►
From YouTube: In the Clouds (E19) | Getting started with AIOps
Description
What do you need to do AIOps right? In this episode, Marcel Hild joins host Stu Miniman to dive into some practical realities of AIOps and how it can help our businesses and cloud infrastructure achieve new levels of agility. The term AIOps has been around for a few years now, but the conversation around what it is and how to achieve it isn't entirely clear, so we'll try to demystify the topic, look at some different AI approaches to managing infrastructure and illustrate some of the ways businesses can start taking advantage of this exciting new technology.
Follow us:
Stu Miniman https://twitter.com/stu
Marcel Hild https://twitter.com/durandom, https://github.com/durandom, https://www.linkedin.com/in/marcelhild/
OpenShift https://twitter.com/openshift
Subscribe to Red Hat's YouTube channel: https://www.youtube.com/redhat/?sub_confirmation=1
About OpenShift:
Red Hat OpenShift is an open source container application platform based on the Kubernetes container orchestrator for enterprise application development and deployment.
Openshift.com http://red.ht/3nJ6AKX
OpenShift on github https://github.com/openshift/
OpenShift on redhat.com http://red.ht/34xQoER
#RedHat #Cloud #AIOps
A
A
A
Welcome
to
in
the
clouds,
I
am
your
host
stu
miniman
and
we're
going
to
be
talking
today.
One
of
my
favorite
topics
we're
talking
about
data
and,
of
course,
we
know,
there's
been
huge
explosions
of
data
over
the
last
few
decades.
It's
one
of
those
exponential
things.
You
know
the
only
constants
in
our
industry
are
our
change.
In
that
you
know,
all
of
the
information
will
continue
to
pile
up.
You
know
we.
We
we
create
the
the
library
of
congress.
A
A
A
Right
in
the
clouds-
yes
yeah,
yeah
yeah
in
the
clouds-
and
you
know
we-
we
we
do
these
events
you're
coming
to
us
from
from
your
home
office
in
in
germany.
If
you
could
just
give
us
a
little
bit
about
you
know
your
background.
What
led
you
to
you
know
this
focus
that
you've
had
for
a
number
of
years
on
on
the
apps
trend
that
we're
going
to
talk
about.
B
Sure
so
my
background
is
in
open
source
for
like
25
years,
I'm
doing
open
source
since
since
university
and
I've.
I
think
I
played
most
of
the
roles
in
this
game
from
a
solution
architect
to
a
developer
freelance
developer,
to
somebody
operating
machines,
and
I
came
back
to
red
hat
like
six
years
ago
and
worked
on
a
cloud
management.
B
Software
called
manage
iq
or
cloud
forms
as
the
as
the
product
and
then
this
ai
center
of
excellence
at
red
hat
started,
and
I
was
listening
to
an
internal
talk
by
old
trevor,
the
this
guy
who
worked
on
the
lip
c
for
some
time-
and
I
figured
this-
is
a
super
interesting
topic.
I
want
to
be
part
of
this,
and
so
I
came
to
aiops,
because
cloud
management
and
this
overlap
with
ai
is
ai
operations,
and
that's
basically
my
journey
into
this.
B
A
Well,
yeah
marcel,
of
course
you
know
being
at
red
hat.
You
know
we
all
have
some
angle
on
open
source.
It's
crazy
for
me
to
think
you
know
I
was
a
product
manager
for
linux
over
over
two
decades
ago,
just
to
date,
myself
a
little
bit.
So
I
had
you
know
quite
a
bit
of
history
with
open
source
before
I
joined
red
hat
a
little
over
a
year
ago.
A
I
also
have
a
little
bit
of
history
with
with
octo's,
so
I
was
really
surprised
that
chris
short,
you
know
who
used
to
host
this
had
not
yet
interviewed
chris
wright,
and
if
I,
if
I
have
it
right,
you're
the
first
octo
member
we've
had
on
the
program.
So
every
company
does
things
a
little
bit
at
red
hat.
Of
course,
everyone's
involved
in
open
source.
So
can
you
help
us
understand
a
little
bit?
The
structure
of
you
know
the
octo
inside
red
hat.
B
Sure,
apart
from
a
fancy
name
octo,
which
always
reminds
me
of
an
octopus,
but
it's
it's
also
also
very
describing-
and
it's
I
think,
it's
a
good
picture,
because
octopuses
are
super
super
intelligent
if
they
wouldn't
or
if
they
wouldn't.
If
their
parents
wouldn't
die
when
they
were
born,
I
think
they
would
even
maybe
they
would
exceed
human
intelligence,
so
they
are
really
learning
during
their
short
life
span.
B
So
when
I
joined
it
was
a
smaller
team,
mostly
pictured,
as
the
officer
of
the
cto
stands
at
a
conveyor
belt
and
identifies
all
the
open
source
projects
and
communities
out
there
and
the
good
ones
keep
on
stay
on
the
conveyor
belt
and
then
products
and
technologies
turns
that
into
products.
I
think
that
evolved
over
time
and
now
we're
also
looking
more
also
into
how
our
internal
culture
looks
like
our
relationships
with
with
communities.
I
mean.
That's
that's
been
there
before,
but
we've
been
growing
our
family
of
the
office
of
the
cto.
A
All
right,
let's
turn
and
discuss
a.I
ops.
So
you
know,
hopefully
you
know,
most
of
our
audience
are
familiar
with
many
of
the
you
know
things
like
dev
ops,
the
movements
that
pull
together
you
know
is
some
some
technologies.
You
know
devsecops
says
something
we
as
a
company
overall
are
doing.
Ai
is
a
little
bit
of
a
fuzzy
term,
sometimes
there's
artificial
intelligence.
There's
machine
learning
and
the
like.
A
I
remember,
I
attended
a
conference
back
in
2019,
which
was
all
about
ai
ops,
so
there
had
been
a
real
spike
in
in
this
discussion.
If
you
could
just
level
set
our
audience
for
ai
ops.
What's
what's
the
high
level
message?
Where
has
it
been,
and
where
are
we
today
when
it
comes
to
the
industry
as
a
whole
for
aiops.
B
B
Algorithmic
algorithmic,
I
I'm
blanking
on
the
term,
but
it
was
basically
algorithms,
so
not
really
ai
and
now
then
they
change
it
to
ai,
because
if
you
say
ai,
you
get
more
funding
for
it
rather
than
doing
analytics
and
just
algorithmic
stuff.
But
I
think
it
still
boils
down
to
this
idea
to
use
data
science,
to
use
algorithms
and
machine
learning
data
engineering
applied
to
operations
to
monitor,
to
automate
and
to
maybe
self-heal
your
your
environment.
Basically
everything!
That's
that's
hap,
that's
happening
in
an
operations
environment
or
in
a
devops
environment.
A
Okay,
so
so
marcel
when
we
looked
at
devops,
one
of
my
favorite
lines
is
devops,
isn't
something
you
can
buy,
even
though
there
were
lots
of
companies
that
tried
to
sell
you
on
on
their
service
as
something
devops.
Are
we
talking
the
same
time?
The
type
of
things
with
ai
ops
is
much
of
this
organization
and
cultural
versus
you
know
a
a
product
that
you
buy,
that
will
magically.
You
know,
make
your
data
better.
A
B
It
depends
on
who
you're
talking
to
right.
So
if
somebody's
selling
you
an
aiops
product
which
has
ai
ops
in
their
name,
maybe
they
want
to
sell
you
this
product,
which
enables
a
iops
in
your
team
or
which
brings
ai
ops
to
your
to
your
setup
to
your
company.
I
don't
know,
and
we
see
a
lot
of
rebranding
of
old
tools
with
some
ai
ops
stamps
slapped
on
it
and
basically
it's
the
same
wine
in
new
bottles.
I
don't
know
if
that's
a
common
idiom,
but
just
rebranding
stuff,
the
devops
movements.
B
So
really
using
the
best
of
both
worlds.
To
create
this
new
persona-
and
I
think
now
we
came
to
that
to
that
time
in
in
our
in
our
journey
to
the
clouds
and
to
scale
where
it's
just
too
complicated
for
humans
alone
to
understand
it,
where
our
automations
might
not
suffers
anymore,
where
our
best
practices
may
not
be
enough,
where
we
maybe
meet
some
unknown
unknowns
or
something
is
super
unpredictable.
A
Yeah,
so
you
you
brought
up
the
the
data
scientists,
and
this
is
one
of
the
challenges
we
have.
You
know
most
companies
usually
don't
have
an
abundance
of
data
science
resources
they're
difficult
to
find.
You
know
you
want
to
maintain
them,
but
getting
the
value
out
of
data
at
least
experience.
I
remember
talking
to
data
scientists
five
or
ten
years
ago,
they're
like
we
have
all
of
this
data,
but
we
don't
necessarily
know
the
right
questions
to
ask.
So
there
was
the
gap
between
the
subject
matter,
experts
and
the
data
scientists.
A
It
sounds
like
if
I'm
hearing
right,
maybe
we're
flipping-
is
where
we're
rather
than
having
the
data
scientists
be
the
center
of
all
this
activity.
Are
we
allowing
the
subject
matter
experts
to
be
able
to
leverage
some
of
the
the
things
that
normally
I
would
have
had
to
be
a
data
science
statist
to
do?
Are
we
democratizing
some
of
those
that
tooling
and
skill
set
that
we
had
from
data
science?
B
Absolutely
it's
about
democratizing
us
and
the
com
commoditizing.
It
also
that
we
can
that
I
can
do
stuff
on
my
laptop
these
days,
where,
like
just
some
years
ago,
I
needed
to
have
multiple
gp,
really
expensive,
gpus
and
hardware
to
do
the
same
stuff.
And
now
it's
really
just
I
mean
you
saw.
Maybe
you
saw
these
videos
on
youtube
where
you
just
it
just
takes
like
five
lines
of
python
codes
to
build
a
neural
net
that
can
actually
identify
images
of
cats
right.
B
But
it's
really
just
five
lines
of
python
code
that
I
copy
and
paste
into
my
browser.
Maybe
I
need
to
install
some
stuff,
but
then
I
can
actually
do
this
without
understanding
the
real
math
that
is
being
applied
there.
That
doesn't
mean
that
I
shouldn't
understand
the
intuition
behind
it.
So
I
would
encourage
people
to
watch
some
stuff
on
youtube
to
get
the
intuition
on
what
data
science
is
all
about.
B
Then
he
will
also
start
asking
questions
so
it's
it's.
Then
you
need
to
smarten
up
the
data
scientists
on
your
domain,
so
I
think
it's
way
easier
to
smarten
up
your
folks.
That
already
know
your
domain
really
well,
namely
operations
and
devops,
and
then
invite
the
data
scientists
to
help
them
solve
problems
that
you
post.
So
it's
not
the
other
way
around
yeah.
A
Yeah,
no
marcel,
that's
great,
it
is
one
of
the
you
know
huge
value,
propositions
of
cloud
in
general.
Is
I
no
longer
need
to
be
a
nation
state
or
have
some?
You
know
ridiculous
budget.
You
know
with
that
democratization
that
you
know
the
availability
of
the
tooling
and
a
lower
bar
for
the
skill
set
that
we
need
to
learn.
We
actually
got
got
a
question
in
from
the
audience
andrew
sullivan
who's.
A
B
It's
a
yeah,
it's
a
good
question.
I
think
it's
it's
essentially
it's
the
same,
and
I
think
it
can
be
applied
to
the
same
ideas
so
being
preemptive
or
proactive
means
that
you
anticipate
something
happening
in
the
future,
and
you
can
do
that
with
some
hard-coded
rules
or
maybe
some
some
some
more
advanced
logic,
that's
codified
into
these
operators,
which
then
take
action.
B
So
first,
the
first
step
is:
you
need
to
have
operations
right,
so
you
need
to
get
your
you
get
your
your
house
cleaned
your
your
environment
really
well
in
shape
for
having
all
the
access
having
all
access
to
all
the
data
that
you
need
to
do
proper
root
cause
analysis
to
do
proper
monitoring,
if
that's
all
in
place-
and
you
have
some
automation
attached
to
it
because
you
can
script
stuff
and
then
you
reach
a
barrier
where
you
can
even
take
that
next
step.
Maybe
you
don't
need
to
take
that
next
step.
B
Maybe
all
is
really
super
easy
and
you
don't
need
to
apply
ai
to
it.
But
if
you
fail
with
your
automation
or
you
could
do
better
apply
some
machine
learning
to
it,
apply
some
statistics
to
it.
Maybe
it's
then
learning
the
data
and
the
observations
had
been
in
the
past,
so
you
might
store
your
data
over
the
past
and
then
you
build
up
that
ai
knowledge.
In
your
team
and
then
you
apply
ai
ops
to
it.
A
All
right
marcel,
I
wonder
if
you
could
help
us
connect
the
dots
you
know
we
talked
mostly
about
the
cloud
on
this
show.
So
is
most
of
you
were
talking
about
things
that
you
know.
Oh,
it
doesn't
require
too
much.
You
pull
out
your
laptop,
you
do
it.
When
you
talk
about
data,
though
you
know
a
lot
of
the
data
lakes
now
are
living
in
the
cloud.
A
You
know
moving
data,
you
know,
isn't
necessarily
an
easy
thing:
physics
still
rules.
So
how
much
of
what
you're
talking
about
you
know
is
it?
Is
it
a
public
cloud
thing
how
does
edge
play
into
it?
Any
any
guidance
you
can
give
as
to
where
do
people
start
looking
when
it
comes
to
there's
a
question
up
there,
you
know
reference
architectures,
the
whole
flow
of
activity.
You
know
we
don't
have
time
to.
B
Yes,
very
good
question
so,
first
and
foremost,
I
don't
think
that
you
need
to
start
from
scratch.
It's
pretty
much
like,
or
it
should
be
like
this
image,
detection
or
cat
image
detection
proper.
That
I
said
no,
nobody
will
train
a
a
complete
imagenet
based
on
all
the
images
they
just
use
the
pre-trained
models
that
are
out
there
to
identify
cats-
and
I
think
the
same
is
true
for
identifying
outliers
for
anomaly
detection.
So,
most
of
the
time,
it's
time
serious
data
that
we're
dealing
with.
B
So
you
don't
have
to
reinvent
the
wheel
to
do
some,
something
like
resource
prediction
like
predict:
the
future.
When
will
my
storage
run
out
of
space?
When
will
my
what's
the
seasonality
of
my
of
my
access
locks
right
so
in
winter
I
have
more
more
access,
because
I'm
selling
tires,
and
I
have
that
spike
where
the
climate
changes.
Maybe
I
use
use
that
to
extract
seasonality
out
of
my
time
series,
because
I
have
an
influx
of
requests
to
my
to
my
webshop,
so
I
don't
write
the
software
from
scratch.
B
I
use
something
that's
already
out
there,
which
is
dealing
with
time
series
data,
the
good
and
that's
where
cloud
comes
in.
If
we
are
talking
about
cloud,
I'm
talking
about
cloud
native,
a
cloud
native
approach
that
means
containers
that
means
probably
kubernetes
or
openshift
orchestration.
That
means
cncf
projects
that
that
contribute
to
this.
So
for
time,
series
data,
it's
prometheus,
it's
that's
all
over
the
place.
People
are
doing
these
days,
monitoring
with
prometheus.
B
B
So
you,
you
will
see
the
same
metrics,
not
the
same
metrics,
but
the
same
schema
of
metrics
being
emitted
throughout
the
whole
stack.
So
you
don't
have
to
reinvent
your
tooling
if
you
travel
up
or
down
the
stack,
but
you
can
just
reapply
it
right
and
I
think
that's
that's
that's
the
beauty
of
it.
It's
a
little
bit
like
lego
blocks
and
they
all
mix
and
match,
but
I
think
even
duplo,
lego
and
linux.
A
Marcel,
maybe
let's
let's,
if
we
can,
can
we
make
this
a
little
bit
real,
so
my
understanding
one
of
the
ways
that
we
internally
do.
This
is
red
hat.
We
have
what
we
call
the
remote
health
monitoring,
so
you
know
all
of
the
you
know
all
the
clusters
out
there
from
a
kubernetes
standpoint,
data
comes
back.
A
Of
course,
it's
all
anonymized,
but
being
able
to
understand
trends
and
learn
from
what
the
community
is
doing
is
something
that
you
know
open
source
has
a
long
history
of,
and
is
one
of
those
great
drivers
of
data-driven
insights.
So
maybe
walk
us
through
that
as
an
example
of
how
you
know,
aiops
fits
into
learning
things
and
maybe
changing
what
we
do
with
something
like
the
remote
health
monitoring.
Yes,.
B
I
think
that's
a
there
are
two
questions,
so
one
is
this
remote
health
monitoring,
which
I
think
is
an
excellent
example
where
we
apply
machine
learning
to
something
that
on
the
first
look,
doesn't
look
like
operations,
but
I
think
it's
again
it's
I
think
it's
a
iops
in
its
truest
sense
and
then
the
second
part
is
how
do
we
treat
or
what's
the
open
source
angle
there?
So
remote
health
monitoring
basically
means
all
the
openshift
clusters
out
there
that
have
opted
into
remote
health
monitoring.
B
So
it's
not
mandatory,
like
you
said,
but
you
can
send
a
piece
of
your
operational
metrics
back
to
red
hat.
That's
like
like.
What's
the
overall
error
rate
of
operators?
Failing
and
what's
the
state
of
operators
are
in?
What's
the
memory
consumption
so
really
something
that
doesn't
expose
any
of
your
of
the
workloads
that
you're
running
there,
but
it
exposes
the
like.
B
B
So
they
can't
monitor
all
the
bugs
and
issues.
But
we
can
identify
issues
that
that
affect
a
larger
fleet
or
have
a
higher
severity.
So
it
helps
us
to
basically
guide
our
attention
and
focus
where
we
put
our
efforts.
Next
to
so,
that's
also
a
iops,
although
it's
not
really
us
operating
a
cluster,
but
we
apply
ai
to
the
an
operational
problem.
A
B
So,
that's
that's,
probably
not
really
open
sourcing,
because
we're
not
open
sourcing
that
data,
it's
customer
data-
maybe
it's
like
crowdsourcing,
but
I
think
it's
going
into
that
into
that
important
direction
where,
let's
think
about
it,
this
these
these
image
nets
that
identify
cat
images
or
these
agents
that
we,
the
the
smart
home
assistants
from
amazon,
alexa
or
google
or
ziri
they
work,
because
we
threw
a
lot
of
data
at
those
companies.
So
google
has
all
the
images,
so
they
could
label
all
the
cat
images.
B
How
is
that
with
operational
data?
Show
me
one
customer
that
exposes
his
operational
data,
meaning
those
metrics
that
we
just
talked
about
the
log
files,
the
incidents,
basically
everything
that
you
need
to
operate
your
environment
and
share
that
with
the
world.
Nobody
does
that,
for
obvious
reasons.
B
Where
not
products,
but
projects
like
a
mysql
project
or
a
postgresql
project
on
kubernetes
project
would
operate
parts
of
a
cloud
environment
completely
in
the
open,
creating
true
open
source
data
sets
for
metrics,
for
failures
of
systems
for
the
log
files
and
all
the
incidents
that
have
been
created
and
then
have
been
resolved.
That
creates
a
large
set
of
data.
B
I
think
that's
the
next
step
and
then
ship
that
to
the
larger
industry-
and
I
mean,
if
we're
talking
about
community,
it's
not
like
back
in
the
days
where
we
did
something
as
students
and
we
haven't
seen
a
paycheck
for
it.
No
it's
it's
actually
people
being
paid
for
this
and
they
are
working
in
these
communities.
So
that's
my
call
outs
to
all
the
hyperscalers
and
customers
out
there.
If
we
operate
stuff
in
the
open,
we
can
also
create
data
sets
that
are
truly
open
and
then
make
aiops
a
a
real
thing.
A
Yeah
marcel,
I
think
it's
a
great
step
along
the
the
trajectory
we've
seen
for
a
long
time.
You
know
when
I
think
early
in
my
career,
it
kept
everything
that
they
did
very
close
to
the
vest.
Oh,
this
is
special.
We're
going
to
build
a
temple
to
every
application,
we're
not
going
to
tell
anybody
how
we
do
this
now
most
customers
understand
if
I'm
going
to
take
advantage
of
all
the
innovation
happen
in
the
industry.
I
need
to
be
talking
to
my
peers.
You
know
running
certain
pieces
of
it.
A
Isn't
what
differentiates
me
it's
the
relationships.
I
have
it's
the
people.
I
have
it's
the
the
business
that
I
run
on
top
of
it
that
that
matters
and
it
needs
to
be
close
to
that
and
not
only
sharing,
you
know
best
practices
but
as
as
you're
saying
if
we
can
leverage
community
data
that
would
take
things
even
further,
so
marcel.
You
know
we
mentioned
at
the
beginning
of
the
conversation
that
there
was
a
lot
of
hype
for
a
few
years
about
ai
ops.
A
Is
it
the
the
natural
diffusion
of
technology
and
the
wave
of
adoption
feels
like
we're
a
little
bit
past
the
peak
of
the
hype?
Oftentimes
there
are
stumbling
blocks
or
companies
have
failures
where
they
move
away
from
things.
So
what
what
are
you
and
your
team?
What
do
you
see?
What
are
some
of
those
stumbling
blocks?
What
are
the
some
of
the
common
things
that
people
will
go
down
a
path
and
be
like?
Oh
forget
it?
I
give
up,
you
know
how
do
we
help
move
things
forward
as
an
industry
as
a
whole.
B
B
B
Yes,
on
that,
on
that
smaller
scale,
it's
usually
managing
expectations,
so
we
had.
I
had
so
many
calls
that
I
have
been
pulled
into
where
a
customer
says
I
want
to
do
ai,
ops
and
a
lot
of
the
times.
They
are
not
really
operating
so
really
managing
that
expectation
that
a
iops
doesn't
solve
the
mess
that
you
currently
have.
So
it
doesn't
help
you
get
better
at
operations.
B
B
It's
a
mindset
like
you
can't
buy
devops,
you
can't
buy
ai
ops,
but
you
need
to
enable
your
staff,
your
engineers,
to
step
up
from
being
devops
folks
to
become
ai
devops,
a
iops
folks
so
start
with
a
small
team
and
enable
them
give
them
a
lot
of
freedom
to
to
practice
stuff
and
let
them
work
on
something
that
is
not
really
mission
critical,
but
where
they
can
apply
something
and
then
learn
and
adopt
that
culture
be
more
data.
Nice.
B
Even
I
mean
that
sounds
hypocritic
to
some
extent,
even
we,
but
also
red
hat,
is
not
super
super
data
driven.
So
we
also
sometimes
just
do
gut
decisions
and
I'm
trying
to
apply
also
the
data
driven
philosopher
philosophy,
to
my
team,
to
the
way
that
we
are
doing
things
so
make
really
data-driven
decisions,
and
that
means
you
can
also
apply
this
data
science
tooling,
to
your
team
structure
or
to
basically
to
any
decision
that
you're
doing
so
at
the
end
of
the
day,
a
jupyter
notebook.
B
This
is
the
the
table,
stakes
for
doing
machine
learning
or
data
science
in
in
the
browser
environment.
A
jupyter
notebook
becomes
something
similar
to
our
team,
became
something
similar
and
and
equally
important
to
our
team
as
the
terminal
command
line
and
git,
and
I
think
that's
that's
actually
a
good
example.
So
we
had
a
data
science
team
and
a
dev
team
and
the
data
scientists
they
needed
to
smarten
up
on
using
git
using
github
using
pull
requests
all
the
tooling
that
the
other
folks
were
already.
A
B
Sure
that's
easy
operate,
dash
first,
dot
cloud.
This
is
the
website,
there's
a
mailing
list
and
there's
a
lot
of
github
organizations,
and
that's
in
that's
really
this
it's
one
of
the.
I
think
it's
the
first
completely
truly
open
cloud
environment
where
you
can
peek
into
the
back
of
the
data
center.
B
B
A
Awesome
so
we've
actually
we've
shared
in
the
in
the
chat.
The
website
there
looks
like
there's.
Also
some
community
meetups
happening
and
you
know
lots
more
resources.
You
of
course,
did
a
session
with
chris
wright.
You
know
our
cto
and
technically
speaking,
definitely
one
people
should
check
that
one
out,
I'm
glad
we
get
to
dig
a
little
deeper.
So
marcel
final
thing
for
you,
I
guess
is
you
know
the
last
two
years
most
of
us
we're
not
commuting.
We
usually,
you
know,
find
some
hobbies
or
things
to
do.
A
B
B
Actually,
I
just
took
the
example
from
another
red
hat
engineer,
so
it
was
not
based
on
my
work,
but
I
deployed
that
stuff
in
this
operate
first
cloud,
so
I
managed
to
send
that
data
over
there
and
just
apply
the
tooling
that
we
are
using
in
ai
ops
to
the
sim
racing
stuff,
which
I
find
super
super
funny
and
interesting.
So,
even
in
your
hobbies,
you
can
do
this
cross
domain,
fun,
stuff
and
smarten
yourself
up
on
stuff
that
you
actually
need
for
work
in
your
in
your
spare
time.
B
I
started
out
as
a
backer
for
the
oculus
kit,
so
I'm
really
in
there
for
since
the
first
time-
and
now
I
ordered
this
ver
io
vero
thing-
that's
so
I'm
always
trying
to
get
the
the
best
stuff.
That's
I'm
not
allowed
to
travel
anymore,
so
the
the
family
doesn't
get
the
goats
holidays.
So
I'm
getting
there.
A
Yeah
yeah,
I
I
need
to
wrestle
the
oculus
away
from
my
son
because
definitely
there's
opportunity
to
travel
through
the
vr
headset
and
check
out
some
of
those
things
marcel
really
appreciate.
The
conversation
appreciate
you
know,
sharing
with
the
community
and
and
glad
there's
lots
of
ways
that
people
can
connect
and
follow
up.
So
thank
you
again.
B
A
Thanks
for
having
me
all
right,
we'll
be
back,
you
know
we
run
bi-weekly,
please
reach
out
to
me
on
twitter
at
stu.
You
can
always
hit
us
up
on
the
youtube
channel
lots
of
good
programming
coming
to
you
throughout
the
year
so
appreciate
everyone
for
watching
the
comments
in
here
and
joining
us
for
your
journey
in
the
clouds.