Invisible Product Podcast Episode 2: PAST - Where Did We Come From?
Summary
TLDRThe Invisible Product Podcast, hosted by Lisa Cardinal, delves into the company's product evolution with insights from Seb and Casper. Starting as a virtual assistant service for busy executives, Invisible transformed into an operations-as-a-service platform. The episode highlights the company's ability to scale, pivot to meet client needs, and innovate through the development of the Digital Assembly Line (D2), Flow, and the new app. The podcast also discusses the significant growth in the AI training vertical, emphasizing the importance of a flexible and scalable product foundation that has adapted to complex client requirements without a complete overhaul.
Takeaways
- 🎙️ The Invisible Product Podcast is hosted by Lisa Cardinal, a product manager on the customer experience team, and is designed for team members to understand the product stack and processes.
- 📚 The podcast delves into the history and evolution of Invisible's product, with Seb and Casper sharing insights on the company's journey from its inception to the present.
- 🤖 Initially, Invisible was framed as a virtual assistant company, targeting busy executives and offering a single touchpoint for various tasks through a virtual assistant service.
- 🔧 The company built its first proprietary product, the Digital Assembly Line (D2), in late 2018 to manage the high volume of requests from clients, which was later migrated to in early 2019.
- 🔄 Clients began to 'hack' Invisible's service by requesting recurring tasks, which led to the evolution of the product into an operations as a service model, with clients willing to pay more for valuable processes.
- 📈 The demand for on-demand delivery services during the COVID-19 pandemic highlighted the limitations of D2 and led to the development of Flow, a prototype application to manage large-scale operations with multiple agents.
- 🚀 Flow was successful in scaling operations for on-demand delivery clients but was not self-served and required significant engineering effort for onboarding new processes.
- 🛠️ The development of the new app, which combined the scalability of D2 and the user experience of Flow, was a response to the need for a more flexible and scalable system for process onboarding.
- 📊 The pivot to AI training as a significant part of Invisible's revenue came after successful collaborations with OpenAI, which recognized the high-quality data and process management Invisible could provide.
- 🔗 Invisible's product has evolved to focus on configurable interfaces and data transfer capabilities to meet the needs of AI training clients, emphasizing the importance of data quality and process flexibility.
- 🏗️ The foundations of Invisible's product have remained robust, allowing for iterative improvements and scalability without the need for a complete rewrite, which is common in technology companies.
Q & A
What is the purpose of the Invisible Product Podcast?
-The Invisible Product Podcast is designed for team members at Invisible, aiming to deep dive into the Invisible product stack and answer questions about the company's products and processes.
Who are the hosts of the second episode of the podcast?
-The second episode of the podcast is hosted by Lisa Cardinal, with Seb and Casper as the main speakers who discuss the history and evolution of Invisible's product.
What was Invisible's initial product offering when Seb joined the company in 2018?
-When Seb joined in 2018, Invisible positioned itself as a virtual assistant company, offering a single touchpoint for busy executives to delegate tasks via email to a virtual assistant.
What is the term 'delegation' in the context of Invisible's early product?
-In the context of Invisible's early product, 'delegation' refers to the tasks or requests sent by clients to their virtual assistant, which Invisible would then parse, work on, and deliver results back to the client.
What was the first proprietary product developed by Invisible and when was it introduced?
-The first proprietary product developed by Invisible was the Digital Assembly Line, known as D2, which was introduced in late 2018 and migrated to in early 2019.
How did Invisible's clients start utilizing the service in a way that was not initially intended?
-Invisible's clients began to 'hack' the service by requesting recurring tasks and scaling them up, effectively using Invisible as an operations team rather than just for one-off tasks.
What significant event in 2020 impacted Invisible's product development?
-The on-demand delivery vertical exploded during the COVID-19 pandemic, which led to a spike in demand for Invisible's services, particularly from companies like DoorDash and GrubHub.
What product did Invisible develop to handle the scale of operations needed during the COVID-19 pandemic?
-Invisible developed a prototype application called Flow to help scale a single process and manage up to a hundred agents on that process.
What was the challenge with the Flow application in terms of onboarding new processes?
-The challenge with the Flow application was that it was not self-served and required several engineers and weeks to onboard a single process, as each Flow app was bespoke and dedicated to a single process.
What is the significance of the transition from Flow to the new app in Invisible's product evolution?
-The transition signifies Invisible's move towards a more scalable and flexible product that can onboard new processes quickly without the need for extensive engineering resources, aligning with the company's direction of operations as a service.
How did Invisible's involvement in AI training impact the company's product direction?
-Invisible's involvement in AI training, particularly with clients like OpenAI, led to a significant increase in revenue and a pivot in the company's focus towards supporting complex, flexible, and repetitive client process needs with a scalable and easy-to-use tool.
What feature of Invisible's product has been particularly well-received by clients and the industry?
-The flexible interface and the ability to quickly configure and update processes have been particularly well-received, with clients noting the product's ease of use and the 'magic' of real-time process updates.
What is Invisible's strategy for maintaining its product's competitive edge as it scales?
-Invisible's strategy includes continuous improvement of its product, focusing on making the tooling as easy to use and scalable as possible, while maintaining a solid and responsive core technology offering.
Outlines
🎙️ Introduction to the Invisible Product Podcast
The Invisible Product Podcast, hosted by Lisa Cardinal, aims to explore the company's product stack and address team members' queries. The podcast invites questions and topics from the audience via a Slack channel. The second episode features Seb and Casper, who discuss the company's product evolution. Seb, having been with the company since 2018, provides historical context, while Casper, a newer addition, contributes fresh insights. The episode delves into the company's transition from a virtual assistant service to a more complex operations platform.
🛠️ Evolution from Virtual Assistant to Operations as a Service
Seb recounts the company's journey from a virtual assistant startup in 2018 to its current form. Initially, the company targeted busy executives with a single touchpoint virtual assistant service. As clients began to use the service for recurring tasks, the company adapted by modifying their data model to accommodate recurring and scaled processes. This shift led to the development of the 'Digital Assembly Line' (D2) in 2018 and its migration to a more scalable product in 2019. The company's pivot to operations as a service was solidified by the demand surge during the COVID-19 pandemic, particularly in the on-demand delivery sector.
🚀 Scaling Challenges and the Development of Flow
The company faced scaling challenges as clients demanded larger volumes of service. This led to the creation of Flow, a prototype application designed to manage a hundred agents on a single process. Flow was instrumental during the pandemic, helping on-demand delivery companies scale their operations. However, Flow's bespoke nature made it challenging to onboard new processes quickly. This realization prompted the development of a new application that combined the user experience of Flow with the scalability of Dal, resulting in a more flexible and efficient system.
🔄 Pivot to AI Training and the Growth of Invisible
Casper discusses the company's pivot to AI training, which began with a project for OpenAI. The company's ability to provide high-quality human data for training AI models led to significant revenue growth. Invisible's product had to adapt to meet the needs of AI companies, focusing on creating flexible interfaces and efficient data transfer capabilities. The foundations laid by previous developments allowed for these adaptations without a complete overhaul of the product.
🌟 The Power of Flexibility and the Future of Invisible
The final paragraph highlights the importance of flexibility in Invisible's product, which has become a significant selling point. The company's ability to quickly adapt and update processes has impressed clients, leading to increased demand. The podcast wraps up by emphasizing the product's scalability and the solid foundation that supports it. The team is excited about the product's evolution and the upcoming discussion on the company's current state and workplace story in the next episode.
Mindmap
Keywords
💡Product Manager
💡Customer Experience
💡Digital Assembly Line
💡Delegation
💡Operations as a Service (OaaS)
💡Product Evolution
💡On-Demand Delivery
💡AI Training
💡Process Builder
💡Scalability
💡Tech Offering
Highlights
Introduction to the Invisible Product Podcast with Lisa Cardinal, discussing the company's product stack and evolution.
Seb and Casper provide a historical overview of Invisible's product development from its inception.
Invisible initially positioned itself as a virtual assistant company targeting busy executives for productivity scaling.
The concept of 'delegation' originated from Invisible's service model, where clients could delegate tasks to a virtual assistant.
Invisible built its first proprietary product, the Digital Assembly Line (D2), to manage high request volumes from clients.
Clients began to 'hack' Invisible's service by requesting recurring tasks, leading to the evolution of operations as a service.
Invisible's product had to adapt to handle the scaling of processes for clients, which was not initially designed for.
The development of 'Flow' was a response to the inability of D2 to scale to meet the demands of the on-demand delivery vertical during the COVID-19 pandemic.
Flow allowed Invisible to manage large agent teams on a single process, but had limitations in process onboarding scalability.
The combination of Flow's scalability and D2's ingestion capabilities led to the creation of a new product, 'AI Mantra', in 2021.
Invisible's entry into the AI training vertical was marked by a significant growth in revenue after partnering with OpenAI.
The need for high-quality data and flexible interfaces led to a pivot in Invisible's product direction to better serve the AI training market.
Invisible's product has evolved without needing a complete rewrite, maintaining a sturdy foundation that supports scaling.
The importance of a flexible tool that can quickly adapt to client needs was highlighted as a key selling point for Invisible.
Invisible's ability to quickly modify processes and interfaces in response to client feedback was a significant advantage.
The podcast will continue in the next episode to discuss Invisible's current state and the workplace story.
Transcripts
welcome to the invisible product podcast
my name is Lisa Cardinal and I'm a
product manager on the customer
experience team here at invisible these
episodes are designed for you invisible
team members we're going to Deep dive
into the invisible product stack and we
seek to answer all your questions about
invisible products and processes if you
have any questions or topics that you
want to see us dive into in the future
reach out on the slack Channel called
Product podcast let's dive
in hello everyone Welcome to our second
episode of the product podcast today we
are going to be talking about the past
of our product uh Seb and Casper are
going to take us through how our product
actually came to be and how it's evolved
over the past few years so I'm going to
actually start by just handing getting
it over to Seb since he's been here and
has all that context I yeah you say Seb
and Casper what it's going to be is
seb's going to talk for a long time
because he's been here for a billion
years then I might pop up the last five
minutes and like hi I'm here too but
it's gonna be mostly sad because he's
got all the context that works Casper's
a new kid on the Block exactly um yeah
I'm I'm like take that and you're like
and sync kind of I love the fact that
you like n syn you're new
band yeah I'm giving my age um cool all
right let's talk about it so um yeah uh
when I joined the
company over five years ago now in
August
2018 and um and the the company and the
and the product as a result have changed
a lot in those last five years and I
kind of want to wanted to give everyone
a little bit of insight as to how that
happened because I think it's quite an
interesting story um when I joined the
company if if you went to the website in
August 2018 where I went um when I was
trying to learn about invisible um you
would see all sorts of stuff about
virtual assistants we framed ourselves
very much as a virtual assistant company
um and you know single touch point and
we were targeting um kind of busy execs
who were trying to scale their own
productivity and didn't want to hire you
know 20 interns um and so we gave them
like a single touch point a virtual
assistant um that they could name uh any
anything they wanted Superman or
Spider-Man or you know James Bond or
whatever it was and they could interact
with that assistant over email and they
could just kind of delegate as much
stuff as they want to that assistant um
and then you know invisible would take
those
delegations
um understand them parse them work on
them and deliver results back to the
back to the client um and and these were
this is where kind the word delegation
came from and it was in in that period
of the company was a kind of discret
packet of work that we would kind ingest
do some work on and hand back um very
much in the same style as as a as a
virtual assistant um but and and that
required us to build a product that was
able to that was optimized for ingesting
a lot of requests all the time because
with that kind of service a busy
executive is just sending 10 or 20
emails a day each with a different
request right and so we had to be able
to ingest those understand them parse
them route them and find an agent who
had the skills and the access uh to be
able to execute that and then deliver it
back right and so um we built the Dow um
in late
2018 uh the digital assembly line which
was actually D two um the first digital
assembly line was basically slack uh and
d 2 was our first kind of proprietary um
product um uh and uh and we migrated to
that product in early
2019 um and as I said yeah that was it
optimized to ingest a lot of requests
and those requests were small requests
could usually be done by an agent or two
agents um you know uh and that was fine
so you really didn't need to like create
much structure around um being able to
scale that because it wasn't really
necessary um what you had to scale was
the ingestion of requests right so
that's how d 2 kind of came about and
then what we saw happen over the next
couple years is that our best fit
clients actually started to kind of hack
our service um you know they would come
to us and they would say um hey that
thing that you that you did for me you
know the other the other week can you do
that can you just do that every day from
now on and we would go okay we'll we'll
take that request that delegation and
we'll recur it and we hacked our data
model a little bit and we'd have this
thing just recur and duplicate itself
every day and and then an agent would
pick that up every day and execute it
right then they would come back to us
and they would say hey that thing you're
doing every day can you do it like 10x
now and we go okay I guess we'll have to
you know either that one agent or we'll
have to staff another agent guess and
and these agents now are no longer
taking different requests because we now
have volume on this it was like the same
two agents were now pretty much staffed
to this um
this delegation which was recurring and
scaling and then they would come back to
us and they'd go okay that thing you're
doing can you do it now 100x and at that
point we were like H okay now this kind
of breaks our this breaks our system
it's not what it was designed to do um
but really what they were what they were
doing is they were they were hacking us
right they they' figured out that um
they could use our service to uh as as
as their operations team right and it
that that's how we kind of morphed into
operations as a service uh they tested
us they saw that we were doing good
quality work and they were no longer
giving us kind of menial tasks they were
actually uh using us as operations as a
service and that's kind of how that came
about and and at the same time they were
also willing to pay us a lot more money
to do so um because those processes were
a lot more valuable to them the ROI was
much higher for them
um uh and so that kind of took took a
different uh that that required a
different approach and so um we started
getting more of these kinds of process
clients we also started targeting that
kind of um following that Trend and that
product Market fit um and uh and kind of
modifying our our go to market around
that as well and kind of going after
these you know business processes um and
then in in 2020 that kind of
really blew up with uh with the with the
on demand delivery um uh vertical which
was which absolutely exploded during the
coid
Pandemic those companies um like door
Dash and GrubHub and many of our current
clients their operations teams in
totally collapsed um with the spike in
demand from coid everyone staying home
everyone needing uh needing delivery and
uh and they found an invisible and
Incredibly powerful partner to help them
scale their operations and with that um
obviously the Dow 2 was not able to
handle that kind of scale wasn't built
for it and uh that was when we developed
flow which uh some of you may have heard
of which was a prototype application to
help us scale a single process and be
able to add a 100 agents and manage a
100 agents on a single process and um
and that kind of uh you know that
carried us through the wave of of of ond
demand delivery um uh kind of clients
that that that hit us in in
2020 but we realized that you know um
this was now the direction of the
company and uh you know while Flo while
Flo was accommodating this um what it
really wasn't doing was uh allowing us
to scale that kind of process onboarding
it would take us two three weeks and
several Engineers to onboard a single
process onflow um because every Flow app
was bespoke and for like dedicated for a
single process maybe a couple um but um
but it wasn't self- sered in any way
there was certainly no um there was no
process Builder as as we know today and
so that kind of led us to to to take
that ux of flow and the um the kind of
scalability of Dal and combine them into
into what we now uh what we now know as
the as as our as the as yeah as uh as
mantor as we called it back then and
that's kind of the logic was well if we
can do this but we can onboard it as
quickly as we could on board uh new
delegations in Dow then we have a real
then we have gold um and that is what we
kind of embarked on in 2021 and and
deployed at the end of that year uh and
that's kind of um taken us uh that took
us into this current wave Uh current
wave of of AI training so I joined in
early 2022 which was sort of the the
crossover point between flow and the new
app so we we still we still had lots of
processes on Flow and we were getting
lots of benefit from it but but the
problems as you said Seb it was flow was
highly functional but uniquely
functional because you were you were
hardcoding the interface you were
hardcoding the business flow behind it
in a single use case and therefore if
they needed tweaking then you had an
engineer involved and when you wanted to
get something up that's like hey this is
quite similar but a little bit different
you have to build an entirely new app
and it's just a very very yeah it's
non-scalable in a different way the Flow
app was scalable within a process but it
was not scalable outside a process if a
client suddenly wanted three things it
wouldn't have worked and that's why yeah
and that's one of one of the reasons
actually like when I was joining it was
quite important to me to understand like
what the future held and I remember
having a conversation with Scott about
this and it was like yeah we we've
learned what we're good at and now were
actually enabling us to be good at that
more quickly with more flexibility and
that was that was an exciting piece of
it when I when I was coming
in yeah I and Casper how soon after you
came in did we break into the AI
training vertical yeah so so like I mean
what I think there's an interesting um
phenomenon I think of uh it's I think it
was Gary Player the quote which is the
more I train the luckier I get the
concept that you make your own luck and
I think the history of invisible both as
a company and within the product is
really it's two lucky events that you
also make the most of the first was the
onand delivery it was there was this
huge Spike and indivisible was able to
service door Dash especially and and
handle that and that was the first big
wave of growth and actually the second
piece of luck really kicked off pretty
much the month after I joined it was May
2022 I think it was via Devon's former
housemate long uang who works for um
open Ai and he was like oh yeah I'm
working for this client like you may
have heard of them they're like f away
on mask it's quite cool this company um
and we've got these data trading needs
and oh you do processes a service could
you do this could you provide human data
and and I think it was like well yeah we
could it's a process right yeah sure we
can do that and and we we we talked to
them and I I can't remember who did the
deal I think it was Jay and Cameron with
a k um and uh we ended up doing a bit of
work for them and I think and if you
look at the revenue it goes like May
2022 $5,000 June 2022
$40,000 July and then August 1.8 million
or something stupid like like basically
they what we produced to them was way
better than the data they were getting
from any other client and they were like
yeah we want all of that like give us
everything you can they were just like
yeah feed me um and that and that was
that was the next big growth like and it
was just open initially um and actually
openai were in a situation where they
had they had their own sort of platform
they call it feather now um
may have been called ter back then and
they had a bunch of different interfaces
but what we were providing for them was
um the ability to train and hire and and
execute high quality across a large
number of people which is a gap
especially when you're doing this
complex work but very quickly we
realized hang on like we've got a good
fit here like we need to make sure we
can support this and that's that that
and that's been something that has
really been the sort of it's as a
company it's been a pivot in terms of
that's now a huge amount of our Revenue
but as a the product we we've pushed it
in certain directions in ways that we
maybe weren't thinking about before we
spent a huge amount of time on
interfaces because although open AI we
actually sort of integrate in a loose
fashion with their app so the agents are
working in our application but it's kind
of not particularly tight for the next
ones the next dominoes AWS and coh here
and AI 21 and Microsoft coming up and
another another few that are coming up
like it's all been our own interface and
so that's basically how can we configure
the process within our application so
that we're capturing them data needs uh
we're capturing the ability to we're
handing the ability to do QA at a high
quality and also push this data out and
it's led to a huge amount of like slight
change in Direction on our product we've
spent a lot of effort on these
interfaces and now we're focusing a lot
on how we transfer the data because
those are the primary needs and that's
like that's been what's Driven this
growth and we've got to support it um
but the other key thing is the
foundations were there the foundations
were there for us to actually set up
these processes like the fact that you
have the ability to create a complex
process with a complex interface via
configuration is hugely important like
if you go into the app you'll see just
how many coher processes there are a a21
processes and AWS processes and because
we need the ability to update them to
tweak them very very quickly and also to
handle access to them in a very
controlled manner like the data quality
is hugely important and so all the
pieces that make up the application
actually ironic
apart from the automation piece which
was historically some a large part of
our value proposition um but when you're
doing human data training automation
isn't really a thing like if if you
could automate it they'd be doing it and
they wouldn't be talking to us um so all
the other pieces have have been a huge
part of that and I I I can confidently
say that we would not be able to do this
if we if we didn't have this flexible
interface and this flexible tooling to
man these processes and actually we're
getting to the stage where it's becoming
a huge part of our selling point like
I've demoed it a couple of times this
week and people are saying this is this
is fantastic like this can we use this
like it's moving from the like this is a
tool to support us to this is a huge
part of our offering like this is a huge
part of our Tech offering is the fact
that we have this flexible tool which is
better than other things in the market
right now like scale I know don't have
the flexibility and that's why people
are coming to us they they they've
decided this is what you need and this
is what we'll give you and we are much
more responsive to the fact that this is
a fast moving area where reserch need
change yeah and I remember in the early
days of of um of Amazon as well well
first of all I remember demoing to
Amazon um and demoing the platform to
them right which was actually a platform
that they weren't going to interact with
but they wanted it to see what was going
on behind the scenes and it was it
became clear to them that it was very
powerful and that and that that could
serve their needs and then during
Amazon's onboarding and scaling I
remember um I believe it was was Andre
um who was making changes pretty much on
the daily in Builder um based on
requests that he that we were receiving
from researchers right we were able to
add a data point I remember Persona um
and other uh kinds of sentiment we were
adding data points then be able to
quickly also um modify the UI in a
really flexible way that that uh that
captured those data points and then we
could immediately uh deliver that that
kind of upgraded process and that I
think felt like magic to them because
were not used to that they were used to
um needing to make a request and then
weeks later some code rolled out and and
then they could capture that new data
point and we could literally do it in
the meeting with them um yeah and that
was I think like pretty powerful um uh
from from the from the client's point of
view my favorite story around that and
if you were there you remember was the
the Amazon orchestrations Project where
they had this complex knee that involved
the ability to to basically via
supervisor fine tuning i. a human
providing the raw data mock up
effectively API calls so like something
that would look like the call you would
make to a third party service and then
the return from that and like Victor and
Sange because they're Wizards knocked it
up in about a week and it was a very
stressful week and I wouldn't want to go
back to it but in a week Amazon spent
three months not delivering it and still
we still don't have it we still don't
have their fabled interface and I think
a having fantastic velers helps hugely
but also having a solid framework within
which you can apply these sort of new
interface objects on a process framework
that is solid is hugely valuable like if
if we were trying to build the whole
thing from scratch maybe we'd be three
months in and not having done it but the
fact that actually it's an interface
component but we have the data models
behind it in in our base structure we
have the way of training together
workflows in the Builder canvas we have
all the other pieces so it's it's it's
just one complex bit rather than a full
organism of complex
bits yeah absolutely and I think um uh
you know Builder has gone through a lot
of evolution and kind of in the hands of
pre as well has really
taken made a step change in terms of its
usability and even though that's
something that clients aren't um using
per se um the experience of Builder and
the look and feel of Builder is are now
kind of garnering compliments from you
know people that have been in in the
workflow management business for for a
while um I think I showcased one of
those a couple days ago right from um
what was quote from yeah I've seen a lot
of these workflow systems and yours is
one of the slickest um and yeah I mean
let's not sit here and just be patting
ourselves on the back there's definitely
things we've got to improve and like
we've got a big road map of stuff that
we know is still are still things that
will make people's lives easier like
versioning cross processes to enable
more easy transfer of stuff and we've
got some core infrastructure things that
are going to really make interfaces a
lot more configurable there's there's a
whole lot of stuff that we can do and
it's not just in the Builder space but I
think yeah if if you if you were try and
um sort of from my perspective and
having listened to se as well and heard
these stories before like the first part
of invisible is the story of like
realizing that we are good at servicing
these like
flexible repetitive client process needs
and like trying to then combine the
tooling that we have for like demanding
work in the dial and having the
interfaces and the complexity of
business process in flow into one thing
and the next stage has been how can we
then make the tooling that supports that
as easy to use and as scalable as
possible as we scale so that we have all
these teams who are trying to service
needs but they're independent and they
are running their own mini businesses
almost but enabled by the core
technology
offering yeah excellent I think we're
going to get into that in in the next
episode right Lisa for sure yeah exactly
um one word I wrote down that I kept
hearing you both mentioned over and over
and over again is scaling and the thing
that excited me when I was joining
invisible and talking to team members
was that the product itself has not had
to be fully Rewritten which is like very
common in technology companies is you
um you start with a product you start
meeting
with clients changing it for their needs
and then you get to a certain point
where you're like okay it doesn't
actually meet anyone's needs anymore we
have to start all over again whereas
what I'm hearing with invisible products
is it's very much we're doing that but
we still got that really sturdy
Foundation where it was really well
thought through to support this scaling
as we keep growing we we've done it once
right the the manal project was a FY
right but I think since then yeah the
the the the foundations that were set up
in that the famous paper demo um with
with seb's hands exactly the magic
fingers um that that still holds true
like the the architecture and the the
core like principles behind it I think
have have stood the test of time and I I
feel very good about them continuing to
stand the test of time as we put more
complex but also more refined pieces on
top of
it excellent all right well why don't we
uh wrap here uh next episode we're going
to be talking about the present where we
are today um talking about the workplace
story so if that's something you've been
hearing you've been hearing that word
and you're ready to learn more about uh
stay tuned for the next episode and
thank you both so much for your time and
we will be talking again soon thanks
Lisa thanks
Lisa thank you for tuning into this
episode because these episodes are
designed for you we encourage you to
reach out if you have questions or
topics that you want to see us Di into
in the future use the slack Channel
called Product
podcast special thanks to musician the
re for the use of this
[Music]
song
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