September 22, 2025 • 34 mins
Build Real AI Products (Fast): Product Manager to Community Builder with Kurt Yang (Fintech & EdTech, RAG, Embedded Finance)
From banker to PM to community catalyst, Kurt Yang shares how non-engineers are shipping functional AI prototypes, validating with customers, and turning meetups into massive and strong ecosystems.
Chapters:
4:17 – Community lessons for PMs
8:34 – De-risking AI with stakeholders
12:50 – Tooling spotlight: Lovable in practice
17:07 – Prototyping workflows that scale
21:24 – Embedded finance & risk-based pricing
25:41 – RAG in fintech/edtech (what works)
29:57 – Stack, Supabase, and next steps
What you’ll learn
From frustration to community: how Kurt spun up GenAI for Fintech & EdTech and grew it to ~800 members.
Lean PM, real signals: why working, functional prototypes beat pretty mockups—and how to run smoke tests with real users.
Tooling that compounds: using Lovable + Supabase (+ ChatGPT) to ship usable prototypes you can measure.
RAG, demystified: practical walkthroughs and when retrieval-augmented generation actually reduces hallucinations.
Fintech shift: embedded finance + dynamic risk-based pricing and alternative credit scoring to hyper-personalised offers.
Enterprise adoption: winning over senior stakeholders in regulated industries (trust, governance, records).
Founder traps to avoid: solution-first bias, over-scoping; how to pick a lower-risk wedge to earn trust and revenue.
Mindset: “Don’t just think, start.” Momentum over perfection.
About Digital Nexus
A founder-led podcast where Australia’s AI builders go beyond the hype with real workflows, decisions, and lessons from shipping. Hosted by Mark Monfort & Chris Sinclair.
Other Links
🎙️our podcast links here: https://digitalnexuspodcast.com/
👤Chris on LinkedIn - https://www.linkedin.com/in/pcsinclair/
👤Mark on LinkedIn - https://www.linkedin.com/in/markmonfort/
👤 Mark on Twitter - https://twitter.com/captdefi
SHOWNOTE LINKS
🔗 SIKE - https://sike.ai/
🌐Digital Village - https://digitalvillage.network/
🌐NotCentralised - https://www.notcentralised.com/
YouTube Channel: https://www.youtube.com/@DigitalNexusPodcast
X (twitter): @DigitalNexus
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker (00:00):
Within two days.
He actually built an entirefunctional sort of mobile
banking prototype within twodays.
We can build proper functionalAnd like you said earlier,
prototypes that we can actuallytests with real customers,
so they can actually based onBiggest advice to everybody is
(00:22):
just to start not talk about it,not start.
Think about it.
Start.
Welcome to the Digital NexusIn this series, we're going to
be getting down and dirty withleaders and founders in the AI
space.
We want to get an understandingthey're building businesses
(00:44):
strategies and future impacts ofWelcome back, folks, to another
This time we've got a specialThis is Kurt Yang.
Kurt, thanks for joining us.
Thanks, Mark.
Kurt and I have met and alsoChris who's behind the camera
right now.
You would have seen the lastfront of the screen.
We do the Swapsies every now andBut Kurt, you're doing some
(01:09):
terms of like bringing peopleand myself that have run
know what that's like.
So, um, Kurt, if you could justgive us a bit of a background
for the audience that may notknow you?
I'm sure a lot of people do, butif they haven't heard of you
yet, what is your backgroundlike?
What's the stuff that's led toFor sure.
(01:29):
Uh, yeah.
It's a great pleasure to beinvited to this, um, fantastic
venue.
Um, yeah, it's a bit about me.
Uh, my name is Kurt Young.
Uh, I'm a digital productdoing product management for the
various industries across likeenterprise, a little bit like
(01:51):
And so I do have a lot of, uh,product management experience,
and I'm really passionate aboutusing that to help startups,
entrepreneurs to succeed,especially help them to, you
know, like sort of build theirnext innovation cheaper,
quicker, get to success muchfaster.
Um, I think my how I found mypassion to this new adventure,
(02:11):
this made up thing I've beendoing is, was actually through
my frustration of I justcouldn't find a meetup or a
community to, for me as afintech, PM, whatever, to learn
about Jai or even trying totrying to transition into AI
product management.
So hence why my friend wastelling me as a joke, hey, why
don't you just create oneyourself?
(02:32):
Yeah.
And initially I took it as aAnd that evening, I think, I
don't know what got through tome.
I just said, you know what?
Hey, what the heck?
I'm just going to pay a meetupYou know, there was a special
and I paid the fee.
And then that's the moment.
That's the moment I realized,I better just to find a way to
So it took me a little while toYeah.
(02:55):
But I think it's like aI'm sure you've been running
get in, get into that bit of akept going and going.
So I've been running this forYep.
So starting from from scratchnow we got about nearly eight
hundred members.
I held about fifteen events bothI mean, some online, some
(03:16):
virtual, some online, some inperson.
Um, it's just been fantastic,I couldn't tell you the amount
personally, I have gained andactually even trying to do some
well, just out of likeYeah, I think that's great and
But you said VMware, when wereUh, that was twenty twenty two,
(03:42):
About two years I was there for.
Yeah.
Oh, okay.
More recently.
Yeah, yeah.
Okay.
I won a competition throughThey had something called the IT
So I'm dating myself here.
Um, but in any case, a lot of,And then you mentioned, out of
frustration, like setting upthe, you know, the meet up
group, the gen AI and fintechand edtech.
And we'll get to that, settingyou weren't seeing something in
(04:08):
what you wanted.
And we did the same thing.
Whether it was, um, we joinedthe Data Science and AI
Association and became presidentthere.
Um, but we also created the OzDeFi Association because out of
that frustration of not having aplace to chat in between other
meetups.
And I think it's interestingthe frustration of, hey, why
(04:31):
know, at night?
And then Edison invents theSo I am comparing you to Edison
here and stuff, but could youtell us a bit more about what
Gen AI and fintech and edtech isall about?
Some folks, they may havestuff, and that's great, and
But what what to you what whatabout for sure.
Um, to put it very simply, mya it's a community.
(04:55):
You know, right now it's onlineBut the point is that we have
this community, all thecommunity members, we can come
together.
We can learn, share all the youcan call the latest or the most
useful AI or AI in general aswell.
Use cases, um, even likeor even like we actually had one
(05:19):
perspective, you know, like howAI career perspective, right?
From all different aspects.
So that's kind of the but like Ifintech financial services.
That's my background.
But later on we're actuallyknow, broaden it up to including
because I personally have a lotbackground covered that as well.
(05:42):
And also we have a lot ofmembers saying to me, hey, in
the education space, there's alot of applications or
applications happening.
Why don't we just try to see ifwe can get some really amazing
guest speakers to cover thosetopics?
So I'm also going to have somesessions coming up as well.
Great.
And speaking of, um, you know,there's so many different kind
(06:03):
of pathways we could go into,but I'd like to set the scene a
little bit.
Like your background we werediscussing before is in project
management, actually.
Product management, there's aI can go deeper.
Yes.
No. That's true.
But I said the wrong p wordApologies to my project and
I've probably equally pissedanother p word.
But um, in in terms of thethat the background?
(06:26):
You said you were at the banksand you were at some other like,
you know, fintech kind of placesbefore.
Has it always been?
No it hasn't.
Okay.
That's a really good question.
I mean, that probably can go onfor like another ten minutes or
whatever, but I'll try to keepit short.
Sure.
Um, yeah.
I actually started my career inbanking as a commercial guy or
relationship slash commercialguy.
So I was a banker.
(06:47):
I was a stockbroker, sort ofonline stockbroker for a little
while.
Then I transitioned.
I transitioned into more like acommercial contract manager
relation, institutional B2B,kind of like a customer success
manager.
You know, a lot of the today'sthose, um, experiences, I got
I got exposure to some you cancall project management or
(07:08):
product sort of related work aswell.
That's how I got like reallyBut I didn't even know that was
Yeah.
Yeah.
So I landed an opportunity byaccident within the bank, as I
say.
And that's that's when Ithink my skill sets really can
And, uh, and then from thatpoint on, I've just been product
(07:29):
management ever since, you know,at ups and scale ups and
enterprise.
Yeah.
And it's interesting.
And I'm asking all this becauseAI is not something that a we
haven't we have seen that itdoesn't work.
Well if you don't bring inexperience the context per se
for sure.
And everyone has the context ofthe expertise that they bring
in.
Sure.
(07:49):
AI can help level up things.
If you don't know much aboutyou information that you can ask
far faster, but it works so muchbackground of some sort of
edtech, from, you know, productAnd I say all of this because I
(08:10):
interesting perspectivestheir experience with what's
Now, in terms of that, like whatmentioned before that there's
having these events.
Could you could you mention and,you know, framing for the
audience.
You're this product managerYou're running these meetups and
(08:34):
been some of the top of mindjust running these events?
For sure, for sure.
Um, I think the biggest learningor, you know, you can call like
a pivotal moment or something isjust realizing that anyone can
do it.
Anyone can create their own.
Might not be like AI startup orreally get into this space.
(08:55):
And the reason I'm saying this,especially speaking from
someone, not from a technical AIor even tech or data, sort of a
background.
I was a commercial guy before II got a bit of a technical like
sort of stuff throughout theyears of doing this stuff,
right?
But even that's the case.
Um, what I realized is that,like some of my amazing past
guest speakers, they are notengineers or data or whatever
(09:18):
people, right?
So they come from, say, CFO orof the person.
So he was a former owner of aI think it was a tire reseller
or tire expert, sort of acompany.
So people come from all, allwhat they like.
What I saw in common among allpassion, their passion to solve
(09:44):
Right.
Because they were frustratedwith whatever the way they were
solving before.
And all of a sudden theysolving those problems.
Number two is what I felt isSo they they all found their own
Maybe that niche is because oftheir experience or their
background or, you know, maybetheir unique sort of market
understanding.
(10:04):
And they bring that to theI think you probably brought up
like initially a little bit aswell.
Right.
And then they really go hard atAnd then that's how they I can
succeeding in what they're doingI think it's really interesting.
It reminds me of, um, I can'tthe scenario is like Steve Jobs,
(10:24):
He he learnt all aboutcalligraphy and style and stuff
like that.
And that experience coupled withwasn't he was the tech guy.
But coupled with his experiencethinking that's helped shape
And for many people, it's theLike before, it was like, okay,
(10:45):
I only knew education and maybeBut now everyone has this
opportunity because the barriersare lowered, like you said, like
if people are passionate abouttheir space and they can bring
in a second skill set, which islike the AI side of things, it's
a dangerous like they become adangerous weapon and in a good
way, as in, like they can tackleopportunities that they wouldn't
(11:05):
have.
And passion is a big thing asI mean, Mia, I don't know if you
I'm a very passionate personYeah.
And actually talking about whatRight.
There's one thing I've beenbecause I'm also coaching some
startup founders on the side aswell.
One thing I do kind ofbasically the pattern, I've been
their past expertise, theirtheir heavy focus around the
(11:31):
So there's a lot more, um, focusaround the solution, not the
problem.
And even with the problem,because they have their
preconceived notions, but theirprevious understanding of the
pain points.
So sometimes there's a lack ofor maybe a bit less focused
around really understanding theproblem and also validating a
lot of the assumptions withinthe problem they're trying to
(11:53):
solve.
And that can be dangerous in alot of those sort of AI startups
or teams.
That's something as a PM, Iknow, just do that validation
space a bit better.
Because in that way they canpotential, you know, failures or
Okay.
So that's really interestingbecause I think a lot of
founders normally just withoutAI, even with AI, maybe it's
(12:18):
accelerated that problem of justgoing out and trying to solve
what they think is the problemwithout actually validating the
problem.
And it's kind of backwards.
It's like, well, I have tocreate a solution to take it out
there.
And we hear this a lot, but itsounds like it still happens
today.
Maybe there's like a this is thepeople like yourself can help
(12:40):
insights sooner, because theymonths, years, even, without
kind of problem.
Can you give us an example,scenario where, you know you've
describe that to us, because Ithis sort of stuff, like how
change for sure.
(13:01):
Are you talking about thegone down a particular rabbit
Right?
Yeah, exactly.
Let me think of one.
I think there's a couple of Ireally good example.
I can give it to you.
Sure.
Um, there's one example.
Um, this is a lady from the.
I believe she, um, like, she hadan idea around an area she want
(13:26):
to focus on, um, which is a thisis more in the proptech sort of
area.
Okay.
Yep.
And, uh, and she had a verystrong belief because she's been
in that field for a very longtime, um, you know, very
experienced.
And she had very solidUm, but then when she actually
problem, what she soon realizedHey, that problem.
(13:51):
It is worth the while solving.
It's very big as well.
But because of her, you know,early stage startup, hasn't got
capital solving that.
So she used to get very, yougoing deep on that one problem.
And, um, I think where I came inUm, you know, maybe we've
(14:13):
validation of the problem, butreally about understanding, hey,
like, why why is it capability?
I mean, from a resourcing froma, you know, like, sort of
solution solving perspective,what are the, you know, all the
top problems within your spaceand which one is actually more
suitable for you and yourorganization at this stage to
(14:35):
solve?
I think, I think for a lot ofthat, you can call a bit of a,
um, you know, discoveryanalysis.
And we actually found out a muchorganization, her organization
And I said to her, hey, it's notlike our eyes not on that ball,
you know?
I think that eventual problemYeah.
The startups, the journey as youRight?
(14:57):
Because sometimes you mightstart with something really
small.
But the thing is you're gettingMomentum is in terms of getting
customers, getting traction,getting revenue.
Right.
And it's a snowball effect.
Once you gain, you know, thosepeople's trust you most likely
they're going to allow you tosolve their bigger problem as
well.
So it's going to be a iterativeYou don't want to go hard on the
(15:17):
If you don't have enough likeability or resources to solve
it.
Yeah.
So that's a good example.
I think that's interestingbecause it's like they they need
to have those runs on the boardbefore it because it's a trust
game.
Like there's still thisLike we need to see that yes,
someone can actually create thethings that we um, and we can
trust them to solve these likebigger problems.
So so that's certainly true.
(15:38):
Um, I think that's great becauseAI is great, but it does make it
into areas where potentially youSo it really does depend.
But do you think that there'son the one side you can call it
(16:00):
side, um, what are your thoughtswith things and tinker and test?
And some of those tests may leadmight actually lead to new
thought about before.
So do you think that there is ahole one hundred percent?
Another great example I can giveSo, um, this is actually a fun,
uh, it's not a founder, buthe's, um, he's actually was my
(16:24):
first guest speaker in my veryfirst, um, first show at the
end.
I think that's his pronunciationSo he's a technologist.
So, you know, technologists,people like to tinker around
things, but he's always been asoftware solution architect sort
of space.
He's he's also fairly new to AI.
Um, well, I think what he wasdays using ChatGPT.
(16:44):
That was about a year and a halfRight.
That's when you know now theylovable and the sort of interest
So he was actually using ChatGPTHe actually built an entire
functional, um, sort of a mobilebanking prototype within two
days.
Wow.
So, so the point I was trying tosay is that the capability is
(17:05):
there, right?
I'm not talking about building afull on, you know, sort of a
production ready, commerciallyviable financial products from,
from digital bankingperspective.
But if you have something liketo people, you can show to the
alignment, getting customermuch more, um, what do you say?
Uh, much more effective than ifyou just talk about an idea,
(17:27):
because when you talk about anidea to, to to people saying, I
want to do this new thing, um,new things or something,
sometimes people may not reallyable to visualize that well or
on the same page misunderstand,right?
But if you within a very shortamount of time, you can get to
that.
That's really helpful.
But more importantly, andbecause it's actually it's a
functional prototype.
(17:47):
He's able to get real data frompeople sort of using feedback as
well.
Although this is not a, youbanking app that doesn't have a
know, you can get live data, um,I think that there's, um, vibe
sometimes hate in terms of vibepeople think that, oh, you're
(18:11):
do stuff and you don't have theAnd I'm getting to, you know, to
a, as a product manager in youra front end developer.
I've done a lot of stuff withof SQL, a lot of data science
I know how HTML works.
I know you know, that I've seendoing, you know, some of the CSS
(18:33):
kind of stuff and doing frontend stuff.
But the interesting thing hashave that bit of knowledge, even
knows how to actually do all theto work with databases, you can
is more than just a prototype.
It can actually become a productbecause you knew how to set it
(18:54):
up well.
And I think that that's theA lot of people think that AI is
I think it's great.
I think it will get us like to,you know, functional kind of
prototypes.
But I think we can even take itsomewhat what you're doing.
What do you think about allOh, one hundred percent.
This is where, you know, I wastelling you how passionate I am
as a lean product manager,right?
So I really want to focus onSo that's lean.
(19:18):
Lean.
The reason being is that in leanproduct management, we very much
emphasis on the importance ofactually building something that
could be a, you know, maybe aFigma Figma interactive
prototype.
It could be just a mock up or Imean, that's before the AI like
AI age.
Now we got tools like Replitknow, ChatGPT as well.
We can build proper functionalAnd like you said earlier, they
(19:42):
we can actually do smoke testsreal customers, right?
Yeah.
And as a PM, that's actuallyeven more powerful than you
having a just interactiveprototype.
Yeah.
Getting feedback from customers.
What if you're going to use it?
What do you think real customersusing it because you're not
actually selling that tocustomers.
What you want to do is you wantto test out there is that
(20:03):
product somebody's actuallygoing to be using on a regular
basis.
Once you get the real usageproof comparing to just getting
whether they're going to buyYou actually get to see the
Like you've got real data pointsAnd the key difference here is
People are saying, hey, it'sto be able to sell.
(20:24):
That might be true.
But but here's the kicker,Once you've done the validation
using that, getting that trueconfidence, knowing this is
actually the one this is goingto work, then you can you can in
today's world, you can build itproperly.
I guess you can actually hire asmall team, really build it out
front end, back end, but onlyonce you actually done the
validation.
So you do that stuff beforehandjust by yourself using those
(20:46):
tools.
Launch the market.
It's all about what I call getwhere you can have a very high
is something that customersAnd then you can build it up
So there's no there's noI think I think that's great.
And you know, the things thatLike you might initially build
(21:08):
something with a bolt, a lovableone of those kind of tools and
where you've got some, you know,for example, a chat bot or
something else that's got AI init and put in your OpenAI API
key, for example, just in thefront end code, and you don't
realize you think it's like safeand stuff.
And it's not until you get anyour key is being compromised,
(21:31):
going to work and stuff.
So then you think about it andthen you can even if you if you
are passionate and you know whatto look for and stuff, and you
can ask the questions of AI, youcan see that, oh, well, the only
way solution is that I have tohave people log in to super
base, to Azure, whatever it is,and I can put the OpenAI key in
a function that's sitting behindthe scenes.
So only logged in users canthat is more protected than
(21:57):
So the thing is, the problemswhatever, that's one type of
problem.
There's multiple other problemsthat even if you don't know
exactly, you can either talk toyour friends and they can tell
you.
Then you can ask AI, how do IOr you can talk to the AI.
But if you're passionate, goingback to what you said at the
start, you can solve thesethings.
Um, what do you think about likethe how do you think AI adoption
(22:18):
in fintech and edtech is goingto go, given what you're seeing
and given the things that you'repassionate about, where do you
see this space kind of likegoing over the next couple of
months or, you know, it's acouple of months is already a
long period.
But yeah, what are your thoughtsFor sure.
I'll probably start with fintechthis stuff in this space.
Yeah.
Um, from what I've seen so far,bit of with the context first,
(22:45):
on top of the existing sort ofinfrastructure and everything.
So within fintech or financialservices in general, there's the
rising trends is what we callembedded finance.
So there's more.
So I'll just explain verybriefly what embedded finance
is.
So think of this way.
I mean, I don't know if youlike, when was the last time you
opened your NRMA app or Qantasapp and those apps, and you will
(23:07):
see more and more of thosecompanies.
They are not financialThey are like, you know, they
of area context in the airline.
A lot of those non-financialcompanies, if you go into their
applications on the web, onmobile, they offer home loans,
they offer car loans, theyoffer, like all these different
lending.
Or you can call banking orAnd in most cases, those
(23:30):
financial products, obviouslythey are not financial
institutions.
So they would have a white labelI used to work in before.
They are white labeled, sort ofthe financial institutions or
technology partners.
They will be the one providingthose both from a technology
standpoint and also from afinancial product perspective to
those non-financial companieslike Apple.
(23:50):
If Apple tomorrow, you know,like sort of yeah, they sell
iPhones, right.
But they also do like some sortof a, you know, personal loan to
you while you're buying aniPhone.
They must done that throughRight.
So that's what we call embeddedfinance because of the rise of
embedded finance.
So there's a very increasing,very much increasing trend of
providing customer with a whatwe call hyper personalized sort
(24:13):
of embedded finance lendingexperience.
Mhm.
For example.
So just imagine if you go on toa, maybe a carsales.com like one
of those car sales, you want tobuy a beautiful sports car
right.
And you don't want to pay tooYou want to get along or
So right now I mean if you dothat, you have to go to the
(24:34):
dealership.
They they would only have maybetwo or three of their preferred
lenders.
And the whole process is veryvery old fashioned.
It can go very long as well.
But in today's world, we'reactually more and more of those
sort of companies that are usingAI to really providing what you
call a almost like a dynamicallyrisk based pricing, so they can
(24:56):
actually based on your own riskprofile.
And that actually also tied toscoring capabilities, which is
just using your your past creditRight.
There's a lot more social data.
There's different type of dataThere's already engine can help
them to do that, but then thatinformation can fit to the front
(25:17):
end from a risk based pricingperspective.
So for example, your offerpricing might be quite different
from mine because of our, youknow, our risk profile because
of all different things, how itshould be based on the product
as well.
Right.
Which is how it should be.
So hence why I think that'sAnd there's traditionally that
to do that.
But nowadays there's more andmore applications can actually
(25:39):
even further feel that you cancall this improvement of the
front end experience, which isback then backed by this huge
back end of capabilitiesdriving.
I think that, you know, what youpersonalization that was harder
machine learning and you know,people talk about AI.
(25:59):
A lot of laypersons, they think,But like it's a wider spectrum,
as we know, and we're going downthe line of the neural networks,
which is like what is drivingGPT.
But in terms of like an overallum, this space kind of like
(26:20):
usage in finance.
And we maybe even touch onedtech there because like, you
guys are doing more edtechrelated stuff, that how do you
see these things being, um,adopted?
Uh, further, do you think thatmore people need to be coming to
do you convince people that areBecause like robodebt was such a
(26:45):
Australia and people still feelwasn't generative AI.
That was, that was a veryBut how do you convince the
Yeah, that's a that's a reallyI don't have audiences, but all
see there is a need in gettingstakeholders in both.
(27:06):
This is actually not just foredtech as well, because in
education, those industries, um,I mean, generally speaking, a
lot of senior leadership, um,they probably have been very
successful with the wayhistorically they've been
running the organisations and,you know, like whether it's
traditional softwarecapabilities and structured and
(27:27):
everything.
So I think that adoption,both industries are fairly
whether it's from a regulatoryinternal digital ability to
So there has been somestruggles, I have to say, in
both industries.
Yeah.
Coming back to your pointearlier, whether it's from a
consulting consultancyperspective, from a workshop
(27:48):
perspective, industry sort ofawareness perspective.
So there's definitely that needof bringing more, both AI
experts or even people likeourselves.
Actually, you can call communityYeah, basically because like one
with you, one of my vision foredtech, it is actually not just
(28:09):
If I can build enough momentumactually partner up with some
sort of organizations where youreally bring them to together.
I think once they actually haveappreciation of, um, you know,
can just trust us or trust thisThen I think you will see a lot
(28:33):
more adoption or a lot morecollaboration among different
sort of industry bodies andeverything.
That's kind of the goal.
I actually that's very Yeah.
Um, I hope it gets there becausebeen part of that.
It's great to run them, butthere has to be some strategy to
it.
Otherwise you can easily burnpeople don't realize is behind.
(28:55):
And you know this well behindthe scenes, how much work goes
into even running just oneevent.
So I'd love to hear that there'sAnd I think, you know, if more
of these groups get connected,we can all be learning from each
other.
And speaking of the meetup, um,now, obviously this is going out
in a few weeks time and thismeetup will already have
happened, but you just mentionedthat the next one coming up for
(29:16):
us, we're filming this on thefifth of September, so I'm
dating it.
But, um, you've got one comingup next week, which is a
practical one.
Can you tell us about that forUh, the one next Tuesday night.
Uh, I mean, this is Yes.
This is about, um, sort ofdemonstrating, actually a live
demonstration of building a rackmodel.
So for those that doesn't knowwhat Rag is retrieval retrieval,
(29:40):
retrieval uh augmented umgeneration.
So this is basically abilitythat stuff as well.
It's basically if you only fitthat you want from an output
combined or confining, then youactually doesn't allow the AI to
(30:03):
So that's kind of a it's beenit's been around for a little
while over the last year or abit.
I think there's definitelyincreasing adoption for the
different organizations, but Ijust really want to practically
telling everyone, and this isnot me demonstrating I have an
organization.
They're very technicallyThey're going to help us to do
that demonstration of aparticular fintech use case,
just showing to people howaccessible that we can actually
(30:24):
all, you know, sort of do it notjust for the really, really
technical guys.
And there's a lot of you cansort of providers out there.
Yeah.
This is further lowering the barorganization to apply those
set it up yourself.
You can use a service now forAnd, you know, for rag for for
folks out there, if you if youjust ask a question of the model
(30:46):
and it doesn't have theinformation in there, it's got a
higher chance of, say,hallucinating answers.
Whereas with rag, it's onlygoing to answer from what's in
those documents.
But there's still nuance, whichthe, the meetup.
So definitely like I know youwill have missed it people on
the show, but, um, you can watchthe recording later on and watch
the recording.
(31:06):
Yeah.
I was going to say, is it inUh, this one's online.
Okay.
So normally we would have therecording after for members
only.
So make sure you sign up to ourYeah.
But in that way you will get theI think that's great.
I think there's a lot offrom that, and it's great to a
people can go through the backwhat it's looking like there.
(31:30):
But, um, do you have any likethis, do you have any kind of
want to leave them with ifin their AI journey?
Like, what do you want to havewhen it comes to AI?
For sure.
I think my biggest advice toeverybody is just to start not
(31:50):
talk about it, not start thinkabout it, start action on it
today.
So why don't they start actingFor me personally, I started
community and it's just beenI learned a lot and everything,
but at the same time I am doingsomething on my own as well on
the side.
Yeah, so I think really get yourYou don't have to be a technical
(32:11):
do this, this stuff and do ameetup or join my community,
feel that you can do, but do notof watch some YouTube videos or
really kind of just get into it.
That's probably the biggest one.
And just just on that note andlike final, final question, but
(32:31):
like, what kind of let's callthem tools.
What are what's inspiring youmoment in terms of tools to help
seeing really cool that you wantYeah, for sure.
So the tool I used mostfrequently, obviously everyone
used ChatGPT.
Yeah.
Um, but I think me as a PM, as ausing a lot is lovable.
(32:53):
I'm lovable.
I use that to for a couple ofUm, one is actually just to kind
of help me to really build outsome initial designs and just
sort of from a conceptual,conceptual stuff.
But more importantly, I amit a bit of functional because
connect with Supabase andI'm actually using that to build
(33:14):
we can actually get live datasort of related stuff that I'm
Um, but yeah, I think there's aI think the most important thing
actually not most important.
Just find the one that actuallyBecause for me, I'm not as
So I haven't been be using, youknow, basil or a few other tools
(33:35):
as much.
So just really come down to whatreally go deep and hard with
make the most out of it.
Yeah.
Brilliant.
Well, look, we'll we'll put allThere's going to be some great,
we'll have from going to yourback on the show.
But, uh, Kurt Young, thank youThanks, Mark.
(33:56):
Really appreciate it.
Cheers.
Thank you for being part ofIf you could really help us out
would be incredible.
It'll help us to get in front ofin the AI space, to share their
back to you in the future.
Thanks again.
Within two days.
He actually built an entirefunctional sort of mobile
banking prototype within twodays.
We can build proper functionalAnd like you said earlier,
prototypes that we can actuallytests with real customers,
so they can actually based onBiggest advice to everybody is
(00:22):
just to start not talk about it,not start.
Think about it.
Start.
Welcome to the Digital NexusIn this series, we're going to
be getting down and dirty withleaders and founders in the AI
space.
We want to get an understandingthey're building businesses
(00:44):
strategies and future impacts ofWelcome back, folks, to another
This time we've got a specialThis is Kurt Yang.
Kurt, thanks for joining us.
Thanks, Mark.
Kurt and I have met and alsoChris who's behind the camera
right now.
You would have seen the lastfront of the screen.
We do the Swapsies every now andBut Kurt, you're doing some
(01:09):
terms of like bringing peopleand myself that have run
know what that's like.
So, um, Kurt, if you could justgive us a bit of a background
for the audience that may notknow you?
I'm sure a lot of people do, butif they haven't heard of you
yet, what is your backgroundlike?
What's the stuff that's led toFor sure.
(01:29):
Uh, yeah.
It's a great pleasure to beinvited to this, um, fantastic
venue.
Um, yeah, it's a bit about me.
Uh, my name is Kurt Young.
Uh, I'm a digital productdoing product management for the
various industries across likeenterprise, a little bit like
(01:51):
And so I do have a lot of, uh,product management experience,
and I'm really passionate aboutusing that to help startups,
entrepreneurs to succeed,especially help them to, you
know, like sort of build theirnext innovation cheaper,
quicker, get to success muchfaster.
Um, I think my how I found mypassion to this new adventure,
(02:11):
this made up thing I've beendoing is, was actually through
my frustration of I justcouldn't find a meetup or a
community to, for me as afintech, PM, whatever, to learn
about Jai or even trying totrying to transition into AI
product management.
So hence why my friend wastelling me as a joke, hey, why
don't you just create oneyourself?
(02:32):
Yeah.
And initially I took it as aAnd that evening, I think, I
don't know what got through tome.
I just said, you know what?
Hey, what the heck?
I'm just going to pay a meetupYou know, there was a special
and I paid the fee.
And then that's the moment.
That's the moment I realized,I better just to find a way to
So it took me a little while toYeah.
(02:55):
But I think it's like aI'm sure you've been running
get in, get into that bit of akept going and going.
So I've been running this forYep.
So starting from from scratchnow we got about nearly eight
hundred members.
I held about fifteen events bothI mean, some online, some
(03:16):
virtual, some online, some inperson.
Um, it's just been fantastic,I couldn't tell you the amount
personally, I have gained andactually even trying to do some
well, just out of likeYeah, I think that's great and
But you said VMware, when wereUh, that was twenty twenty two,
(03:42):
About two years I was there for.
Yeah.
Oh, okay.
More recently.
Yeah, yeah.
Okay.
I won a competition throughThey had something called the IT
So I'm dating myself here.
Um, but in any case, a lot of,And then you mentioned, out of
frustration, like setting upthe, you know, the meet up
group, the gen AI and fintechand edtech.
And we'll get to that, settingyou weren't seeing something in
(04:08):
what you wanted.
And we did the same thing.
Whether it was, um, we joinedthe Data Science and AI
Association and became presidentthere.
Um, but we also created the OzDeFi Association because out of
that frustration of not having aplace to chat in between other
meetups.
And I think it's interestingthe frustration of, hey, why
(04:31):
know, at night?
And then Edison invents theSo I am comparing you to Edison
here and stuff, but could youtell us a bit more about what
Gen AI and fintech and edtech isall about?
Some folks, they may havestuff, and that's great, and
But what what to you what whatabout for sure.
Um, to put it very simply, mya it's a community.
(04:55):
You know, right now it's onlineBut the point is that we have
this community, all thecommunity members, we can come
together.
We can learn, share all the youcan call the latest or the most
useful AI or AI in general aswell.
Use cases, um, even likeor even like we actually had one
(05:19):
perspective, you know, like howAI career perspective, right?
From all different aspects.
So that's kind of the but like Ifintech financial services.
That's my background.
But later on we're actuallyknow, broaden it up to including
because I personally have a lotbackground covered that as well.
(05:42):
And also we have a lot ofmembers saying to me, hey, in
the education space, there's alot of applications or
applications happening.
Why don't we just try to see ifwe can get some really amazing
guest speakers to cover thosetopics?
So I'm also going to have somesessions coming up as well.
Great.
And speaking of, um, you know,there's so many different kind
(06:03):
of pathways we could go into,but I'd like to set the scene a
little bit.
Like your background we werediscussing before is in project
management, actually.
Product management, there's aI can go deeper.
Yes.
No. That's true.
But I said the wrong p wordApologies to my project and
I've probably equally pissedanother p word.
But um, in in terms of thethat the background?
(06:26):
You said you were at the banksand you were at some other like,
you know, fintech kind of placesbefore.
Has it always been?
No it hasn't.
Okay.
That's a really good question.
I mean, that probably can go onfor like another ten minutes or
whatever, but I'll try to keepit short.
Sure.
Um, yeah.
I actually started my career inbanking as a commercial guy or
relationship slash commercialguy.
So I was a banker.
(06:47):
I was a stockbroker, sort ofonline stockbroker for a little
while.
Then I transitioned.
I transitioned into more like acommercial contract manager
relation, institutional B2B,kind of like a customer success
manager.
You know, a lot of the today'sthose, um, experiences, I got
I got exposure to some you cancall project management or
(07:08):
product sort of related work aswell.
That's how I got like reallyBut I didn't even know that was
Yeah.
Yeah.
So I landed an opportunity byaccident within the bank, as I
say.
And that's that's when Ithink my skill sets really can
And, uh, and then from thatpoint on, I've just been product
(07:29):
management ever since, you know,at ups and scale ups and
enterprise.
Yeah.
And it's interesting.
And I'm asking all this becauseAI is not something that a we
haven't we have seen that itdoesn't work.
Well if you don't bring inexperience the context per se
for sure.
And everyone has the context ofthe expertise that they bring
in.
Sure.
(07:49):
AI can help level up things.
If you don't know much aboutyou information that you can ask
far faster, but it works so muchbackground of some sort of
edtech, from, you know, productAnd I say all of this because I
(08:10):
interesting perspectivestheir experience with what's
Now, in terms of that, like whatmentioned before that there's
having these events.
Could you could you mention and,you know, framing for the
audience.
You're this product managerYou're running these meetups and
(08:34):
been some of the top of mindjust running these events?
For sure, for sure.
Um, I think the biggest learningor, you know, you can call like
a pivotal moment or something isjust realizing that anyone can
do it.
Anyone can create their own.
Might not be like AI startup orreally get into this space.
(08:55):
And the reason I'm saying this,especially speaking from
someone, not from a technical AIor even tech or data, sort of a
background.
I was a commercial guy before II got a bit of a technical like
sort of stuff throughout theyears of doing this stuff,
right?
But even that's the case.
Um, what I realized is that,like some of my amazing past
guest speakers, they are notengineers or data or whatever
(09:18):
people, right?
So they come from, say, CFO orof the person.
So he was a former owner of aI think it was a tire reseller
or tire expert, sort of acompany.
So people come from all, allwhat they like.
What I saw in common among allpassion, their passion to solve
(09:44):
Right.
Because they were frustratedwith whatever the way they were
solving before.
And all of a sudden theysolving those problems.
Number two is what I felt isSo they they all found their own
Maybe that niche is because oftheir experience or their
background or, you know, maybetheir unique sort of market
understanding.
(10:04):
And they bring that to theI think you probably brought up
like initially a little bit aswell.
Right.
And then they really go hard atAnd then that's how they I can
succeeding in what they're doingI think it's really interesting.
It reminds me of, um, I can'tthe scenario is like Steve Jobs,
(10:24):
He he learnt all aboutcalligraphy and style and stuff
like that.
And that experience coupled withwasn't he was the tech guy.
But coupled with his experiencethinking that's helped shape
And for many people, it's theLike before, it was like, okay,
(10:45):
I only knew education and maybeBut now everyone has this
opportunity because the barriersare lowered, like you said, like
if people are passionate abouttheir space and they can bring
in a second skill set, which islike the AI side of things, it's
a dangerous like they become adangerous weapon and in a good
way, as in, like they can tackleopportunities that they wouldn't
(11:05):
have.
And passion is a big thing asI mean, Mia, I don't know if you
I'm a very passionate personYeah.
And actually talking about whatRight.
There's one thing I've beenbecause I'm also coaching some
startup founders on the side aswell.
One thing I do kind ofbasically the pattern, I've been
their past expertise, theirtheir heavy focus around the
(11:31):
So there's a lot more, um, focusaround the solution, not the
problem.
And even with the problem,because they have their
preconceived notions, but theirprevious understanding of the
pain points.
So sometimes there's a lack ofor maybe a bit less focused
around really understanding theproblem and also validating a
lot of the assumptions withinthe problem they're trying to
(11:53):
solve.
And that can be dangerous in alot of those sort of AI startups
or teams.
That's something as a PM, Iknow, just do that validation
space a bit better.
Because in that way they canpotential, you know, failures or
Okay.
So that's really interestingbecause I think a lot of
founders normally just withoutAI, even with AI, maybe it's
(12:18):
accelerated that problem of justgoing out and trying to solve
what they think is the problemwithout actually validating the
problem.
And it's kind of backwards.
It's like, well, I have tocreate a solution to take it out
there.
And we hear this a lot, but itsounds like it still happens
today.
Maybe there's like a this is thepeople like yourself can help
(12:40):
insights sooner, because theymonths, years, even, without
kind of problem.
Can you give us an example,scenario where, you know you've
describe that to us, because Ithis sort of stuff, like how
change for sure.
(13:01):
Are you talking about thegone down a particular rabbit
Right?
Yeah, exactly.
Let me think of one.
I think there's a couple of Ireally good example.
I can give it to you.
Sure.
Um, there's one example.
Um, this is a lady from the.
I believe she, um, like, she hadan idea around an area she want
(13:26):
to focus on, um, which is a thisis more in the proptech sort of
area.
Okay.
Yep.
And, uh, and she had a verystrong belief because she's been
in that field for a very longtime, um, you know, very
experienced.
And she had very solidUm, but then when she actually
problem, what she soon realizedHey, that problem.
(13:51):
It is worth the while solving.
It's very big as well.
But because of her, you know,early stage startup, hasn't got
capital solving that.
So she used to get very, yougoing deep on that one problem.
And, um, I think where I came inUm, you know, maybe we've
(14:13):
validation of the problem, butreally about understanding, hey,
like, why why is it capability?
I mean, from a resourcing froma, you know, like, sort of
solution solving perspective,what are the, you know, all the
top problems within your spaceand which one is actually more
suitable for you and yourorganization at this stage to
(14:35):
solve?
I think, I think for a lot ofthat, you can call a bit of a,
um, you know, discoveryanalysis.
And we actually found out a muchorganization, her organization
And I said to her, hey, it's notlike our eyes not on that ball,
you know?
I think that eventual problemYeah.
The startups, the journey as youRight?
(14:57):
Because sometimes you mightstart with something really
small.
But the thing is you're gettingMomentum is in terms of getting
customers, getting traction,getting revenue.
Right.
And it's a snowball effect.
Once you gain, you know, thosepeople's trust you most likely
they're going to allow you tosolve their bigger problem as
well.
So it's going to be a iterativeYou don't want to go hard on the
(15:17):
If you don't have enough likeability or resources to solve
it.
Yeah.
So that's a good example.
I think that's interestingbecause it's like they they need
to have those runs on the boardbefore it because it's a trust
game.
Like there's still thisLike we need to see that yes,
someone can actually create thethings that we um, and we can
trust them to solve these likebigger problems.
So so that's certainly true.
(15:38):
Um, I think that's great becauseAI is great, but it does make it
into areas where potentially youSo it really does depend.
But do you think that there'son the one side you can call it
(16:00):
side, um, what are your thoughtswith things and tinker and test?
And some of those tests may leadmight actually lead to new
thought about before.
So do you think that there is ahole one hundred percent?
Another great example I can giveSo, um, this is actually a fun,
uh, it's not a founder, buthe's, um, he's actually was my
(16:24):
first guest speaker in my veryfirst, um, first show at the
end.
I think that's his pronunciationSo he's a technologist.
So, you know, technologists,people like to tinker around
things, but he's always been asoftware solution architect sort
of space.
He's he's also fairly new to AI.
Um, well, I think what he wasdays using ChatGPT.
(16:44):
That was about a year and a halfRight.
That's when you know now theylovable and the sort of interest
So he was actually using ChatGPTHe actually built an entire
functional, um, sort of a mobilebanking prototype within two
days.
Wow.
So, so the point I was trying tosay is that the capability is
(17:05):
there, right?
I'm not talking about building afull on, you know, sort of a
production ready, commerciallyviable financial products from,
from digital bankingperspective.
But if you have something liketo people, you can show to the
alignment, getting customermuch more, um, what do you say?
Uh, much more effective than ifyou just talk about an idea,
(17:27):
because when you talk about anidea to, to to people saying, I
want to do this new thing, um,new things or something,
sometimes people may not reallyable to visualize that well or
on the same page misunderstand,right?
But if you within a very shortamount of time, you can get to
that.
That's really helpful.
But more importantly, andbecause it's actually it's a
functional prototype.
(17:47):
He's able to get real data frompeople sort of using feedback as
well.
Although this is not a, youbanking app that doesn't have a
know, you can get live data, um,I think that there's, um, vibe
sometimes hate in terms of vibepeople think that, oh, you're
(18:11):
do stuff and you don't have theAnd I'm getting to, you know, to
a, as a product manager in youra front end developer.
I've done a lot of stuff withof SQL, a lot of data science
I know how HTML works.
I know you know, that I've seendoing, you know, some of the CSS
(18:33):
kind of stuff and doing frontend stuff.
But the interesting thing hashave that bit of knowledge, even
knows how to actually do all theto work with databases, you can
is more than just a prototype.
It can actually become a productbecause you knew how to set it
(18:54):
up well.
And I think that that's theA lot of people think that AI is
I think it's great.
I think it will get us like to,you know, functional kind of
prototypes.
But I think we can even take itsomewhat what you're doing.
What do you think about allOh, one hundred percent.
This is where, you know, I wastelling you how passionate I am
as a lean product manager,right?
So I really want to focus onSo that's lean.
(19:18):
Lean.
The reason being is that in leanproduct management, we very much
emphasis on the importance ofactually building something that
could be a, you know, maybe aFigma Figma interactive
prototype.
It could be just a mock up or Imean, that's before the AI like
AI age.
Now we got tools like Replitknow, ChatGPT as well.
We can build proper functionalAnd like you said earlier, they
(19:42):
we can actually do smoke testsreal customers, right?
Yeah.
And as a PM, that's actuallyeven more powerful than you
having a just interactiveprototype.
Yeah.
Getting feedback from customers.
What if you're going to use it?
What do you think real customersusing it because you're not
actually selling that tocustomers.
What you want to do is you wantto test out there is that
(20:03):
product somebody's actuallygoing to be using on a regular
basis.
Once you get the real usageproof comparing to just getting
whether they're going to buyYou actually get to see the
Like you've got real data pointsAnd the key difference here is
People are saying, hey, it'sto be able to sell.
(20:24):
That might be true.
But but here's the kicker,Once you've done the validation
using that, getting that trueconfidence, knowing this is
actually the one this is goingto work, then you can you can in
today's world, you can build itproperly.
I guess you can actually hire asmall team, really build it out
front end, back end, but onlyonce you actually done the
validation.
So you do that stuff beforehandjust by yourself using those
(20:46):
tools.
Launch the market.
It's all about what I call getwhere you can have a very high
is something that customersAnd then you can build it up
So there's no there's noI think I think that's great.
And you know, the things thatLike you might initially build
(21:08):
something with a bolt, a lovableone of those kind of tools and
where you've got some, you know,for example, a chat bot or
something else that's got AI init and put in your OpenAI API
key, for example, just in thefront end code, and you don't
realize you think it's like safeand stuff.
And it's not until you get anyour key is being compromised,
(21:31):
going to work and stuff.
So then you think about it andthen you can even if you if you
are passionate and you know whatto look for and stuff, and you
can ask the questions of AI, youcan see that, oh, well, the only
way solution is that I have tohave people log in to super
base, to Azure, whatever it is,and I can put the OpenAI key in
a function that's sitting behindthe scenes.
So only logged in users canthat is more protected than
(21:57):
So the thing is, the problemswhatever, that's one type of
problem.
There's multiple other problemsthat even if you don't know
exactly, you can either talk toyour friends and they can tell
you.
Then you can ask AI, how do IOr you can talk to the AI.
But if you're passionate, goingback to what you said at the
start, you can solve thesethings.
Um, what do you think about likethe how do you think AI adoption
(22:18):
in fintech and edtech is goingto go, given what you're seeing
and given the things that you'repassionate about, where do you
see this space kind of likegoing over the next couple of
months or, you know, it's acouple of months is already a
long period.
But yeah, what are your thoughtsFor sure.
I'll probably start with fintechthis stuff in this space.
Yeah.
Um, from what I've seen so far,bit of with the context first,
(22:45):
on top of the existing sort ofinfrastructure and everything.
So within fintech or financialservices in general, there's the
rising trends is what we callembedded finance.
So there's more.
So I'll just explain verybriefly what embedded finance
is.
So think of this way.
I mean, I don't know if youlike, when was the last time you
opened your NRMA app or Qantasapp and those apps, and you will
(23:07):
see more and more of thosecompanies.
They are not financialThey are like, you know, they
of area context in the airline.
A lot of those non-financialcompanies, if you go into their
applications on the web, onmobile, they offer home loans,
they offer car loans, theyoffer, like all these different
lending.
Or you can call banking orAnd in most cases, those
(23:30):
financial products, obviouslythey are not financial
institutions.
So they would have a white labelI used to work in before.
They are white labeled, sort ofthe financial institutions or
technology partners.
They will be the one providingthose both from a technology
standpoint and also from afinancial product perspective to
those non-financial companieslike Apple.
(23:50):
If Apple tomorrow, you know,like sort of yeah, they sell
iPhones, right.
But they also do like some sortof a, you know, personal loan to
you while you're buying aniPhone.
They must done that throughRight.
So that's what we call embeddedfinance because of the rise of
embedded finance.
So there's a very increasing,very much increasing trend of
providing customer with a whatwe call hyper personalized sort
(24:13):
of embedded finance lendingexperience.
Mhm.
For example.
So just imagine if you go on toa, maybe a carsales.com like one
of those car sales, you want tobuy a beautiful sports car
right.
And you don't want to pay tooYou want to get along or
So right now I mean if you dothat, you have to go to the
(24:34):
dealership.
They they would only have maybetwo or three of their preferred
lenders.
And the whole process is veryvery old fashioned.
It can go very long as well.
But in today's world, we'reactually more and more of those
sort of companies that are usingAI to really providing what you
call a almost like a dynamicallyrisk based pricing, so they can
(24:56):
actually based on your own riskprofile.
And that actually also tied toscoring capabilities, which is
just using your your past creditRight.
There's a lot more social data.
There's different type of dataThere's already engine can help
them to do that, but then thatinformation can fit to the front
(25:17):
end from a risk based pricingperspective.
So for example, your offerpricing might be quite different
from mine because of our, youknow, our risk profile because
of all different things, how itshould be based on the product
as well.
Right.
Which is how it should be.
So hence why I think that'sAnd there's traditionally that
to do that.
But nowadays there's more andmore applications can actually
(25:39):
even further feel that you cancall this improvement of the
front end experience, which isback then backed by this huge
back end of capabilitiesdriving.
I think that, you know, what youpersonalization that was harder
machine learning and you know,people talk about AI.
(25:59):
A lot of laypersons, they think,But like it's a wider spectrum,
as we know, and we're going downthe line of the neural networks,
which is like what is drivingGPT.
But in terms of like an overallum, this space kind of like
(26:20):
usage in finance.
And we maybe even touch onedtech there because like, you
guys are doing more edtechrelated stuff, that how do you
see these things being, um,adopted?
Uh, further, do you think thatmore people need to be coming to
do you convince people that areBecause like robodebt was such a
(26:45):
Australia and people still feelwasn't generative AI.
That was, that was a veryBut how do you convince the
Yeah, that's a that's a reallyI don't have audiences, but all
see there is a need in gettingstakeholders in both.
(27:06):
This is actually not just foredtech as well, because in
education, those industries, um,I mean, generally speaking, a
lot of senior leadership, um,they probably have been very
successful with the wayhistorically they've been
running the organisations and,you know, like whether it's
traditional softwarecapabilities and structured and
(27:27):
everything.
So I think that adoption,both industries are fairly
whether it's from a regulatoryinternal digital ability to
So there has been somestruggles, I have to say, in
both industries.
Yeah.
Coming back to your pointearlier, whether it's from a
consulting consultancyperspective, from a workshop
(27:48):
perspective, industry sort ofawareness perspective.
So there's definitely that needof bringing more, both AI
experts or even people likeourselves.
Actually, you can call communityYeah, basically because like one
with you, one of my vision foredtech, it is actually not just
(28:09):
If I can build enough momentumactually partner up with some
sort of organizations where youreally bring them to together.
I think once they actually haveappreciation of, um, you know,
can just trust us or trust thisThen I think you will see a lot
(28:33):
more adoption or a lot morecollaboration among different
sort of industry bodies andeverything.
That's kind of the goal.
I actually that's very Yeah.
Um, I hope it gets there becausebeen part of that.
It's great to run them, butthere has to be some strategy to
it.
Otherwise you can easily burnpeople don't realize is behind.
(28:55):
And you know this well behindthe scenes, how much work goes
into even running just oneevent.
So I'd love to hear that there'sAnd I think, you know, if more
of these groups get connected,we can all be learning from each
other.
And speaking of the meetup, um,now, obviously this is going out
in a few weeks time and thismeetup will already have
happened, but you just mentionedthat the next one coming up for
(29:16):
us, we're filming this on thefifth of September, so I'm
dating it.
But, um, you've got one comingup next week, which is a
practical one.
Can you tell us about that forUh, the one next Tuesday night.
Uh, I mean, this is Yes.
This is about, um, sort ofdemonstrating, actually a live
demonstration of building a rackmodel.
So for those that doesn't knowwhat Rag is retrieval retrieval,
(29:40):
retrieval uh augmented umgeneration.
So this is basically abilitythat stuff as well.
It's basically if you only fitthat you want from an output
combined or confining, then youactually doesn't allow the AI to
(30:03):
So that's kind of a it's beenit's been around for a little
while over the last year or abit.
I think there's definitelyincreasing adoption for the
different organizations, but Ijust really want to practically
telling everyone, and this isnot me demonstrating I have an
organization.
They're very technicallyThey're going to help us to do
that demonstration of aparticular fintech use case,
just showing to people howaccessible that we can actually
(30:24):
all, you know, sort of do it notjust for the really, really
technical guys.
And there's a lot of you cansort of providers out there.
Yeah.
This is further lowering the barorganization to apply those
set it up yourself.
You can use a service now forAnd, you know, for rag for for
folks out there, if you if youjust ask a question of the model
(30:46):
and it doesn't have theinformation in there, it's got a
higher chance of, say,hallucinating answers.
Whereas with rag, it's onlygoing to answer from what's in
those documents.
But there's still nuance, whichthe, the meetup.
So definitely like I know youwill have missed it people on
the show, but, um, you can watchthe recording later on and watch
the recording.
(31:06):
Yeah.
I was going to say, is it inUh, this one's online.
Okay.
So normally we would have therecording after for members
only.
So make sure you sign up to ourYeah.
But in that way you will get theI think that's great.
I think there's a lot offrom that, and it's great to a
people can go through the backwhat it's looking like there.
(31:30):
But, um, do you have any likethis, do you have any kind of
want to leave them with ifin their AI journey?
Like, what do you want to havewhen it comes to AI?
For sure.
I think my biggest advice toeverybody is just to start not
(31:50):
talk about it, not start thinkabout it, start action on it
today.
So why don't they start actingFor me personally, I started
community and it's just beenI learned a lot and everything,
but at the same time I am doingsomething on my own as well on
the side.
Yeah, so I think really get yourYou don't have to be a technical
(32:11):
do this, this stuff and do ameetup or join my community,
feel that you can do, but do notof watch some YouTube videos or
really kind of just get into it.
That's probably the biggest one.
And just just on that note andlike final, final question, but
(32:31):
like, what kind of let's callthem tools.
What are what's inspiring youmoment in terms of tools to help
seeing really cool that you wantYeah, for sure.
So the tool I used mostfrequently, obviously everyone
used ChatGPT.
Yeah.
Um, but I think me as a PM, as ausing a lot is lovable.
(32:53):
I'm lovable.
I use that to for a couple ofUm, one is actually just to kind
of help me to really build outsome initial designs and just
sort of from a conceptual,conceptual stuff.
But more importantly, I amit a bit of functional because
connect with Supabase andI'm actually using that to build
(33:14):
we can actually get live datasort of related stuff that I'm
Um, but yeah, I think there's aI think the most important thing
actually not most important.
Just find the one that actuallyBecause for me, I'm not as
So I haven't been be using, youknow, basil or a few other tools
(33:35):
as much.
So just really come down to whatreally go deep and hard with
make the most out of it.
Yeah.
Brilliant.
Well, look, we'll we'll put allThere's going to be some great,
we'll have from going to yourback on the show.
But, uh, Kurt Young, thank youThanks, Mark.
(33:56):
Really appreciate it.
Cheers.
Thank you for being part ofIf you could really help us out
would be incredible.
It'll help us to get in front ofin the AI space, to share their
back to you in the future.
Thanks again.
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