►
Description
Layer 2 Scaling Solutions in Ethereum and Beyond - Danno Ferrin
If you've looked into Ethereum Mainnet scaling then you've seen a whole lot of terms thrown around like Layer 1, Layer 2, Layer 0, sidechain, rollup, zk snarks, state channels, sharding, and so on. This may leave you confused about what each of these approaches do and may also leave unanswered questions about which solutions are available now and which ones are available "soon." In this presentation I will survey the major scaling solutions including the principals behind the solution and specific projects implementing those solutions and their current deployment readiness. Included for each will be a brief discussion about the viability of hosting each solution on a non-Ethereum DLT.
To learn more about Hyperledger, the open source community focused on developing a suite of stable frameworks, tools and libraries for enterprise-grade blockchain deployments. Which serves as a neutral home for various distributed ledger frameworks including Hyperledger Fabric, Sawtooth, Indy, as well as tools like Hyperledger Caliper and libraries like Hyperledger Ursa click here: https://www.hyperledger.org/
Session Details & Slides (if available)- https://sched.co/j3fn
Access All Sessions: https://events.linuxfoundation.org/hyperledger-global-forum/program/schedule/
----
Hyperledger – Open Source Blockchain Technologies
Hyperledger is an open source community focused on developing a suite of stable frameworks, tools and libraries for enterprise-grade blockchain deployments.
It serves as a neutral home for various distributed ledger frameworks including Hyperledger Besu, Fabric, Sawtooth, Indy, as well as tools like Hyperledger Avalon, Cactus and libraries like Hyperledger Aries, Ursa. Learn more about Hyperledger projects: https://www.hyperledger.org/use
Case Studies: https://www.hyperledger.org/learn/case-studies
Training & Certification: https://www.hyperledger.org/learn/training
Tutorials: https://www.hyperledger.org/use/tutorials
Webinars: https://www.hyperledger.org/learn/webinars
Events: https://www.hyperledger.org/events
Vendor Directory: https://www.hyperledger.org/use/vendor-directory
Subscribe to the Hyperledger Newsletter: https://www.hyperledger.org/newsletter
Follow-us on Twitter @Hyperledger
Learn about Hyperledger Membership: https://www.hyperledger.org/about/join
#Hyperledger #HyperledgerGlobalForum2021 #Blockchain
A
A
A
I
used
to
be
on
the
all-core
developers,
which
is
kind
of
the
equivalent
of
the
tsc
for
ethereum
and
right
now
I
am
working
at
reddit
on
the
community
points
team
and
one
of
our
concerns
is,
of
course,
ethereum
scaling.
So
I
have
a
bit
of
insider
knowledge
as
to
what
the
issues
with
a
lot
of
these
are,
which
is
why
I
thought
this
talk
might
be
valuable
to
the
community
as
a
whole
into
hyperledger
community.
A
So
first
I'm
going
to
talk
about
you
know.
Why
does
ethereum
need
a
scaling
solution
and
then
I'm
going
to
give
an
overview
of
the
various
scaling
solution,
approaches
and
some
of
the
trade-offs
and
some
of
the
things
you
might
need
to
do
to
scale
it
beyond
just
what's
carried
as
the
carrier
on
the
chain
and
finally,
I'm
going
to
talk
about
specific
categories
of
these
solutions
because
there's
you
know
it
goes
deeper.
It's
like
you
could
do
these
sort
of
things
and
when
you
combine
them,
here's
what
you
get
out
of
it
and
then.
A
A
So
that's
an
account
that
you
know
you
can
do
a
transaction
with
each
day
there
are
about
a
billion
transactions,
a
billion,
and
one
was
one
estimate
that
I
found
and
if
this
was
evenly
distributed
across
the
entire
24
hours.
Hence
it's
not,
but
if
it
were,
that
would
be
11
500
transactions
per
second
on
an
even
basis.
It
clumps
up
it's
busier
in
the
morning
when
everyone
buys
coffee
in
the
evening
when
people
buy
dinner-
and
it's
not
so
busy
at
two
in
the
morning
when
people
buy
their
steam
video
games
off
of
steam.
A
Of
course,
you
know
like
trades
and
finance
and
high
frequency
trading
it
can
get
even
higher
than
that.
So
what
is
ethereum
looking
at?
How
do
they
compare
to
these
scales
today?
Ethereum
hazard,
at
least
last
week,
when
I
put
the
numbers
on
these
slides,
155
million
998
485
unique,
addresses
they've,
probably
gone
up
to
156
million
by
now,
it's
not
too
hard
and
that's
addresses
in
use
on
the
network.
A
That
is
about
an
order
of
a
thousand
less
than
what's
going
on
with
mastercard,
or
you
know
more
than
a
thousand,
not
a
thousand.
That's
only
it's
it's
about
five
x.
The
second
thing
is
the
daily
transactions.
This
is
the
thing
that's
at
least
a
thousand
less
than
mastercard,
and
they
would
do
a
million
247
000
daily
transactions
and
that's
14.43
sustained
transactions
per
second.
Now
surprisingly,
it
does
hit
about
that.
A
It
doesn't
go
too
far
from
that
and
that's
because
the
blocks
on
ethereum
are
basically
full
all
the
space
that
you
can't
put
a
transaction
on
there.
There's
enough
market
demand
that
they
are
filling
that
up
and
that
contrasts
with
the
54.9
tps
theoretical
maximum
of
the
current
15
million
gas
per
block
divided
by
a
block
every
13
seconds
and
here's
the
real
kicker.
Is
it
21,
000
gas
per
transaction?
A
So
what
happens
and
what
people
have
seen
is
that
when
there's
more
demand
than
space,
the
cost
of
gas
goes
up.
So
that's
why
people
complain
about
the
price
of
gas,
because
there's
people
engaged
in
decentralized
finance
and
see
that
there's
money
to
be
made
and
they're
willing
to
pay
a
higher
premium
than
someone
who
just
wants
to
trade,
their
crypto
kitty
and
give
it
to
their
nephew.
So
the
prices
go
up
and
only
the
people
who
are
willing
to
pay
the
price
go
in
there.
A
If
you
do
it
just
at
layer
one
because
the
fundamental
scaling
issue
with
ethereum
is
that
at
a
certain
key
critical
aspect,
ethereum
is
single
threaded.
All
the
transactions
that
you
read
into
to
create
the
state
route
are
executed
in
order.
Each
previous
transaction
can
impact
each
subsequent
transaction.
A
You
can
do
some
parallelization
parallelizations
if
the
transactions
don't
interact
with
each
other,
but
in
a
lot
of
these
decentralized
finance
scenarios,
the
ordering
is
very
important
because
that's
how
people
make
their
money,
so
ethereum
is
single
threaded
when
it
comes
to
evaluating
the
transaction,
so
that
puts
an
intrinsic
limit
on
it.
So
so
the
solution
for
layer
two
is
to
find
ways
around
the
single
thread
and
that's
really
what
makes
layer
two
a
separate
thing
as
layer.
A
So
when
we
do
the
sort
of
a
solution,
there's
some
concerns
when
we
move
stuff
for
for
layer.
Two.
The
first
question
is
data
availability.
If
you're
going
to
take
all
these
transactions
you're
going
to
combine
them
together,
where
do
these
transactions
live?
And
this
is
a
fundamental
question
you
ask
for
all
of
your
layer,
2
and
all
of
your
off-chain
scaling
solutions
when
your
chain
is
full,
how
do
you
scale
off
chain
and
get
the
data?
A
So
one
thing
you
can
do,
is
you
can
store
your
data
on
the
chain
and
that's
what
things
like
call,
data
and
ethereum
events?
You
can
get
the
equivalence
of
your
calls
of
what
would
be
executed
in
your
layer,
2
without
actually
executing
them.
You
can
say
I
do
this
really
complex,
d5
transaction
with
these
parameters
and
you
don't
actually
execute
it,
so
you
can
fit
it
in
that
gas
limit
on
the
block.
The
second
option
is,
of
course,
to
do
it
off-chain,
rather
than
putting
the
transaction
on
the
block.
A
A
A
lot
of
the
early
private
transaction
solutions
on
things
like
quorum
were
kind
of
a
proto
layer,
two
solution
off
chain
using
this
off
chain
as
a
feature.
So
I
won't
go
into
that.
That's
in
this
talk,
but
that's
you
know.
Some
of
these
ideas
are
have
been
around
for
quite
a
while
and
they're
just
formalizing
into
this
new
idea
of
what
layer
two
is.
A
The
second
question
is:
what
sort
of
things
are
you
putting
on
layer
two?
What
does
the
data
mean
and
there's
a
continuum
of
about
four
steps
of
what
these
things
on
the
on
layer
two
mean.
The
first,
of
course,
is
just
data,
there's
no
intrinsic
chain,
meaning
it's
just
whatever
you
want
to
put
on
there.
It
has
no
meaning
to
ethereum,
it
might
have
meaning
to
your
layer
too,
but
there's
no
meaning
to
the
ethereum.
A
The
second
layer
is
it's
the
native
transactions,
the
same
transaction
you
might
put
on
layer
one.
It
means
the
same
thing
to
ethereum.
It's
the
same,
smart
contract.
It's
the
same,
peer-to-peer,
networking
solutions
they
just
might
have
different
chain
ids
in
different
sub-networks.
They
transmit
upon
the
the
next
layer
is,
of
course,
transaction
claims
and
fraud
proofs,
and
this
is
where
you
get
the
roll-ups,
where
what
you're
putting
on
layer
one
is
the
net
effect
of
a
of
a
clustering
of
several
transactions.
A
Where
you
might
have
you
know
five
transactions,
and
that
effect
is
that
one
column
goes
up,
the
other
column
goes
down,
and
so
what
they
put
on
there
is
the
transaction
they
claim.
This
is
what
it
means
and
if
you
don't
believe
me
post
a
fraud
proof
to
show
me
that
we've
done
it
wrong,
and
so
that's
the
essence
of
a
lot
of
these
optimistic
rollovers
as
they
optimistically
post
the
results
and
then
they
say,
prove
me
wrong
and
they
put
money
on
it.
So
there's
an
incentive
for
you
to
prove
them
wrong.
A
I
put
one
east
to
say
this
transaction
is
correct,
come
and
get
the
ether
and
prove
me
wrong
and
of
course
the
fourth
solution
is
a
bit
different
from
that.
These
are
the
zero
knowledge
proofs.
So
there
is
no
fraud
proof
and
there
is
no
full
knowledge
of
what's
going
on
there,
but
there
is
an
argument
using
zero
knowledge,
encryption
technologies
to
say.
Well,
I
did
a
transaction,
all
the
rules
were
followed
and
here's
a
proof
to
say
that
all
the
rules
were
followed.
A
A
A
Two
solutions,
this
kind
of
goes
into
the
the
complexity
continuum
of
what
the
data
is,
because
if
you're
just
drawing
data,
you
might
not
be
able
to
put
ethereum
there,
but
if
you're
using
zero
knowledge
proofs,
then
you
have
some
very
structured,
meaning
that
I
can
only
take
ethereum
and
these
five
smart
tokens
in
here,
and
I
can
prove
that
it's
correct.
But
then
you
have
the
question
of
how
do
you
bridge
it
between
layer,
two
and
layer,
one
and
layer,
one
and
layer,
two
getting
into
layer?
Two
is
easy:
there's
a
smart
contract!
A
A
So,
in
a
lot
of
these
cases,
it
takes
about
two
weeks
to
do
this.
It's
like
the
agreed
standard,
for
you
know
the
censorship
resistance.
Some
people
have
found
this
as
a
market
opportunity.
I'll
give
you
a
fast
exit,
I'll
engage
that
counterparty
risk.
All
I
want
is
100
basis
points,
so
there
are
solutions
that
will
do
these
fast
exits
and
it's
kind
of
built
on
the
financial,
complex
financial
systems.
A
For
some
of
these
items,
if
you're
willing
to
wait,
you
get
it
all
if
you're
not
willing
to
wait,
pay
a
fee
and
someone's
willing
to
take
the
risk
for
you,
you
might
not
like
the
fee,
but
that's
the
way.
The
markets
work
and,
of
course,
the
one
that
I've
alluded
to
in
some
of
the
other
slides
is
censorship.
Resistance,
controllability.
A
Does
the
layers
2
solution?
Is
it
open
to
all
providers?
Can
you
make
sure
your
transaction
gets
on
there?
Can
you
make
sure
that
people
aren't
manipulating
it
without
being
seen
on
chain?
It's
simple
because
you
can
put
any
transaction
on
chain.
You
can
interact
with
contract,
it's
fully
there
and
in
the
public,
and
you
can
prove
that
the
data
either
was
there
or
wasn't
there.
A
A
It's
a
second
so
on
off
chain.
You
know,
there's
there's
ways
to
prove
that
you
are
actually
your
data
is
being
included
and
it
wasn't
there,
but
those
are
involved,
more
complex
systems
that
affect
the
impact
and
the
complexity
of
the
layer,
2
solution
and
there's
some
advantages
that
only
some
people
may
see
it.
That's
a
privacy
and
security
concern
that
some
solutions
find
advantageous.
A
So
in
this
it
forms
a
complexity.
Continuum
data-
it
is
what
it
is
transaction
is
the
same
as
what's
on
layer,
one
fraud,
proof
this
is
actually
a
roll
up
and
it's
actually
executed
somewhere
else
and
relies
on
external
watchers
to
catch
the
errors
and,
of
course,
zero
knowledge.
Big
fancy
math
easy
to
verify,
but
a
few
people
can
tell
you
why
it
works.
So
there's
a
lot
of
mathematical
faith
put
in
it.
A
Luckily,
you
know,
faith
in
mathematics
is
actually
kind
of
easy
to
prove
if
you
can
get
the
right
person,
so
you
know
I.
I
can't
explain
how
some
of
these
work,
but
I
believe
that
they
do
so
from
this.
We
get
what
I
call
the
periodic
table
of
scaling.
You
take
on
chain
and
off
chain
on
the
vertical
axis
and
you
get
the
four
scale
con.
A
The
four
entries
on
the
constant
contin
continuum
of
complexity
along
the
top,
and
this
gets
you.
What
I
think
is
a
more
complete
solution
of
what
the
types
of
solutions
are.
The
four
boxes
on
the
right,
the
fraud,
proof,
zero
knowledge
on
chain
and
opt
chain
when
most
people
talk
about
layer,
two
on
ethereum
they're
talking
about
one
of
these
four
solutions.
A
Now,
when
it
comes
to
off
chain
data
for
data
and
transactions,
some
people
say
it's
layer.
Two
some
people
say
it's
not
so
I
call
that
category
quasi
layer
two
and
if
you're
doing
non-chain
transactions
you're
just
doing
layer
one
and
if
you're
just
putting
data
on
chain.
Please
don't
do
this,
there's
more
efficient
ways
to
do
it.
A
It's
happened
before
so
I
have
a
slide.
That'll
show
you
some
of
the
details
on
this
later
on
so
again,
each
one
of
these
have
put
a
name.
Some
of
these,
the
ones
on
the
right
are
the
industry
norm
for
if
ethereum
optimistic
roll-ups
are
on
chain
fraud,
proofs
plasma
is
off
chain
fraud,
proofs,
zk,
roll-ups
are
on
chain,
zero,
knowledge
and
volidium
is
off
chain
zero
knowledge
side
chains
are
the
typical
off-chain
transaction
solution
and
hashed
commitments
and
graffiti
are
the
typical
data
solution
where
they
have
no
meaning
you're.
Just
committing
a
document.
A
You're
hashing
it
or
you're.
Putting
the
document
literally
on
so
the
on
chain
and
off
chain
data
solutions
are
pretty
simple
on
chain
is
just
graffiti
a
famous
example.
If
you
go
to
bitcoin
block
268060,
it
contains
an
entire
rickroll
and
op
return
before
they
lock
down
the
size
commitments
off-chain,
you
might
hash
a
document
or
a
merklized
data
tree
or
use
fancier
structures
to
put
it
on
on
chain
to
put
the
data
off
chain
and
put
the
hash
on
chain.
So
you
can
later
come
back
and
say:
hey.
A
I
invented
this
20
years
ago.
Here's
a
hash
of
the
invention
document.
So
that's
the
sort
of
things
you
would
use
off
chain
data
for
some
of
the
simpler
solutions:
transactions
on
chains,
just
layer,
one
data
and
off
chains.
You
would
use
something
called
side
chains.
Basically
any
or
peer
change
is
another
example.
I
might
give
to
a
name,
it's
basically
any
other
blockchain,
where
there's
some
sort
of
mechanism
where
you
can
bring
the
the
the
tokens
and
the
data
back
and
forth
between
the
two
chains.
A
Atomic
swaps,
custodies,
oracles
and
lookup
and
lockups
are
typical
ways
that
you
might
do
this
quasi
layer.
2
is
kind
of
a
layer,
2
system,
so
you
know
what's
the
difference
between
a
quasi
layer,
2
and
a
real
layer
2..
In
my
opinion,
the
real
layer
2
system
depends
on
the
transactions
of
the
layer,
1
block
chain
and
the
layer
2
systems
just
roll
them
up
and
make
them
behave
like
the
other
systems,
quasi
layer,
two
systems
may
or
may
not
follow
the
transaction
semantics
of
the
layer,
one
that
they're
being
put
on.
A
They
may
respect
the
conventions
they
may
not
and
in
some
cases
you
could
consider
these
blockchain
integration
systems
like
cactus
and-
and
you
know
it's
probably
a
good
example
and
weaver
is
another
example.
These
might
fit
into
the
quasi
layer
too,
where
you
can
integrate
data
between
them,
bring
them
back
and
forth.
They're,
not
strictly
a
layer
too,
because
in
some
cases,
you're
bridging
peer
systems,
so
one
can't
be
said
to
be
a
layer
of
another.
Sometimes
one
might
be
entirely
a
layer
of
another.
A
So
I
think
this
quasi
layer,
two
space,
is
an
interesting
space
to
look
into
that.
I
don't
have
time
to
dive
into
in
this
talk,
so
fraud
proof
systems
are
the
the
next
layer
of
complexity,
and
they
only
like,
I
said,
have
recorded
the
net
effect
of
the
change,
the
results
of
whoever
posts
it
is
trusted
until
it's
proven
false,
and
these
systems
have
ways
where
they
can
put
in
where
they
can
say.
Here's
the
proof,
here's
the
math,
here's
the
transactions,
here's
the
rules,
you
would
run
it
by
some
systems.
A
You
need
to
find
the
block
and
run
the
entire
block.
There's
one
time
where
you
run
the
entire
thing
and
show
that
the
output's
different
on
the
layer,
one
chain
and
the
other
ones
have
more
interactive
systems
where
you
kind
of
do
a
binary
proof
where
you
find
the
one
transaction
where
things
were
different
and
we
have
to
do
it
interactive.
A
So
what
happens
is
whoever
posts
the
result
will
put
a
stake
on
there
and
say:
hey.
I
put
one
in
theory
to
say
that
this
works
and
people
who
post
a
false
one
and
are
proven
wrong
or
who
their
software
is.
Buggy
will
have
their
stake,
taken
from
them
and
typically
whoever's
proving
it
will
get
the
lion's
share
of
that
stake
because
they
proved
it
wrong
as
kind
of
a
bounty.
For
you
know,
I
dare
you
prove
me
wrong.
Okay,
you
were
right
here.
Have
you
have
your
share
of
the
money?
A
So
that's
how
these
fraud
proof
systems
work
as
they
incentivize
people
when
you
post
it
on
chain
to
prove
it
wrong
and
when
they
do
prove
it
wrong.
You
get
the
money
kind
of
the
financial
incentive
to
make
it
work
in
a
permissionless
system,
so
on
chain.
You
would
store
the
inputs
and
results
on
the
layer,
one
there's
no
computation
on
the
layer,
one.
It's
just
execute
the
fraud
proofs
themselves
are
posted
on
the
layer,
one
off
chain.
It
gets
more
difficult
because
not
even
the
inputs
and
results
are
always
stored
on
layer.
One.
A
When
you
do
your
fraud
proof
you've
got
to
bring
those
with
you
and
you've
got
to
have
mechanisms
to
prove
that
those
were
done.
One
very
simple
thing
is
a
counterfactual.
You
just
have
you
know
one
thing
that
says
you
know
what
I
give
up
you're
lying
and
you
know
you
push
the
everyone
loses
button.
So
that's
one
way
to
do
an
off-chain
solution,
but
there's
better
solutions
where
you
can
prove
that
they
did
this.
They
did
that
and
they
lied
about
their
battleship
and
put
it
here.
So
the
results
are
posted
on
chain.
A
Zero
knowledge
systems
are,
of
course,
the
very
end
of
the
continuum.
These
depend
upon
a
lot
of
advanced
polynomial
math
that
I
don't
understand.
I
didn't
take
nearly
enough
math
in
college
to
understand
this.
The
big
one
that's
out
there
is
called
the
zk
snark.
There's
zero
knowledge,
succinct,
non-interactive
argument
of
knowledge,
a
couple
key
things:
each
one
of
those
words
is
improvement
is
important,
zero
knowledge.
There's
you
don't
need
to
know
anything
about
the
transaction
to
prove
that
the
rules
of
the
system
are
followed
sustained.
A
The
next
step
is
also
important,
not
interactive,
because
some
cases
to
prove
it's
interactive,
like
you
know,
if
you're
thinking
of
odd
or
even
numbers,
you
might
give
them
a
number
say:
five,
okay,
you
might
check
and
see
if
it's
odd
or
even
at
that
point,
but
the
non-interactive
they
need
to
set
up
a
system
so
that
you
don't
have
to
do
these
challenges,
so
the
challenges
are
kind
of
built
in
and
the
last
part
is
argument
of
knowledge,
because
this
is
cryptography
at
the
end.
It's
probabilistic.
A
These
probabilities
are
cranked
up
to
the
extreme,
but
again,
at
the
end
of
the
day,
there
is
an
outside,
and
I
do
mean
outside
chance
that
you
can
find
two
solutions
for
the
same
key,
highly
unlikely.
It
would
take
the
burning
out
the
entire
joule
strength
of
the
sun
to
calculate
those.
So
it's
a
very
strong
argument,
but
at
the
end
it's
an
argument
that
you
know
that
someone
did
this
math
and
they
follow
the
rules,
so
these
transactions-
nobody
can
really
see
what's
going
on
in
these
transactions.
A
All
you
know
is
that
the
three
column
argument
you
know
like,
for
example,
if
you
do
three
column
math,
that
is
it's
proven
well
in
the
that
it
follows
the
rules
of
like
you,
know
three
column
or
two
column
counting,
but
whatever
the
rules
for
the
transaction,
the
the
circuits,
the
integer
circuits
are
set
up.
So
that
proves
it
now.
The
zk
snarks
don't
have
fraud
proofs
and
they
call
these
validity
proofs,
and
these
are
what
you
post
to
the
chain
and
once
it's
there,
the
math
either
works
out
or
it
doesn't.
A
A
So,
as
peter's
question
we're
going
to
list
some
sample
projects.
These
are
the
ones
that
I
have
here
today.
That
I
have
slides
on
baseline
protocol
is
an
example
of
off
chain
data,
where
you
can
just
put
your
data
on
there.
A
good
example
of
a
side
chain
is
x,
dye
for
optimistic
roll
ups.
Arbor
trim
is
one
and
for
zk
roll
of
validium
loop
ring
off
the
top
of
my
head.
Some
of
the
other
examples
of
side
chains
would
be
polygon,
has
a
side
chain
solution.
A
Anyone
that
bridges
off
to
you
know
the
the
other
x
die.
The
x
chai
the
side
chain.
Solutions
tend
to
be
specialized,
so
there's
that
the
optimistic
roll-ups
there's
another
company
called
optimism
running
something
called
the
ovm,
and
that
is
something
that
should
be
shipping
here
in
july.
Arbitrary
has
actually
shipped
their
mainnet,
so
these
optimistic
roll-ups
are
actually
rolling
out
now
arbitrary
and
off
and
optimism
are
the
two
best
examples.
Polygon
is
said
to
be
working
on.
One
hasn't
shipped
it
yet
polygon
is
trying
to
do
everything.
A
Loop
ring
is
a
zk
roll
up.
Validium
aztec
is
another
zk
zk
sync,
I
think,
is
another
one.
So
those
are
some
of
the
names
of
some
of
the
projects
and
I
should
update
the
slides.
Actually,
there
is
I'll
update
the
slides
on
there
and
I
will
post.
I
will
post
these
on
the
well
I'll
update
my
slides
so
question
from
tom
klein.
A
Can
you
give
some
examples
of
where
zeke
snarks
is
appropriate
and
where
not
so
the
most
simplest
place,
where
zk
snarks
are
most
appropriate
or
where
you're
simply
trading
ownership
of
tokens
and
you're
not
doing
anything
fancy
with
it?
If
you
just
have
a
simple
erc20
contract-
and
all
it
is,
is
it's
who
owns
the
token
and
who
doesn't
you
can
send
tokens
you
have?
A
You
can
not
send
tokens
you
don't
have,
and
that
is
a
great
example
of
where
a
zero
knowledge
type
system
works,
where
it's
a
very
small
self-contained,
well-defined
loop,
bring
it
implements
such
a
system
like
that,
and
they
support
up
to
there's.
I
think
it's
a
thousand
and
they're
loopering
v32
there's
a
limit
to
the
number
of
erc20
contracts
that
they
can
support
because
of
the
math
say,
support,
ethereum
and
basically,
all
the
major
value
bearing
tokens
that
you
would
have
on
there.
A
So
a
situation
where
it's
very
simple,
very
repetitive,
stock
ownership
would
be
another
great
example.
I
mean
that's
essentially
what
tokens
are,
if
you're,
just
sending
stock
back
and
forth
to
people
and
doing
something
very
simple:
that's
where
it's
appropriate,
where
zk,
zero
knowledge
proofs
are
not
appropriate,
are
where
you're
doing
rich
smart
contract
type
interactions,
for
example,
if
you're
doing
stuff,
like
you
know,
interest
bearing
things
like
ave
does,
if
you're
doing
more
complex
logic
in
there.
A
If
you
have
like
the
crypto
kitties,
they
have
some
special
on-chain
logic
that
goes
on
when
you
breed
the
kitties,
that's
kind
of
unique,
the
more
specialized
and
the
more
distinct.
What
you're
doing
on
the
blockchain
on
on
your
smart
contracts,
the
less
and
less
appropriate
zk
roll-ups
are
now.
You
can
always
write
your
own
zk
circuit,
as
you
can
integer
circuit.
That
is
always
an
option,
but
the
more
and
more
different
you
get
the
more
and
more
harder
it's
going
to
get
to
write.
Those
and
you'll
need
to
get.
A
You
know,
there's
less
and
less
people
who
have
the
knowledge
to
do
that.
So
I
think
that's
an
excellent
question.
When
would
you
do
zk
over
smart
over
an
optimistic
roll
up
and
if
you're
doing
fancy
stuff
with
your
evm
contracts?
The
optimistic
role
is
almost
always
the
better
solution.
If
you're
doing
very
simple
standard
industry
standard
token
trading,
zk
roll
ups
are
an
excellent
solution
and
you
get
a
lot
of
privacy
out
of
them.
A
So
I've
got
about
four
minutes.
So
that's
going
to
be
one
minute
per
each,
so
baseline
protocol
is
a
way
that
you
can
baseline.
Your
records
into
layer
one
they
got
some
fancier
way;
they
don't
just
store
data.
They
got
some
fancy
way.
Where
you
can
close
your
baselines,
you
can
tokenize
what
you've
done
with
what
you
put
into
those
protocols.
So
the
theory,
the
the
story-
that's
always
told,
is
you
can
take
your
you
can
take
your
bill
of
sale
and
you
can
baseline
it
or
your
your
purchase
order.
A
You
can
take
your
purchase
order.
You
can
baseline,
it
make
a
token
out
of
that
and
trade
that
token
on
an
insurance
basis,
and
that
data
is
committed
on
the
blockchain
through
the
various
servers.
You
can
get
your
data
and
you
can
prove
that
it's
been
on
there
that
it's
been
proven
and
you
can.
You
can
tokenize
it,
which
I
think
is
something
that
people
are
really
excited
about.
A
The
next
one
is
x-die.
I
think
this
has
to
be
the
oldest
side
chain
that
is
still
running
on
ethereum,
that's
functional
and
provides
value.
I
actually
used
this
about
two
or
three
years
ago
at
east
denver.
They
started
paying
the
food
trucks
with
xdy,
so
you'd
actually
use
the
blockchain
and
you
would
be
able
to
buy
all
your
lunch
with
xdy
xdich.com
is
the
solution.
Is
the
location
for
that
and
it
does.
You
know
bridging
protocols
with
proof
of
stake
and
all
that
other
stuff
arbitrum
just
launched.
A
I
don't
have
nearly
enough
time
to
talk
about
it,
but
it
has
interactive
fraud
proofs
and
you
can
put
any
evm
smart
contract
on
there
that
you
want
it
compiles
it
to
its
custom
solution.
Offchainlabs.Com.
It's
probably
one
of
the
hotter
because
they
just
launched
their
their
their
beta
net
and
they're
gonna
be
opening
up.
It's
gonna,
be
one
of
the
ones.
You're
gonna
see
a
lot
more
press
about.
Optimism
is
the
other
one
they
we're
gonna
launch
in
march.
A
They
had
us
late
to
july,
so
you'll
probably
see
another
press
cycle
about
optimism
coming
up
here
in
july
as
well
and
loop
ring,
I
think,
is
one
of
the
ones
that
is
actually
running
on
ethereum
right
now
is
a
zk
as
a
z
as
a
zero
knowledge
circuit.
It's
been
live
since
about
2020.
Each
block
root
is
committed
to
layer
one,
but
you
do
all
the
0k
circuit
stuff,
mostly
offline.
I
think
it's
more
of
a
lydium
than
a
zero
knowledge.
Roll
up,
michael
bregoni
asks.
A
A
So
one
of
the
values
of
doing
l2
on
ethereum
is
that's
where
economic
value
is
solana,
for
example,
doesn't
have
the
same
community
around
it,
yet
they
may
get
it
they
may
not.
But
today
it
doesn't,
but
solana's
also
doesn't
have
multi-party
backup
failures.
They
had
one
node
can
bring
the
whole
network
down
and
they
had
to
do
some
rollbacks.
A
So
there's
one
risk
there:
it's
not
really
bft
tolerant,
but
as
far
as
scalability
it's
it's
you're
still
running
on
the
same
issues
in
that
when
you
do
a
layer,
two
you're
spreading
across
multiple
computers
and
you're,
creating
a
solution
that
you
don't
have
to
get
everyone
through
the
door.
At
one
point,
you
can
do
lots
of
transactions
per
second,
but
if
it
has
to
go
through
one
cpu
to
prove
it,
that's
going
to
be
the
problem.
A
Only
the
people
participating
in
there
need
to
worry
about
it.
An
anonymous
question
is
what
is
a
side
chain.
So
a
side
chain
is
basically
a
side
chain,
isn't
just
another
block
chain
network
and
when
they
call
it
a
side
chain.
What
they're
doing
is
they're
making
statements
that
this
is
subservient
to
this
other
blockchain.
A
Otherwise
you
could
have
near
protocol
is,
is
very
popular
in
doing
some
of
the
stuff
where
they
treated
not
as
a
side
chain
but
as
a
peer
chain
near
protocol
has,
you
know,
stands
on
its
own
has
its
own
economic
value,
but
they
have
logic
where
they
bridge
things
back
and
forth.
You
can
take
your
nfts,
you
can
put
it
on
near
and
you
can
trade
your
nfts
on
near
by
by
pinning
them
at
a
particular
address
on
each
one,
and
you
can
move
them
back
and
forth,
but
near
has
its
own
economic
value.
A
So
near
is
not
a
side
chain
of
ethereum
in
the
same
way
that
xdi
is
a
side
chain
of
ethereum,
but
all
three
of
these
these
chains
are
independent
block
chains
that
run
their
own
consensus.
They
have
their
own
nodes
that
if
you
want
to
participate
as
a
node
in
there,
you
have
to
process
all
the
data
in
there.
So
I
think
that's
probably
the
best
description
of
the
slide
chain
is
just
another
block
chain
that
is
dependent
upon
one
of
the
other
block
chains
to
get
its
meaning.
A
So
again,
these
are
my
last
two
slides
it's
coming
in
I'm
two
minutes
over,
so
I'm
going
to
keep
talking
until
the
room.
Proctor
tells
me
that
I
need
to
that's
probably
what
they've
done
in
the
chat
room.
Tell
me
that
I
need
to
be
quiet,
so
yep,
and
so
it's
I'm
over
time,
but
these
are
some
of
the
baseline
examples
and
I
thank
everyone
for
coming
to
my
talk.
You
can
get
me
online.
I
am,
I
think
I
have
my.