:30,960
something else this week and I 
just don't have time to sit down

1073
00:56:30,960 --> 00:56:34,080
and learn. 
So if you can persuade your boss

1074
00:56:34,080 --> 00:56:37,560
to have completing some courses 
as part of your quarterly 

1075
00:56:37,560 --> 00:56:40,040
targets, if your boss is on time
with this and it's your boss 

1076
00:56:40,040 --> 00:56:42,360
nagging you to complete the 
learning, that's much better 

1077
00:56:42,360 --> 00:56:45,320
than being like, you know, I've 
got to wait and like, try and 

1078
00:56:45,320 --> 00:56:48,200
learn on a weekend when I'm 
tired or whatever because I 

1079
00:56:48,200 --> 00:56:51,560
couldn't do it during the week. 
So yeah, get it as part of your,

1080
00:56:51,560 --> 00:56:54,200
like, quarterly targets. 
Get it built into like something

1081
00:56:54,200 --> 00:56:56,320
official at work. 
Get your boss on side with you 

1082
00:56:56,320 --> 00:56:57,960
learning. 
That's going to make it so much 

1083
00:56:57,960 --> 00:57:00,360
easier to devote these new 
skills because. 

1084
00:57:00,360 --> 00:57:04,680
Yeah, it pays to have you 
competent and productive and 

1085
00:57:04,680 --> 00:57:06,760
knowing about all the latest 
technologies, it's going to pay 

1086
00:57:06,760 --> 00:57:10,040
off for your boss. 
So yeah, it's going to make your

1087
00:57:10,040 --> 00:57:14,520
life easier. 
In that respect, I suppose you 

1088
00:57:14,520 --> 00:57:17,680
know people need that discipline
certainly for self-paced 

1089
00:57:17,680 --> 00:57:19,720
learning such as Datacamp 
provides. 

1090
00:57:20,120 --> 00:57:23,560
Any other tips around? 
OK, get your boss to set aside 

1091
00:57:23,560 --> 00:57:26,040
some time and maybe actually pay
for it as well too. 

1092
00:57:26,360 --> 00:57:30,320
Any other tips in kind of, you 
know, how much somebody should, 

1093
00:57:30,320 --> 00:57:33,560
how often they should try, you 
know, is it 20 minutes a day or 

1094
00:57:33,560 --> 00:57:37,920
40 minutes a day perhaps? 
Or do they set aside half a day 

1095
00:57:37,920 --> 00:57:40,240
and, and just do the rest of the
work the other four days? 

1096
00:57:40,240 --> 00:57:43,720
What, what would, what do you 
know works best or how, how, how

1097
00:57:43,720 --> 00:57:46,160
does it pan out? 
Do you hear anecdotally from 

1098
00:57:46,160 --> 00:57:47,880
your learners? 
Yeah. 

1099
00:57:47,880 --> 00:57:51,800
So I mean, your two options 
really are you set aside like a 

1100
00:57:51,800 --> 00:57:55,280
regular time each week. 
So it's like maybe it's like 

1101
00:57:55,640 --> 00:57:57,600
after lunch on a Friday, it's 
like, OK, I'm going to spend an 

1102
00:57:57,600 --> 00:57:59,680
hour learning maybe, maybe twice
a week. 

1103
00:58:00,560 --> 00:58:03,320
The other alternative is you 
save up and you do an intensive.

1104
00:58:03,320 --> 00:58:07,720
It was like OK, once, once 1/4 
and they do like a three day 

1105
00:58:08,280 --> 00:58:10,200
learning binge or something like
that. 

1106
00:58:10,200 --> 00:58:12,480
And just like really get to 
grips with the new technology. 

1107
00:58:13,840 --> 00:58:18,360
So again, it's going to depend 
on like what the rest of your 

1108
00:58:18,360 --> 00:58:20,160
life is like for what your flow 
is like. 

1109
00:58:20,440 --> 00:58:22,400
But yeah, building regular 
habits for learning. 

1110
00:58:23,080 --> 00:58:25,960
Actually there's also a did get 
mobile app, so there's like 

1111
00:58:25,960 --> 00:58:28,800
practice modes if you just want 
to do like 5 minutes on the bus 

1112
00:58:29,680 --> 00:58:32,040
during you commute. 
You can also do things that way.

1113
00:58:32,320 --> 00:58:36,280
So yeah, you've got a choice 
between that like regular small 

1114
00:58:36,280 --> 00:58:38,400
amounts of learning or big 
binge. 

1115
00:58:38,560 --> 00:58:41,080
Like certainly a lot of 
companies that will run sort of 

1116
00:58:41,080 --> 00:58:44,400
like training days where it's 
like, OK, we're going to get 

1117
00:58:44,400 --> 00:58:47,760
everyone in a department to take
the same course at the same 

1118
00:58:47,760 --> 00:58:50,600
time. 
So yeah, there's more than one 

1119
00:58:50,600 --> 00:58:52,680
way to learn, just as long as 
you find time somewhere. 

1120
00:58:53,640 --> 00:58:55,280
Yeah, and find what suits you 
most. 

1121
00:58:55,280 --> 00:58:57,000
The fact that it's it's on 
mobile too. 

1122
00:58:57,000 --> 00:58:59,960
Obviously you can use that 
downtime people have commuting 

1123
00:58:59,960 --> 00:59:03,800
or, you know, just hanging 
around to maybe kind of upskill 

1124
00:59:04,480 --> 00:59:06,600
and leverage some learning of 
data camp as well. 

1125
00:59:06,600 --> 00:59:10,440
To any parting thoughts, Richie,
then that did I miss any of the 

1126
00:59:10,440 --> 00:59:12,760
questions or topics you wanted 
to cover today? 

1127
00:59:12,760 --> 00:59:14,720
Anything to say? 
Oh man, no. 

1128
00:59:14,880 --> 00:59:18,400
I mean I think trick is just to 
get started and do stuff. 

1129
00:59:18,560 --> 00:59:22,280
So, I mean, we talked before 
about like, good ways of like 

1130
00:59:22,320 --> 00:59:24,520
adopting AI. 
And it's like, well, you know, 

1131
00:59:24,680 --> 00:59:26,400
all the experts I speak to, 
they're like, well, you know, 

1132
00:59:26,400 --> 00:59:28,440
first you need to like, figure 
out what the business problem 

1133
00:59:28,440 --> 00:59:29,440
is. 
And then you need to get all the

1134
00:59:29,440 --> 00:59:30,720
data and the infrastructure in 
place. 

1135
00:59:30,720 --> 00:59:32,520
And only then should you start 
building AI. 

1136
00:59:32,800 --> 00:59:34,800
And that's fine. 
But you know, you'll never ship 

1137
00:59:34,800 --> 00:59:37,680
anything. 
Just get started, try a project,

1138
00:59:37,880 --> 00:59:41,840
learn some new things. 
And yeah, make mistakes and it's

1139
00:59:41,840 --> 00:59:44,960
cool, you know, just just make 
progress, do stuff. 

1140
00:59:45,920 --> 00:59:49,200
Make progress, Yeah. 
And you will make mistakes, but 

1141
00:59:49,200 --> 00:59:52,520
they're all learning experiences
from, you know, the you learn 

1142
00:59:52,520 --> 00:59:55,160
more from mistakes than you do 
from kind of anything that goes 

1143
00:59:55,160 --> 00:59:56,720
smoothly. 
I think is kind of one of the 

1144
00:59:56,720 --> 00:59:59,640
mantras out there as well too. 
Absolutely. 

1145
00:59:59,640 --> 01:00:03,000
And you know, especially in a 
live situation like this, it's 

1146
01:00:03,000 --> 01:00:06,920
like make mistakes on camera. 
So always the fun stuff. 

1147
01:00:07,880 --> 01:00:09,800
Perfect, Willis. 
I certainly ascribe to that. 

1148
01:00:09,800 --> 01:00:12,440
I've been doing that for a 
couple of years now, making tons

1149
01:00:12,440 --> 01:00:14,760
of mistakes from people on 
camera, but you get used to it 

1150
01:00:14,760 --> 01:00:16,960
as well too. 
Richie, listen, it's been a 

1151
01:00:16,960 --> 01:00:19,640
pleasure to have you on board. 
Thank you for setting aside the 

1152
01:00:19,640 --> 01:00:22,640
tie. 
I hope that the folks that have 

1153
01:00:22,720 --> 01:00:26,360
learned a lot drop into data 
camp, see what we do there. 

1154
01:00:26,520 --> 01:00:29,280
I know we're a little early for 
the new courses that we have out

1155
01:00:29,280 --> 01:00:33,520
with you from MongoDB, but as 
you said, uh, first one end of 

1156
01:00:33,520 --> 01:00:36,080
July or so and then onwards from
there as well too. 

1157
01:00:36,080 --> 01:00:38,440
So certainly we'll keep an eye 
out for those. 

1158
01:00:38,680 --> 01:00:41,880
And, but for me, uh, if you want
to learn a little bit more about

1159
01:00:42,200 --> 01:00:46,000
MongoDB, we have a developer 
center, developer.mongodb.com 

1160
01:00:46,000 --> 01:00:48,560
where you can go. 
And as I said in the intro, we 

1161
01:00:48,560 --> 01:00:50,560
do these live streams. 
And I'm not the only one. 

1162
01:00:50,560 --> 01:00:53,720
My colleagues do these, I think 
every Thursday or so, which are 

1163
01:00:53,720 --> 01:00:57,560
much more hands on coding than 
me with the guests and and 

1164
01:00:57,560 --> 01:00:59,400
talking through some demos as 
well too. 

1165
01:00:59,400 --> 01:01:01,560
But Richie, it's been superb to 
have you on. 

1166
01:01:01,560 --> 01:01:05,120
Thank you for sharing all the 
insights and agreeing to have 

1167
01:01:05,120 --> 01:01:07,280
that click Bailey title on the 
vibe coding. 

1168
01:01:07,720 --> 01:01:11,440
I think it worked well and let's
see how that one plays out in 

1169
01:01:11,440 --> 01:01:13,800
the long term. 
But it's been a pleasure to have

1170
01:01:13,800 --> 01:01:14,840
you, Richie. 
Thank you so much. 

1171
01:01:15,760 --> 01:01:18,760
Yeah, thank you for inviting me.
And yeah, thanks to everyone in 

1172
01:01:18,760 --> 01:01:20,040
the audience for the great 
questions. 

1173
01:01:21,080 --> 01:01:24,400
Indeed, Yeah, thank you for all 
our viewers and do stay tuned 

1174
01:01:24,400 --> 01:01:28,280
and keep an eye out. 
Follow us on the various YouTube

1175
01:01:28,280 --> 01:01:29,960
and LinkedIn. 
It's a follow or subscribe. 

1176
01:01:29,960 --> 01:01:31,840
I can't remember which ones 
which, but you know what to do. 

1177
01:01:31,840 --> 01:01:35,160
Press those buttons and you'll 
get alerts for future shows such

1178
01:01:35,160 --> 01:01:38,000
as this. 
So for me, Shane McAllister in 

1179
01:01:38,000 --> 01:01:41,560
Amsterdam and Richie over in New
York, it's been a pleasure, 

1180
01:01:41,560 --> 01:01:43,280
everybody. 
Thank you for your time and 

1181
01:01:43,280 --> 01:01:45,160
thank you very much, Richie. 
Appreciate your time. 

1182
01:01:46,600 --> 01:01:47,040
Take care.