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Welcome to Epicentre, the show which talks about the
technologies, projects, and people driving decentralization
and the blockchain revolution. I'm Felix and I'm here with me
here today We're speaking with Iliad, who is the Co founder of
Nir and CEO of the Nir Foundation.
Nir is a sharded layer one blockchain.
So welcome, welcome Iliad, welcome back on Epicenter.
It's great to have you for a second time.
Yeah. Thanks for having me and
Congrats on, yeah, 10 years, epic, epic achievement in this
space. Yeah, thanks so much.
Yeah, like you said, it's it's basically 70 years in crypto.
So we've we've all aged a bit yesterday in the episode we
recorded yesterday, the 10 years episode, they had like the
slideshow and you could see the progression of Maher, Brian and
Sebastian like from their youth to their 40s or late 30s.
So yeah, that's great, cool. Yeah, we actually wanted to
start unconventionally with your background, but in in your case,
it's a very interesting background in AI and machine
learning. So we wanted to 1st sort of talk
about your work there. You're one of the authors of the
original Transformers paper. Can you.
Yeah, maybe start by like telling us about your start in
the AI and the mouse space. For sure, yeah.
I mean, so I started tinkering with AII think.
Even in high school I was actually excited about neural
networks as a concept and I worked for machine learning
company that was pretty old school machine learning company
starting from first year of college.
But when I saw kind of deep learning resurfacing in 2012
thirteen there was this kind of Seminole work at a time which
now now feels like duh. But back then was very exciting
which was they trained in neural network to encode and like
encode the image and then decode it back into the same image.
So pre training what we know now as and that model without any
supervision learn to detect cats.
And so there was a neuron in the network which if you activate it
it would it would generate a cat and like different types of cats
And so it learns something like semantic without any training
like in any like input data from here it's right just by looking
at images. And so when I saw that and that
was done by Google by Jeff Dean and Andrew Ng and they did it on
a bunch of GPUs and like they they managed to scale it up.
And I'm like I want to do that. I think that's that's the thing
that's that's going to, you know actually change things.
And so I joined Google research. My believe all this was that
natural language not images going to be the driver for
reasoning and for kind of like intelligence because you know
there's many, many species in the world like hundreds of
thousands of species that see and only one species that talks
and has language. Right.
So there's way more semantic information language.
And so my team worked across a variety of things specifically
question answering. So when you like type questions
on.google.com we were actually running a neural networks to try
to read our like pages that you see and respond to you was like
a short answer so like you would see some damn short answers.
Now the challenge was the neural networks at a time specifically
recurrent neural networks were too slow to be put in
production. And so we were just using bag of
words models, which means you just literally throw all the
words without any order into the model and it kind of tries to
figure out what's going on. And it worked.
It worked reasonably well. But and this is where kind of
the Transformers gave birth, was like we could not use RNN in any
practical use case. And so we were looking kind of
for something. And so Jacob who was the manager
and had like another team came up with this idea like they were
using attention on top of words without any recurrence for for
another task. And so kind of merging that idea
was recurrence. Like can we use attention to
somehow figure out which words are relevant in the order when
you do answer questions or translate something.
And that kind of gave birth to the Transformers really was like
we need something that's really performant, that can be highly
paralyzed. And attention is really good
mechanism, you know, logically to do this.
But if you package it all kind of the way this models really
work is that everything happens in parallel.
Like the way I like to describe it, there's this movie Arrival
where aliens talk in the whole sentence at the same time.
Like there's like a circle of scrub list, but they produce it
at the same time. And that's kind of how
Transformers actually read articles.
It's not like one word at a time.
It's literally reads the whole article, all the words in
parallel and then like has multiple steps to kind of
process it and reconcile this like the understanding of it and
then it answers the question and so.
So that lays out really well for the modern hardware GPUs that we
use. And so it allows to have like
this massive kind of performance improvement which means also you
can scale out the models. And so I've was a, you know,
worked on that was a team of amazing researchers which now
all went to do really cool stuff.
And then at the time I decided to leave Google to start an AI
company near AI, which was supposed to be pretty much
teaching machines to code. So my belief, and I still
believe this, that now given this, this steps of models, you
can change how we interact with computing.
You can actually talk to computers and they do work for
you instead of needing to have an engineer to write code for
you, right. Which again like now it seems
more obvious that that's possible.
Back in 2017 there was like huh And so so we started near AI but
we only we gave us us a year because obviously at that time
it wasn't moon shot and we didn't have that much resources.
So we're doing some interesting stuff around data collection and
some machine learning. But one thing we ended up doing
is getting a lot of people around the world actually doing
like writing some code for us, writing some descriptions for
the code. And so we we had to struggle to
pay them because they were mostly students in China and
Ukraine and Russia in kind of some other countries.
And like some of them don't have bank accounts, some like
Ukraine, for example, PayPal doesn't work.
In China, PayPal doesn't work and so there's like no good way
to do it like programmatically to send people money.
And so we started looking at blockchain as like, hey, can we
just send people money easily in in code.
And the answer was in 2018 the answer was actually no because
even back then the fees on Bitcoin and stadium were way too
high And and then as you probably know when you start on
the blockchain rabbit hole, you cannot stop and just keep
digging and you're like wait what is this?
And so, so we kind of as we kept digging and researching
different block chains and different technologies, we're
like wait, we actually know how to build something of this sort,
right. So my Co founder Alex, he was
building Sharda database company before and we have like you know
systems background, we're like we can probably do this, but we
can focus on user experience, developer experience while kind
of solving the scalability underneath and making sure fees
are staying stable. And so that's kind of how we
went from near AI to becoming near protocol in 2018 and and
starting this journey. So Elia in this current wave of
LLMS. Of course like this attention
mechanism is a key part but another key part is just the
idea of you know like just the idea of scale.
I'd like collected a lot of data from the Internet from books and
then pre train the model and of course the ideas of RLHF and all
they came later. But the fundamental idea is you
throw in a lot of data, you pre train, you make a big model sort
of produce good results. Did you anticipate that scale
was going to work this well, and if so, why would did you use
that approach in Near? AI, no, I I that that's a part
that definitely kind of was interesting to see that as
people scaled up the models, they became like they started
exhibiting kind of properties like more and more sophisticated
reasoning properties. And it's like it makes sense now
that you know you think about it like the capacity of the model
is higher. It's able to like generalize
better. It's able to kind of learn quote
UN quote programs that it can execute.
But yeah at the time that wasn't like particularly clear that
like it will be that kind of step function change.
And so we yeah we were not at nearly I we're not doing that
partially because we also just didn't have you know like we
raised you know small C like pre C drown actually And we we
thought we could get better supervised data instead and we
did we did some pre training on like GitHub and and things like
that. But we we didn't thought, didn't
think of like training the whole Internet, that's like large
scale and we did have resources to do something like that
either. And the other interesting thing
is kind of like this. This attention mechanism also
seems to like it's built for natural language processing, but
also seems to kind of work across different modalities like
such as like images and maybe video in the future.
And like how does that come across to you right?
Like is that unexpected or is that is that something you
expected in the past? I mean like when the
Transformers were just in development there was like like
the teams actually tried them on different modalities, I mean not
like multi model models but different modalities and it was
pretty interesting to see it worked really well.
So I think it's that was kind of known that it works on on
different modalities pretty early on.
I think the kind of the intuition there is really that
you know the way kind of we work as well as very much like like
our our eyes actually like move all the time every like I I
forgot how many milliseconds. And so we actually kind of pay
attention to different parts and then our brain kind of
reconstructs the image right at different levels and kind of you
know residential language same right.
You read sentences you like build some semantic meaning and
then you know it kind of continue building out this the
meaning of the what you read. But sometimes you like zoom in
on specific words when you need to answer a question and so like
I think like generally speaking there is like intuition behind
this but obviously again it's like it it's interesting to see
how well it all works right Definitely you know like we had
we had a pretty good models like even before it.
It just like they were super slow and like non you couldn't
use them in production at all but this you know obviously like
the the scale is which for example Open AI went and and
scale it up and and by the way they did a tremendous amount of
work to make it work like it's not we cannot take it for
granted. They're just like oh we just
increased parameters and hit enter like no it was a ton of
work across the board from you know low level engineering to
like fine tuning to you know they changed some of the model
kind of details of model architecture as well.
But yeah, like the. It's it is.
It is It was surprising for me like, I think like that when it
went from 2:00 to 3:00 that was like interesting like two.
It was kind of like, OK, yeah, I get it.
Like we've trained models like that at Google kind of thing
from 2:00 to 3:00. It was like, OK, that's that's
really interesting because I can see the, you know, there's like
something more now happens and obviously it's 3.5 is where
like, OK, yeah, that's like it actually learns something that
is like beyond just language modeling, right?
Like there's some reasoning that this is extractable now through
kind of this instruction fine tuning.
On a high level, I'm I'm actually curious what your
stance is on this stochastic parrot versus understanding
spectrum. So, so there are, there are
people in the AI community that that say that LLMS actually
don't understand anything. They are stochastic parrots in
the sense that they have understood the statistics of
what word follows what other word in language because they
have seen billions of examples. And when you talk to an LLM and
it's generating words, it's just replicating the statistics of
what it has seen in the past without actual any understanding
behind behind the box. That's the like, at the extreme,
that's the stochastic parrot view.
And on the other extreme, perhaps there's a view, maybe
like the ES upscale view, which is kind of when you force a
model to predict the next word and you force it to do it again
and again in order to predict the next word.
It has to Start learning something about the world itself
to do the the job of prediction well.
And in kind of trying to predict it well, it is forced to learn
about the world and so it has actual actual intelligence about
what world it has in it is in. So it's not just a stochastic
parrot. This is actually when you're
talking to GPD 4, you're talking to something which has
understanding distilled into it and they seem to be like these
two extremes in the in the space and I'm I'm curious like where
you stand on on that on that debate.
Yeah, I I mean I definitely closer to Discover's view like
from my perspective kind of you know it at the end is bunch of
math, right. And so like you can kind of
decompose what this math is doing and you know try to build
an intuition around like types of transformations.
It can, it can or cannot do. And so from my perspective kind
of you know the first step is you take the document and you
embed it, right. So you went from words into a
multi into dots and multi dimensional space, right.
So I mean let's let's for a second imagine it's
two-dimensional although it's multiple.
And so there is like the words that are similar, right.
Are you know close to into space.
The words that are far. Now you have a next layer which
transforms us words, right, to kind of give them more context.
And so just, you know, think of it as rotation in the space and
then you have a tension which is you know, you're trying to kind
of given the, the current word, you know, try to pull in the
context of the words around it to give to give it more semantic
meaning. And so that's another
transformation, right. So like in a way you take kind
of set of words, right, and then you kind of keep transforming
them. And so it it what it learns is
the transformation function which in a way is a program.
It's a program that is trying to transform the words into a level
which is useful then to predict next word right or and then like
later respond to questions. And so kind of is this like a
pure stochastic parrot where it's like well pure stochastic
parrot we had when we were doing just like you know, we were
generating Wikipedia articles for example, right.
You just give it a name and just say generate a Wikipedia article
like that's pure like you know there there's it just makes up
stuff because like that that name doesn't exist right.
There's no there's nothing So just generate something that
looks like an article But when we when we starting to look at
like OK well how would you answer to this question right.
It it to be able to do that right it needs to kind of
process information right. It it does this kind of
transformations on the on the article and like it's trying to
connect contextualize that and and give give the answer.
So in a way like I think of it as it's learn some set of
programs that like our world has right.
So like it's not a complete word model, right.
It clearly has a lot of gaps, but it is a kind of set of
programs that our like world model has that it can apply to
be able to to answer well or predict next word for a training
and that in itself is really useful, right, as we see.
But it's also because it has so many gaps it it, it has issues
with doing some you know, kind of specific things and the more
precise it needs to be, the less well it does, right.
Because it kind of ends up being like if either as a program, the
programs are very probabilistic and kind of semantic versus you
know, if you ask it to like describe the steps of of
something. But at the same time a lot of
the things we do is kind of like there's like few thing core
things and then everything else you kind of fill in
automatically, right. So that's why it's really good
like even at coding like most of the most of the coding we do
right is actually kind of boilerplatey and so and so
there's like few not just you can actually get to like a
reasonable code and that's why I think like things like copilots
are in pretty good products in result.
Cool. So look, turning to applicative
views, so now this LMS are are pretty amazing and you have some
applicative ideas on applying them to the near ecosystem.
So yeah, what? What are they and how do you see
that unfold? Yeah, I think I think of this
kind of across 3 dimensions. So the first dimension is
actually less about AI itself and more about our kind of
society. And this is the idea that kind
of as more content is generated as there's more kind of
information wars in general misinformation.
And again, the important part to note, misinformation is not AI
problem, it's a human problem. the IT, you know, we are in
crypto space and so Byzantine generals is something that our
space is based on. And that's literally the, you
know, the miss cite citeable misinformation like.
And so the idea of like misinformation of of malicious
like attack on information is something that exists from, you
know from like early on. And so from my perspective, the
way to kind of start solving that is to to bring the kind of
security, cryptography and reputation to a level of of the
content of the of individual pieces of content.
So right now, for example, we are, you know, using websites,
we have HTTPS and so we have, we have some set of security
guarantees around accessing specific websites.
But the content on the website can be coming from anywhere.
It can be saying anything and there's no way to kind of
maintain reputation, contacts, comments, etcetera around it.
So we need a new set of standards around that, so that
you can hover an image and it shows you or a video or piece of
text and it tells you like who published it, when it was done,
if there's any side comments or contacts etcetera from reputable
sources that should be attached to it.
So for that we need blockchain, we need, you know, set of
standards, we need browser support and we need kind of
publishers to be supporting this.
And I think that's a really important part for our society
generally because otherwise we're going to be living in a
world of kind of, you know, all the content is like you never
know what if it's true or not, right.
And it's constantly like kind of manipulation around that.
Now kind of the second pillar for me is I call it kind of
decentralized AGI, right. So if we assume you know this
models are getting more powerful, more intelligent, what
you definitely don't want is a single company or you know two
or three companies deciding what's right and wrong for this
models to do. You don't want to like them to
decide what you're allowed to do and what you're not allowed to
do as models. It's also like it's the same
thing that happened with social networks, like being a kind of
moral police for the world just doesn't work.
The world is very multi dimensional.
Something that's legal in Amsterdam is completely legal in
a lot of other countries and the other way around.
And so like you know what moral is, is even more complicated.
And so it's really important to have community be governing kind
of the alignment safety as well as kind of the, you know,
instruction data sets that these models are trained on and as
well as like being able to validate that the model you run
is actually the model that you wanted to run, right.
So right now if you call, you know, GB TAVI or Google API, you
get a response, you have no idea which that could produce that
response. You have no guarantees that it
was the model that you wanted to run.
And actually sometimes it's not because they're trying to
optimize costs. And so like how do you actually
have this guarantees? And especially for something
that's mission critical, right, like like if I'm doing trading
on this, if I'm doing healthcare, like any kind of
business decisions, right. You want to make sure to, you
know you're accessing the model that you have predictable
parameters and outputs. And so for that we need
decentralized inference. We need kind of model
marketplaces, We need kind of community data, crowdsourcing
and data management governance and took kind of the whole stack
of tooling that really manages this.
And then, you know on top of this you'll be able to to kind
of interact with it in a hopeful like.
I think the other way is like making sure it's privacy
preserving so that when you interact with it, you have it.
So there's a lot of work to be done.
There's a lot of like there's a bunch of startups doing
decentralized inference. There is still privacy gap.
I think that people are researching, but it's still
pretty far. There's some data marketplaces,
there's some other kind of pieces, but it's not really, I
would say like combine into like a product story yet.
But I think like that's a really important for like humanity
period. Because otherwise, you know,
like tomorrow you go to your favorite, you know, AI model and
it says like, oh, you're banned or you know, you use the
incorrect word and so now or something, right.
So all the usual stuff we've seen before.
And then finally, I actually think the the flip side of this
is local models, right? Because although like this big
models, they have the world knowledge, they have maybe
access to lots and lots of context.
But actually what you want most of the time is a model that
knows everything about you. But you don't want all this data
to go anywhere else, right? You want it to live with you on
your machine, on your, you know, your private encrypted data
store and you want to model the table to access that.
So you want a local model that is personalized for you.
You control it. It's not affected and
manipulated in any way by, you know, advertisement giants.
And so it's actually on your side and there's just responding
kind of the way you would like to not the way you know tied
once you or whatever. And so I think that is a really
important side of kind of as well.
And so we're actually been playing around with like Edge
Intelligence and I did a couple events and being kind of talking
with some projects around this space and it's like it's
actually it's lasts VEP 3 like you know sense of blockchain,
but it's more of a three in a sense of principles, right.
It's user owned AI is you know controlling your own data.
It's like all of those values that we to talk about and I
think that and kind of the web series of custody will be
converging more kind of in a in in on the principal side, right
maybe and like on technology side as well and this kind of
the area I'm I'm most excited and working on right now.
So in practice how how are you like approaching this?
Are there like teams you are funding or is like there AI team
in the year or how? How can we imagine this?
Yeah. So we've been working with some
of AI teams. We actually just had a Neo con
about a months ago and we headed AI track there with some
projects presenting that we already working with as well as
kind of I'm working as like advisor with a few projects kind
of more closely and we do have I would say like AI efforts more
on also just automating our own operations like.
So the other side of this is I think kind of the ecosystem
itself should become AI enabled and over time AI ran.
So like ideally my, you know, my job and kind of the job of
coordinating the ecosystem should be done by AI.
And by the way the AI is a kind of like this approach actually
solved the core problem of humanity and of resource
coordination. The core problem of humanity is
principal agent problem is that when we want somebody to to to
do stuff on our behalf like we select, you know, in elections
or we hire someone to manage our money or something else, they
have their own needs and and they have their own wants and so
their decisions are usually not fully aligned with us who hired
them. So that's called principal agent
problem. And so AI actually being the
agent that behaves on our behalf, acts on our behalf is
the way to solve that. And if you scale it up to kind
of governance level, right, like actually having AI, being the
actor that you know, makes decisions based on what the
population wants, is the way to solve a lot of the current
challenges was, you know, when you're like someone, they do
stupid things, right? Or not, things that they
promised to do. Kind of that's a way to like
really address it. And so there's a really
interesting kind of future of governance there.
But like, we can start applying it now in this decentralized
ecosystems because they're already fully digital, they
already have kind of like all the actions are in chain, right?
So you can have traceability, you can have like veto power,
etcetera, if something goes wrong.
And so I'm really excited about also that side of the applying
AI in in the three space and obviously you need that whole AI
like decentralized stack to do that.
But we are kind of starting to do it from bottom up on on our
side just in foundation for example like hey what are things
we can automate, what are things that we can like start
leveraging this technology for as well as maybe build some of
the tooling for developers to build tariff AI enabled things
in in in the space. We also have, yeah, a bunch of
projects that are kind of experimenting with this across
different areas. Yeah that's super awesome.
I I also saw actually your Co founder like the Alex working
directly on like smarter LLMS. Can you maybe also like what
what's that about? Is that related to me or is it
like some sort of totally different thing or what can you
share about that? Yeah.
So I mean it's it's a stealth project right now.
So I'll I'll not go into too detail maybe you'll have him you
know in a in at some point to go more in depth into it.
Well, yeah, I mean we kind of so I'm advisors there and we
working kind of I would say side by side.
But yeah, he's focusing more on the lower level and like kind of
preparing for the future of of this as well.
Yeah. I think, I guess maybe you
you're mentioning right, like AI sort of also like making our
life easier in the sense of operationally in the
organizations, but also I guess, yeah, in the wider society.
And I guess that's always been like a huge focus of Near.
So yeah, we wanted to sort of dive also in that side of Near
where basically you're branded now in many places like as the
blockchain operating system. And I think, yeah, one of the
core features around that is like sort of the the UX focus of
new. So maybe yeah, can you explain
to us how Near has sort of approached yeah basically
usability for for developers and and users in the in blockchain
systems and and what you're currently doing there?
For sure. Yeah.
So I mean this was our vision from the start because when we
started ourselves kind of diving into the blockchain and again
this 2018, so things were different.
You know you needed to install Mist for the and so the I mean
the experience was pretty like painful and and it's also it was
built on top of kind of a very different set of primitives I
would say like conceptual primitives said than what
normally people both users and developers expect right.
So, you know, you need like to understand the Xerox wallets,
you need a seat phrase, You need to like kind of pay gas.
You need to like have, do all these things which are like
strange when you, you know, when you're just starting.
And what we've tried to do from the start is like how do we
design kind of still like a blockchain that is secure that
it has all all the same properties that we all want, but
is able to kind of hide a lot of this complexity, ideally most of
it, and make you know blockchain kind of abstracted out such that
developers when they build applications can just build like
as close to normal that to experience.
But using the benefits of that three, using the kind of all of
the value. And then also enabling users to
have like more composibility, right, more ownership kind of
being able to interact with multiple kind of applications
and and have this like transportability of data.
And so the near itself, right, kind of was designed was this.
So we've like our accounts for example, you know the account
obstruction part of the accounts have been designed from scrap
from the start on the protocol level.
There's like a bunch of kind of differences that was done
including that accounts themselves are you know just a
username that follows kind of domain name structure.
We have lots of different keys with different permissions which
allow us to have like multiple devices securely.
It allows to delegate access, it allows to like applicate the
front end of application to have a session key for example to
transact for a specific set of kind of interaction.
So kind of all of this functionality comes in by
default. And then on the developer side,
the choices we made around first of all choosing Web assembly
which but at this point is like seems that everybody's kind of
agrees on, but pretty much it's like it's an engine that runs in
all the browsers. It's something that you know is
like on billions of devices at this point it's supported by you
know, large network of kind of developers.
It runs on edge. It you know it supports lots of
languages. You can run a lot of software in
it and so so we kind of picked that and made it really easy to
build like in a way from a developer perspective what when
you write nearest my contract it's really just a service which
has message like messages in and out and you have a kind of local
key value database which is pretty much like the limits
there are so big that like I don't think anybody ever hits
them like you can't you. I think we have contract that
have like 4 gigabytes of storage in their in their database
right. So you can build like massive
massive contracts specifically you can build all the chains as
a smart contract on there. So we have Aurora which is an
EVM as a smart contract just like taking you know the EVM
that's usually run people a separate chain just put in smart
contract their their database is where all the state of the of
this storage right. You can do the same as Bitcoin.
I've I've been suggesting somebody to like fork Bitcoin
and put it on there make it ultrasound money.
We have JavaScript running as well.
So you can run JavaScript smart contracts you can potentially do
Python And other stuff. So like it's kind of enables
developer experience across the board.
And since then we kind of following the same principle is
like, OK, well, now that you can build anything on smart contract
side, what's the next part? Well, actually you want to get
the data out of this out of the blockchain and blockchain are
not optimized for reading data. They are all kind of we've tend
to optimize them for writing and kind of maintaining security.
And so for reading data you want a completely different data
structure and so that hence there's like this principle of
indexing and kind of in the way of chain computation.
And so we've been building indexing framework and that
actually culminated in what we call query API which is a
service that indexes that you can like write a smart contract
that describes the indexing of data that executes off chain.
So in a way it's like a off chain computation framework that
allows you to store output of that computation in kind of SQL
databases that then you can query and finally, well OK now
you have back end and and middleware, now you need a front
end, right. And again it seems weird that we
are like oh you build everything decentralized but now run a
server on a specific domain that you will need to maintain.
It's like OK well that kind of violates the whole part point of
what we're doing. So, so we created this kind of
decentralized front end framework that allows to store
the front end code itself on chain.
So again the smart contracts code on chain, the middleware
code on chain, the front end code on chain and now anyone,
any kind of we call them gateways can render this code on
a user side, right? So we have a desktop app, we can
have a mobile app and we have obviously web apps that can bode
that from the blockchain directly into your browser and
render it there. So there's no kind of middle
server that's needed to render you, you don't need to have a
domain. You can obviously if you want to
and so you can just you know launch kind of part of your web
app as as this decentralized front end component and now it
will live forever on blockchain, right?
But side by side with your smart contracts, have the same
operability, have security, cryptographic security, who has
it, have versioning. So if I as a user don't hate a
new version I can go to version before and so like all the same
properties we really like about smart contracts we now get for
front ends. So, so all of that is really
enables like a full stack decentralized development that
is you know familiar with to normal developers.
It's React JavaScript components, it's JavaScript for
middleware indexing it's JavaScript Ross C++ and like
other languages for smart contracts.
So you have like a full stack decentralization that you can
have. And interestingly as we were
building the front ends, we realized actually the front ends
can work with any blockchain. And so we kind of just turned on
all the Avms and some other blockchains and people started
building all the AVM fronts as well.
So we have a UNISWAP for example, for linear, the kind of
official UNISWAP front end is served out of the decentralized
front end, right? Because by the way, it also
doesn't charge extra fees and we have like partnerships with
others, the KVM, Mantle, etcetera.
And so the idea is like actually as you start looking from that
lens, from a user lens, right. As a user, I don't really care
which blockchain the apps is on, I just want to use them.
And like if you go to you know some like you know some of this
gateways where you can access this front ends, you can just go
and search for whatever app you want, click on it and start
using it that that's how it should be.
And so, and this is kind of where we get to this concept I
started with which is like, hey, we want to abstract the
blockchain for users and developers.
We're getting back to it with kind of this now that we have
this full stack decentralization where like actually this works
for all blockchains, for all chains, for roll UPS, for
whatever, because you can actually abstract out all that
on the front end side and make it really easy for people to
interact with it. And so hence we kind of started
going backwards now with some of the other launches we had,
right, allowing pretty much as a kind of how do we make it really
easy now for one experience to to unite all of the block chains
and kind of recall the chain abstraction principle.
And so this goes into like GA and and some other things we,
yeah, we can discuss. So, so India is it, is it
correct to imagine so when you talk of like this indexes
service or the service of hosting a front end, is it
correct to imagine it as the indexing logic or the front end
logic is stored on the chain. But then there is some kind of
off chain actor that is actually taking that, taking that logic
and the data and actually serving it much as a traditional
server. And somehow the chain is
guaranteeing that this server's work is correct and it is
compensated. Is it?
Is it correct to imagine it like that?
Yeah, pretty much. So the idea is, I mean similar
to maybe blotching validator nodes as well, right?
There's a kind of a logic that is conceptual and then all the
validators are doing that job and like you can always have,
you know, more validators, less validators.
It's kind of independent of that.
Similarly, yes, the indexing logic and the front end kind of
source code itself is stored on chain and so any server can run
on and kind of create the same, you know outcome from this,
right? Again similar to RPCS for
example, RPC server right is serving your data but it's you
know anybody can run RPC server and get the same results.
So like it's it's part of protocol.
In a way it becomes part of protocol and so similar thing
we're trying to do for about both front ends and middleware
indexing as well. So maybe one way to think about
this is that, so on on on the TDM, right?
If you look at the TDM, there's a basically a blockchain and
then there are separate protocols like the ENS for
naming your blockchain address to a human readable name.
That is the graph which kind of like indexes a smart contract
and kind of presents historical data about the transactions and
events in the smart contract. And maybe maybe there are other
examples that I'm missing. So in the TDM layer, these are
like different systems and usually they are competing
systems as ENS, but they won't be a competitor to ENS the graph
and they won't be a competitor to the graph.
But in near NIA has kind of taken the philosophy that some
of some of these things are like are like really key to the UX of
a blockchain and therefore they should be supported
out-of-the-box by the layer one itself.
Is that is that the philosophy here?
To extend, yeah, I think, I think the way to think about it
is it's more than just layer one, right.
Like at the end when we are interacting with applications on
any of these chains, like there's a whole host of tools
and and more importantly standards that we are
interacting with, right. And so like ERC 20 for example
is a standard and it's a standard that kind of came out
of the application space. But it's now like you cannot
imagine the CDO without the ERC 20 standard and So what we're
doing here is really defining standards for this key
primitives that are just going beyond just you know token
transfers but going to like how to how to define indexing, how
to define this interest run it's now implementation of those
things can like you can have many implementations, you can
have you know mobile render and web render.
You can have indexing like you can have you know external
partners who are competing with each other, how to implement it?
Same same for RPCS, right? RPC is a standard but then the
way it's implemented right can be very different like
underneath you may be like cached everything in database
and maybe using cloud flare like whatever the architecture you
want to use. But the standard is there and I
think what we've been trying to do is define a standard and I
mean have a reference implementation but for for this
more key pieces to make indeed the experience more aligned and
kind of have this like singular journey for developers and users
that you know is cohesive. And yeah, like the way you know
some of the things like you can have businesses around the
standards that are, you know, very profitable.
But like the core principle for me of decentralization is
actually in the standard. It's the fact that like if you
define a standard, it means that you can swap in and swap out any
participant. And so you're not you don't have
this like lock in effect. You don't have the effect of you
know you go to a bank and you cannot move your money out
because it doesn't allow you to. Or you know the cannot cancel
your telco provider because like or like your positive telco
providers don't even work for you.
The here we can always have like a competitor that comes in and
if they're more effective and can provide better prices people
can switch to it. But the stand because the
standards is the same. And so for me that's kind of the
key principle of like Web 3IN general.
And so I think the challenge that I've seen is like not
having the standards actually leads to kind of huge
fragmentation of experiences. And as well, like actually
Monopoly has been built because like now that you've built all
your software towards some API, you cannot switch because nobody
else provides us. And like you need to rewrite
half your code to do that. So how much is kind of analogy
with the Apple ecosystem versus the Microsoft ecosystem for
desktop, how much, how well does it map?
So in a sense when you look at kind of like the the Apple, the
Apple ecosystem, it's a company that has kind of maintained
control over, it's kind of like operating system, supply chain,
it's way of like delivering music, it's way of delivering
books, it's way of how kind of applications kind of like appear
to the end user. And in the in the beginning, I
think they also wanted control over the hardware, but maybe
they have retracted on that now. Whereas like kind of like
Microsoft is 1 where just like the raw operating system and
then applications emerge. And if there are standards
needed for their interoperability, the market
kind of like figures it out. And from the outside it feels
like OK. Near is kind of like more going
towards that Apple philosophy that we are going to define all
of the standards for, for many of the things that are key
determinants of the user experience.
Whereas other ecosystems like Cosmos or Ethereum might be more
kind of like the Microsoft approach where we are providing
like transaction throughput as the center, the account model as
the center. And then kind of a lot of the
interoperability between the standards is left to the market
to figure out how much does that analogy map and how much does
doesn't map. I mean the, I would say the.
Part that's like agree on is we're definitely trying to focus
on user experience, right. And so with that, it's important
to figure out like what are the touch points that you want to
have standards on. Again like my perspective is for
example RPC, Jason. RPC is part of Ethedium
standard, like it's part of kind of the protocol even though it's
actually not but like it it for both accounts.
If you try to change it RPCAPI like you will break everyone And
so, so we kind of see in a similar way, right, like if RPC
is part of the standard, why not some other parts.
But as I said like you know Near for example has like number of
contributors that they're building things like the, the
actually the VM that's built right now for the decentralized
finance is built by proximity, right.
And for example you know the query API kind of other
companies can implement the same standard and provide kind of
better services. So I think the idea here is that
by defining the standard we kind of actually opening up the
market for people to fill in like with better products in
this. And again like it's pretty early
still. So like a lot of stuff we still
build like reference implementations, but similar as
a CDM by defining a standard for protocol, it opened up a place
for all of these clients to be implemented, right.
Like that's kind of the idea is like you you, you define a
standard and then you open it up so that others can contribute to
it. In the same way versus competing
on AP is and competing on like kind of you know in a way
marketing what's happening right now in in in like talking price,
what's happening in the CDM for some of this like infrastructure
tooling, right. It's like can can we get a
bigger AirDrop by using a product versus like hey, this is
a standard, everybody will be using this standard and so now
what's the best product people can build for the standard.
So I think like that's kind of the difference.
I don't think it's as applicable to like this, you know big
commercial for profit companies versus like this is an ecosystem
that we're building and really defining more kind of this I
would say like layers of the stack.
Going back a bit to like this, what you said about the
switching cost from your telephobic, I guess like related
here. I guess one big thing in
blockchain is generally like bridging or like if you want to
switch the ecosystem you have to like go to some other chain and
then like move the liquidity there which can be like
cumbersome. And you did mention the chain
abstraction for a second there and I I saw on your Twitter a
bunch also like this concept you have like of account aggregation
that you teased. So maybe you can yeah, can you
talk, tell us a bit about like what what are you doing there or
how are you like sort of solving this interoperability problem in
in the blockchain space? For sure, yeah.
So that that's a very important topic.
So although we have started building bridges I think so the
our Rainbow Bridge been built from 2019.
So I think we started building you know kind of in line with
IBC kind of timing and we've been, we've been running since I
guess in the beginning of 21. And at the same time like
bridges are really bad as a concept because they created
honeypot for security. They are the place to siphon off
assets and like there's you know if there's any attack on the
protocol itself bridges kind of how you exit and and they like
that just the amount of failing modes between different block
chains is pretty like it's pretty big right.
But between multiple block chains it's like insane like you
know chain stop blocks didn't publish like all those things
you need to like as a developer now you need to handle and then
on application side, OK yes you know the fungible tokens
transfers is maybe reasonable but as soon as you add any
logic, right be that you know rebasing or be it LFT.
Now when you bridge it you lose all of the logic on the other
side and so so the concept I've been kind of exploring for a
while now. I was calling it originally
remote accounts, but we we kind of reframe it as account
aggregation. This idea that ideally you're
going to have one account and there's mapped accounts to this
on other chase. So imagine you know you have my
root dot near account on near and then I have an address on
this idiom, I have an address on Bitcoin.
I have an address on Solana which I control with this
account. And so now if I want to buy a
Solana NFT, right, right now I would need to like set up a new
wallet, you know bridge some stuff to Solana by the NFT.
And then I like, I don't know and then like go and look at it,
you know from time to time because I'm mostly sitting on
here or you can you have the Solana address that's linked to
your to your new account. You pretty much through this by
an NFT for this address and we can talk how that works.
And now you have the front end that actually shows you
everything you own across all of this chains from all of this
addresses. And the way mentally to think
about it is when you go to Binance or Coinbase and you sign
up with your Binance or Coinbase accounts, you have addresses on
all chains, right. And I mean they they are deposit
addresses usually. But imagine those addresses were
actually normal addresses. You can use apps and buy NFTS
and tokens etcetera Wiz so that and but your account is your you
know, Coinbase account. And so that's kind of where
you're, you know, like ownership is.
And so that's what we're trying like we building, we're going to
be launching end of the first quarter is this concept of
account aggregation that now allows together with
decentralized front ends allows to actually collapse the kind of
this whole, you know, multiple chains switching networks, you
know, bridging all of this into a very simple experience of you
get an account, you know you deposit some funds into it and
now you can transact across all block chains, across all of
their apps. And it kind of will get executed
on on your behalf on those chains.
And you have this addresses but it's all self custodial and all
kind of hidden from you. You don't need to think about
gas fees on those chains etcetera.
So that's kind of the experience we're going after.
And again, this is just an extension of what we've been
building was near by trying to abstract out the near
blockchain, we're just like OK, well we can actually do that.
It's the same thing for everyone and really provide like a unique
and valuable experience because you know anything multi chain
you want to build near will be actually the place to build it
because you will be able to transact across all of the
chains without having to bridge, without having this like
complexity. You want to build for example
Bitcoin D5 well on near, you know every near account or smart
contract will have a Bitcoin address you can deposit to it
can start you know doing stuff right.
And so that's kind of conceptually what we really
bringing to market with this and like kind of finishing our I
would say arc of chain abstraction that we started with
doing near in the first place. So on a on a high level India,
I'd like this idea is is in the Cosmos ecosystem.
There's a chain called Neutron and because the Cosmos ecosystem
has IBC, so Cosmos chains can bridge to each other in quite a
good way. Neutron has the idea that in
Cosmos you have the idea of delegated account control, which
is like on one chain you have an address and that can control
many other Puppet addresses on other chains and neutrons.
Trying to build that kind of that that Puppet master chain
where like you would have your central account and you will
control other addresses on a lot of chains over IBC through
Neutron. Does it feel similar?
But the reason it works in the Cosmos ecosystem is you assume
my BC that there's a secure bridging solution underneath
available. For this to work in Neutron my I
almost start to saying that OK, the only way like this can work
for Near and Solana. For example, having like a
address on Near that in control of puppet address on Solana, you
need a secure bridge between Near and Solana, is it not so
the bridging problem? Solving the bridging problem
seems like a prerequisite to this.
Yeah, so we're trying to go away from bridging almost completely.
I mean there will be some places where you still need bridges,
but so let let's look at Bitcoin as just to get way more clean.
Example, right. With Bitcoin you cannot have a
smart contract bridge because, well, Bitcoin still have smart
contracts and so the only thing you can do is to own addresses.
And so the core idea here and it's conceptually the same as
yeah, what Neutron is doing, but the core idea is different.
The core idea is that we make near network itself be able to
sign transactions for other block chains And so near network
becomes in the way custodian of all of this address mapped
addresses on other chains. And you as a near user telling
network right be that through smart contract or or or user
user interaction to sign a transaction on Bitcoin to send
some bitcoins from your remote address from your delegated
address to some other address right.
And so because of this like you don't need to actually bridge
Bitcoin to near to do anything right?
You you just literally the bitcoins live on Bitcoin
network. The you know OP coins live on
optimism the you know Salon and NF TS will live on Salon, NFT on
Solana and I just control all that by just sending
transactions there. But as a user like I just
interact with near and I kind of pay near guest fee which is very
small. I say like do this, you know I
attach whatever also you know if I need to buy something etcetera
on near and then we have kind of intent relayers that actually
execute stuff like you know the transaction gets signed by near
network and then intense relay of you know sends that
transaction on your behalf on the other.
And so there's no actual like bridging.
There's no kind of security kind of issue where like if this
bridge gets broken or whatever or that network gets forked
etcetera, like none of that exists.
And because near account there's also like a very interesting and
kind of a little bit crazy thing because near accounts are are
actually tradable. So you can actually list near
account as NFT and somebody can buy it and get access to it
because you can rotate keys on there.
What is allowed to do is you can have lots of assets across all
kinds of networks and then you can list that as a bundle on
near as like you want to sell some BRC 20s, some so on NFTS,
some Ethenium NFTS and some and OP coins and GMX at the same
time. You can list all that as a
bundle under one near account and then somebody can buy all
that with one transaction on near paying near transaction fee
and within one second block time.
So you don't need to wait for Bitcoin transfer, You don't need
to wait for all of this. You can do it on what so you can
actually start. Bundling all of these things and
trading kind of across all chains on near very easily
without actually sending transactions of bridging
anything anywhere else. And that's kind of the the shift
that we're trying to do. I called on bridging that we
like. You have the account level kind
of ownership that's maintained indeed, but it's maintained by
very specific security parameters that are near
parameters. And then if if the, let's say
Solana network fails for whatever reason, there's no
bridge problems, right, That would you know, rise from from
this like because you own stuff on on Solana.
So whatever Solana has to deal with, right, like whenever it
recovers etcetera, like you will get it back.
But like it's it's kind of the same like you know you have this
kind of relationship with that network but not like there's no
bridge that you need to deal with and kind of think of as
like an intermediate you know complexity.
So that's the idea. It's like you know again we're
going to be rolling out more documentation.
It's like we have a test net version coming out for people to
hack on in in kind of January. And so we we actually invite
people to start building because again like a multi chain
experiences like you'll be able to build this this way easier
because you don't need to think about all of the complexity of
like oh this message didn't deliver that that work is like
paused. You know like something crashed
because of inscriptions like you don't need to deal with any of
that right. It's like you can literally sell
your your you know network failed.
You can sell the account that that has assets in failed
network right to somebody else for example if they want to take
that risk, right. So like you can do that without
having that network life Even so.
So that's kind of the the, the level of experience we won by
and like this leads to fully obstructing the block chains
right. Because now from a user
interface I just go I use the app and like I just see that I'm
using my for example near account and it doesn't really
matter for me that that was a Solana like NFT that I bought
right. I just see it in My Portfolio
view and like you know for that we need like indexing of Solana
and all the other chains data. So the same stack there we need
decentralized front end that kind of aggregate all this and
so kind of that. That's like how we package all
the stack into by abstracting the blockchain.
So quick quick nerd question which is so OK so Nia's like
this is awesome. First of all I mean NIA becoming
like a distributed custodian essentially imagine it as like
Coinbase but distributed and the distributed custodian can have
hot wallets basically on all of the other all of the other
chains. But as an engineer my my my
questions really starts to be in in Bitcoin you have like a
single single account or a multi sig account.
That's what Bitcoin provides available right?
Like it, it assumes that there is like maybe like 1 private key
and then one public key and there's a signature to that
public key. Whereas near as a distributed
network has lots of validators. So how do what fancy
cryptography makes this makes this work?
Yeah, so it's called chain signature.
And so this is a threshold signature with where the valid
as validate is rotated. You can maintain actually the
same set of public keys. So even though you rotate and
like have different parts of the private key being rotated, they
all like when they sign threshold signature you get the
same public key then. I mean you can have derivations
of this, so you can have like as many public is possible, but
they all deterministic within the whole blockchain, so, so
that's a pretty cool technology. And yeah, like it's kind of
reasonably new. Some of the folks from the city
have been pioneering that and yeah, we've kind of leveraging
that as a way to have needed to become this, this decentralized
kind of custodial. Right.
I think maybe also XLR works a little bit like that or am I?
I think, But anyway, one question that I had like in this
scenario where you have the divider NFT on Solana, you need
the liquidity right on Solana as a user.
So maybe I have like funds on the air, but I don't have on
Solana. Like is there some system that
you're thinking of to to balance that out without bridging or
yeah. Exactly.
Yeah. So we so this is where we call
them intent through layers or I mean we'll we're still shopping
the name but so this is the idea that on near we have this well
we have this principle of trial account to this idea where I can
send you right now a link you click on it and you'll have some
near in it. So you can do stuff on near but
you cannot withdraw that near. So we actually like, we kind of
like what what it'll do is like actually send you a one time use
private key which when you click it actually create a new private
key in your in on your browser switch that private key.
But that private key is limited access to that account.
So you can transact but you cannot withdraw funds And so
that kind of concept applied now to other kind of chains in a way
a lot what it allows to do is we can have other parties to, to
fund the account to execute things, right.
They can put some Solana tokens to pay for gas or for NFTS but
you cannot withdraw that by sending a like direct
transaction to withdraw Solana. So, So what this allows now to
do is you can pay the somebody on near with near token and then
they will put Solana tokens there and then execute your
transaction. And kind of by doing that right,
we kind of have pretty much a way to that's why I say it's
intent, right. You say like my intent is to buy
some Solana thing but I don't have Solana token like here's
punch and near tokens execute that there and so and you know
now you need somebody who has liquidity on all the chains to
execute the stuff. But that's like having a third
parties doing that is way easier right than to have like whole
bridging and automatic execution.
So this is like, yeah, I really like some sort of fee attached
to it and the relayer can grab it.
It's not like a blockchain network or anything.
Yeah, yeah, exactly. Yeah.
There's like a surf body, like, you know.
Like a market maker or whatever. Whoever it is, it's.
Like yeah, any market maker or any like bot arbitrage bots can
do this kind of stuff pretty much and they also is doing that
they'll just relay the transaction as well.
So like you don't need to actually also send the like
submit transaction because like the valid is only signed
transaction right now and somebody needs to like actually
ship it to peer-to-peer network. So they they will do that as
well, yeah. That's pretty awesome.
Yeah. I like looking forward to
reading more about it once the the more documentation is there
and stuff. But yeah, thanks for for sharing
it here. And yeah, I guess further in the
near journey like we we didn't actually talk about much about
the chain itself, right. I think you were basically one
of the first, if not the first like sort of sharded block
chains and and been like staying with that sort of narrative
wire. I think the others have pivoted
from that. So yeah, can you tell us a bit
like how has the like near sharding developed or what?
What is sharding actually again for people that forgot about it
and you know where, where, where is it going?
Yeah. So as I mentioned, right, my Co
founder Alex who was you know, building sharded database, I
mean I'm coming from Google where everything is charted just
like you cannot have, you know, billion users and put them into
one database. This just doesn't work.
And so and like on my computer and so for us it was like, you
know, kind of pretty obvious that you need charting.
And so sharding, I mean at at the core of the idea is like you
as you process, you know, as you store more data, as you process
more transactions, you need multiple machines doing work in
parallel and you want this machines to be kind of doing
similar work, right. And like distributing load And
ideally as more load comes in, you actually increase number of
computers, right. So this is how all of the VEP 2
giants work. You know, again, imagine your
Gmail, right, or imagine Facebook, right.
There's like a database underneath which you know is
charted. It has hundreds or thousands of
servers that store for example user data.
And you know when you're a user requesting it routes you to the
server where your user data is and retrieves it and then when
you need to update something or process you know transaction, it
kind of routes a transaction there.
So that's kind of the core concept.
And like you know again logically you cannot have like
you cannot have billions of users using the same like 1
server, right? And this is what's currently
happening where for non sharded systems it means like they
relying on pretty much one server replicated, but one
server nevertheless to process everything that happens on their
chain and so. So for us it was kind of, you
know, pretty obvious when to do this.
Now blockchain adds extra complexity compared to that too,
where you have all of the, you know, security that you need to
deal with. And so we've been kind of
obviously iterating on, on a design kind of within this
conceptual thing. And so we introduced Nightshade
back in 2019, which was our sharding design where in a way
every single near contract or account is actually a separate
chain and we just bundle them in such a way such that as users
and developers you don't know about it, right.
And so we kind of bundle them to the number of machines that you
know parallel processing machines at a time you need to.
And so again this is very similar how VEP 2 works where
you know every like user account is in a way independent and they
store and they can be like moved around between different
databases like between different computers in the database and
so. So this kind of allows to
abstract out the complexity of the sharding from the user,
right? As a user, if you go to near
blockchain, you will not see shards.
We don't actually show them like you need to go to our PC and
like query the block headers and stuff like this.
Now the thing that we in 19 were planning to do was for security
was based on challenges and that's proved to be very
challenging. And this is across the whole
space, right? We've seen like a number of
other chains actually struggling with implementing challenges.
And so kind of earlier this year we ended up kind of doing
research and and refocusing on instead doing stateless
validation. So what this means is now when
block is produced, block actually contains all of the
state that that execute the transactions touched and that
information is being sent around to everybody else.
What this means is that other validators don't need to have
state of the Shard, they can just validate the block on its
own. And it means we can have, you
know, hundreds and thousands of validators validating every
Shard. It can be completely random,
they don't need to be assigned to specific Shard at any time.
And this also means we can you know have now a lot more kind of
nodes and validators in a network kind of proving the
whole system now kind of on a low level what it what NIR is,
is really a decentralized shared sequencer that then sends out
the data availability of this transactions across the whole
chain. We use erasure coding and then
we have this execution which now is stateless execution which
then is being proven by number of other validators and and
settled right. So we kind of package the whole
what now is modular framework actually in one you know
pipeline way on top of the same set of validators, right, kind
of just being rotated constantly across the network.
And so that's you know at the core what near is.
And so we actually going to be launching the new testing
network for stateless validation to kind of as a part of our
phase two launch. And so this is kind of finalizes
like core road map of of sharding that we've outlined
since 2019 and this should be coming kind of January or
February and we're going to have you know the full mainnet launch
probably in April. And this is the idea that
actually kind of conceptually if people read like with Dalek's
end game this is in the way that structure you have block
producers who are charted or kind of can you know we can keep
adding more block producers in parallel, so you can keep
scaling the network. We also moving the kind of a
somewhat in because of this block producers now don't need
to rotate as much. We're actually moving the whole
state into memory which gives us about 10X improvement on each
shards kind of transaction processing.
And so like each Shard gets 10X and then you can have more
shards And so then they kind of you know they do there's a
coding date availability and then they do processing creates
this blocks with state witnesses send them out and then you have
large network of validators who who don't need to be this large
who can just validate this blocks without having the full
state of the chain. So that that's kind of the you
know in a way finalizing our road map but also very much end
game. There's kind of bundles a lot of
the current like roll up concepts and you know sells a
base concept that is idiom is talking about into one product.
And then we announced we we working on ZK Vazem with Polygon
because this kind of just sending out state witness with
the block is actually a lot of bandwidth.
And what ZK Vazem allows us to do is actually to prove the
whole block execution with state witness on the block producer
directly. And so now instead of sending
like potentially you know MB of data we can just send you know
whatever 10 KB proof out and everybody else can just validate
that without re executing all the same transactions.
So that is kind of actually you know final game.
I mean there's like a few more pieces that to complete the
picture but that is the structure that we we think is
pretty much final kind of architecture that you know you
have tenship resistant shared charted sequencer, right.
So and you can you know you have like all the data availability
underneath to provide you so that and like we do data
availability first before execution because that means all
the other indexers and other piece of infrastructure can
start executing in parallel. And so you don't have latency on
user interfaces before the kind of finalization of the execution
on the validators themself. Then you have kind of execution
on validators send out witness and now you know large network
of validators can validate it and prove it without needing to
have state for dated. And all kind of having like you
know potential state is like 50 gigabytes for example.
So they don't need to like have that 50 gigabytes on them.
They just receive whatever relevant for the transactions
are being processed. And so that's kind of the, yeah,
I mean it's it's a little bit complicated as a as a scheme but
but like really it's powering this again like the kind of the
end game structure that people been talking about at the same
time it's it is like kind of that modularity just like
reusing the same set of servers right to to ensure kind of
throughput and latency low latency.
Yeah, like that's. That's like an episode on its
own, to be honest. To dig through that, is it
correct to think that like the stateless validation requires ZK
wasam as a primitive? So no, because you you can do
stateless validation without ZK. So what you do is you execute
transactions, you record which pieces of state you touched and
then you just send those pieces of state with with witnesses,
right, with kind of proof that it's part of the state together
with transactions. And so we're actually launching
that first while in parallel kind of working on ZK Wasim and
so Zikiwasm, what it allows to do though is just compress all
of these and execution of invalidation of this into just a
proof right. So in a way that Zikiwasm will
prove the execution of this BLOB pretty much state plus
transactions into just a fixed size proof but it but it's so
it's more of an optimistic Zikiwasm from this perspective
is optimization and it's obviously like way better for
like longer term you know storage but it's not a
prerequisite. I mean maybe I'll I'll try to
present my simple imagination of like of the system.
So the way I imagine it is like if you imagine I'm a I'm a
validator I'm an accountant right automated accountant.
Essentially in near I have the capability I'm assigned somehow
like some piece of work and and some of my work is also rotating
right like it's not part there's a massive Ledger, massive
Ledger, massive state and I am assigned hey go and make some
changes to this part of of the state so I can I can basically
go to that part of the state. There are bunch of transaction
associated with it. I execute the transactions and
1st I can I make the data available.
Hey these are the transactions I am I'm going to execute.
I make the execution, I update the state and today I somehow
provide some witnesses so that for the other accountants I can
sort of provide a proof. Hey, I did my job correctly,
here's proof. And they don't need to download
my part of the state to verify my work and the ZK proof will
make that even easier. So the state imagine as a
massive massive tree or something.
I can, I can modify some branches of the tree and I
create a proof and then I that proof is witnesses today,
Ezekiel wasn't tomorrow and I can send that thing to others.
They don't actually need to have my part of the tree in order to
verify my work. And and then there's a separate
system that says OK, in modifying this part of the tree.
What are the transactions? I did some somebody duplicates
duplicates that work and because because I can modify a part of
the tree quite independently and there are many like me, so there
are many accountants like me. All of these accountants are
kind of modifying like different parts of the tree in parallel
and like that is fundamentally why the system is able to scale.
Yeah, very well put. So you have partnership with
Eigen DA. Why do you need a partnership
with Eigen DA in that case? Yeah.
So, so this kind of maybe, yeah, changing gears right.
So so this is like near itself. This is near itself, right?
Like it has no interaction with other things but.
Yeah, yeah, so so and again Near itself right now is you know top
use blockchain by number of addresses.
For example, you know daily active, monthly active, weekly
active. And so like Near itself has like
a bunch of utility and and value already.
But again we kind of when we frame this like chain
abstraction thesis, right. What it means is that for the
developers and users on top, we're trying to provide a smooth
experience across using other chains as well.
And this is where we kind of looked around and like oh, NIR
already has data availability built in like that's just part
of our protocol. And so we have seen a bunch of
layer twos that we can plug in into this to kind of hooking
into the rest of our systems, right.
And so that's where we kind of you know started in in like kind
of pretty much provided a way to hook in AP stack, CDK, Stark,
Nets, kind of stacks. How do you publish your data on
NIR? Now if you just publish data on
NIR, it's useful it, you know, it's obviously very cheap.
It's you know way cheap like cheaper than pretty much
everything else in the market. And because near as shorted you
actually have more capacity than anything else can that can take
your data already and we're going to add more shorts, but
it's not as useful because you cannot route messages between
between kind of smart contracts on roll ups between each other
and near in near contracts. And so that's where we had a
partnership with Eigen layer not Eigen DA to help us actually do
the work for this layer twos to get to executed state and
outgoing messages such that the applications that want to route
messages faster within one and two second, they can actually do
that through the near network. So Eigen layer validators will
execute this roll up given the data published on near, they'll
execute it and they will have a new state route for the roll up
itself now. So think of it it's it'll be
extra accountants, Etherium accountants who will be actually
looking at the roll ups and updating statehood there, but
then publishing back to the near like telling it to near
accountants as well. And so now near accountants and
Etherium accounts together know the state of both near and all
of the roll ups that are plugged into the system.
And so now you can route messages between roll up
contracts and near contracts and you know back and forth.
And so this allows us to kind of again like align more the the,
the space of of the the space. And so again for chain
abstraction for kind of aggregation it means we can do
things way faster between all of the roll ups that that fit into
the system. So that's kind of how like DA
plus Eigen layer kind of provides this fast finality.
And then you know there's other kind of tooling that we you know
plug in on top is decentralized front ends to really kind of
abstract it from a user. But like we need that kind of
alignment again neat near in the way each account like each each
element of that tree is is separate roll up right And we
kind of we have a system for managing them And so we kind of
trying to fit the other roll ups into the same system and you
know obviously we need to like plug in some pieces to to make
it work under the same security parameters that roll up expect
right which is a theorem security hands dagger layer and
then DA is kind of way to get this data you know into the
system as well and and provide some guarantees there.
Hard to unpack, but but like logically it's like, yeah,
imagine, yeah, it's exactly that.
It's imagine near as this massive tree and then there are
like lots of accountants in near itself.
There's one group of accountants and then accountants can kind of
modify parts of the tree independent of each other.
We can send proofs about their modifications so that other
other accountants can trust their work.
And then kind of like this, eigen layer partnership is in
some way saying that. There is CDM accountants, yeah.
Yeah, it's like near, Near says. We have an awesome group of
accountants. But if you want your own
accountants and if you want your own roll up, you have created a
separate group of accountants. But then your accountants and
the Near accountants we we sort of need to interface in some way
so that so that the work, your accountant's date can be
deduplicated on near and the other way around.
And via this deduplication we can somehow achieve like
trustless interactions between Ethereum roll ups and Near
something like that, right? Yeah, pretty much like I would
say that. So the roll ups is pretty much I
want my own accountant, right that runs everything.
But then I I trust Etherium accountants to revalidate
everything and and finalize it, right.
So like Etherium accountants are are the final final.
My accountant is the one who can do quickly, right.
He sits right by my side. And So what we say here is near
accountants can, you know can provide a bunch of value by, you
know either connecting your accountant to the other guy's
accountant, right. So you can connect together or
to our applications. But we still need to see them
accountants because the finality of the roll ups is on Etherium,
right. And so that's why we have Eigen
layer pretty much to lend us their Etherium accountants to
kind of use the let the you know as a roll up publishes the
Ledger right from their account. And 1st like we have the sedum
you know accountants or Eigen layer to like validate
everything quickly right before the fully sedum sinality will
happen. And so that allows to kind of
near accountants and to have like trust into into the
execution of what happened on the roll up while also have the
way quicker you know time to finality and to communication of
messages for this roll ups and maintaining the same security as
as they have through ECDM. So that's kind of like, you
know, it's like roll ups near an ECDM coming all together into
like 1 happy family of accountants.
I think that's, that's a great note to end on, right.
Like big happy family of accountants.
Yeah. Thank you so much for coming on.
It's been like a massive episode, I think.
Yeah, I need to like process this and I'm I'm sure our
listeners will take some time to process everything too.
Well, we can do another one in a few months as we launched all
this stuff, so. Yeah, totally.
And yeah, we still also have Alex episode about the Smarter
LLMS. Outstanding.
So lots to do. But yeah, thanks so much for
coming on. And and thanks for to our
listeners, We'll have like 1 1/2 hours of of content here.
Raise guys. Thank you for joining us on this
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