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NEAR AI Ecosystem - What Did You Ship This Week? @near_ai & @NEARWEEK
Recorded: April 3, 2025 Duration: 1:03:17Space Recording
Short Summary
In this week's episode of 'What Did You Ship This Week?', the Nier ecosystem showcased groundbreaking projects including Public AI's crowdsourced data platform, Asimov's decentralized knowledge marketplace, and Verita's privacy-focused data solutions. Key innovations such as token incentives for data builders and the integration of MPC technology highlight the ongoing evolution of DeFi and AI in the crypto space.
Full Transcription
Hello everybody, welcome to episode 7 of What Did You Ship This Week? My name is Cameron Dennis. We do this every single Thursday at 11 a.m. Pacific,
where we really just want to create a space for the Nier ecosystem to share, well,
what they showed this week. It's pretty straightforward. We have an amazing set of
speakers today. Jordan from Public AI, Chris from Verita, Matt from Asimov, Zahid from Jutsu,
Matt from Proximity, Charles from
DiviAgent, and then at the very end, I'm going to give a little bit of a lowdown on what has been
going on in the Near AI team. So without further ado, I'm going to bring the first speaker to stage,
Jordan, welcome. Great, you are muted, and if you unmute and just quickly explain a little bit about what
public AI is, introduce yourself a little bit, and then spend about seven to eight minutes
or so talking about what you guys shipped this week.
Absolutely. Thanks for having me. Appreciate it.
So what we're doing at public AI is crowdsourcing data for AI training.
I am the co-founder and CMO of Public AI.
And that's what we're doing is crowdsourcing AI data.
And the why we're doing it is because we want to see a future where we have humans
actually being able to make their expertise
and experiences and everything it means
to be human equitable.
So to do that, what we have is basically
a crowdsourcing platform where as our clients come to us
with specific needs to improve their AI models,
usually they've done an LLM experiment or something like that
and need to get it up to production grade
in terms of reliability and cost effectiveness,
we're able to go and source the new on-demand data
that's required to get that to production grade.
They've already scraped the internet for what's available there
or through synthetic data that's already been trained
on the huge Reddit, Google, et cetera, datasets.
And they need fresh data to be able
to get their models performing.
And we're able to go do that.
We have close to a million data builders, we call them,
contributing all different modalities of data.
And then when it comes to making it equitable, that brings us to what we
shipped this week. And let's see if I can share screen. Do the window where we are paying people
out using stable coins and points. So here we have a recording of what it looks like to sign
on with your near wallet and after you've earned on the platform can go in and claim your earnings
and get paid for both the uploading of high quality data and verifying each other's data.
uploading of high quality data and verifying each other's data. So because we have so many
data builders showing up, like I said, close to a million total accounts right now,
when we go and run a campaign, generally some fraction of those show up, like say 20,000 people
come and are actually contributing data. And then the next video walks us through
what that looks like to contribute data.
Currently we're doing a campaign
where everybody is recording audio samples
in their native tongue.
So this shows the process of what that looks like
in our Data Hub DAP.
And as everybody is coming and adding data,
we say read the rules,
not everybody always reads the rules. There's going
to be a great diversity of people that are contributing. That's why our platform is valuable
as we've got people on every continent, different demographics, all of those things. But then we
also get a very high diversity of quality in that data. So as another step after this uploading process, we use
the Web3 primitives such as staking, on-chain, reputation, and then voting to be able to get
high quality data out of the data sets that are being uploaded. So I showed you how you can earn on the platform,
what it looks like to upload. And then let's get into an actual live demo of what it's like to vote.
So the voting process inside of DataHub, after you log in, is to go and look at all of the different campaigns that are going on.
And the deal with voting is you don't actually need to be an expert in, for example, speaking
Chinese or one of these other languages to be able to vote and say whether or not the
data sample is good.
And that's what we're doing with the verification process is just
making sure that the AI will be able to use this data and that it'll be meaningful for AI training.
So the requirements are that the voice is clear, that it's natural flow. So it sounds like an actual
speaker, like it's not a robot voice. It's not just an AI
recording of a voice, that the volume is okay. It's not totally blasted out or too quiet,
and that the voice is steady and smooth. So basically looking for a native speaker of the
language and that the audio quality sample is okay and that it'll be useful as a sample. And
that's what we're voting on, not voting whether or not we like the content or not, or if we understand it.
It's more just to ensure the quality of the data.
That's what the voting is about.
So to actually go in and do that, then we hit start.
And then we can listen to the data sample.
Not sure if it's going to come through on the recording.
But if you can hear it, this to me sounds...
No, we can't hear it, unfortunately.
Okay.
Well, it sounds like an actual Mandarin speaker
after playing the audio file.
And if it didn't, then I would say no.
Like if it was all distorted or it was choppy,
but it sounded like somebody just saying a natural sentence.
So we go ahead and confirm.
And then you get your notification that you voted and can go on to the next question.
So that's what we shipped this week is both the ability to claim with Near, your Near wallet,
The ability to claim with NIR, your NIR wallet, both stablecoins and points.
And then we shipped this new data collection campaign.
So we're looking for people speaking all different languages.
We currently have 10 languages on deck.
Like if you go into the DAP at beta.publicai.io, you can see if your language is listed there.
And if not, we will be cycling through
44 languages in the next couple weeks so your turn will come up um join the telegram and discord to
connect with the community of other experts and then be sure to give us a follow on social media
so you hear about new earning opportunities um and everything else exciting that we've got going on.
So that's it. Thanks for having me on.
This is amazing. I'm curious,
or is there like a premium of like on like, you know,
rare languages? Like if you.
Absolutely. Yeah.
A lot of people when they were coming to the campaign for the first time, we're like, Hey, I speak this niche language in Africa.
It's very regional.
And should I put down English because I know that English is a popular language?
Or should I put down my regional language?
And 100% regional languages is what we're after or what the client is after.
And we see this as being like a huge opportunity.
Just data we know is already valuable.
This is something that was kind of the start
of the story of of near to begin with and it's something that it's it's not a new insight either
like I came from the advertising industry where market research and so on is is valuable to this
day as long as people are spending money then human data will be valuable. And the more diverse and niche the data is, the more valuable
it would be. And through Web3 Primitives, we're able to get folks of all the different walks of
life actually contributing and make it equitable for them. And it's still cheaper for our clients
than having to run their own like warehouses of data labelers or you know in cases where we're gathering new data which is
our main focus there's just there's no way to get people to who are working in a factory to go take
a picture of a bird on another continent that's just not possible so yeah totally that's that's
what we're excited about hey man this is so cool a real use case of crypto who would have thought
yeah uh super exciting thank you for joining It's always great to have you here.
And I'm going to bring on Matt from Asimov. Matt, how are you doing? It's good to have you here.
Hello. Thank you very much. Hey, everybody. I'm Matt from Asimov Protocol. And yeah,
let's look at what we have done today. So it's been a lot of little incremental bits and pieces this week that we've been working on.
So let me just present here, share screen, and here we go.
Right, cool.
So the Asimov protocol, for those of you that don't know, we're working on a public decentralized knowledge marketplace.
So the big like beachhead use case we're solving for at the moment is when you go and ask an LLM a question and it comes back with a response, you have to double check that response.
response, you have to double check that response. If you were on a few weeks ago, you'll remember
us chatting about this. And Cameron mentioned about going to Thailand and asking ChatGPT,
are there any dangerous animals here? ChatGPT saying no, you're fine. And then him finding out
that the hotel had warned that elephants had been trampling people. So just a random example there. But that's the thing
we're trying to solve is LLMs often hallucinate information. And so we're trying to give them
verified knowledge to work from. So within our repository, we've got a whole bunch of
sort of packages. Some of these public, some of these not quite yet, but I think all these ones are public here.
But just to highlight two that we've been doing some work on at the moment, Asimov CLI that you can go to.
And Asimov CLI is our main CLI that allows you to interact with the Asimov platform.
And you can install that directly now using Cargo or Homebrew as well.
Plus, we've got package managers for other platforms as well, Scoop, Nix, Flatpak, etc., depending upon what platform you're on.
But that's your main entry point as a sort of end user and a producer of information to the Asimov protocol.
What does that allow you to do?
Well, one of the things is to use the dataset CLI,
which is an addition to this.
And the dataset CLI that you can install, again,
through similar means,
allows you to actually publish data to the network.
I was going to do a live demo here,
but was having a few issues just beforehand.
So here is a screenshot showing what was going to do a live demo here, but was having a few issues just beforehand. So here is a screenshot showing what was going on.
But here we have the Asimov Dataset CLI, and we are publishing to Testnet, to a particular repo here, two different RDF datasets.
IDF data sets that are taken from OpenStreetMap. So these are two data sets from the OpenStreetMap
So they're taken from OpenStreetMap, right?
data set that describe countries, boundaries, roads, all of that kind of stuff, but in a
knowledge graph format. So that allows you to load those up. So you can use the CLI to publish them.
It will go ahead and prepare them, chunks them into batches, and then uploads them to the
testnet. On the other side of things, we've got a few widgets here we've been working on. So we
have a map widget here that allows you to render some of the information that you might see. So
again, open street map information and a graph widget that lets you do network graphs, for example.
So this is the graph widget running here.
You can see here we've got an area of France here, Breton, and we can actually click on this and it will bring up the original open street map data.
So this is just a widget on its own, but this is just one of the parts that we are working on towards the whole visualization
of this marketplace.
So yes, that's the main thing.
So Asimov CLI, Asimov Dataset, Asimov Dataset Publish,
and the two widgets that we have there for rendering stuff.
Just as a bonus to throw in here as well as a demo,
I've been working on something to automatically transcribe messages, voice stuff, and put it into
a knowledge graph. And as with all live demos, I've been trying to demo it right here to pick
up everything I've just said, and it's come up blank. Demo gremlins.
But just to show you.
So this is taking a query from the last update that I did in which I was talking about our trip to San Francisco.
So this was live coding, live creating a knowledge graph from that.
So you can see on here bits and pieces about problems with LLMs, hallucinations, artificial intelligence.
We're in the US talking about blockchain, the Nier ecosystem.
And you can then query it.
So what problem is Asimov solving?
And actually referencing the graph with specific relations, it comes back and says that Asimov develops the Asimov protocol.
The Asimov protocol aims to address hallucinations.
And it defines what hallucinations are.
It says, therefore, Asimov is developing the Asimov protocol
to address the problem of hallucinations in large language models.
That's just a demo of some of the stuff that we're using just to test and kick
the tires of some of the bits and pieces we're working on.
There we go. Thanks very much.
Matt, that's super exciting, man.
How can you and Public AI
kind of work together here? Because it seems
like they have a lot of people who can
provide data and
help address these allotinations.
That's great. I was watching that just then
and I was thinking we should be collaborating on this.
So yeah, we'll
reach out to them and chat
and see because they've got all of that language information,
for example, that they were demoing there.
To be able to surface that through Asimov would be fantastic.
So, yeah, definitely we'll be in touch.
And that is one of the reasons why we run these spaces
is like we can all actually work together
to solve this giant issue of open source AGI.
So Matt, thank you so much for hopping on.
Chris, bringing you on.
Welcome, Chris.
It's good to see you.
Can you hear me?
Cannot hear you.
It did work in the green room, but let's try to maybe just change the output
or input of your testing. Hello. Beautiful. There you are. How are you doing?
Very good. Thanks for having me. Of course, man. So just real quick, can you please explain
one, who you are, two, what Verita is, and then three, what you shipped this week?
And I feel like you're sort of a special case.
Verita's been doing a lot of stuff over the years.
And so it would be great to kind of maybe talk a little bit about the journey to where you led today, what brought you here today.
And, yeah, take it away.
Yeah, would love to.
So I am Chris, based here in Australia.
So it's currently 10 to 5 in the morning.
So nice and early.
So Verita started out really as a decentralized private database storage platform.
So effectively allowing users with their private blockchain keys to take ownership and control of their personal data.
And of course, with the advent of AI, the ability to connect your own personal data and services you use to AI is a really interesting emerging use case.
And particularly doing that in a self-sovereign way, privacy-preserving way, where you as a user are in complete
control.
So what we've done is built out a whole set of tools for developers to easily get access
to user data.
And when I'm saying user data, I'm talking about things like accessing someone's Gmail
or their Telegram, their Google Calendar, their Drive, their Discord server, Slack, Notion, etc.
And so our vision here really is to make this data available for AI agents
and create these sort of personalized AI agents.
And so I'll show you some stuff today, but this is a little bit unusual.
It's a bit of a call out as well to the near um ecosystem and community because what we've built we're really keen to
integrate uh into the near ai agent stack um and uh i'll sort of talk to that a little bit um in
a second so uh let me just share my screen uh get the right one. Okay. So effectively, how this works is a user creates an account. So
VREA has its own network specifically designed for encrypted private database storage. A user creates an account. So VREIT has its own network specifically designed for encrypted private database storage.
A user then connects their different accounts,
so like a Google account, Telegram, Discord, et cetera.
And we have a deep in network of private TE nodes,
and they effectively synchronize the user's data
so that data is retained private.
No one can see that data except the user with their private keys.
And then we allow third-party apps.
So this is where you could be a builder building an AI agent on Nier
and a user can click a connect button
and effectively authorize your agent to have access to different types of data.
At the moment, we support things like chat messages,
emails, calendar entries, files or documents.
And in the future, we'll also support actions.
So being able to send a message, send a telegram message,
send an email, order an Uber, whatever those capabilities are,
we want to have that full read sort of write type capability.
And then moving forward to what we shipped this week, we have deployed or
released a set of lane graph SDKs. So you can effectively easily use these tools within a lane
graph environment. And the next step, as I mentioned,
is that we want to support integrating this
with the Near AI agent infrastructure.
So what that would look like is as a user,
if I sign in here, this is what we call the Vrida Vault.
This is like the home for a user of their data.
So I've just got a test account here
and I've connected some data.
If I, I'm gonna just jump across now
to a different screen and show,
where is it?
Here.
Okay, so this is a little command line chat bot
that we've built.
And this is just a demo.
It's all open source.
This is a part of our SDK.
And effectively, this is using those lane graph, I guess, modules that we've developed that has a whole bunch of tools.
And this is just a simple interface to interact with those.
Now, behind the scenes, I've actually generated an API key from my data,
and I've put it in an environment variable.
But this is just modeling what you could also do in Near AI
in terms of putting it in an environment variable
and then integrating that into the agent infrastructure.
So I can do something like list my recent emails.
Obviously, with this, you can connect to integrate this with different llms and things
like that but as an example here you can see it's invoked a tool and it's queried my email
to generate that response i can then follow that up with something like when is the demo day
like when is the demo day?
This account's just a test account,
so I've only got access to a few emails.
Sometimes this happens because Lama doesn't always
do the right type of query.
It's not doing the right type of query at the moment,
but that's okay.
You get the idea.
So you could do all sorts of things here,
like list all of the crypto telegram groups I'm in
or anything that you want with user data.
So if I jump back now to my window again.
So as I mentioned, this is like the vault where a user has their data.
It is possible as a user come in here and click on authorized apps,
and you can effectively create your own API key.
So you can create your own, or you can actually have other applications request them,
and then you can send to those keys.
So what we're looking to do next is allow people
to create their keys and then within the Nier AI assistant,
put them in an environment variable and then have AI agents
in Nier being able to access user data.
So, for instance, you could do something like, you know,
as the example before with Telegram,
list all of the crypto-related Telegram groups I'm in
and then do something on-chain relating to those
or provide some suggestions on, you know,
some projects that you might want to invest in,
things like that.
So really thinking about how you can connect this sort of stuff.
You do the same thing with email.
You can sort of search, you know, query someone's email
about all of the crypto communities that someone's a part of
or what types of crypto projects is somebody interested in. you know, query someone's email about all of the crypto communities that someone's a part of, or
what types of crypto projects is somebody interested in? And the AI will do a lot of the
heavy lifting to kind of answer those types of things. So that's a very quick overview.
We do have a whole bunch of documentation here at docs.vrida.ai. And we do have some
documentation here around all the different types of AI queries and things
that are available to developers.
This is awesome, Chris, and thank you again for taking this
at 4.45 in the morning, which is insane.
So I appreciate that.
And can you just drop all these links and these docs and stuff
into our shared Telegram group where we can share them after?
Yeah, we'd love to, mate.
No problem.
Awesome.
And if you can kind of have a magic wand and wish, you know,
the Nier community or anyone watching this would engage with Verita in any way,
what would you want them to do?
Yeah, I'd love to collaborate with anyone that would want to take user data
and actually deploy an AI agent on Nier
that's actually incorporating that data
and doing some really interesting stuff with that
because we're huge fans of Nier and huge supporters of Nier,
and we really see that being able to create personalized AI agents
is going to be a huge leap forward.
So we're trying to enable some of that infrastructure,
and we'd love to work with people in the community to enable that.
Well, I actually think what you're building is an absolute prerequisite for people to give a shit about AI, because if it's not personalized, then like it doesn't really matter to like my mom and my dad.
So love it.
We have a person who is building an AI agent that can post on LinkedIn on your behalf and will need your personal contacts in order to make sure that the posts are actually sound like you.
And he's coming up next.
And so, Chris, thank you for joining.
Zahid, you are up next.
See you later, Chris.
Zahid, welcome back.
I think your seven episodes, you've done every one of them thus far
it's pretty impressive yeah it's the consistency that counts in life so I want to be consistent
thank you for the invite yeah so you work on a bunch of stuff um you have about eight minutes
take it in whichever way you want.
But before, please, just for those who might not know you,
introduce yourself, introduce what Jutsu is,
and then talk about what you should this week.
Okay.
So, hey, everyone.
I'm Zahid, founder of Jutsu.
And Jutsu is an AI agent platform.
And we are working with Near AI to build a TypeScript SDK.
And our main goal is to build a developer experience where from zero to production will be in minutes, not hours.
So most framework today is like it takes a lot of time just setting up the infrastructure.
But we want to create experience where people can get from zero to productive in few minutes so we have been working on this sdk for uh one and half uh almost two months now uh and i'm happy to say that now
we have a ai agent uh kind of typescript agent platform or agent agent SDK, where you can build a AI agent locally
and deploy right now locally,
the demo that I will show,
but everything with using just TypeScript.
So you don't need complicated configuration or anything.
You just say NPM install near AI CLI,
and then it install that,
and you say NPM create your agent name it will
create and then you say npm run it will run the agent so i think as far i know because i pretty
much use every single agent framework out there so i think near ai sdk typecriptsdk especially uh will have the fastest productivity time
uh let me prove it to you uh so i'm going to share my screen uh where let me share my screen
and i think you can see my screen so this is uh this is a demo that we kind of instead of live coding, just recording.
So in the left side, you can see the CLI.
And in right side, we are running the entire Near AI hub locally.
And we are working with Near AI team to push it to production pretty soon.
production pretty soon.
Hopefully by next week we'll have something.
Hopefully by next week, we'll have something.
But you can see all you do is linear AITS create
and then it create, give it a name
that create a TypeScript agent.
And in the agent of TS, you add your code.
And after you add your code, it's pretty similar.
It's one-on-one matching with the Python SDK.
So we wanted to keep both SDKs same.
When you have the agent, then you can just say upload.
So now we are uploading locally,
but in the future, you will be able to upload
that agent in the hub.
So when you upload your agent, that will show up in the left side in the hub. So when you upload your agent,
that will show up in the left side in the hub.
And it's pretty similar experience
when you are building with Python,
then you can run your agent from the hub.
So let's see if the hub can answer the query, user query.
So in a few seconds, it will come up with the answer.
So one of the other thing we are doing
is like trying hard to improve the performance.
So you can see like this is not a kind of a demo setup.
So it's something that's the output is coming from from the hub so right now
we can create so the current status is we can create a agent using one command we can run a
agent using one command and we can deploy so the next step for us is like a configuring everything
so that you can deploy from local to the near hub and then use it
you share it with your user so that's kind of the um sdk side update and those who know me
they know that i do a bunch of stuff so i was uh there was a hackathon this weekend in SF.
It was about generating videos.
So how do you generate a video?
So some of you know that I have this,
I work on this platform called Post
where you can create a LinkedIn post easily
with just providing a prompt and then it will produce a LinkedIn post.
And some of you already know also, like, our platform produce really realistic images and videos.
So some of the images and videos are realistic.
So these are all auto-generated video.
And then I thought, like, it will be an interesting time to kind of figure out, like,
if I can take this video generation to the next level.
So allow me to introduce these videos where these whole videos are generated using AI.
The caption, the storyline caption and everything is generated by AI.
So I know that in Homebrew, they organized a vibe coding kind of event.
What if they could also add a video without any kind of work?
So I don't know if you can hear this video.
In this video, we're diving into the world of vibe coding, where creativity meets technology.
If you've ever felt lost in the coding jungle, stick around because we're about to unlock the secrets to coding with style and flair.
I don't know if you hear the audio, but the audio and the...
Tired of boring drinks? Let's shake things up.
Today, we're diving into the refreshing world of Spindrift,
the sparkling water that's anything but ordinary.
Get ready to discover why this drink is making sparkling water and real fruit.
No artificial flavors, no added sugars, just pure delicious. You get the idea.
So like what is possible is kind of extraordinary now, like with, let's see, stop the sharing.
So now imagine like if you want to create a promo video for something for your product launch,
or maybe you are running events,
you don't have to do anything.
So from like a, just a simple topic or a prompt,
we can generate a storyline.
We can generate all the videos,
stitch together the video and then the caption
and the voice.
So all in one place.
So that's the future we are going uh after and yeah privacy is also very important
so like um that's something we will work in the future but right now we are using um
linkedin api so your data we don't post your data we don't keep your data So if you want to give it a try, go to post.ai and give it a try.
So like all I'm asking at this point is give feedback.
That's it.
Thank you so much.
We've gone.
We've come so far, like since a year ago.
And I remember seeing like a very similar like AI generated ad where everyone's faces are merging together and the hands are, you know, doesn't make any sense, but this is really cool. So people could go check out post.ai,
but where do they actually give that feedback? Is there somewhere in post.ai where they can
provide that feedback? That's a good feedback from you. So thank you for that feedback. So
yeah, right now just DM me in telegram or just send me a message. But this is a great feedback.
So this week, we will add a feedback form or something that people can give feedback.
I got more feedback for your feedback.
And that is what your Telegram or, you know, open up your Twitter account so people can actually, like, send you notes.
Yeah, so I can share it with you and then
you can share uh with everyone perfect awesome thank you hope to see you next week and up next
we have mr matt lockyer how you doing matt it's good to have you here um i'm good you you got me like I'm coming in off of some severe release PTSD here.
We had a really fun time releasing.
I know I got seven, eight minutes.
You're pretty good, man.
It's pretty well, is it fair?
Just let it run.
Just let it run.
Charles, you're up next.
Just get ready.
We're coming off next very exciting uh release we released an agent for um for base
names um so these are basically ens uh names that you can get on uh on base and um what we thought
would be interesting is that you know i i really wish i i can't get through to x.com right now. So do you mind sharing your screen, Cameron?
Yeah, sure.
I can do that.
For some reason, it's just spinning for me.
So I'll let you know if it clears up later.
But I just wanted to show a couple of accounts
and interactions and stuff.
For sure.
All good.
Essentially, let's check out Banker banker bot if you can pull that up
thank you banker with just bank bank r
oh thank r yeah that guy little tv one yeah so a banker bot really awesome bot created by this guy, zero X deployer.
It's an awesome concept. So basically it gives everyone with a,
with an X.com handle an EVM address basically.
And with that address,
you can go to the banker bot website and use this thing called the terminal,
or you can just tweet at it. So if you click replies for banker bot,
we can check out the terminal is not the most exciting part of it. But if you look at replies, you scroll down, you can see some people, there we go, at BankerBot, sell 0.01 ETH for
this other token, I'm presuming, this contract address on Cal protocol. So you can even just put in a contract address.
It'll figure out how to do stuff.
Now, right now it only kind of sends ETH.
It swaps tokens and you can choose the protocol
you want to swap on as long as it's on base.
And it'll also issue tokens
so you can get it to like launch new tokens.
Pretty cool stuff.
So we thought with shade agents,
it would be cool to create an extension
for something BankerBot wasn't doing.
So if you go to just at base names,
based names, base names, yeah.
So this is our agent.
And if you click on replies here,
you can see that basically people are like, if you go to Kendall's, hey, at base names, grab Kendall.
So basically we can now purchase these base names.
Now what's really interesting is that, you know, our bot kind of kicks off the flow,
says, hey, I'm on it.
And what we do is we use near chain signatures
to create a one-time deposit address
where users can send the funds to that address.
And that address is specific for their X user ID
plus the name that they want to register.
So basically it's a complete one-time address
for just that main registration for just that X.com user. So basically it's a complete one-time address
for just that main registration for just that x.com user.
Now that's their custom one-time deposit address
for that name purchase.
So then Kendall tells BankerBot,
hey, send the above funds.
BankerBot sends the ETH to the one-time address.
And then our bot goes and looks up the deposit and you can see at the
bottom, it says done. And then it also, in the case of kind of overages, like in case there was
extra ETH left in the account, it'll automatically issue a refund from the one-time deposit address
to basically leaving nothing but a tiny bit of dust in the account. So we basically have created like this BankerBot extension bot or another bot.
You don't have to use BankerBot, but the flow with BankerBot plus base names
is now super seamless because you do not have to leave x.com at all to register base names.
So that's the good, that's the happy path that's what
that's what happened you know that's that's what we say what happened but obviously when we release
something like this we get all sorts of crazy stuff that goes on um i won't get into that's
what to ask yeah yeah i kind of want to know. Well, I mean, like, there is some really fun stuff that happens.
So, for example, like, we were using, like, we're using a bunch of different technologies here.
We're using near chain signatures.
We're using the Twitter API.
We're using BaseScan, which is basically EtherScan for base.
We're using their API.
We're using regular base.
I think it's like public node RPCs, right?
So if you put all these things together,
you have a lot of things that can go wrong.
Basically, exceptions can be thrown
and you got to handle like,
oh, okay, so I get an exception.
How many times should I retry this?
Because retries obviously would count towards rate limits
and you're rate limited on various APIs, especially X, right? So we did run into, when we first launched,
we ran into X rate limit. So we just had to increase the account and pay, you know, the
Elon tax. I hope I don't get shadow banned for saying that, but basically, you just got to handle these things as they come.
There were some really interesting edge cases from users though.
So people were sending base ETH from smart contract wallets, which was causing basically an internal transfer instead of a normal transfer of ETH.
a normal transfer of ETH. So we kind of had to handle that edge case as well. So there were a
So we kind of had to handle that edge case as well.
lot of things that we had to jump in and sort of just handle these interesting kind of cases.
Different, you know, different pricing for different lengths of names. And then also,
like, when we originally launched, I think people were trying to say stuff off of replies,
were trying to say stuff off of replies. The bot was getting confused. It was like double responding
to replies and stuff like that. So we had to shut off all replies and quote tweets and stuff,
much to the chagrin of some people. Some people just, they see a thread and they want to try
something too. So they just kind of post at the end of the thread. Fair enough. But I mean,
They just kind of post at the end of the thread. Fair enough. But I mean, we had just launched.
So for us to kind of jump in and disambiguate that case of like replies and quote tweets and all that stuff, it was just easier to shut off any reply, quote, tweet or retweet.
So anyway, long story short, there's a lot of interesting lessons learned what we're doing now uh because we just learned all those
all those lessons in uh in sort of um like it was like a baptism by fire kind of thing like we we
really got uh we really got scorched on a few things and but we were taking those lessons and
we're going to uh basically distill those down into the repos, the readmes for the base names agent, which is all
public. It's all out there in open source. And again, this is built on top of shade agents.
So it's built on top of the shade agent stack, which is a completely non-custodial
and the worker agent code that talks to X, talks to the EVM, you know, compares the transaction
and then eventually calls the smart contract and gets the signature, that code base that's the
worker agent kind of brain, that's verified. So we have blown away the key to the near smart contract.
There really is no way for us to change that code base on people.
And, you know, it's a truly non-custodial agent just kind of floating out there in the cloud.
Now, we're using FALA, and they've been a great infrastructure partner.
And the code for the worker agents deployed in a trusted execution environment.
These are great guys.
They've been awesome with supporting our work with shade agents.
And if the infrastructure, like if the agent were to have a problem
or kind of get stuck or there's some wild exception was thrown
and it was to be thrown offline, we can redeploy the same code,
but we can't change the code. So we can redeploy the same code but we can't change the
code so we can redeploy the agent boot it up again and sort of put some funds into it and
get it going again so it can make those um those near chain signature uh requests like to get to
get evm transactions signed but we can't change the code. So we can't change what it does. So it's just
doing, it's doing what it does and it's going to keep doing what it does. And as long as we keep
paying the x.com API and, uh, and we keep, uh, you know, we keep the, we keep the Fala instance
online, then it's just going to keep on going. And the near smart contract obviously just sits there and just expects that a worker agent
that's currently deployed on FALA talks to it with that verified code base. So yeah,
so lots of stuff going on, lots of moving parts, but we're still, we're working very hard on kind
of distilling a lot of the shade agent learnings and knowledge and getting that out there for people.
And yeah, we're in the works.
We're working on some small mini hackathons
to try to get people interested
in sort of building agents like this.
So just look for that in the future.
Hey, how do I top off my account
so I can press post and apply for me?
How do you top off my account so I can press post and advise for me? How do you top off your account?
Yeah, how do I give the actual agents the crypto to get me the name?
Oh, first you'd have to post this, and then it'll respond.
It usually responds in like a minute.
So it'll just take a sec, and then you'll see a reply to that. And then,
and then, and then it'll tell you which one-time address to send the funds to.
So I don't know if you're not using BankerBot that that's fine. You can still send base
ETH to that address. And then, and then within, I don't know, within 30 seconds or whatever of you, it depends because it sort of, it has to check for the deposit.
Then it goes and it prepares the transaction, gets a chain signature, comes back, broadcasts it, and then you get a success.
And it kicks off the, I think it responded to you already.
Boom.
All right.
There you go.
Yeah. So I got to do the next 10 minutes. All right.
Actually, I want Cameron.Bates.E.
Okay. I will
play with this. You don't have to send to that
address. It'll just eventually the check for the deposit
will just climb out and then it gets,
it gets kicked into a refund list. And basically if you send way late,
there'll be an automatic refund kicked out. But, but yeah, we don't,
we don't kind of keep you in the live flow for very long.
I'm doing this.
So yeah, that's the story. We're really excited about how this went, though. It got
it got a lot of it got a lot of attention, got a lot of eyeballs. People like the flow. I think
you're going to see more agents like this. Yeah. And like we said before, I was on here a couple
of weeks ago, I think for the hot wallet service and things like that,
we're still looking for teams that want to potentially use shade agents and chain signatures
to create kind of a universal, you know, wallet substrate for various web two kind of services.
So for example, like, you know, like Cameron, your X handle would be used for, you know, maybe one wallet on BankerBot.
But then if there's five other alternatives, you have different wallets, right?
We're trying to kind of see if we can maybe tie those together, at least for some of the bots, so that developers don't have to like reinvent that wallet infrastructure
every single time they want to make a bot that does different things. Yeah. And other ideas,
hackathon stuff coming up shortly. That's really all I got to share, but we're really excited
about it. We think it's pretty cool stuff. And we're hoping that, you know, that people want
to hack on Shade agents.
There's more you can find out for Shade agents at just near.ai.shade.
Beautiful.
Matt, thank you.
We'll be sure to share that with the ecosystem.
Is there anything else that, anywhere that people can go learn more about you,
learn about what you're constantly shipping?
Well, yeah, I'm on X.
Just Google the name and you'll find me.
And then also just follow,
you know, follow what,
I think it's at Proximity Fi is doing on X because we get a great,
you know, we get a great marketing guy. He's pumping out lots of stuff.
Vishal does great work and we're plugging into a lot more other ecosystems here.
Some people maybe that are sort of in the near ecosystem might say, hey, why are you doing all
this stuff on base or this or that? Well, we really just want to show off chain abstraction and chain signatures. We want to show off the fact that we can build these
agents where the brain is in a trusted execution environment. The smart contract is on near. So
all of the transactions are basically a near transaction to get that signature. And that's where you also add
that extra smart contract guardrails. So you can have like an LLM running in a T, but you don't
know what the LLM is going to do. And if it does something really kind of silly, you probably want
your smart contract to kind of add the additional guardrails there. So it doesn't kind of, it doesn't
do anything really stupid with user funds. And then that's all happening on Nier.
But then, sure, the ultimate broadcasting or maybe token issuance or swap or whatever
could be occurring on another chain.
And that's what we think is really exciting about this tech is that the verifiable agent
and the sort of the secure kind of stack that we've built around shade agents is a near stack.
It just happens to have the added bonus of being able to execute on any chain.
Yeah.
Everyone asks me like, oh, you're doing all this multi-chain stuff.
And I'm like, near is the new internet.
The blockchain was just the database to maintain state we've checked that box the second box is all chain
abstraction a prerequisite for you know the user on internet to actually work because users don't
actually give a shit if their app is running on aws or azure they just want something that's easy
to use why do we care about all this because ai is the future and AI is not going to care about what blockchain it's interacting with. So that's sort of my framing of it all. And it just makes
sense. I, you know, poo poo to those who are, you know, saying only do stuff on here. That's kind of
silly. So Matt, thank you for joining. I'm going to be bringing Charles on.
Charles, welcome aboard.
Can you see Charles?
Hey.
Yes.
Hey, thanks for having me.
How's it going?
Thanks for joining.
Quickly, just brief intro on yourself, intro on what you're building and what you shipped
this week.
Sure.
I'm Charles.
I'm one of three
contributors at Benevio Labs, including my co-founder Jacob Nall, former guys from Pagoda.
And we are focused on building DeFi apps for mainstream consumers. Right now we're working
on a project where we have this idea for an overall app experience that we want to build.
But as we're working towards that, we want to release core experiences of this app as soon as we can.
So one of the things that we shipped this week that I'll share
is the designs for the Divi agent that we're building.
These are the overall designs,
but we're also using the interface that we don't have to build
that already exists, the chat interface with Near AI.
So let me share my screens.
Yeah, so generally we want to onboard mainstream consumers, non-crypto natives, so that they
build comfort and starting to build wealth
and digital assets. And we want to allow them to use automated conditional swapping of tokens to
stable coins as a means to regularly provide financial support and for crypto natives as a
means to regularly trim off your portfolio as the market drives it up so that you can always buy the dip, for example.
This is the UI that we, the designs we landed on this week, in addition to the UI that we're using with Near AI.
And I just like how we found this kind of sweet spot between using the chat interface and then providing additional context with these quick buttons and actions that you can do to interact with with agent.
So that's one thing that we shipped this week. And then if I can switch and share.
The terminal. Another thing that we did this week is we improved the LLM on near AI that we're interacting with with our agent or improved its ability to match user input against agent functions.
So as we're writing Python functionality to grant the LLM capabilities to do things like save user information to be able to save conditional swap conditions.
How do we persist that between messages and just kind of found how we navigate between
saving information into the class where the agent is written versus
saving things into the file context that the LLM uses in between commands.
So now when I give it different information, I previously gave it my near account.
when I give it different information I previously gave it my near account so now
I can see what the agent is doing and see that it recognizes hey I should use
a certain tool to fetch the near account ID I know that I saved that in a file
and I can get that file to grab that information and then return it to the
user so now we're able to move forward with prompting the user to add all the information that they need to add in order to set conditions that the agent will use to autonomously issue a swap in the future.
So I can tell it, hey, I want, now that you have my NEAR account, you know, all of the Omni tokens that are in my portfolio.
I want to set a growth goal of $10,000. So I want to set a growth goal of $10,000.
It can recognize what functions it should use to save that goal.
I want to set an allowance goal of $3,000 such that when my portfolio grows by 10,000,
I want to do a swap of some combination of stable coins, some combination of tokens,
excuse me, that the agent will determine into the stable coins, some combination of tokens, excuse me, that the agent will determine into the stable
coins. So we improved the LLM's ability to recognize what functions to call. Now when you
give it different commands, it can appropriately read all the different files that it saved in that
info to determine how to satisfy your requests. So I then asked it, okay, now that you know my
goal is 3000 in USDT and you know all the Omni tokens associated to my NEAR account, recommend a set of tokens and quantities that I could sell to realize that value.
So now we're getting these values back.
And one of the things that we need to improve upon here is we can see it returning the value.
It's calling the correct function.
It's suggesting here's the combination of these different tokens to sell to realize your goal in USDT.
But the LLM is still not parsing that as appropriately to tell the user exactly the best way, like what that means, and still the LLM is doing it a little bit differently than just the raw output.
So that's one area to improve upon next. And this all fits into the context that we want to design for trust and allow an LLM, allow an agent to act autonomously on your behalf using an approach that users will feel comfortable with. So one of the things that we were thinking about this week is how do we design to improve trust such that one would be comfortable giving an agent the ability to do this type of trades on its behalf?
have access to that they would use to send a smaller quantity of tokens to.
And then we can leverage NIR's MPC network and NIR Intense, such that the agent can use
the MPC network to request a signature to be able to leverage the proxy contract with
restricted access to certain methods and restricted number of tokens to be able to act
autonomously on a user's behalf at any time without giving an agent access to your entire account. So this is one of the ways that
the way that we're right now experimenting with how to design for trusting autonomous agents.
And we want to leverage MPC and Near Intense to do this. And thanks to the Near One team and Near
Intense that chimed in on the Telegram chat, because we found that with this experiment, the signature that we're able to generate from the M see that there is a PR that was merged just last week to add ED25519 signature types into MPC.
So really excited to be able to test that out, to be able to generate the signature type that we can use to allow an agent to act autonomously on your behalf with near intent and really tie together what we
shipped last week of doing this all manually with near intents.
Oh, this is so cool. It seems like a prerequisite, another prerequisite to just ensure everyone in
the world can actually understand DeFi and not have to worry about constantly updating their portfolios.
This is super cool.
When Mainnet, not to be that guy.
When Mainnet.
And yeah, I guess where can people learn more about the project and you?
Yeah, so we're OpenWebEconomy on Twitter.
And that's where you can see everything that we're shipping, including the experiments
that we want to get feedback on different pieces of this app.
We'll launch those there.
I'll also add in the notes a link to our GitHub repo where you can build with this agent.
If you're doing something, you're building with agents, you want to do autonomous trades.
We're also doing a lot of this work open source so we can build faster and help others build faster as well.
So you can find everything we're doing there and maybe that helps you move a little bit faster too.
I have a lot of builders in our network.
What kind of builders are you looking to work most closely with?
I'm really curious about people interested in DeFi, people that are solving similar problems of how to bring users
into this experience um one person that's contributing with us has a lot of experience
on the llm side so it's been really great to see his knowledge of llm sdks from other environments
and bring that over to the near side but really anyone who's building or using defy apps and has
questions around common patterns that work in some context, but in DeFi there's
friction. We're dealing with those as well. So I'd love to chat about that.
Totally. You should absolutely get in touch with the Delta trade team if you haven't already.
Happy to make intros. Yeah, thanks.
Awesome. Charles, thank you so much for joining. Hope to see you on a future one with more progress.
It's amazing to see your journey thus far. We've been chatting about this for a bit, and so it's cool to see you guys. We are building the team at Near AI. It's very exciting. It takes a long time to get all the budgets and people and interviews and everything going,
but making amazing progress on that front.
Also making great progress on the Auditor Agent RFP.
Found an amazing security team
to kind of take that project and move it forward.
There's a lot of things that,
my role is primarily external integrations.
So I don't want to be the one to break the news of some of these exciting external integrations
that happen, so I can't talk about a lot.
Something that I've been thinking a lot about are governance agents.
And I'm actually just going to drop a doc I wrote about what kind of governance agents
I would like to see.
Because I don't know if
many people here probably aren't familiar with a very interesting experiment that the
NIR ecosystem ran last year called the NIR Digital Collective.
This was an experiment to slowly decentralize the NIR Foundation by handing funding power
to the community.
And we ran an open election to determine who should those people be.
There was a lot of amazing learnings. I have a whole presentation dedicated to that that I'm
not going to get into. But we pretty much created this incredibly complex DAO that had all these
levers of stopping funding and approving funding. And so the problem was that there's a lot of bias in governance.
And I want agents to help address that bias
and help people make better decisions.
So I'm probably just going to post this on Twitter
with view access only.
If you're able to check out
some of these governance agents that we like built,
we'll probably put out a request for proposal
for some of them.
Charles and everyone
else, like if you guys are interested in governance and especially for DeFi protocols, like might
be helpful. And also people sometimes, you know, can make money participating in governance. And
so there could be some interesting things that we could all do together. So I'll drop that.
Other things I'm working on are like encrypted memory solutions for the Newer AI Hub.
So like the Verita example, like how do you personalize your agents with your actual context?
I've been focusing on that and focusing on getting compute for the Newer AI Hub,
specifically H200 with TE support, which are very hard to find.
And so I scraped the earth, found some.
It's been pretty great.
Shout out to our partners who are doing that, Exibit specifically.
And then also, let's see, managing the Twitter account.
That's something that takes much more time than I originally thought.
So these are some things that I'm working on personally,
but the Near AI team is shipping a lot more. We actually have
next week Alex Comaford
come back on to talk about the progress happening
on the MCP side of things.
And then hopefully we'll also get Pierre
on to talk about the agent cloud.
I don't want to be the one that breaks
the news. It's not my news to break,
so you guys are going to have to hold off
and wait. But a lot
of things coming super excited
that's all i got um thank you for joining and if you want to you know keep follow the near ai
account the twitter account follow in your week um check out the near jobs jobs board we will be
kind of posting all those open roles there and that that's about it, guys. Hope to see you next week.
Thanks for joining.
