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NEAR AI Ecosystem - What Did You Ship This Week? #5
Recorded: March 20, 2025 Duration: 1:17:01Space Recording
Short Summary
The latest episode of 'What Did You Ship This Week?' showcased a vibrant array of project launches, partnerships, and fundraising efforts within the Near ecosystem, highlighting innovative tools like Instant Apply and shade agents, while also emphasizing the growing community and hiring initiatives.
Full Transcription
Hello, everybody.
Welcome to the fifth episode of What Did You Ship This Week?
My name is Cameron Dennis. I will be mainly just facilitating introductions
and giving a quick update on what's going on near AI land at the end of the call.
But to kick it off, we do this about every week just to have people show off
what they're building in the ecosystem, kind of treat it like a decentralized community stand-up.
Most of the time, people display demos of what they've actually built. ecosystem, kind of treat it like a decentralized community stand-up.
Most of the time, people display demos of what they've actually built.
Sometimes it's just general updates from a lot of the teams working in the ecosystem.
And today we have an amazing set of builders joining us.
Some of them have been working on near-implementations for quite a while, but not quite ready yet.
Let's have them like Xtrace for encrypted memory and private rag.
Silverstream talking about browser agents.
And we actually have a guest here that is, won the hackathon or was a winner
of the hackathon that we ran over the weekend from instant apply.
Looking forward to hearing from Jeevan.
Next we have Matt Lockyer from Proximity Labs to unpack some progress we made on the
shade agent side of things and HotWallet service, which I'm really excited to hear about.
And then we'll move on to Asimov Protocol where Matt Hamilton, who's a new member on
the team, will be unpacking kind of what they've been focused on with their CLI tools, their
SDKs, and some of it, I guess, recent experience being in San Francisco fundraising.
And close to last, but definitely not least, is Fala Network, giving a quick update on the
Red Pill and Fala Cloud integration, their Cloud CLI, and a couple other things.
And then Jutsu, Zahid Islam from Jutsu, will be talking about the near TypeScript SDK,
and then maybe a little bit about the hackathon experience
because he was also the hackathon. He won the hackathon actually first place for building a AI agent that manages your LinkedIn campaigns.
And then at the end, I will be giving a quick lowdown on what's happening in the near AI company.
So without further ado, we'd love to bring on our first guest, Li Wen,
bringing you up to the stage.
And just FYI, if you're going to share your screen,
make sure you're sharing the specific tab on your screen,
not your whole screen.
Yeah, yeah, but I need to kind of switch back and forth from a text editor and my browser.
That's totally fine. Just make sure you're not sharing anything you don't want to accidentally share.
That is the only request.
Just real quick, it would be great if you can quickly introduce yourself,
give a little bit of background on Xtrace, and then unpack what you've been shipping.
Yeah, so hello, everyone.
My name is Li Wen.
I'm the co-founder of Xtrace.
My responsibility is mostly on building stuff.
And yeah, so Xtrace is basically building a private memory
for AI agents, which is enabled by our encrypted vector database.
So I guess without further ado,
I'll showcase our product, which is not open yet,
but is pretty functional, I would say.
Yeah, let me start sharing my screen.
Okay.
Can you guys see my screen right now?
Yes, we can, yep.
Okay, so basically we have this,
so how you would use X-Trace is very much similar
to how you would use X-Trace is very much similar to how you would use any vector database.
So you will have a user developer portal.
Once you log in, you can see all the knowledge bases
and API keys on your portal.
So in order to use X-Trace SDK or vector database,
you first need to create an API key and give you a name as a demo, whatever.
And then, yeah, you need to create an API key,
and copy it, and save it somewhere
so that you can use it later with the actress SDK.
And then in addition to an API key,
you also need to create a knowledge base.
So as a near AI demo, knowledge base, create, right.
You also would need the knowledge base ID
in order to specify which knowledge base
you want to post your data to, right?
So once you have these two things,
yeah, you probably also need to manage.
So give an API key, you can manage the permissions
of this API key to multiple knowledge bases.
So right now I'm going to give this API key the right access,
the read and write access to the knowledge basis I just created.
So, right, so these are all the setup you need
before you can use our SDK.
So now let's dive into our SDK.
So basically our SDK has like two major components.
One is data loader and one is retriever.
So the data loader basically kind of takes in
bunch of crypto primitives and a storage and compute unit,
which kind of decides where to post your data to.
And then, you know, it runs the compute script
on your data locally, and then post the encrypted data to whatever
storage you specify, as you can see here from my screen
in the Jupyter Notebook.
So I'm going to run this cell.
Right.
So let's wait.
Oh, in the meantime, I'm using a news article I scraped,
which is this one, as the document
that is going to be posted to X-ray server.
So yeah, the data loader is now running.
Let's wait.
I think it's going to, yeah, okay.
So now it's done running.
So now the data should be successfully processed
and loaded onto X-Trace server.
So right now I want to show you how the data
actually looks like on our X-Trace server, right?
Just to convince you that they are all ciphers, right?
So these are the data that is loaded onto X-Trace server.
As you can see, the chunk, the index,
they are just a bunch of ciphers,
which means X-rays doesn't know anything about your data.
Okay, switching back,
I'm going to talk about how you,
once your data is successfully loaded onto X-ray server,
how you are going to use it.
So to use the data stored on X-rays is as simple as,
you know, import a retriever from our SDK.
So right now we only support a simple retriever,
which basically does nearest neighbor search
on your ciphers, but in the future,
we're planning to roll out more advanced retrievers,
such as, you know, the ones that can do metadata search,
can do complex queries, like range queries and all that.
Yeah, so that's the plan.
So now let me run this cell.
Once you have the retriever and the query,
you can just retrieve the necessary data.
So yeah, so now you can see it retrieved,
when I ask why is Elon Musk partnering with X-rays,
sorry, Microsoft, the retriever successfully retrieved
a bunch of context that is semantically similar
to the query.
So basically it retrieves the context for the agent
so the agent can use all this text to answer this question.
Now, switching back to the server side,
I want to show you the log so that you know the queries are
really encrypted.
So this is the log of that query. And if you check the API key here,
matches the API key I specified here.
So that means the demo is actually a demo,
not a stage thing, okay?
So let's go to the body.
As you can see, the query is this.
So it's just a cipher, right? So that basically means X-rays, when you query X-rays
database, X-rays doesn't know your query either. So X-rays doesn't know your query,
X-rays doesn't know your data, but still X-rays can return the necessary context to your agents.
Right. So once you have the context, you can just, you can just feed it to whatever inference engine you want
to use.
So for example, it actually provides a simple implementation of DeepSec.
So if you run this, it is going to take the context, return here, feed it to DeepSec,
and DeepSec will use this context to answer the question, why is Elamma partnering with Microsoft?
And yeah, so this is going to take a while
because I don't have GPU on my machine,
but I think you get the idea, right?
And yeah, I think that's it.
This is amazing.
And just to be crystal clear,
the primary use case of this is for people
to better train their AI agents
with their personal data
so they can be as helpful as possible for you.
Is that correct?
Yes, yes.
I wouldn't say it's training per se,
because X-rays is not modifying the weights of your model.
It's more, yeah, it happens, it's rag.
It happens during inference.
But yeah, but the idea is you can use your personal data
to make your AI agents better suited
for whatever use case you want
without sacrificing any privacy.
Cool, so this is a pretty important foundational prerequisite
for AI agents to actually solve problems for people.
Like you need a encrypted data store.
Yeah, yeah, yeah. So yeah, pretty amazing.
So it's incredible, man.
Yeah, yeah, yeah. I'm working tirelessly on making it better, adding more features.
But yeah, happy to be here.
This is amazing. Yeah. We'd love to have you back on. This is super exciting.
We do have to move on to the next builder, but thank you, Liwen, for coming.
What's the best way for people to kind of stay updated on the project and you?
Uh, I guess, uh, I'll post, uh, my, my telegram handle here so people can just,
you know, DM me.
Cool.
We'll make sure people follow your, your Twitter account.
Oh yeah.
Right.
Right.
Right.
Also, I need to do that.
Awesome.
Cool.
Awesome.
Well, thank you for coming. Uh, we. That was all. I needed to do that. Awesome.
Cool.
Awesome, everyone.
Well, thank you for coming.
We're going to move on to Manuel from SilverStream.
Manuel, how's it going?
Hello.
Hey, nice to meet you.
Hello, hello.
It's good to have you here.
So just as a quick reminder, a quick overview, what is SilverStream doing?
Who are you?
Why are you building SilverStream?
And then what have you shipped recently?
Yeah, so nice to meet you, Emmanuel. I'm a scientist. I come from a strong AI background,
and we're building SilverStream to make sure people can have their agents go on the internet
and interact with the web. As x-rays show us, people are going to share their personal information with agents, and
so we want these agents to do something with that information, which mostly is going to
the internet and buy things or do something in the name of the users.
So at Silverstone, we focus on getting things reliable.
You want an agent that does something every time, but also general. That's why we focus on giving tools to agent developers
like you all to use web web-based interactions.
What we've been focusing on.
Just real quick, sorry, hi.
So there's just to be kind of give someone like an analogy.
Is this most similar to like an entropic computer use
or an open AI operator?
Yes.
OK, cool.
It's like the agent goes on a website,
it clicks around for you.
And let me skip ahead and share a demo of one of our agents
on the near platform.
Let me share only the things I need to share.
There we go.
Can you see it?
Coming up? Is it? Camera?
Is it showing up?
Yeah, yeah, it shows up now.
So this is our agent on the near platform.
You can see on the bottom here, there is a request.
This request can be directly from the user,
or it could be from another agent.
And this video is sped up for X because we don't
want to be waiting for too long.
But the agent is going to stream back
a sequence of interactions with the web
to achieve some task, in this case, booking an Airbnb.
So if you call our agent as a tool,
you will get a stream of images or video.
We're still working on integration with Neonet.
And agent goes and does something for you.
Then it will eventually get to the checkout page.
We don't do payments yet.
I hope we can present that maybe next month
in one of these conversations.
But this is the direction in which you are building.
And the focus of right now, the focus
is to get things reliably working every day.
So in this case, for example, you
see the agent at a translation window
pop up, which was unexpected.
And you get out of it by itself.
Now, we want this to happen 100% of the times.
Let me skip forward.
There you go.
And then it goes to the booking.
So what we are going to release this week
is a data set for people to fine tune and train
their own agents, mostly because coming from a science
point of view, the most important thing is data.
You want to be able to fine tune your own agent from a good
enough starting agent.
And for that, you need data.
That's why we decided to open our data sets
to people that need it.
In fact, it's not public yet.
But if you go to SilverStream AI,
you can sign up for the waiting list, which
is going to be unlocked very quickly to get access
to our data sets so you can fine tune your own agents.
And the other point of user-owned agents
is that you need to be able to see how the agent works,
but also to create your own.
Maybe start from an initial open-weighted model,
but then fine tune it in an agent
that you care about for some specific interactions.
With the data set, we come also some kind of collabs,
like training kits, so people can learn how to train their own agents.
But our focus is really to get the most reliable agents,
deploy them on the near platform so everybody else can use it.
Do you...
That's basic. Yeah, super cool.
So just to be crystal clear, people should be going to silverstream.ai
to sign up for the demo.
Yeah, we'll throw the link somewhere here.
There are two waiting lists.
One is for the data,
which is the one that is going to be released soon.
The other one is for to be a user of our agent,
which is going to come later on in time,
but you can sign up so you get the early spots.
Cool.
Awesome.
Well, this is super exciting.
Where can people learn more about SilverStream generally,
besides the website?
It will be on the website.
We have a Twitter account.
It's kind of stale now.
It doesn't say much.
And also, there is my own Twitter account, which I guess is,
you can Google my name.
Can I say it? can people read the chat?
No, but we can follow up in a thread after.
Yeah.
Cool.
Awesome.
Cool.
Check out Manuel's Twitter account, sign up for the beta.
This is all super exciting.
Looking forward to having you on to talk about payments sometime.
Like let's make these agents useful for the real world.
So thank you for coming on well.
Next up, we actually have one of the hackathon winners
who is building with SilverStream.
And so, Jeevan, welcome to the show.
And we'd love to kind of get you to go and demo
what you're building or just talk about it.
But first, introduce yourself, what you normally work on,
and then what you should last week.
Hello, I'm-
And actually real quick, I see Luliana is also in the chat,
so I'm gonna bring her on stage as well.
Yeah, take it away.
Yeah, hello, I'm Jeevan, and me and Luliana,
we built Instant Apply last week in the hackathon.
Both of us are CS majors at Minerva University.
We are juniors right now.
Normally I just build web apps,
or try to play with some ML algorithms,
and Ulyana, you want to introduce?
Yeah, I also normally work more in science.
I currently work as a research assistant
at Harvard Psychology Lab. So that was an unusual project to make but we're both very excited about
it. And the idea about the project is to help people apply to jobs. So the
audience is basically everyone who needs to apply for jobs. The way it works is
basically it collects the information about you from your resume
and from the separate fields that you fill out like
full veteran status that you don't normally put on the resume
but that are often asked on the job applications
and it fills out the job applications fully for you.
So the from start to finish it finds the job applications for you. So the from start to finish, it finds the job applications for you and Chiwan will
talk and show the search in a bit. It finds the links to job pages that fit your resume
and description of what you want to find, like the job site, like the company size
like the job site, like the company size city you want to work from, etc.
It goes to the link, it navigates any pop-up windows, multiple pages in the application,
clicks through them in a way similar to what Manuel was just presenting was SilverStream.
But specifically for job applications.
Yeah, do you want to show the?
Yes, so just for the sake of time,
I already filled my profile.
Has basic information that they would ask in a job application,
your summary, resume, job titles, portfolio links,
all of the details, and demographic demographic information which is hidden for now.
And the first thing that you do is what type of job you want.
Say I want a software engineering intern and I want it to be in San Francisco remote.
I would search for jobs.
And right now we're using API,
but next week our plan is to use a SilverStream
to scrape and find all of the job applications
that are relevant, take some time from the API.
The way it works on the backend is it saves all the found jobs to a database from which
it then pulls to apply for them, but you can also check out the jobs manually.
I might have a little trouble sharing my screen.
Let me see. Oh yeah, and I can also see how we actually apply to the jobs.
Have you guys used the tool to apply to jobs yet?
Like yourselves?
Not just yet.
It's a little bit rough yet and there is a lot of for many job application websites. There are many different structures for how it works.
So it's difficult to optimize for every website.
But it's working quite well for single page sites right now.
And also, sometimes they ask for cat saw, and you have to log in.
And we just don't want to randomly put their password for user and save it in the database.
But if it does not have a sign up page, it works pretty well so far.
Got it.
Soon with with Xtrace, you will be able to put credentials in and ensure their privacy.
So yeah, you can see all these things are coming together.
Or I am.
Am I sharing my screen?
Can you see it?
Yeah, ignore the rough frontend.
That is not what we're all about.
So I, like, oh no, wait, can you actually see it?
G'know it, can you?
No, I only see the apply to jobs page.
Um, sorry about that. It's okay. I'll just go ahead and do what you just told
us not to do. Yeah, okay. That sounds great. So I go back to the apply page. I link the apply to jobs and it starts the whole process all by itself
and it fills it. It's not from my resume, I'm not Anne, but yeah, it can select stuff
from the data folders. It's not currently filled because it was a fake resume but it does work and it will do
the same thing with a resume. We were a little bit short on time because we just
built it on the hackathon literally but we're very excited about the idea and I
think for now we have a solid proof of concept kind of situation that is
actually working
and can interact with different websites. So that's where we're at now and we plan to build on it.
That's super exciting. What next? Like what are you doing to improve it?
So one of the things I just did, majority of what I worked was working on the Near-Ear Asintov,
seeing if we can actually have the source functionality
stuff there.
We tried a bit.
But I guess the next goal is to use SilverStream
and some other existing AdSense we have to fast,
safely store the user password so you can log in,
go through CAPTCHA, and also work on multi-page websites.
Workday, I think,'s really hard to work with.
So over the next week, we have a break,
so we are planning to utilize that week
to make it a proper MVP.
I love it.
Using your spring break to ship.
Love to hear it.
Cool.
Liana, Jeevan, is there anything that you guys
want to add, any place that people should follow you?
Should they reach out to you on LinkedIn, Twitter?
Anything else you want to say before we hop off to Matt?
Yeah, I think people can follow us on LinkedIn definitely.
We'll put links in the chat.
That works.
Perfect.
Yeah.
Awesome.
Do you have anything on your end?
Same.
I'll share my telegram and LinkedIn in the chat as well. Thank you.
Beautiful. Awesome, guys. Cool. Well, thank you for joining.
Hope to see you on in the future to highlight exactly all the progress you've made.
And without further ado, I want to bring Matt Lockyer to the stage.
Matt has been an OG Nier builder for a very long time. And now I think he is shipping one of the most impressive
and I guess inspiring pieces of technology
in crypto right now, which are shade agents.
And so Matt, I'd love for you to kind of further explain
what these shade agents are and what you've been working on.
Well, thanks, Cameron, for the glowing intro there.
Yeah, so I'm working on shade agents and I'll give a quick
refresher on that in just a bit.
And then I'm going to talk about some ideas we have for shade
agents that are coming up really shortly,
and we'd love to get the near community and other builders involved.
So I'm going to. Share screen.
Window. And basically just a quick overview.
If you haven't seen shade agents yet.
We basically have created a framework on near that allows
you to deploy arbitrary worker agent code in a
trusted execution environment.
So this code can talk to LLMs, which can also be in the T.
So they're running kind of in a confidential
compute situation.
Or I mean, the worker agent can talk to external APIs.
It could be talking to external language models
and stuff as well.
The real like core part of the shade agent stack
is that the worker agent will derive an ephemeral
Nier account so that it can basically talk to a corresponding Nier smart contract down
here and it will get the remote attestation from the trusted execution environment, including
the code hash of the actual code that it's running.
Now the reason that's important is that once we register
this agent in the smart contract,
this arbitrary worker code can just go ahead
and call methods in the smart contract.
And those methods are protected by virtue
of this worker agent running that particular code base.
So I can't come up with a better way to describe it protected by virtue of this worker agent running that particular code base.
So I can't come up with a better way to describe it other than there is a very tight coupling
between the code that is run in the trusted execution environment for the worker agent
and the smart contract.
So we have this real sort of tight connection there that we think is pretty verifiable and
pretty like sort of tight connection there that we think is pretty verifiable and pretty
like sort of secure. And that would look like that that sort of like manifests
itself in some cool design patterns where you can do method access control in
a protocol based on the worker agent having a particular code hash. You can
have multiple worker agents working on different parts of the smart contract.
Okay, so that's just a little overview on shade agents.
And I just wanna jump back and basically say,
there's a couple of different things
we've been doing with us.
So one for East Denver, we launched a shade agent
that basically would listen for tweets on X,
ZEETs or whatever whatever they're calling them
these days I'm old school I'm just gonna call it Twitter but basically he would
listen for tweets and it would it would look for people mentioning shade agents
and whether they were sort of throwing shade on AI slop that's out there or
whether they were mentioning shade agents in a favorable tone, they would get
rewarded with tokens that were minted on hyper liquid. So they
would get either base tokens if it's positive sentiment, or they
would get shade tokens if it's negative sentiment. Now, a lot
of people in the near community were like, Wow, why are you
minting these tokens on hyper liquid? We're showing off chain signatures as well.
So, you know, we can do this agent in a T
that has this tight coupling with the near smart contract.
But then from that near contract,
we can make calls to the MPC, the chain signatures protocol,
and we can basically get signatures for any chain.
So our next iteration of these agents is,
we're gonna be launching this next week,
is an agent that will register a name for you on base.
So on base they have base names or base.eth names,
and basically all you have to do is tweet at this agent,
and it'll create a one-time deposit address
that you can
that you can send the requisite you know funds to based on you know the price of
the name and once you fund that account those funds will be put towards
registering a name on base so the agent will just sit there and you can tweet
hey shade agent you know register my name on base.
And what we're going to do is we're going to have an interaction here with Bankerbot
as well.
So you can send the funds from your Bankerbot account and we'll register the base name to
your Bankerbot address.
So it'll create a little bit of like agent to agent stuff as well.
And again, like we're trying to show off that this is like a multi-chain solution as well.
And so the last thing I wanted to talk about
is we've got this,
this is kind of a bit more early,
early kind of green field idea here,
but basically we've got these agents out there
like BankerBot and there's a whole bunch of other ones coming out
where they all take sort of your ex username
or your telegram handle or whatever,
and they create a wallet for you
based on your ability to authenticate through that service.
Now, a lot of people are gonna create agents that do that.
And so for one Twitter handle, like my ex handle, whatever,
my name is like, at Matt D Lockyer,
I might have, if there's 10 different agents,
I might have 10 different accounts.
What we're trying to do, because we have this shade agent stack,
and we can sort of create a secure connection
between something running in a T
and the underlying smart contract
that can create these accounts for you.
What we're gonna try to do is we're gonna try to get
the like some of the near community
and maybe beyond a multi-chain community
to rally behind kind of a hot wallet service.
So one like one developer could launch multiple agents
that do different tasks, but they all
have the ability to access the underlying same wallet.
So for my handle, at MattDLockier,
I'll have one account, but it can be accessed
by hundreds of different agents.
And what we're trying to avoid here is like,
everyone launches an agent that talks to a wallet
that's derived from your ex username.
There you go, you've got hundreds of agents,
now you have hundreds of wallets.
That's gonna create a big problem.
So we're trying to kind of propose this hot wallet service
by using the Shade Agent stack as sort of a core primitive
to create this underlying like liquidity layer
that can be accessed by hundreds or thousands of agents.
And I think there's gonna be hundreds or thousands of agents
that are specified to do different tasks.
And then, you know, people might say,
hey, what about security?
This and that, obviously, there's some issues there.
But the whole Hot Wallet service would
run on the premise that you would have to mention the agent's
username to basically allow that agent to get access
to your underlying wallet and create a transaction,
call for chain signature and get that signature.
So we can do this.
Like I've thought through it kind of technically
and I think we can do it,
but we obviously want some support
and we wanna know that there's like a market
kind of reason to do it.
So we're seeing what the community says about it.
We have a Shade Agents Telegram group as well.
Yeah, if you want an invite to that,
send me like a DM on Twitter, X, whatever.
Anyway, yeah, happy to chat about it, Cameron.
Yeah, so I guess let's say someone wants to create a Shade Agent today.
Where should they actually go besides the...
Oh, you can start at near.ai slash Shade.
We've got a little notion page up there
with all the info about Shade Agents.
Yeah, and the stack that I showed earlier,
that's a great question.
The stack I showed earlier,
it's a framework that's ready to go,
ready to be built on today.
You basically just fork the Shade Agent template
and away you go.
And something like the HyperLiquid Token Minting Agent,
that took me a couple of days after forking the template.
And the Base Names Agent,
it's only taken me a couple of days
since forking the template.
So this is very fast and easy to use sort of hackathon fodder.
Besides some of the agents that you had highlighted, is there anything that you guys want to see built?
Like you guys don't have the internal capacity to build. There's a lot of builders probably
watching this. I think there's like over 2,000 right now. What are you looking for?
over 2000 right now.
What are you looking for?
Absolutely.
So I haven't been able to get really, really deep into LLMs
and using them with the Shade Agent framework.
So I'd love to see somebody connecting to an LLM,
doing some meaningful stuff.
So there's a lot you could do.
You could have Shade Agents doing image generation
or writing poetry or whatever,
and just doing some fun kind of interactions
and behaviors around the LLMs themselves
and the generative aspect of them.
I've mostly basically been using the sort of the Twitter
kind of command prompt and then basically do something.
So I've been sort of doing some seek and destroy
kind of example use cases,
but I'd like to see somebody do something more expressive.
And also, mind share trading agents, whatever, you know,
yeah, any sort of like web markets or whatever,
creating even like peer toto-peer like betting
kind of things.
Like you could have people kind of talking on X basically saying, hey, I think, you know,
the price of whatever coin is going to go to this.
Do you want to do the over under?
Somebody else joins up, deposit into some chain signatures derived accounts, and the
agent basically finishes
it off. And because you can kind of trust the agent code, the worker agent code, you
know, it can basically sit there, listen and resolve the market, and then basically execute
on the chain signatures accounts.
Cool. It sounds like you're using Twitter's distribution channel to get essentially the apps built and distribution is everything in AI and crypto.
So yeah, this is super exciting.
Last but not least, is there anything else that you want to add before we pass it off to Matt from Asimov?
No, I think we're good.
And I'm just really excited about the whole near AI community and what everyone's building.
And I love that you're doing this, Cameron.
So thanks for having me.
Absolutely.
Actually, other thing, does Proximity invest in teams
building with shade agents?
I believe we do.
Yeah, so we're involved in sort of a lot of the investments
in the Near AI space as well.
So yeah, just reach out.
I think we have something on our page, proximity.dev.
Cool.
Amazing.
Agent builders out there,
go build shit agents and try to get investments.
But first you obviously have to build something useful
that will ideally make money.
So yeah, awesome Matt.
Thank you for coming.
We'd love to have you on in the future.
Gotta pass it off to other Matt.
Thanks for joining.
Great.
Other Matt.
Hey, Matt.
We got a double header here.
Hell yeah.
Okay.
Let me just share my screen here.
Beautiful. Um, beautiful.
Um, and just as quick, quick, quick, quick retro,
uh, introduce yourself, introduce what Asimov does, and then let's unpack what you've been working on this week.
Great. Okay. Uh, you can see slides here.
Hopefully. Yes, yes. Okay, cool. Hi, folks. My name's Matt.
It's great to be here.
I'm going to give you a quick update on Asimov Protocol, a little bit about myself.
I am new to the Nier ecosystem, something I'm genuinely excited about.
I've recently, I've worked in a vast number of Web3 projects and crypto projects over the years, from the XRP ledger and Ripple
through the protocol labs at Filecoin, Arbitrum Foundation,
Arbitrum, BitTensor, doing decentralized AI and blockchain
stuff.
And now most recently, Asimov.
And I'm currently here in San Francisco. We're doing a US
fundraising trip so this is gonna be slightly different what did we ship
because in this case it's really more about shipping kind of ideas and sort of
progress towards what we're building but I thought it'd be good to give us a you
know give everybody an update as to what we're up to at the moment. So yeah I
joined Asimov shortly after meeting
Arto and Joshua, who you may know from the near ecosystem
already, at East Denver.
Their vision and energy immediately resonated with me
and drew me into their mission of decentralizing knowledge
and tackling some of AI's most pressing problems. So Asimov was specifically focused in addressing the challenge of high quality,
trustworthy, structured data. Something that we feel is currently underserved,
yet is critical for reliable AI. So our platform leverages decentralized, verified
knowledge in the shape of knowledge graphs, which not only help store and retrieve structured data efficiently,
but enable powerful reasoning capabilities.
So this approach allows us to integrate diverse data sets dynamically
and ensure that information is being used both accurate and verifiable.
So at the moment, we're wrapping up our fundraising round.
Like I said, we're in San Francisco. We've been running around San Francisco and the Bay Area,
chatting to many people, going to a lot of events.
So we're wrapping up our fundraising round at the moment.
I'm thrilled to announce that things are going really
positively.
We have an anchor investor, Brian Pellegrino from Layer Zero,
has come on board, which has given us significant momentum
to continue on
and when we get back home to roll up our sleeves
and continue building.
So the last three weeks, a lot of major events,
ETH Denver, Ethereum SF, Stanford blockchain AI Summit, GDC.
We've had numerous discussions that have been
incredibly valuable in helping us clearly frame our MVP. We've identified
our beachhead use case, which is addressing the critical issue of hallucinations in LLMs,
specifically when answering factual questions. So at the moment, you've probably experienced
you ask a factual question of an LLM, and the answer it comes back with may sound reasonable,
may seem feasible, but may be factually incorrect.
The example I often give is you ask it for a flight route
from A to B, and it'll come back and say,
fly this particular airline to this airport, this airline
to that airport.
Sounds all reasonable, and then you check it out
and find out that actually those airlines don't fly those routes.
So by ensuring the integrity of data fed into the models
through, like I said, decentralized verified
knowledge graphs, we can significantly
reduce these hallucinations and inaccuracies
when dealing with factual information
and boost reliability.
This is crucial because misinformation and inaccuracies
in AI outputs can have significant real-world
implications, affecting everything from daily tasks
to critical decision-making processes.
So whilst we've been here, we've opened a whole lot
of promising conversations with several institutional investors.
The enthusiasm we're seeing from these groups
is a fantastic validation of our vision. One event asked people who'd experienced hallucinations,
90% of the audience put their hands up.
I imagine the other 10% are probably lying.
And it's been fantastic validation of our vision
and gives us a lot of confidence in the strategic direction.
We've talked to a lot of people from a lot
of different industries about how verified
knowledge can help them.
So on the community and side event side, the last few weeks have been really hectic, but
extremely rewarding.
We've been traversing all over the Bay Area, actively participating in a whole load of
events from specialized hackathons to unique community gatherings.
So we intended things like
Near AI's Useful Agents Hackathon.
You've already seen on this stream some outputs from that.
We were chatting to people there.
Well, we've also joined really diverse events
such as a local art exhibition
at the former Boston Dynamics Building,
which blended like robotics and creativity.
And Arto at the moment is at the opening of a robotics co-working space.
So these kind of things are offering us useful insights into different sectors that Asimov can help with.
One particularly fascinating visit was to AGI House,
which is a four-story, multi-million dollar mansion overlooking San Francisco.
Imagine the geekiest frat house you've ever seen,
filled with the greatest minds working
on cutting-edge artificial intelligence,
talking about things like consciousness and really
high-level concepts.
And the energy and collaboration and depth of conversation
there was truly inspiring for what we're doing.
So it made significant connections,
spread our vision at prominent industry gatherings, Ethereum
SF, Prime Intellect's decentralized AI day, Shelling Point, ETH rising, we've
met a bunch of influential figures and innovative thinkers, Robert Scobel, Alex
Skidinoff as well from the EO community, Alex Chima, and these have sparked a lot of
interesting ideas around collaborations and what we're doing here. So yeah, we went to Stanford Blockchain AI Summit and we participated in DomSagola's
Developer Camp where we exchange insights alongside investors and industry leaders like Bill Tai and
Amanda Terry. And most recently we've been at the intersection of AI and Web3 with the GDC
conference that's happening here. So we've been going along to GDC events, community meetups
like Orbis86 as well.
So we'd love to continue the momentum.
You can follow us both the Asimov protocol main accounts
here.
And this QR code will take you there.
It's also asimov underscore protocol on Twitter, X.
And my personal
account hammer toe as well that you can find me there and follow me there.
Amazing that well thank you so much for giving the lowdown for everyone else
listening. This is how you do fundraising. If you are an agent
builder and want to actually get you know money, you have to show up you have to go to all these events you have to talk get money, you have to show up, you have to go to all these
events, you have to talk to customers, you really have to actually get your ass out there
and talk to them.
We were trying to make a list of all the different events we've been to.
It's been like three events a day we've been going to.
So yeah, it's paying off.
But it's a balance, right?
So like you do this now, so you can focus on shipping in the next couple
months.
To go ahead and also, you know, hopefully, you know, connections
for future funding rounds as well. That's, that's, that's
one of the main things, you know, that we're doing here as
well. So yeah, it's been very good, but also just validating
the idea, right? Talking to investors talking to so many
different industries, to care, talking education, you know, what does, you know,
verified knowledge and large language models mean to them? You know,
what are the problems they're seeing around using LLMs in, in, you know,
in the case of something like healthcare or even illegal or like I said,
education as well. So that's been really good.
And if you can solve hallucinations, holy crap, that is a really big deal. I was make this story very short, but I was in Thailand, asking LLM,
Hey, are there any like dangerous animals around and going to the jungle? And it said no. And then
I go talk to the person that's working in the front desk of the hostel that I was staying at.
And they said, Oh yeah, right across the street, there's a whole family that was trampled by elephants yesterday. And so I'm like, wow, I wish that this LLM let me know that I have to watch out for elephants.
And so it literally can mean life or death in a lot of situations.
That's a fantastic use case. I mean, we're not going to be able to solve hallucinations in
general for like everything, but definitely for factual information, for questions like that.
This is perfect. That's the kind of thing we're going to be looking to solve. Amazing man. Well, thank you for coming on.
A little bit different than the average. What did you ship this week? But builders take note. This
is how you fundraise. Yeah, great. Thanks for having me on. Awesome, Matt. Thanks. And I'm going to bring on Hong
from Fala. And just give me one sec while I bring people to the stage.
How's it going, man?
Hey, great, great.
I feel like I see you a couple of times a week now.
I'm very lucky.
But just to give everyone a quick lowdown, remind them,
what is Fala Network? Who are you?
What's your role at Fala? And what did you ship this week?
OK, sure.
Yeah, I think this is actually the second time
I've been here to share what we have built last week.
So I'm Hang, co-founder and the CTO of Fala Network.
Fala Network is a TE-based solution blockchain,
as well as the cloud.
So our major product so far is called the Fala Cloud.
So it's kind of like AWS,
but as long as you put your program deployed on our cloud,
we make it fully trustless.
So how does trustless mean?
Although it's not like a fully distributed,
fully decentralized cloud,
it's still hosted on some data centers.
But at the same time, you get two properties.
One is your program deployed on this cloud is verifiable.
So integrity is protected.
And second, you also get the privacy.
So all the inputs, outputs, and the intermediate states
is all encrypted.
And nobody else except the program and the user
can see the raw data.
So by using the TE, there is actually
a lot of interesting news cases.
One of that has a huge overlap with near AI.
So when we develop AI, we always want
to make sure that AI is not controlled
by any single entity.
So if you just run the AI on AWS, then no matter how secure your AI is, it's still
limited by the security of the infrastructure.
As a company, you can always, as an admin, you can always access the AWS and change whatever
that's already deployed on the server.
So essentially, although it's an AI,
but you have no way to prove it's the real AI
and it's not going to do anything evil.
But if you put it on a TE,
TE is like a super, super secure box.
So you can prove that this is all controlled by the AI
and you can even trace it back to your program,
your source code, you can trace it back to the L. And you can even trace it back to your program, your source code.
You can trace it back to the LRM you're using,
and even how you train this as a LRM.
So everything can be transparent and fully auditable
and fully secure.
So this is the power of TE.
And in today's event, I'm going to show you
a bunch of interesting progress we have made last week.
So here.
All right, so under the hood, our team do a few things.
First of all, we do the development
on open source projects.
So no matter you're using
our product or not, you can just feel free to use our open-source project, including Dstack, which
is the backbone of all the T infrastructure. It's an open-source project. You can deploy it on any
capable hardware to turn that into something that can be used easily. On top of that, we also have the collaboration with Nier
to ship the private ML SDK.
It's another layer built on top of DStack.
So last week, a big update from DStack side
is that now we enable the integration
with the private ML SDK.
So that means you can use the GPU with the features already provided
by the underlying open source SDK.
So give you some feeling.
So now if you use the private ML SDK,
you're going to do something like this.
So there is a combined new and give all the parameters
and then you run combined,
this stack run your CVM.
Then you have your program loaded into the GPU TE.
But at the same time, if you check our cloud,
then we actually give user very user-friendly web UI.
So you don't need to write any code or write any command line.
You just click Deploy, and then everything is already there.
So the updates
of this integration actually enable to connect the GPU SDK with the CPU SDK so that in the CPU
SDK we give you all the like convenience feature including the web UI in this CPU SDK. So even without using our commercial product,
you can also use the exact same user interface
on your customized build or on-premise deployment.
So this is the first thing.
We do also have a few other updates,
but that's a little bit technical, deterministic RTMR3.
This makes verifying your T TE application on-chain easily.
And another one, add TCB status check on the on-chain KMS.
And this can make the verification of your TE program
on-chain more secure.
So yeah, this is the updates on the open source SDK side.
And second one I want to mention is that last week we also
did a really interesting thing.
If you're familiar with AI agents,
one of the very popular AI agent SDKs is called Eliza.
And they just ship V2.
And here we actually have a very brief demo
how you can run Eliza V2, which
supports multiple agents running in the same program.
It's like a swarm, not just a single agent.
And you can already deploy that with our CLI tool,
integrate it to your GitHub action,
and you can make sure it's like the user experience is
pretty much like a bird's-eye-catch. You make the change and
Push it to your github then everything can be automatically
Pick up by the github action compile build and push it to the cloud
Everything can be done within just around 10 minutes
So if you are interested in that, you can check our blog post on our website so that you
can learn how to use that. So this is the second one, Eliza B2 on the Fella cloud. And the third
one is actually when you're using a TE, very important thing is you want to check how you can prove this program is
indeed running in the TE and it's secure. In the past we do have a proof web UI
where you can upload the proof and see the technical parameters of the proof.
But as you can see all those fields are just the hashes.
And for non-technical people, they
have no idea what it is.
All they can do is to check, OK, it shows Verify.
It seems very secure.
But we want to make this even better for the average users.
So here on the cloud, we do have a new version
of the Testestation explorer.
So as long as you turn on your application showing
on this dashboard, it can be a part of it.
And if you check, for example, this one, Timelock,
if you click View Testation, we're
going to bring you to a page with detailed explanation.
And for example, it shows this is running in the verifiable hardware.
The system is good. System means the operating system as well as the infrastructure and the KMS,
which is another component that secures your program.
And in the future, we're also going to give you the application level verification.
That means you can link from the hash of your Docker container
into your original source code.
And this can make the auditor or even non-technical people
way easier to be convinced this is the real TEE, not a fake TEE.
And there are a bunch of explanations here.
So I'm not going to talk too much about that.
And finally, last week we launched our Cloud CLI.
So for some of the people, you feel really good
to just use the web UI.
But if you go into a professional,
then you want to automate everything.
So that's why we bring the Cloud CLI.
So in this CLI, you can almost do everything
that's available on the web UI, but always the command line.
So all you need to do is to first install the CLI,
and then you can use the command to do everything you need.
For example, link to your cloud account,
create virtual machines, manage virtual machines,
or retrieve the attestation of that.
So yeah, because time is really limited,
so I'm not going to talk too much about that.
Oh, yeah.
Finally, one more thing I want to mention
is that we just, let me check if I can find it.
Yeah, here it is.
So one more thing is when you're using the,
when you're using, like, building the AI agent,
usually you not only build your agent itself,
you also need to use the AI models, right?
So another thing we just shipped last week,
but actually not shipped yet,
this is the preview version,
is that now we have integrated the TEE GPU inference into the cloud as well through another project
called RedPill.
So that is kind of like an open router, where you can use just
one account to access many, many different AI models available
on the platform.
But here, what we want to highlight is that now we have the models running in the GPU TE.
So not only you can get your AI agent fully secure,
you can also get the model fully secured with the TE.
So nobody else, including some Altman or whoever you can tell,
they have no way to say what's running in this model
and what is the input or the output.
So yeah, if you want to do something sensitive,
deal with the user privacy data, use this one.
This will bring your AI agent to the next level.
All right, okay.
So that's a very quick overview
of what we have done last week.
Yeah, you guys are doing too many things
to fit in a single session.
So it's, we'll make sure to follow up
with in greater detail with a thread
because there's so much going on.
So congratulations on all the progress.
Thank you for joining as always.
As I've asked everyone else,
what is the best way for people to stay in touch with Fala?
What is the best place for people to like, you know, learn more?
Oh, yeah, of course.
Just check our Twitter account and we're going to push a lot of updates every day
and also a lot of memes.
All right. If you like memes and updates,
go follow Follows Twitter.
Thank you, Hong.
And last but very much so not least, we have Zahid.
He is a frequent attendee of the Wedding Ship this week
because he's always shipping, and it's always exciting.
And also huge congratulations on winning the useful agents hackathon
Your agent was by far. Well indeed the most useful
And I if you want to kind of give a quick overview about what that is and then also then talk about what you
What else you've been working on? Okay. So hey everyone. I'm Zahid. I am
founder of Jutsu and kind of a,
we are working on two main things at this moment.
So the first thing is the main priority is kind of a,
building a really good TypeScript SDK for builders
because Cameroun and Nia did a great job of the hackathon and
It was great for two different reasons one
I get some time to kind of a hack on something random stuff that typically I will not do in my day job
As a founder and the other thing was like I had the opportunity to kind of see how people
are building and using the near AI Python SDK and what are the problems they have.
So, based on all this learning, we are kind of working and improving the TypeScript SDK.
So, it's not at this moment ready. But I'm going to give you a preview of how it will look
like.
Let me start sharing my screen.
And then share screen.
I'm going to share entire screen, and I promise I don't have anything that I don't want to
share.
So this is I'll run a video. So it's about the, one of the main thing I learned
during the hackathon was getting started to build agent
was not easy because people had to fight
through the Python version mismanagement
and kind of from zero to agent took a lot of time for a lot of people, even for
experienced Python developer. So, our goal is to build a kind of a mechanism where all you do is
just an NPM install, something, and then you just create and start building
and start testing locally.
So if you see the command line, so for near TS,
it will be near EITS, and then you just,
it's a similar, same kind of a way to create a agent,
but all of a sudden you have now a project
with two file where you can run any TypeScript
and you can run any kind of TypeScript,
but you get all the functionality for near AISDK
through the core.
And then you can add your code to run,
I'll speed it up.
So you probably miss that one.
So you will just say NPM run.
So, and then it will run the project.
And in the pro and then you can kind of give your input.
We also saw like how difficult it is
to do the multi-line thing.
So because if you, if your agent have a multi-line kind of input,
local testing is a nightmare.
So with this one, it's as simple as you don't have to,
as a user, don't have to do anything.
The framework will figure it out.
So two big important kind of a thing.
One is you just have to do NPM install
and then it will feel like any other standard
TypeScript project, like if you are familiar
with Create React app or Next.js app.
So it should feel like that for JavaScript developer.
So that's kind of the SDK update,
and we are working on it, and we'll give more update.
The second thing I'll show is the hackathon project.
So the hackathon project, the main idea
was I don't like to do social media, especially LinkedIn,
but I understand how important it is to do LinkedIn posts.
So imagine I have an event that will be next week.
So in fairness, I am going to run SFNode on next Thursday.
So now I need to start posting for that event
so that I can attract more attendees. So it's a work. Every day I need to kind of start posting for that event so that I can attract more attendees
So it's a it's a work like every day I have to do so instead. What if I could have a agent?
Something that kind of a do it for me. So that's kind of the main goal. That was the main goal
So that's why like we added this campaign feature where you will say, in the description section,
maybe you will just copy paste your event description.
And then you will say, what is the goal you have?
What kind of writing style?
And what is the call to action?
And which date to which date you want to have this event?
And then you will see, so I asked from 24 to 28.
And then it created all this post for me and
then if you are
Interested you can go and so this is about the post product launch. So you will see like a
This is I did not add anything
kind of a but you are like a you are always welcome to kind of a check the
generated but you are always welcome to kind of check
the generated output and then kind of, let's say I want here probably a new line
or you want to add something else,
but in few second you get post for the whole week.
So that's kind of one of the important,
that was one of the main goal.
So that's kind of one of the important. That was one of the main goal.
So that's what we did.
And now, during the hackathon and outside hackathon,
when I'm showing this feature, and it
looks like a lot of other people are like me, who don't have
time or who don't necessarily like to do social media,
and they want to use this tool for that.
So it is available.
So if you want to use, just go app.post with 2t.ai.
So the other feature that I think is super interesting
is if you want to create kind of a single post,
so you not only just create a text post,
but also can add images. And then let's say this is a generated post, so you not only just create a text post, but also can add images.
So, and then let's say this is a generated post,
but it looks like too long.
And if you want to make it shorter,
so it's just as simple as like choosing it
to make it shorter.
So now, yeah, I will kind of make it short.
You can generate images,
you can generate more realistic images, or if,
so, and then this is probably because we all are engineers,
so we put a diff.
So you'll be able to see what are the things that this AI
agent changed.
So you can see this new version is pretty much
half of the old version.
And then if you are OK with that, accept.
And then you can improve it if you want.
Image is same.
So if you want to generate a new image,
so in this case I am regenerating,
I can regenerate,
but of course you can always generate
with all the different other style and other features.
And then the last thing is we are working with videos.
So if you want to add a video,
so then you can just use the video.
Yeah, so, and the last thing I would say,
like some people also, I found like a true research,
like a carousel is very important for LinkedIn
and I have no time or interest to build a carousel.
So with this one, you can just say, okay,
near AI agents,
and then it will kind of do a Google search,
and it will try to find information about near AI,
and then I don't know what it will find.
Welcome to the future of AI,
introduction to near AI agent capabilities. So whatever it found, it's kind of
a... But you can see, like, you can... It gives you... Think of
it, this tool, as a v0, so where you get the version 0, and then
you can quickly iterate, because I don't know about you, but for
me, writer blocks is a thing and English is not my first language.
So I always find this kind of tool useful.
And I don't have to copy paste from a chat GPD
or kind of create a really nice prompt in chat GPD.
So that's kind of the main reason for building this tool.
And if you want to use it, go to kind of a app.post.ai.
And if you are using, if you want to take LinkedIn seriously,
so start using this tool and follow the post page in LinkedIn.
Awesome.
Super exciting stuff.
Looking forward to the improvements to TypeScript SDK.
People come to me all the time saying,
hey, I'd like to deploy an agent I built on the Near AI Hub,
but it's not in TypeScript.
So I think this would be a big unlock for the ecosystem.
So thank you for coming.
Is there any last words that you want to add,
any place that people should follow you?
Obviously, try post.
I'll be talking a bit more about that later.
Is there anything else?
LinkedIn, like I'm probably more active in LinkedIn.
Yeah.
And send me a message in Telegram.
You can send me a message in Telegram, anything
about TypeScript Update,
or if you have any suggestions.
We have open source repos, so you can follow the project, our progress.
Any constructive feedback, you're welcome.
Awesome. Well, thank you, thank you.
I guess I'm the last one to go. And the TLDR is that there's been a lot going on in the near AI world.
Right now, let me just share my screen.
I'm going to be giving you guys a quick update.
Everything can generally be found on Twitter on exactly what we shipped the last week.
We do our best to build the public. And so kicking off from, I guess, this week is the hackathon.
We ran this on Saturday.
We had over about 280 people sign up, about 140 people actually attended.
I had about 18 project submissions.
The whole focus of this hackathon was to build useful agents.
Useful agents, the utility piece is defined by some sort of benchmark
that you create to will prove its usefulness.
And so that was really well received.
That was in partnership with Electro Capital and SF Compute.
Electro Capital is probably the best, if not one of the best VC funds in the crypto land,
and they've been doing a lot more deals in AI.
One of their portfolio companies is SF Compute
where you can actually buy, sorry, rent or lease GPUs
by the hour without any long-term contract.
This is a really big deal for anyone that is, you know,
training models or, you know, needs very flexible
compute contracts for inference,
depending if you like get a lot of inference
at a given time.
And so both of those partners are great.
We had an amazing set of bounty partners.
There's a whole thread kind of unpacking the photos.
We had Adam Wolf from Anthropic give an amazing presentation about...
He actually helped run Claude code, yeah, Anthropic, talking about what makes
a useful agent. Evan from SFComputes, Ilya from Nier, of course, and that was really an impactful
and educational talk. These are some of the winners. You just heard from Zahid with Post.
Second was Diligence AI. They built a VC due diligence platform and a set of agents that
decreases the time that they
actually need to take to do quality technical and non-technical due diligence. And this was a
winner. Near Food is an AI agent that can help you find free food based on Luma. If people are
organizing events, it essentially scrapes Luma and recommends you events to go to for free quality
food. And I will say there was amazing food at the Useful Agents hackathon, which some people may
have just gone for the food. So hopefully this doesn't make a bunch of food grifters come to
our feature events, but it's a cool agent.
I'm not going to walk through all of the agents, but I will say it was a pretty
cool and eclectic group of builders.
People, everything building from like research tools for biology to test time,
compute routing agents to, you know, improve reasoning, uh, on for agents and
other LLMs to, you know, vacation mode, which is an AI agent that can try to save corporate
knowledge bases. So if someone goes out of office for a while, they are not totally lost when they
come back. The classic wallet portfolio managers, other apps to agents to identify the cheapest
version of an item you're trying to buy,
fact-based agents to fact check news, AI agents to demonstrate how you can
confidentially navigate robotics. So it's like agents doing everything. It was pretty cool. Really enjoyed the event. And next up, another big thing that Alex Kovaford
from the NIR AI team did talk about last week
is NIR's implementation of MCP.
This is Anthropics Model Context Protocol.
And the whole point is now,
if you are using any type of building framework
that is supportive of Anthropics MCP,
you can now use those tools to build agents
that can interact with Nier.
They can do things like manage accounts,
check balances and tokens.
You can read this thing here, but it's pretty cool.
And I think a really great way to sort of plug
into the greater MCP ecosystem,
which is growing very quickly.
And obviously there's a lot of future work to be done.
This is, I think, really exciting stuff. which is growing very quickly. And obviously there's a lot of future work to be done.
This is, I think, really exciting stuff.
There's a bunch of really big, sensitive projects
building in the near AI world right now,
or the company near AI that I can't talk about,
but hopefully in the coming months or weeks
we'll have more to share there.
Oh, I forgot to mention Ilya did speak.
He had his own keynote at Nvidia GTC,
highlighting a new paper that was announced called
Decentralized Confidential Machine Learning that essentially
highlights how model builders and fine tuners and really
agent builders as well can get paid for their contributions
where they can even have a close source model where you're able to verify the inputs
and reward people based on their contributions to fine tuning a model.
Pretty exciting stuff. A lot of it is TEE based.
There's a whole paper here that will unpack it.
If you are looking to kind of get the TLDR, I suggest checking out Ilya's
Twitter thread. You can check him out and follow him at illblackdragon. This is what he pitched
at the NVIDIA GTC conference. And you know, more to come on this front. This is sort of just the
paper explaining how everything would work when we are trying to incentivize model builders and fine tuners with money.
Because everyone knows the whole world of AI right now is the business model is not truly figured out yet.
And I do think that crypto and payments based on contributions are pretty much a prerequisite to making AI business model sustainable.
So really cool paper unpacking that. What else do I have here? are pretty much a prerequisite to making AI business models sustainable.
So really cool paper unpacking that. What else do I have here?
Oh, we are, you know, I'm still working on the development of an AI auditor agent.
This was all reviewed and announced in an RFP.
There will be more RFPs coming out.
So if you are an agent builder looking for very clear scoped work, there will be more RFPs coming out for agents that we want built and hosted in the near AI hub.
Besides that, I mean, I manage Nears Twitter account,
even though I'm not really a marketing manager or, you know, really have a ton of experience.
So I do manage all of that and that's sort of what I...
Some of the things I've been doing this week,
right now, really just following up with all the partners that you heard here today. do manage all of that and that sort of, but I, some of the things I've been doing this week,
right now, really just following up
with all the partners that you heard here today.
There's a couple of other really big integrations coming out.
I'm also, the whole Near AI team will be start hiring
a lot of people soon.
And so at least some of the stuff I'm working on
is building out entire hiring plans and scaling the team
because we are going to need a lot more capacity
if we're trying to build a truly user-owned AI.
These are some things I'm working on,
and obviously we'll share updates next week.
But that's about it for now.
Hopefully we can get more new AI engineers
on this call in the future trying to do it, but sometimes they would rather focus on shipping
than talking about what they're shipping.
So I guess that's my role.
That's about it, guys.
Shout-outs in your week for facilitating this every week.
And everyone who came to participate, we do this again every Thursday
around 11 a.m. Pacific.
That is 2 p.m. Eastern.
And come back next week. Good to see you then.
Thanks for coming.
Thanks for watching. Subscribe to NearWeek.
All right, still live. All right. See you. you
