EP. 271 The "Vibe Coding" Controversy: What Devs Are Getting Wrong About AI

Everyone's talking about AI taking over coding jobs, but what's the real story? Shane McAllister and DataCamp's Richie Cotton dive into the "vibe coding" phenomenon and expose the biggest misconceptions developers have about AI. Learn how to shift your mindset from a pure coder to a "vibe curator" and future-proof your career. Don't miss the full video discussion, available to watch now in the Spotify app.

Episode Transcript

(00:06):
Welcome to the Mongo DB Podcast.I'm Shane McAllister.
I'm one of the leads on our developer relations team and
it's great to have you join us today.
So in a world when AI can write your code, well, at least some
of the time, software development and data careers
need rethinking. How you work and how you learn
are utterly changing. Do you vibe code everything and

(00:29):
hope for the best, as we've seenmany, many articles recently?
Do you spend your evenings and weekends pouring over all the
latest tools and frameworks? Well, today's guest, we're going
to have a great discussion and dive into the controversial area
of keeping up with the vibe coders.
And today, I'm really, really happy to welcome Richie Cotton,
a senior data evangelist, Pardonme, at data camp to ruminate the

(00:54):
changing relations chip between engineering teams and data teams
and modern developer workflows and everything else that we can
decide to get on to on today's topic.
Richie, you're very welcome to the Monetary podcast.
How are you? Life is good, Shane.
Really pleased to be here. I'm based in New York City.

(01:14):
We're having a heat wave today. It's got a message from the
building saying they are turningthe air conditioning down in
order to save energy. If I start sweating throughout
the show, that's what's happening.
Hey, this is a live stream, anything can happen and I'm not
usually for regular viewers, I'mnot usually in my normal place.
I'm in Amsterdam at the moment doing a, what we call a

(01:34):
developer day at Mongo DB where we get some of our clients into
a room for a day or two and we run through some are going to be
fundamentals and also some deeper topics with them as well
too. So if the Wi-Fi goes or anything
else happens or somebody comes in and knocks on the door, I do
apologize. So if you pass out from heat
exhaustion, Richie or I get moved out of this room.

(01:56):
Everyone knows that this, this chat is live.
I'd love to start with most guests to kind of dig back a
little bit your career path to date.
Richie, where did you start? What roles have you gone through
all the way up to your current role as evangelists in Data
camp? Absolutely, yeah.
Uh, my background is in data science, so, uh, I started well

(02:20):
before it was called data science.
Really. So this is back in 2005, uh, I
was working on data for chemicalhealth and safety.
So that was the start of my career.
I've also done data science in other industries.
So I moved from chemical health and safety to debt collection.
And then I moved from debt collection to proteomics.
And there it was like it was trying to work out what's going

(02:42):
on with like proteins. I hadn't done any biology since
I was 16. I started asking stupid
questions about, you know, what's that thing in a, in a
cell again? But yeah, industry shifts are
very difficult, but the data hasbeen sort of persistent
throughout. I joined data camp in 2016.
Since then I've been teaching things around data and more
recently, AI. OK, excellent.

(03:04):
And and you're I'm always super impressed when I get published
authors on the show as well too,because I totally I see the work
that's required into creating content like that.
Tell us about the the two books that you put together as well.
Sure. Yeah.
So I wrote two books on our programmes.
I suppose it was called LearningR and then I wrote Testing R

(03:24):
code. So I really enjoyed the process
of writing. Actually, that was how I kind of
ended up working in education. You know, I enjoy teaching
people. It's kind of sad to me that R is
sort of declining as a language.I think it's a great language
for working with data. Python second or everything.
I like Python as well. But yeah, 2 great books now
obsolete. And these days, yeah, I, I do

(03:45):
all my teaching via, well, as you say, evangelism.
So that means I host our, the Data for the Data Camp podcast
called Data framed over in our webinar program.
I also organize virtual conferences.
Actually the next one is on Thursday if you want to sign up
for Data Camp Radar. I don't think I have a link for
you, but yeah, Internet search for data on radar, that's

(04:06):
happening on Thursday. Excellent.
So what's it like to be the guest instead of the host?
So it's going to be tricky if I start asking you questions,
that's usually for some habits. That's fair enough.
That's fair enough. So like I was familiar with data
Camp, I know Mongo DB has done stuff and has lots in the future

(04:27):
with data Camp coming. But for our viewers who wouldn't
be familiar, give us a little audit history of Data Camp and
and where it's at today. Sure.
Yeah. So Data Camp is an education
company. So we do online courses for data
and AI skills and as well as courses we also do projects.
We have certifications for testing your skills.

(04:49):
And then we have productivity platforms.
Basically, it's a hosted tube tonotebook platform called Data
Labs, designed to make it easy to get started and easy to
collaborate with others. OK.
And you've been there since 2016, as you said, obviously
massive changes right in the, inthose nine years or so in this

(05:09):
space, more particularly certainly in the last, well,
publicly in the last two, 2 1/2 years in the AI world.
How has that changed Datacamp's approach to, to learning?
And if I could slot in a second question there longer to be
redesigned its university platform about that probably
about 18 months ago to make themshorter, more succinct courses.

(05:32):
How does Data cap appeal to thatas I would call them the TikTok
generation Richie? Oh yeah, yeah.
So things have changed widely. Like when I started data camp in
2016, there were eighteen of us.There's more than 200 now.
So obviously like it's it's a growing space, a lot of people
wanting to learn about things. And I think our focus to begin
with was very much on just data scientists and data analysts.

(05:54):
And since then, everyone wants to know about data.
And the last couple of years, everyone wants to know about AI.
So we've created a lot more slightly less technical context
or more introductory stuff for people who like, OK, maybe your
background is like marketing or HR, but you need to understand
things about like the basics about data and AI.
So we have a lot more basic content.
And then I'll just say like the the rise of the AI, the AI

(06:17):
engineers with the big story in the last couple of years.
So we have content for that crowd as well.
But yeah, you mentioned the TikTok generation.
So when we started all our courses, they were they were
four to five hours long. And this is like, OK, we need to
go faster because before that itwas like, you know, you go on
courser and you've got to take an 8 week course.
No one has time for that. So we're like, OK, we can do

(06:38):
fast content and yeah, we are getting increasing pressure for
one hour courses and shorter format content.
Yeah, yeah, I think so. Listen as the as the father of
three kids who are teenagers andupwards, yeah, their attention
spans are just non existent Richie.
So those damn. Kids.

(07:01):
I just sounded like an old curmudgeon now.
But anyway, when we have people with the data science background
on the podcast, there are pains to point out that AI is nothing
new. Machine learning has been around
for a long time. The principles behind AI, I've
been there for for, you know, decades at this point in time.

(07:22):
But we've obviously seen this huge impact.
Certainly as I said earlier, in the last two, 2 1/2 years, the
impact of AI on coding practicesand obviously copilot, one of
the first at the gate there withwe can be your peer programmer,
we can sit beside you, we can help you.
How has, in your view, AI changed the day-to-day work of

(07:45):
developers? Yeah.
I mean, I think this is interesting because developers
tend to adopt new technology a lot faster than data scientists.
It's just a fight for life. Like all the tooling gets built
for developers first because it's developers creating stuff
for themselves and it's obviously a much bigger target
audience than than the smaller sort of data realm.

(08:05):
So yeah, I think things like Copilot and more recently you
got like a person wins up all these kind of newfangled ID ES
that are very much AI powered, so these target developers
first. So I think, yeah, you hit the
nail on the head. The big difference this is AI is
actually getting quite good at writing your code for you.
And that means you need to thinkless about, well, what's the

(08:27):
syntax and more about what do I actually want to build?
And so these kind of these product skills are deserving,
like what do I need to do for tosolve a user problem?
That's becoming increasingly important to think about also I
think because it doesn't work all the time.
Like you've got to spend more time just looking at like code

(08:48):
smells like what's gone wrong with this code or what's weird.
So looking, looking at for weirdness, identifying things
that might be going wrong. That's that's seems to be a
great proportion of people's time compared to just trying to
remember syntax. Yeah, I think and at Mongo DB we

(09:10):
spent quite a while probably, but about two years ago we first
started, uh, been concerned a little bit about coding
assistance in, you know, obviously there's a different
discussion about the value and the use case and is it a junior
developer, should they be using it and they don't know what
they're doing versus senior developer, which is reducing the
mundane and the boilerplate. But we were, I suppose

(09:33):
preoccupied in Mongo DBS, particularly in trying to
understand how good these code assistants were with Mongo DB
operations, making sure that they had the ground truths and
the best practices, etcetera. So we did a lot of projects with
many code assistant providers tomake those better and to good
effect. It was it opened up a different

(09:54):
area for us as well too. But when we were preparing for
the live stream, we deliberatelychose the Kick Bailey title with
invite. Cody.
Talk to me a little bit about where we are there now, Richie.
Is it a viable strategy or incredibly risky shortcut?
Yeah, I mean, a lot of it depends on what the consequences

(10:15):
of writing bad code are. So, you know, you're developing
something for like a satellite that's going into space.
It's like you want every single line of code to be thoroughly
checked and optimized, because if you get anything wrong, the
satellite crashes, you've lost 10s of hundreds of millions of
dollars. On the other hand, for if you're
a data analyst, you're doing exploratory data analysis.

(10:38):
If the code's terrible, it's like, OK, I've drawn a plot that
doesn't give me any value. You've just wasted like 2
seconds looking at a stupid plot.
It's like it doesn't matter. So I think actually in data use
cases, vibe coding has got a lotof value.
You don't want to care so much about the code you like,
particularly for exploratory stuff, you just want to generate
lots of things fast. So it works really well there.

(11:03):
Yeah. So it's all about the trade-offs
of like, what happens if things go wrong?
Hmm, I think so. And I suppose that look, that's
what I was trying to allude to with the the junior developers
using using code assistance. Yes, they can get started super
quickly in an area that they they mightn't have the skills or
expertise to potentially debug further down the tracks.

(11:24):
And and you know, I think, and Iknow the developers that I know
using the code assistance to great effect are, yeah, it's
replacing the boilerplate. It's replacing the mundane.
They're still a little skepticalon the business logic going to
the coding assistance per SE, but they're using it as a
canvas. In other words, they're almost
like a a writer getting a draft going.

(11:46):
I want to build this. Can you start some of it?
And I'm going to tweak and shapeit.
How do you feel in that as well too?
Have you seen that? Yeah, definitely.
So I like the idea of like, coming up with ideas using AI.
Like even for you, you start writing code, it's like, well,
what do I want to do? What should the structure of
this be like? Yeah, generative AI is great for
that, but don't you point about,like, junior developers?

(12:08):
That's a tricky one because you're right that like,
understanding your code is becoming incredibly important.
But if you've only ever been using AI assistance to develop
stuff, it's like, as a hiring manager, do I really want to
hire someone who can generate code really well, but they can't
understand it? And it's a tricky one, but it's

(12:28):
not a clear answer. If for example, if you're a
marketing analyst and you only write code like 5% of your job,
it's like maybe that's fine thatyou can only use AI assisted
tools to build something becausemost of it you just your
marketing knowledge is the most important thing.

(12:48):
If you're a software developer, that's a very different
proposition. Like I think I would only want
to hire software developers who can write code themselves or at
least understand code without having the AI explain it to
them. True.
I, I hear you. And I think on, on other shows
I've done on similar topics, thenotion of checks and balances.

(13:09):
There's the notion of, yeah, by all means use AI to write some
of your code, but go review thator get some peer review of that
and, you know, put those checks and balances in place.
And we've had Red Monk who are basically, you know, consultants
with regard to the developer space and, and then and on the
show as well too, talking about this very topic.

(13:31):
And they were discussing the risk of skills atrofying if
you're not used to this. And if you're just relying on
the code assistance over time. And the more you rely on it, the
more that kind of fundamental first principle skills are going
to disappear. And I suppose the more
potential, as you might have said, upstream problems you

(13:52):
could encounter if you can't debug that code.
Yeah. And so I like the idea of having
backup plans. So one of my hobbies, I go
backpacking and there it's like,well, it's fine to have
technology until you don't have it.
So for example, you can take Agps with you and then you can
see like where you are at all times.

(14:13):
And it's great till you drop your phone in a Creek.
And it's like, OK, fine. Do you want a backup plan?
Like it's nice to have a map, preferably washproof like a
paper map. And then you need to have some
map reading skills to find out where you are.
And suppose the map blows away. Well, it's actually also quite
useful. Felt like a vague sense of where
you are in the countryside. So having these backup plans for

(14:34):
like when stuff fails is really useful.
I think the same is true in coding.
So it's like, OK, great to use AI assistants, but when they
ate, the AI API goes down. Whatever you want to still be
productive. Yeah, you need a backup plan for
when stuff goes wrong. Yeah, I think so.
I, I, I was laughing there because I wrote, I, I did

(14:56):
electronic engineering in college, which was basically
four years of maths, pure maths.And I remember giving out to our
professor lecturers that, you know, I thought I'd be building
electronic circuits and cool stuff.
And he goes here, we Teach Firstprinciples so that you will be
able to do those, or at least know how they should work.
But you're not a technician, youare the person who designs how

(15:16):
they should work. And I suppose, you know, using
any tool you need to have a fundamental knowledge of kind of
the grounding in it etcetera as well too.
You mentioned, you know, people playing with AI and play, you
know, who wouldn't be coders etcetera as well too.
Do you think we're narrowing thegap between people who wants to

(15:36):
dabble and, you know, have an idea or have a concept or
something going back to this vibe coding and being actually
able to see whether that's viable or not?
Absolutely. I mean, this has sort of long
been the dream that everyone cando technical things.
So like, for example, in the business analytic space, the big

(15:57):
story the last decade has been around self-service analytics.
And what you really want is likeall your sales team, your
marketing team to be able to answer their own damn questions
about data rather than to get the data team involved every
time. And I think the same is true in
terms of coding. It's like you, you want to build
an app. It's like you shouldn't have to
rely on someone technical to build something simple.

(16:20):
And I'm hoping, I'm really hoping that all code generation
and I guess no code tools as well as as just AI just allow
more people to be able to build stuff that they want to build
without having to rely on the extra technical person.
Yeah, and I I'm going to kind ofhave dig down into the change in
relationship, but I see a question in from Rahm and I

(16:43):
don't know the answer to this. I hadn't.
Have you heard about the Super Bass paradox?
Richie is that? Familiar to you?
Not heard of the Super Bass paradox?
Do you want to give us a a bit more explanation and then then
we can argue about it? Yeah, and I said at the
beginning, obviously we streamedlive on LinkedIn and YouTube.

(17:03):
I've just got some DMS internally here going.
It doesn't seem to be on LinkedIn at the moment.
So I apologize if there's less interactivity or you're trying
to get this to work on LinkedIn.It doesn't seem to be, I don't
know, some connection somewhere has gone.
So I think we're solo on YouTubeat the moment, Richie.
So that's, that's our audience anyway.

(17:24):
The, you mentioned there the, you know, the business
intelligence and needing data teams and data engineers to get
involved with the marketers and the people looking for the
information. And that relationship having
changed because AI is bridging that gap.
Umm, how is that changing how those teams work together?
How's that changing how those teams collaborate together and

(17:46):
how does and the evolvement of, you know, how that relationship
will be? Yeah, so actually, umm, this had
a big effect on where data people are placed within
organizations. So one thing we've been seeing
is that the data analyst role itself has been kind of dying
out as a job title in favour of more commercially focused

(18:10):
alternatives. So for example, rather than just
being a data person in a data team, you are now a marketing
analyst or you're a sales analyst or you're a product
analyst and you're embedded within one of these commercial
teams. So there's been that move of
like you need data skills plus some other like secondary
business skill. But on the other side of things,
you get data engineers and they are becoming more centralized.

(18:32):
So if the law, the further away from customers you are, the more
likely you are to be in a central team.
And the closer your work is to customers, the more likely you
are to be in one of these sub commercial teams.
So those job roles have been changing a bit.
And I think as you maybe throw this back to you, have you seen
like a difference in who's working with Mongo DB?

(18:53):
Are there different job titles interacting with it?
I suppose there is. And I look, we obviously, uh, to
use that American expression, eat our own dog food.
We're using AI in some of our own tools.
And we started off putting an AIchat pod in our own docs so that
you couldn't, didn't have to search through the docs per SE,
but you could kind of just describe what you were looking

(19:15):
for and it would surface up the most appropriate results.
And we've taken that across the board into one of the areas that
we used to actually do some training on quite a lot was
aggregation pipelines in, in thedata context.
And now we have a natural language to aggregation pipeline
generator built into Mongo DB Compass.
You can tell it what you wanted to build and it will output the

(19:38):
code for you. So we're seeing, I suppose, uh,
and more recently, certainly in the last 6-7 weeks, we actually,
for those who are familiar with them, MCP servers, MongoDB Atlas
is now an MCP server. So in essence, you can have, you
know, direct access to your databack on Mongo DB without really

(20:00):
knowing how to query that data on Mongo DB and that's for
yourself or that's through agentic systems or anything as
well too. So we're seeing a big change in
how people are working directly with the data.
In the past, Richie, we were obviously, you know, Mongo DB is
very idiomatic it, you know, we're using drivers to directly
interact with the data. Now a lot of that has been

(20:21):
extracted away. Yes, under the hood that.
What's going on in essence, but the, the requirements to really
understand about, you know, collections and documents and
sharding and, and you know, how we manage all of that, that's
just seems to be not, not disappearing, but less of an
onus as to I just need to work with this data.

(20:44):
And so big change on our side aswell too.
Definitely have to say, like, I'm sure that like the theory of
sharding is very cool. It's not something I want to
worry about like on a day-to-daybasis because it's just not
directly adding value to my role.
I think for a lot of people it'slike, well, you know, how does
it make me more money? It's like, well, yeah, it's

(21:05):
better if it's someone else's problem if you can't answer that
immediately. But Shardim was one of those I
started among going to be about 5 1/2 years ago.
And back then you had to very carefully choose your Shard key.
In other words, what were you deciding where, how and where
your data was split up amongst essentially your, your primaries
and secondaries and all of your servers, etcetera.

(21:26):
Now and a while ago you could change your Shard key on the
fly. So this this whole, you have to
think really long and hard because you're never able to
change this went to you can change it on the fly to exactly
what you're saying. I don't really need to worry
about it. I'm going to use the tools and
we've got performance monitors baked into Atlas now.
So you can go in there and say, yes, I can use, they're using AI

(21:49):
tools themselves to show you howyou might be better able to
structure your data, change yourscheme and where most of your
queries are coming from, what part of your data collection
most of those queries are hitting and therefore how you
get more performance, more speedas well to Yeah.
So it's it's a yeah, an ever changing space.
And on that note, obviously in data camp, you're creating this

(22:13):
content, creating these courses with that rapid changing of the
ecosystem that we operate in. What are your kind of top
strategies, Richie, for, you know, continuous learning data
camp aside, because that's what we're going to get to, we're
going to have a demo. But you know, how do you keep
ahead of things as well too in this space?
Yeah, sure. I mean, there's a definite

(22:35):
problem in that. I mean, particularly with AI,
there's just so many companies, it's impossible to keep up with
everything. And there's half life of the
skills seems to be getting shorter and shorter.
Like you learn stuff and then itgoes out of date like maybe a
year or two later at most. So yeah, you just have to spend
more time learning in order to keep up with stuff that there's
no way around. But I do think there are a lot

(22:58):
more sources of information. So I mean, you've got sort of
formal learning through courses,but you've also got, I mean, the
my LinkedIn feed is full of people just blathering about
the, the latest tool and there'stons of influencers like willing
to tell you like what the latestcool tool is.
There are tons of reports going on.
I really like the reports from McKinsey Quantum Black.

(23:20):
That's their AI arm of McKinsey.They do some very good research,
but there's there's tons of stuff around.
So I mean, it really is just a case of like keep keep listening
to what other people are trying to tell you.
I mean, of course, podcasts and webinars are also our race on
Tetra, both of us. So yeah, keep keep attending

(23:42):
those sorts of events as well. Yeah, we had Eguazio, which is a
company acquired by McKinsey andQuantum Black.
They're on the live stream therelast week as well too.
So we had a, we had a great conversation about that as well
and, and how they are bringing AI to, you know, that's a space
that they've been in a long time.

(24:03):
And I think in the learning context then, Richie, how do
identify, how do developers identify kind of the new tools
and the frameworks that are worth their time?
It's changing, it's moving so quickly.
You don't want to bet your stackon something that might be gone
in six or eight months. You know, what's your advice

(24:24):
there? Yeah, I mean it is tricky.
So I think just checking the thefinancial viability of some of
these AI startups is kind of worthwhile at the moment.
Like there's been a lot of like cash thrown at AI companies in
the last sort of couple of years.
It's not clear how much longer that's going to last.
So we might be looking at like sort of 2027 like big crash

(24:48):
where half of these things disappear again.
One thing you'll see like I'll. Hold you to that, Richie.
I'll get you back. Absolutely.
I mean, we've seen a few big failures already, but like I
think edge of the ecosystems fairly fairly cash flush still.
But. In terms of getting started, I
always think like just making use of some of the APIs around

(25:12):
like work on the large language models and some of their data
storage tools is a good place tostart.
LLM frameworks are also like a very useful thing.
Other than both, there's only like 3 of them.
It seems to be standardized around like Lang chain, LAMA
Index, and Haystacks are one of those three seems to be useful.
And then beyond that, yeah, it'sthe data storage tools that are

(25:35):
that are very important to learn.
And I suppose going back to yourearlier commented that it's easy
to get started, it's easy to experiment.
How important are, you know, is that a level of experimentation?
I meet a lot of developers when we're doing Mongo DB events, et
cetera, who may not be using AI in the day job, but they're

(25:55):
doing it in their personal projects.
They are experimenting, they're jumping onto platforms like
Lovable to build something quickly, those sort of things.
How important are those kind of personal projects to a
developer's career pathway and progression?
Oh yeah. So having a portfolio is pretty
essential. So thinking about this from a

(26:18):
hiring manager's point of view, whenever I've like trying to
hire people, it's like you startoff, you like at the moment,
like, you know, you put a job out there, you might be getting
500 applications just because, you know, things are tight in a
lot of industries. So then you look at their resume
or CV and you filter out, OK, well, you know, let's get rid of
like 400 people who obviously don't have the qualifications is

(26:40):
like sent in, but know what the job was.
And then you read the, the covering letter.
It's like, OK, we'll weed out anyone who can't string a
sentence together because they're going to be hard to
communicate with the work. And that leaves you with more
people than you can hire still. So then you're looking through,
you want to find reasons for like, well, which people are
going to get rid of. And having a portfolio is just a

(27:02):
sanity check to make sure the person has the skills that they
claim to have in their resume. So that means, yeah, if I'm
hiring manager, I'm probably going to only like spend a
minute or two looking at each thing in the portfolio.
But I'm just checking. Do you actually have the skills
that you say you have? So really you want to have

(27:22):
simple projects that are easy tounderstand in under a minute or
two. It does not be something really
complex, it just has to be something that demonstrates that
you have the skills that are relevant to the job that you are
applying for. OK, And I suppose something new
that we did in Mongo DB recentlyas we produced skills by just
going back to the TikTok generation that I was talking

(27:45):
about. These are small, really, really
bite size. Pondering our pieces of learning
that you can do and get a badge that you can display on your
profile on LinkedIn, for example.
Do you have something similar inside a data camp?
You know the level of courses that people have taken or go
through? Is that something that would add

(28:05):
to their CV or resume? Yeah, sure.
So there's sort of two alternatives here.
So first of all, if you completea course, you get a certificate.
So this is just a statement of completion and this just proves
that you've put in some effort into learning.
We also have certifications which are basically you have to
pass exams in order to say that you've got qualifications.

(28:28):
And so some of these certifications are with existing
companies. So for example, like Microsoft,
Amazon, whatever, they have the these big long standing training
programs like for all the sort of cloud technologies.
So you take data camp courses and then you go and pass a
Microsoft certification. But for things like data

(28:49):
science, where there's no sort of standard certification, we
provide our own. OK.
And for that data science certification, how long is that
course pathway, Richie? How long does that take to go
through? So it depends whether you've got
a background or I think from scratches it's about 50 hours of
learning, then you might have todo a bit of practicing as well
on top of that. OK.

(29:10):
It's probably similar to our larger accredited Mongo DB
certification then that we have it's yeah, it's a similarly
decent chunk of learning in order to send over the badge or
the certification that you get at the end of that as well too.
Have you seen obviously the typeof courses that Datacamp are
pulling together and putting outtheir change in the last two

(29:32):
years or so with the onset of AIand all the tools in that space?
Yeah, definitely. Umm, so umm, interestingly, like
from a corporate point of view, because we do subscriptions for
individuals, but we also do a lot of corporate training as
well. And from a corporal point of
view, the most popular AI courses is all about like

(29:55):
getting started with gender of AI.
How do I do prompt engineering? How do I do the basics
understanding like what's possible with AI on saying
what's not? And what we're?
Finding is that there are so many people who previously never
had any interest in the space whatsoever, they now suddenly
realize that they need to learn this part of the job because
you've got like every CEO going.We are now an AI first company.
We're going to put AI everywhere.

(30:15):
And so regardless of whether youthink your job needs AI, you do
need to have some AI skills because your boss or your boss's
boss is going to demand interview.
So those skills are obviously augmented by, you know, whatever
ID the developer is using the code assistants that can either
be natively in there, that can be put through as extensions or

(30:37):
plugins, etcetera as well too. But you still see the the need
for proper pathway of learning as opposed to just as we said
earlier, vibe your way through it.
Yeah, definitely. I think every hiring manager
wants people who do have skills,so there's definitely a need for
learning. I would say please don't

(30:59):
outsource your entire brain to AI.
It's not going to do you any good in the long term, actually.
Oh, so there was an MIT paper that went viral recently.
So it's all about like they did like brain scans of people
writing essays, both with that with AI assistance and without
AI assistance. And unsurprisingly, the people
who had to like, think about what they were writing were

(31:21):
thinking more than those who were just like typing a quick
prompt and saying generate this.So kind of an obvious result.
But it was cool they had the thesort of brain scans and in
general, this is a good life lesson.
It's like it's cool to be more productive with AI assistants.
But actually, yeah, occasionallyyou do want to engage your own
brain. Critical thinking is going to
like become like a highly commodified skill or highly

(31:43):
valued skill. A highly valued skill such as
common sense has become a highlyvalued skill as well too.
A rare thing. A rare thing, yeah.
A rare, a rare thing. Umm, I know we've been doing
some work with, with our data camp of doing some Mongo DB work
and we've got more in the futureas well with yourselves.
Tell us a little bit about that.What's what's been done already?

(32:05):
What's coming down the tracks? Richie, uh.
Yeah. So, uh, we are currently in the
middle of remaking our course, just introduction to MongoDB in
Python. So this is like how to use
MongoDB Python tools. Uh, this is going to be
launched, uh, I think late July and then in, in August, we're of
course called building AI agentswith Landgraf and Mongo DB.

(32:29):
So that's really sort of gettingcutting edge.
I'm hoping they're going to be more courses.
So this is just so data campaign, Mongo DB recently
signed a formal partnership. So this is just the start of
things. I'm hoping they're going to be a
lot more courses around Mongo DBcoming soon.
Excellent and and am I correct in thinking we were the first

(32:49):
non relational database data camp had a course on back in the
day? Yeah.
So actually this intro to Mongo DB in Python course, it's a
remake of a course that was built I think back in
20/17/2018. So yeah, you would.
Mongo DB is definitely the firstno sequel base course around,
and I think pretty much the onlyone for many many years.

(33:12):
Yeah, okay, that's great. And I suppose obviously MongoDB
gets to participate in this AI space primarily through to the
fact that two years ago we launched vector search on
MongoDB, which has been an enabler for us.
It's meant that, you know, regardless of what large
language models you use, regardless of what embedding

(33:33):
models, if you store the invectors out of those embedding
models in MongoDB, the beauty isthey're stored in the same
document as the original data, which has a lot of advantages as
well too. Are you seeing that that
resonates with with when building these forces and
putting together the AI agents with land graph, are you seeing
that that resonates as well too,making it easier for people to

(33:56):
have their data and the embeddings associated with that
data side by side? Yeah, absolutely.
So I think one thing for people who've been in the data space
for a long time, we get used to the idea data is mostly about
like numbers and categorical data.
But actually that's no longer true.
It's like images of data now, words of data now, video is data

(34:18):
now, audio is data now. Basically anything you think of,
it's data now. And so unstructured data is just
so important. And there's a lot of people
who've been around in data for along time.
It's time to like retrain their thinking to be like, OK, you
need to think about unstructureddata.
And I think this is where Mongo DB shines.
And the fact that you've got the, the sort of see traditional

(34:41):
no sequel, I mean, it's not beenaround that long, but it's been
what, like less than two decades?
It's been getting old again. So yeah, you've got no sequel
stuff and you've got voted database stuff together in one
place. I think that just makes things
so much nicer. It's yeah, not having to learn
different systems or different tools and different things.
Is is definitely. It streamlines your workflow.

(35:04):
Yeah, yeah. No, I couldn't have plugged
Mongo DB better. Thank you for that.
The other thing too is that Datacamp actually use Mongo DB
underlying your product and platform as well too.
Tell us a little bit about that.Sure.
So I can't give you too many details on the engineering team,
but recently in our Slack Wind Channels, Windows channel,
they'll talk about how they've just done an upgrade on how we

(35:27):
store experience point data. So Datacap has a gamification
systems. Every time you click complete
and exercise, you get some XP. All this XP data is stored using
Mongo DB. There was recently an upgrade to
using Mongo DB 8. The engineering team got very
excited because the performance improvements from Mongo DB 8,

(35:48):
that stuff goes faster and they're actually it was more
efficient. So they actually managed to
start running or storing all this on a slightly lower powered
server, save some money. So basically it's going faster
and saving money, which two goodthings.
Getting both at once is is very nice.
Yeah, and that was one of the things with Mongo DB 8 at the
time when we released it, it wasn't chock a block with a ton

(36:09):
of new features, but the main messaging was around speed and
performance and costs as well too.
So it's great to see that in real life.
It's probably prudent maybe to have a, you know, we talked
about data camp a lot. Maybe if you can do a bit of a
screen share, Richie, so that wecan, you know, for those that
aren't familiar, have a bit of alook around a bit under the hood

(36:30):
as well to and to show us how itworks.
The platform. You know that XP data as you
said, that gets gathered as people complete things as well
too perhaps? Sure, absolutely.
Let me share my screen now. OK so this is the the home page
for data camp and. So dating up is very simple from

(36:54):
a platform point of view. It is basically Netflix for data
and AI education. So basically you just choose the
courses you want, you click on them, go and take them.
So from a navigation point of view, very, very
straightforward. So one thing I would like to
show you though is our tracks. I'm just going to go to the
learning. So tracks are series of courses

(37:15):
that make sense in order. We have got 24 career tracks.
So depending on what job you want, whether you want to be
data, data analyst, whatever data engineer, we've got a track
for you. I'm going to click on the AI
Engineering 1. So we've got two of these.
So. One's a series of courses for if
you have a background in data and you really want to get into
AI engineering. 1 is for if you've got a background in

(37:37):
software development, you want to get into AI engineering.
So these have got, yeah, basically this one's 15 courses.
So there's about 60 hours of content and there's a
certification available at the end.
So if you want to prove that youhave the skills, you can take
that certification. There is a certification coming
from the developer track sometime in Q3.

(37:59):
So if I click through to this, you can see an example of what
is going on. So this one's slightly shorter.
So it takes about 26 hours to complete.
And the first version of this, so it's focused on the open
AIAPI. There's some hugging space stuff
in there and then you learn about Pine Cone and Lang Chain.
One thing we're hoping to do with sort of future versions of

(38:21):
this, we want you to have a choice of model.
So it doesn't have to be open AI.
It could be the Anthropic Cloud API, could be Google Gemini API.
Same with the the the data storage.
So safe. We're just starting a
partnership with Mongo DB, so maybe you want to swap out Panko

(38:43):
and have Mongo? DB in.
There, of course. Maybe, of course.
Of course you. Is we'll fight the Panko people.
And then yeah, if you maybe don't want Lang Jane, maybe
maybe you want to use Llama index, maybe you want to use
Haystack. So we're going to have a bit
more flexibility in those. But.
So that's the idea. You basically care about like
APIs, data storage and LM development frameworks.

(39:07):
Those are like the three most important sort of broad skills.
So I want to show you an exampleof an exercise.
So within this course you can see sort of split into smaller
chapters. And within that, if you have a
look at the flows, these play things, that's an example of a
video. So we do short videos, like 3 or

(39:29):
4 minutes and then the rest of the course.
So at least 75% of the course isgoing to be hands on learning.
So let me show you just an example of this.
So we've got a short video and basically, yeah, you can see
it's what it's like 4 minutes long, nice and pretty soft.
You get a transcript once you dowatch that.

(39:50):
Let me just reset this. You have a coding exercise, so
all the coding is done in a browser.
You don't need to install anything in this case, it's just
asking you to. So no external ID you needed,
you can do it all straight on the platform.
Exactly. So yeah, it's basically like
built in IDE. It's called complete stuff.
So in this case, it's like sending a message to the open AI

(40:13):
client. So let me just type that.
So you got to create an open AI client and then you've got to
create a chat completion and then submit that to see if
that's right. And yeah, it's, it's, it's
generated stuff. So you don't need to worry about
like API tokens, you don't need to worry about setting

(40:34):
everything up. You get a nice success message.
Actually, I can show you if you do something we've got for next
exercise. I'll, I'll, I'll fill this one
out wrong. So maybe we do the wrong model
there. So if you do something wrong,
it's going to tell you you've got like.
Auto. Graded feedback.
There's an error in your code. You can see what the error

(40:55):
message is. There's an AI assistant which
will explain what went wrong. That's horrible.
Brilliant. So yeah, so helps you learn
faster, basically. And if you get stuck, you can
also take hints about what's going wrong.
And then, yeah, you can see if you get stuff right, you get XP.
You've got to pay XP to. Take the hints.
So all very straightforward. You lose your.

(41:18):
XP, Yeah, Yeah, exactly. Yeah, you'll ask for the answer,
but you don't get any XP there, so.
You ask for the gamification, then I suppose there.
Yes, I have to say, gamifications been one of the
things you've been working on a lot over the last few months.
So I kind of realized that I'm actually better at keeping my
Duolingo streak than my data camp streak, even though data

(41:38):
skills are more important to me than Spanish.
It's just because there's like stupid OWL giving me messages
like 3 * a day saying please practice.
So we're pushing a bit harder ongamification just to give you
that extra motivation in order to keep learning.
OK, good. So that's something coming down
the tracks from Data Camp then as well too.
And I put up a link there. I'll post it in the comments as

(42:00):
well too. But for anybody interested in,
you know, taking some of the Data Camp courses, you've
generously given a 50% discount there.
So appreciate that, Richie. I'll make sure to copy that and
get it into the comments as well.
Is there some? Is there?
Can they get started for free aswell too?
Is there trials? How does it?
How does it generally work? Yeah, so you can register for

(42:23):
free. You get to take a few exercises
and there are a few costs that are entirely free.
So yeah, there are things you can do without.
Before you pay money, you want to kind of try the platform and
learn a few bits and pieces. But yeah, if you, if you want
the full experience, access to the modern 500 courses, then
yeah, yeah, you've got to sign up.

(42:44):
Yeah, half price. So yeah, of course, one thing I
find even better than buying data camp yourself, we also have
corporate plans. So if you want to get your boss
to pay for data camp, get them to speak to the sales team.
That's certainly the best, the best way to go about it.
I'm just scanning through some of the comments.
As I said earlier, I think we had a breakdown in the LinkedIn

(43:07):
connection, then I see it's picked up again.
So something has fixed itself inthe meantime.
So thank you for everybody who'sjoined us on that as well too.
There was one in about seeing the boundary between the data
engineers and software engineersshifting.
We did touch on that a little bit earlier obviously.
Do you want to maybe if if that was comment was missed by this

(43:33):
person joining us from LinkedIn,maybe touch on that a little bit
again in terms of that shrinking?
Yeah. So I think there is a bit of
blur between those as increasing.
So one thing I'll say is that traditionally dated teams have
been bit scrappy and I think there's been increasing
requirements about them becomingmore professional in terms of

(43:54):
software development. So software development like
processes have been pretty well defined for decades now.
Yeah. I mean, you got like the agile
process around me. You've got a lot lots of kind of
tools around like quality control that haven't been
present as much in the data space.
I think data engineers are beingtreated as though they're a type

(44:14):
of software engineer. So there's a lot of more, so a
lot of professionalism around software quality there.
OK. And on the other side of things,
I think software engineers are requiring, are requiring more
data skills. Like there's a lot more
requirements around observability and just being
able to understand, oh, wait, isthis an anomaly And, and what's

(44:36):
going on? Just being able to like predict
what's going to happen? I think so things like time
series forecasting are increasingly useful.
I think on the on the on the product side as well.
AB testing has been around forever, but well, certainly in
the last decade he's been incredibly popular.
And maybe software engineers need to understand like what the
product manager's doing with with those AB tests.

(44:58):
So that's definitely an important skill to learn.
OK. All the time when we speak about
data, there's always questions generally come up around
authorization, security, encryption, access.
Is there those sort of courses on data camp as well too,
Richard? Yes, we have of course around
data privacy, data quality, datasecurity, data governance, all

(45:19):
these sort of things. Most of them are conceptual
courses, because it's not just technical people that need to
understand these is everyone in your organization really.
So yeah, all that's covered. Yeah.
Yeah, I, I, I think these are kind of becoming increasingly
universal skills. Like you need to at least have
the basics. Like, oh, well, maybe we
shouldn't put our sensitive commercial data out out into the

(45:42):
public. Same with personally
identifiable identification. It's worth understanding what
those things are and how you should treat them.
And in a similar vein, I supposewhen it comes to authorization
and security and all of that with the rise of AI agentic
systems. So AI that's due in a series of
tasks on your behalf before surfacing back with the results.

(46:05):
Two things, how does is that addressed maybe in some of the
content you have there. But I suppose more secondly,
more importantly for me is kind of your thoughts on that agentic
space. We discussed AI.
We didn't discuss the kind of AIdoing its own thing until it
comes back to you with a viable results.
Thoughts on that, Richie? Yeah.
So AI agents, it's a very broad definition.

(46:28):
People can disagree on exactly what constitutes an agent.
So I think that there are two fields of thought.
So 1 is that the AI agents you should build should just be
basically business processes encoded in the software.
And maybe it calls an LLM somewhere, some really, really
simple agents just to make things automated and more
efficient. So a good example of this, when

(46:52):
our sales team make a call, they're supposed to write down
what was said in the call according to it's called the Med
Pick framework. I can't remember what the
acronym is, is something sales, but it's like basically about
the state of the deal and sales people hate doing this because
it's really tedious and it was thinking and it's taking away
time from them actually like doing something there's a it's

(47:13):
going to help their targets. So they do it badly.
So we have a very simple agent which takes the call transcript
and then it categorizes it according to this framework and
then dumps the the results into sales force or wherever they're
supposed to be in order to do this.
So really, really simple agent really, really useful because it

(47:33):
makes sales team happy because they don't have to do something
they hate that's tedious and boring.
And this is a great use case. On the other hand, you've got
some companies trying to build really, really advanced employee
replacement agents. So you've got some Cognition
Labs has Devin it's like an AI software agent.
You've got Julius AI creating like AI data scientists.

(47:56):
You've got called Emos trying tocreate like a universal AI
employee, which sounds way too ambitious.
I think it's mostly customer service Asians at the moment,
but like trying to get real people.
And I feel like the sweet spot is kind of halfway in between
that. Like if you have a lot of
repetitive processes, then yeah,OK, do very simple agents.
But the modern sort of reasoningAIS mean you can actually do

(48:19):
something a little bit more advanced there that and have
slightly more flexible processesbeing dealt with using agents.
And this broach is on the usual elephant in the room topic when
it comes to AI. It's AI is going to do everybody
out of a job. And I think my personal take on
that is, you know, from a developer perspective, yeah, who

(48:43):
doesn't want, like, I don't wantto write boilerplate code.
I don't want to write the mundane code.
I want to do the work that, you know, gives me a key
differentiator. And I think that the types of
jobs that developers will do will change the areas that get
involved in a change. What's your thoughts on that?
AIS come in to take all our jobs, Richie.

(49:06):
Yeah, I mean, it just very much depend on your job.
So for example, like earlier this year there was the Duolingo
CEO talking about Duolingo's going AI 1st and no more
contractors is going to be a hiring freeze.
Everyone has to use AI and it's going to be your performance
reviews. So pretty extreme take.
And if you're a Duolingo contractor, then obviously,

(49:27):
yeah, your job is being taken byAI.
But I think for developers at the moment, there is there are
more development tasks to be done that there are developers
in the world. There's like there's definitely
a shortage of people with enoughtechnical skills.
So I don't think we're going to see developers losing jobs

(49:48):
rapidly. I think may there may be some
hiring freezes at some companiesat least in the short term.
But I think like being able to build stuff is just incredibly
valuable. Like there is no shortage of
problems where a bit of softwareskills, it's not going to come
in handy. Like I feel like the world has

(50:08):
so many problems we need all thetechnology help we can get to
solve them. Software is eating the world,
Richie, right? I think from our perspective
that most definitely. Software is eating the world.
AI is eating software I guess. I got a question in from Sri,
which I'll answer because it's more of a Mongo DB question than

(50:29):
a data cow question. You know, we, we have recently
introduced A Django longer to beback in.
So Sri go check that out. If you just search for that,
you'll find our developer article around that.
I think you'll find some examples as well too.
We have a lot of Django developers.

(50:49):
It's kind of we've been at the Django cons.
I think we're going to the US one in another couple of months
as well too. So it's a key, it's a key key
area for us in terms of Django as yourselves on Datacamp.
Do you have much course in content around that?
No. So Django is very much for web
developers. It's slightly out of our
wheelhouse at the moment, so we don't have any content on that.

(51:13):
I did use Django briefing about 2010 with it in the last 15
years. Well, I look, we have a lot of
Django proponents in here in Mongo DB.
So it's something that we generally keep alive.
And as I said that Mongo DB backin when public preview about 6-8
weeks ago I think. So it was something new from us

(51:35):
and it's certainly an area you mentioned obviously or your
books, the language and you're kind of go, where's that gone?
Obviously Python has has a resurgence with the AI.
You've seen a lot of that yourselves as well, too.
Yeah, so certainly, I mean, one thing I say like Python is sort

(51:55):
of eating everything. Like my background I mentioned
like I came for the our community, I wrote books on our
and yeah, that's all gone away. So I think, yeah, it seems like
the AI community is sort of settled on like Python And
JavaScript being the two main languages.
Yeah. So that's it's incredibly
popular. I don't think the developer can

(52:16):
we will ever settle on anything being the two main languages at
all. This is this is a battle that's
always going to happen. If it was, you know, there's,
there's, you know, we know the Ruby on Rails, the Ross, you
know, there's a whole host of languages that were the next big
thing, right? Yeah, actually one thing I've
still been waiting for. Like Julia was supposed to be
the next big language in data has never quite happened.

(52:39):
Like maybe, maybe one day it will.
But yeah, not just yet. Great.
Well, look, this has been superb.
I kind of think we're getting towards the end.
Anyone got any questions in the comments, please put them there
before Richie and I disappear. We touched a little bit early on
how to keep up to date and data camp aside, obviously you you

(53:02):
need to prioritize that over your Duolingo, Richie.
But how else do you consume the new stuff?
Is it blogs? Is it videos, podcasts?
How do you like to absorb, I suppose, your information to to
keep it on top of things? All of these things actually, I
have to say, if I want to learn about something new and then we
find an expert and I get them tocome on on the webinar podcast

(53:25):
and I just get to interview themfor an hour and that's that's
how I find out about stuff. So yeah.
That's something that I need to do as well too.
I do get dropped in it from timeto time.
Totally the deep end doing thesepodcasts as well to go ahead.
I know nothing. I've got to do my homework
before this guest joins me. So it's certainly a good way to
go. That's that's what you on on

(53:46):
age, right? You're going to say right?
And I want to learn, and here's the person who's going to teach
it to me. Exactly.
Yeah. So I have to say, quite often I
do end up like having conversations about topics I
have no idea about. The hardest 1 though, is the
sports analytics things because I'm, I'm not really like a huge
sports fan. We did an episode of the of data
framed on like how data is changing like the NBA, and I had

(54:11):
to study so much. Just basic basketball technology
just did not sound like an idiotin front of everyone.
So yeah, AI still fine. You can get away with a bit of
black there because not that many people understand
absolutely everything. Yeah, sports is hard to get away
with. Yeah, sport.
Well, at least basketball has a lot of action.
I find there's sports that have lots of downtime.

(54:33):
Baseball, American football, cricket, they are the ones that
have lots of data associated with them because that's where
you keep the interest up in between the downtimes of action
on the pitch of the field or or wherever it might be.
I'm always amazed and I know Moneyball, you know, that is at
stats.com is the company that the Moneyball guys found.

(54:54):
It's still going very, very strong.
So it's a super interesting areaand most definitely.
Yeah, and I have to say, just learning about the the data
analytics behind things has given me a greater appreciation
for the sports because, yeah, it's knowing like how people are
thinking about stuff and like what actually works well with
data. It's like, Oh yeah, kind of
makes sense. Yeah, no, it certainly does.

(55:16):
It's certainly churning out a lot.
And I suppose obviously we put up the code for Datacamp
earlier. It's in the comments there as
well too. You can grab that so people can
jump across there and get started.
Any final thoughts for our viewers, Richie on?
You know, somebody's saying and I think we'd one of the we'd one
comment come in if I can just find it is, you know, I want to

(55:39):
learn but I have some difficulty.
How can I start with app dev? You know, obviously data camp,
go Start learning some stuff there, Go other frameworks and
use their courses that Mongo DB has a university as well too.
But any other final thoughts forpeople who I think AI is one of
the areas that I think people are happy to experiment in my

(56:00):
view. In other words, it's around, you
know, relatively new that there isn't, OK, People will always
say they're experts, right? But there isn't anybody who's an
expert in this space per SE, because it's so new.
And there's a lots of experimentthat you can just do and get up
and running yourself. Yeah, absolutely.
And so 11 tip I'll have is like it's always hard to find time to

(56:24):
learn if you have a day job and like there's always this
competing pressure. Well, I've got a deadline
something else this week and I just don't have time to sit down
and learn. So if you can persuade your boss
to have completing some courses as part of your quarterly
targets, if your boss is on timewith this and it's your boss
nagging you to complete the learning, that's much better
than being like, you know, I've got to wait and like, try and

(56:45):
learn on a weekend when I'm tired or whatever because I
couldn't do it during the week. So yeah, get it as part of your,
like, quarterly targets. Get it built into like something
official at work. Get your boss on side with you
learning. That's going to make it so much
easier to devote these new skills because.
Yeah, it pays to have you competent and productive and
knowing about all the latest technologies, it's going to pay

(57:06):
off for your boss. So yeah, it's going to make your
life easier. In that respect, I suppose you
know people need that disciplinecertainly for self-paced
learning such as Datacamp provides.
Any other tips around? OK, get your boss to set aside
some time and maybe actually payfor it as well too.
Any other tips in kind of, you know, how much somebody should,

(57:30):
how often they should try, you know, is it 20 minutes a day or
40 minutes a day perhaps? Or do they set aside half a day
and, and just do the rest of thework the other four days?
What, what would, what do you know works best or how, how, how
does it pan out? Do you hear anecdotally from
your learners? Yeah.
So I mean, your two options really are you set aside like a

(57:51):
regular time each week. So it's like maybe it's like
after lunch on a Friday, it's like, OK, I'm going to spend an
hour learning maybe, maybe twicea week.
The other alternative is you save up and you do an intensive.
It was like OK, once, once 1/4 and they do like a three day
learning binge or something likethat.
And just like really get to grips with the new technology.

(58:13):
So again, it's going to depend on like what the rest of your
life is like for what your flow is like.
But yeah, building regular habits for learning.
Actually there's also a did get mobile app, so there's like
practice modes if you just want to do like 5 minutes on the bus
during you commute. You can also do things that way.
So yeah, you've got a choice between that like regular small

(58:36):
amounts of learning or big binge.
Like certainly a lot of companies that will run sort of
like training days where it's like, OK, we're going to get
everyone in a department to takethe same course at the same
time. So yeah, there's more than one
way to learn, just as long as you find time somewhere.
Yeah, and find what suits you most.
The fact that it's it's on mobile too.

(58:57):
Obviously you can use that downtime people have commuting
or, you know, just hanging around to maybe kind of upskill
and leverage some learning of data camp as well.
To any parting thoughts, Richie,then that did I miss any of the
questions or topics you wanted to cover today?
Anything to say? Oh man, no.
I mean I think trick is just to get started and do stuff.

(59:18):
So, I mean, we talked before about like, good ways of like
adopting AI. And it's like, well, you know,
all the experts I speak to, they're like, well, you know,
first you need to like, figure out what the business problem
is. And then you need to get all the
data and the infrastructure in place.
And only then should you start building AI.
And that's fine. But you know, you'll never ship
anything. Just get started, try a project,
learn some new things. And yeah, make mistakes and it's

(59:41):
cool, you know, just just make progress, do stuff.
Make progress, Yeah. And you will make mistakes, but
they're all learning experiencesfrom, you know, the you learn
more from mistakes than you do from kind of anything that goes
smoothly. I think is kind of one of the
mantras out there as well too. Absolutely.
And you know, especially in a live situation like this, it's

(01:00:03):
like make mistakes on camera. So always the fun stuff.
Perfect, Willis. I certainly ascribe to that.
I've been doing that for a couple of years now, making tons
of mistakes from people on camera, but you get used to it
as well too. Richie, listen, it's been a
pleasure to have you on board. Thank you for setting aside the
tie. I hope that the folks that have
learned a lot drop into data camp, see what we do there.

(01:00:26):
I know we're a little early for the new courses that we have out
with you from MongoDB, but as you said, uh, first one end of
July or so and then onwards fromthere as well too.
So certainly we'll keep an eye out for those.
And, but for me, uh, if you wantto learn a little bit more about
MongoDB, we have a developer center, developer.mongodb.com
where you can go. And as I said in the intro, we

(01:00:48):
do these live streams. And I'm not the only one.
My colleagues do these, I think every Thursday or so, which are
much more hands on coding than me with the guests and and
talking through some demos as well too.
But Richie, it's been superb to have you on.
Thank you for sharing all the insights and agreeing to have
that click Bailey title on the vibe coding.
I think it worked well and let'ssee how that one plays out in

(01:01:11):
the long term. But it's been a pleasure to have
you, Richie. Thank you so much.
Yeah, thank you for inviting me.And yeah, thanks to everyone in
the audience for the great questions.
Indeed, Yeah, thank you for all our viewers and do stay tuned
and keep an eye out. Follow us on the various YouTube
and LinkedIn. It's a follow or subscribe.
I can't remember which ones which, but you know what to do.

(01:01:31):
Press those buttons and you'll get alerts for future shows such
as this. So for me, Shane McAllister in
Amsterdam and Richie over in NewYork, it's been a pleasure,
everybody. Thank you for your time and
thank you very much, Richie. Appreciate your time.
Take care.