Trust is a function. It is more than just a feeling. In a world of infinite noise,
the only signal that matters is the one you verify. We are here to map the graph,
order the protocol, and build the immune system for the decentralized web.
Welcome to the signal processing layer.
This is Say What?
Welcome, everyone, to the third episode of Say What?
A special series here on Pleb Chain Radio,
diving deep into one of the toughest and most important challenges in all of freedom technology,
The web of trust.
What exactly is web of trust?
Why does it matter so much right now?
And how do we actually build it in a world where technology, especially AI, is accelerating faster than ever?
These are the questions we are here to unpack.
Today, we're exploring the fascinating intersection of artificial intelligence, web of trust, and everything in between.
I am your guest host for this episode, David Strayhorn, stepping in for our usual host, Avi Burra.
He's one of my co-founders at Nosfabrica alongside John Gordon and Vitor Pamplona.
Avi's done an incredible job kicking this series off and I'm excited to keep the conversation rolling.
Joining me today is Matthias de Bernardini, brilliant software developer and agentic engineering expert
who's already pushing the boundaries of what's possible at the AI Trust Frontier.
And as of this week, Matthias is officially joining us
to collaborate with us at NOS Fabrica
to help build open-source web of trust tools on NOSTER
and to bring them, hopefully, to billions of people.
So, Matthias, it's great to have you here. Let's dive in.
Thank you for having me. It's a pleasure.
Well, why don't you tell us about yourself?
Tell us about your journey to where you are now.
Yeah.
So I originally, I was really interested in engineering.
I loved chemistry when I was in high school.
So I went to school, university in Milwaukee, Wisconsin for material science and engineering.
And I finished that and had a great experience.
And eventually I learned about Bitcoin.
I think the first time I learned about Bitcoin was in 2014.
and I have this really funny story where I, you know, I was telling my dad, I was pitching him
Bitcoin. It's like, you know, I was, I was making a little bit of money at the time working as a lab
technician and I wanted to buy this. I didn't know what I was going to do with it, but I just wanted
to have some because I had seen Roger Ver's commercial and I had read about it on Slashdot
as well. And it like really, really piqued my curiosity. And but I just wasn't like an investor,
like I didn't really see myself as that. You know, so like the whole mindset of like,
oh, buy it and I can sell it later. I just wanted to like experiment with it.
I told my dad about it, you know, tried to get his opinion. And, you know, as I was explaining
to him, it's like, oh, yeah, and it's like $90 right now. And my dad's just like $90 for one
Bitcoin? No, no, no, no, no.
That's too much. That's too much.
It's too expensive.
Like, okay, dad.
I don't know if we were calling them sats back then.
So you're keeping the pull-em-out cheat if that was.
I know, I know, right?
I should have.
I mean, the sats, they always, they were in the code base,
but I don't think back then it was like a thing.
I don't think so.
Yeah.
That would have been the smart thing to, you know,
hit him with the unit bias and then he would have been like,
oh yeah, that's a great deal.
Why not? Just get a couple.
But it sounds like you weren't coming at it from an ideological Austrian economics.
That's not kind of what brought you into it?
It was the technology?
I just wanted to play with it and just see what it was like.
Why are all these people talking about it?
It's this totally made-up thing, and yet it's grabbing all these people's attention.
That's interesting. I should I should get some and look into it. Why not?
And at the time, I you're right. Like I didn't really have I didn't really understand investing.
I didn't really understand money, how the economy works and all of those things.
It was just so it was just out of mind, you know, really just focusing on living my life, going to school, trying to get a job.
um and you know once i i got to that you know and started making my own money uh then i was a little
bit more interested in all of these things and i'd come across jim rickard's book uh the road
to ruin and currency wars and i i read through both of them and you know i think in like the
matter of a couple weeks and i just you know very clear to me after reading this like okay
wait a minute he's just explained everything to me about how the economy works and like this giant
yeah you know this giant mess we've made and in the book he was advocating for gold
uh because Jim Rickards is he's like a famous gold bug but I'd also known about Bitcoin you know from
like reading that article didn't really pay too much attention to it but then a friend from school
was trading it and he was telling me about it more and more and he was making really good money with
it uh trading uh and at that point like I had money and I could buy some so I just did you know
I didn't really have to get anyone's second opinion and it really just interested me just
so much just the idea that you have this proof of work and that is what underpins the money and
there's this decentralized network and we're all double checking each other's work and then writing
it to this thing called the blockchain and everybody shares it and if anybody makes a
mistake everybody else rejects that mistake and rechecks their own work and you know writes it down
to their personal storage.
I thought that was so interesting.
Just being a part of that,
that's just what I want.
I want to run a node.
I want to be a part of all this.
And I remember I had the,
I think it was the Air Wallet, Air Blitz.
It was like an old iOS wallet that had Bitcoin.
And I bought a little bit off of this exchange.
It was a really cool exchange
because they didn't have a custodial wallet.
you would have to put in an address for them to send it to you.
And so you would give them fiat and then they just sent you Bitcoin.
So there's no like there's no custodial wallet like a like a Coinbase.
And I remember I was just I had like like 50 bucks or something like that.
And it just showed up.
And then I realized I'd also bought a Ledger wallet because I understood like it was kind of intuitive.
to me, it's like, I shouldn't keep my password on the device.
If I get hacked, they can just take the Bitcoin.
That to me just seems so obvious.
I should probably get something, what is the easiest way to solve this issue for me?
And I got the original Ledger wallet that didn't even have a screen.
And I remember I set it up, made an address, and then scanned the QR code with the iOS wallet,
and then just saw the balance change on my laptop.
And like that moment, I just got goosebumps, you know, that I could just like move money around like that just so seamlessly.
And I didn't have to call a bank.
I didn't have to do anything.
It was just software running.
And that was like a big aha moment for me, just being able to, you know, just seamlessly move money around.
But then I quickly realized, like, wait a minute, this Ledger device, it was like a really small USB device at the time.
It doesn't have a screen.
So if I want to see the password, it has to be on my laptop because that's where it shows you the key or the backup for the mnemonic phrase.
And I was like, if I have a screen logger on my laptop because I downloaded it by accident, they're going to be also going to be able to do my Bitcoin.
So like this thing doesn't really help that much.
Obviously better than just storing it locally.
But when you initialize it, it shows you the password.
And then at that point, like I got a Trezor and that to me was like awesome.
you know it's like okay finally like the super minimal device I can see the all the words there
and at the time I was just doing engineering work but I just wouldn't shut up at work about
you guys should get some Bitcoin like this thing is really cool you know and like I convinced a few
people to get it but they all like quickly sold it basically like everyone and then there was this
one guy who's a bit older he was like a huge silver bug you know and so like he's probably
laughing his ass off right now you know with how well silver is doing but at the time like silver
was totally dead in the water and bitcoin was going like crazy so uh that was really fun and
eventually you know i just like couldn't keep shutting up about it and i realized i'm not doing
the right thing here. My focus is not on this job. It's on Bitcoin. How do I get involved in this?
What were you doing at the time?
I was doing packaging engineering. I got out of my degree and I found this company. It was a
chemicals company. And I basically would do all the product testing and validation. It was pretty
interesting. It was a good company. Honestly, it was a great business, well-established.
They had products all over the world, and people bought the products and loved them.
And they were a curious case of a chemical company where they actually cared about not dumping chemicals into the environment.
They went out of their way to try to do it as environmentally friendly as possible, which is pretty rare.
usually those companies, you have to get the government to tell them what to do.
But they were pretty adamant about, even if it cuts into your margins,
we're still going to make sure all the waste is getting disposed of properly.
And there are just a lot of things that they were doing that they didn't have to do
because the law didn't require them to do it.
But they understood that, you know, we should do it our way.
And as a result, the products end up being more expensive, but they're just a higher quality.
It was like cleaning products and, you know, that sort of like household chemicals, that sort of thing.
So great company.
You liked working with them, but didn't have the excitement of.
Yeah.
You know, it's just like it just wasn't anything to do with Bitcoin.
And also I just like wasn't able to do anything entrepreneurial in that space.
Like I, for me, the idea of like making my own chemicals company would just seem like
just so, just so unrealistic.
And like I, it would just like never, ever happen.
And at the time, you know, I just saw everything happening with machine learning and AI.
This was, you know, 2017.
um back then like you know it was much more academic and there were just very few companies
working with ai and it was mostly like recommendation engines um but you know it was just so
obvious to me that it was just early and you just had to wait for the technology and someone will
come up with something better and you know it's better to just start to get involved rather than
just sit and wait for that to happen.
So I found a really good program for computer science and this was happening to me in the
Netherlands.
So I was able to, you know, I have Italian citizenship at the moment.
So I was able to study in Europe basically for free, even though I had already like,
you know, gotten an education, um, expensive one here in the United States.
But it just, to me seemed like, yeah, the fastest and most efficient way to get up to
speed with everything.
And I knew that like, if I wanted to work in Bitcoin, I really had to know my stuff,
you know, like how, how am I going to make a wallet without actually knowing about the
fundamentals?
Um, at least that's how I think I, that's how I thought at the time, you know, that
was my my sort of how my brain worked And now I know that like that not really true You know you can just jump in whenever you want And as long as you just putting in the effort and you running code and you trying stuff out and you thinking critically you know you can you can just learn on your own
And now with with, you know, now with ChatGPT and Claude, like that's even more true for anything.
So I basically, I was just doing school. I was doing honors research and distributed systems. And then COVID hit. And I realized, like, why am I in school? Like, I can already make websites, I can already make apps.
You know, I already knew about open source and GitHub and Git and, you know, just working with other people.
And I was like, why am I working on this school project?
With all the COVID stuff happening, you know, you're just sitting at home bored.
um so i just dropped out on my last year and just tried to desperately try to use bitcoin
lightning to like start a business i was um writing a bunch of python scripts to like analyze
the lightning network and my in my head my business plan like i can like parse the lightning network
and then offer like routing hints or lightning nodes at the time.
Like that seemed reasonable.
So, you know, just using LND, I would just pull down the graph
and then just like, you know, calculate eccentricity and the diameter,
you know, shortest, longest path from any two peers.
Just all these like different metrics.
And my thought is like, okay, I can make an API and try to sell it.
And I was working on that.
I was making some progress.
But then I was like, damn, this Python is so slow.
I'm never going to make any progress.
Even though I was using NumPy under the hood,
which is as fast as you can get with Python without writing it in assembly yourself.
and I think it was like the NetworkX Python library
which at the time was really good
and it was like
I should just write this in Rust
and see if I can make it better
and I started doing that
but then a friend of mine from a Discord group
got me this job at a company
that was like, it was this guy that made a ton of money in crypto and wanted to start a crypto
exchange. And so they got me to like run it for them. And even though I was just like fresh out
of school, you know, like I knew a little bit about programming and, you know, Bitcoin and I
have been doing it. But the reason why he recommended me is because I was helping all
these people like with their Bitcoin wallets on this discord channel for, for traders that my,
my other friend, you know, got me into. And, uh, I remember somebody got their like
funds lost in a samurai wallet. And, you know, I knew how to like get it out and like, you know,
do all the derivations and, um, you know, I, I helped them, you know, rescue some coins. Uh,
and eventually, you know, it just goes to show if you just like put yourself out there,
you go and you know you help people like eventually someone's just gonna like you know
prompt you to help them and maybe for a business and uh that then i moved to the united states
back to the uh united states um and was just working for this company you know doing basically
just everything like it was a small uh shit coin exchange that used binance as their back end so
So they had like all this liquidity from day one and access to all these shit coins.
And man, what a piece of crap that that company was like.
I was like, I cannot believe how unprofessional this is.
And we're handling like, you know, all this money.
I like I tried to do my best, but because it was like a front end for Binance, we didn't have any access to any of the wallets.
You know, we didn't have it was just like a dashboard that honestly was like looking back at it.
It's like, did they have did like Binance have ChatGPT back in like 2021?
Because this back end was so buggy.
And like, did they code this?
Like, how how is this so buggy?
Like all these labels like were messed up.
And it was just funny.
But, you know, I just remember just trying to help people with their wallets.
put these shit coins it was just so hard to work with like because they were just so buggy
the dot all the docs didn't make any sense and i ultimately didn't have any access to any wallet
so all i could do was just ping binance that hey this user they're having issues like this is what
they're telling me can you help them out and the back and forth would always take forever um and
And eventually the company just went out of business.
And at that point, I was like, all right, well, I'll just keep back to doing what I know, which is just open source and Bitcoin.
I would just look for projects, you know, that that sounded interesting in the in the space at the time.
Like I thought Lightning was doing like really well.
Like it seemed like they had enough people like working on it that it didn't really need any more people to like.
because like all the ideas were there you know they had all the bolts it was just a matter of
like implementing them and i saw that they had like great programmers working on it so it's like
all right well i'll go do something else uh and so i found the fetty project and just started
contributing to that um you know i was part of this hackathon uh in austin and then that was sort
of what like really prompted me to get really involved with Fediment. And then I participated
in this hackathon. It was like a month long hackathon. And I like just went all in on it.
And I had a partner at the beginning, but then they dropped out. So it was just me.
And I was able to make this distributed storage system that used the federation to make multiple copies of your data.
And it was basically a key value storage that would get stored by all the members of the federation and use the underlying fediment consensus algorithm to make sure that the data is actually consistent.
across all the members.
And that was a lot of fun.
I got second place in the hackathon.
And at the time, I was helping this other guy
who was also in the hackathon
because he had some questions about Rust.
And I was just helping him about how to get access to it
and just do several things with the Fetty Mint dev environment.
and um it turns out that he was working with this guy called rob hamilton and then that's how y'all
met that's how i met rob from anchor watch yeah and like he um you know it's like hey i need this
back end looks like you know rust and bitcoin do you want to help me and at the time uh you know
Rob had created this, like he, it already was very like cool.
What he had for as a backend, like it all worked the whole anchor watch,
you know, mini scripts vault system. Um,
and what do you call it? But it was, it was just,
it was using Python and, uh, calling BDK CLI using a sub process,
which is, you know, inherently not great. Um, so yeah,
He just got me to just rewrite all of it in Rust.
And I just worked for him for, yeah, I think it was for like six months.
And then he offered me full time.
Is that how you ended up in Nashville?
Yeah, yeah.
He said, hey, if you want full time, you got to come down to Nashville and work out of Bitcoin Park with us.
Yeah. And I think that was, you know, I'm really glad he did that because, you know, it's just so much better to just focus with your team when you're all you just know you're all in.
Everyone is in for the project. And like it just like it's just so much more motivating rather than, you know, knowing that everybody like uprooted their lives to move to Nashville because it was it was three of us working on the code base.
and it just motivates you so much more to like yeah just like work and like get it done you know
even if it's kind of complicated um but it was just yeah i had the time of my life honestly just
like shipping that to production and just dealing with all the bugs and at the time it was crazy
At the time, yeah, ChatGPT was coming out and Claude and all this stuff.
And so I just kept experimenting with these agents and learning more about AI as much as I could.
And you had already been thinking about machine learning, you said, before COVID even.
Yeah.
And distributed systems.
And it's interesting, you said, recommendation that people were using machine learning for recommendations.
but not in a purely decentralized way.
No, definitely.
It's all, I mean, at the time,
like when I was learning about it,
you know, the biggest users of machine learning
were like Netflix
and they would use machine learning algorithms
to see what movies you had watched
and then they would run that
through their own internal recommendation engine
that was all machine learning based.
with neural networks and all of that good stuff.
And that's what will populate that list of like,
hey, you might be interested in watching these movies.
So Facebook is also another big,
a huge consumer of AI workloads.
And many others,
more and more social-based companies,
like Tinder, for example,
or pretty much anything where you have some kind of graph
that you want to iterate over
and then suggest different members of that graph
to the user as potential things you might be interested in.
So this is where the knowledge graph you learned about?
That was a networks and graph theory course I took in school.
And I just thought it was super interesting because I immediately was just applying it to the Lightning Network and to Bitcoin nodes.
That's basically how I got through all the school is every course I took, I found a way to apply it to Bitcoin and Bitcoin Core.
And then that immediately made it so much easier to understand all the concepts.
And then also just like have enough like dopamine running through me while I learn about it.
Because like I'm interested in Bitcoin.
This is how Bitcoin works, you know.
So like my computer, my, you know, systems architecture class, you know, it's like, okay, this is the computer system that runs Bitcoin Core.
Like how, you know, how does that work?
The algorithms course I took is like, okay, the blockchain is just a giant, you know, data structure.
in the algorithms, data structures, you know, programming, obviously, computer networks,
same thing. Everything I was learning, I was applying it through the lens of like,
how would Bitcoin Core use this? And that just made it so much easier to just keep thinking about
what it is I'm trying to learn about and just like do more cycles and then just have it ingrained in
my intuitive understanding. And that honestly, I think that's like probably the best cheat,
quote unquote cheat code that you can apply when you're trying to learn something new
is how can you apply this knowledge that seems kind of random? Like what, you know, and it kind
of is because the teachers have to decide like which one, which topics to like discuss in the,
in the course. And that's up to their discretion to like what they think is most useful.
and like you know they're not into bitcoin um in fact the big reason why i just you know didn't keep
uh why i wanted to drop out is um i had like my senior thesis and i wanted to do it on bitcoin
but i couldn't find any teachers that cared about bitcoin or were willing to like sponsor me
to like you know do bitcoin research um and i guess i could have looked harder at like other schools But I was just like whatever I may as well just you know do just do it on my own Like I you know if I have a good idea I can put it out there and people will like
it, you know, like, and if not, then whatever, I don't need to go through the school process.
And I'm honestly really glad I didn't like stick with the academic system. Cause it's, it's just
not great. Like it has a lot of issues. Obviously I think school is very important.
And I don't think AI is going to replace school.
I think it's going to make it much more valuable, actually, because already the whole point, like a big point of going to school is just meeting people.
And networking, just like making relations, like hanging out, you know, making friends, like getting to know people.
What are they working on?
That is never going to go away.
and, you know, be using AI to learn with your friends and teachers.
And like, it's just going to make it more valuable, not less, in my opinion.
Definitely, there will be schools that like, if you don't,
if there's schools out there that you don't learn,
then yeah, they're in trouble, you know, like,
but actual institutions that like, yeah, want to expand human knowledge
and give young people a good, you know, environment
to actually improve themselves.
I think it's going to be even more important.
We're just going to get more schools, I'm thinking,
and it'll just be easier to administer classes
and administer the entire educational system.
A lot of learning we can do on our own.
And like you were saying, you would learn something
and you'd kind of connect it to something else
of great interest, like Bitcoin, right?
kind of integrate that, all the different things you learn into that.
Going back to the recommendation engines that you were talking about,
that Netflix is doing, did you ever think about,
okay, that's a lot of what we want to do with Web of Trust
is we want to build recommendation systems,
but we don't want to do it in a centralized manner.
We don't want Netflix to be in charge.
We don't want some big company to be in charge of the system.
Did you ever think about how could that happen, that service happen, and be effective, but in a way that doesn't put Netflix in charge of it?
You know, at the time, like what was available for people was a lot different in terms of like what kind of hardware you could buy.
It really was a totally different class.
And, you know, if you really wanted to run big workloads, you had to run it in a cluster or rent a cloud system to do it for you.
and so at the time
like
it just seemed like not possible
for people because you'd have to run the workload
yourself with the data
and it's like
hard drives were still too small
and you're talking about training
the data or about just using
it after it's trained yeah you need
to train the models and then also
do inference and
either doing either of
those like it you know the
it just wasn't it just wasn't possible with what was available at the time. And so I just kind of
like this thing is just going to be a cloud thing, you know, and I still kind of feel that way,
even with modern systems and even with all the talk of running it locally. You know, what you
can buy from Apple these days is just so much better than what they had in the past. And it's
even better than in some cases what you can get from NVIDIA on a, you know, on a per dollar basis.
so um what do you call it that math is now a lot different um you know again just because so many
advancements were made in hardware uh over the last five six years but at the time it just kind
of felt like yeah it's like these things only get better with more data you know it's like they
don't get better with less ever and a lot of how they kept improving models was just
throwing more data at the problem.
Which means that you're going to have to,
you can't just be a regular plebe and doing all that.
You've got to be, the bigger the better.
So if we want recommendation engines
to be truly decentralized,
then traditional AI as we know it
sounds like not the answer.
Well, I think it just comes down to the incentives.
And if you can create, and also comes down if new methods to split up the work and distribute it,
and then incentivize people to label data and then run it through their system.
But there's also a lot of issues with that because you run into all this overhead whenever you try to distribute work.
and that's why nvidia is worth so much money is because they can efficiently distribute work
and induce very little overhead and that's because they have custom hardware that lets
all the nodes in their cluster efficiently communicate with each other so one of the
things that nvidia has is infiniband and that lets you connect it's basically like the super
powered version of Ethernet, you know, instead of connecting everything through a network switch,
you connect to this InfiniBand switch, and that lets you efficiently communicate
without all the overhead of TCP. And that's, you know, if you want to train something bigger and
better than what open AI can, even if you had all of the computers, you know, in the world,
they're still going to be connected with TCP, you know, like the internet protocol.
That's going to induce a ton of overhead. So it'll always just be a lot slower than,
and so instead of taking you a month, I haven't ran the numbers, but you know,
you'll never be as fast as deploying the latest models, even if you have more data than they do.
So as far as like training, like, you know, if you're okay with just always being behind,
then and there's a way to incentivize people to like label things.
And honestly, I think data labeling will just be a very important like,
that might be like UBI in the future, you know, where you're just getting paid by AI companies
to label stuff in your day-to-day life,
and then they give you money for that.
That just goes to train the super AI.
So that would be decentralized in the sense that you'd have
billions of people going and collecting data.
Yeah.
But the entity paying you would be this great big institution
collecting all the data.
So that's not the solution we're looking for.
No, because you just have to trust them
that they're actually using the data the way you labeled it
instead of in a way that's more beneficial to them.
And we'll never know how to,
even if they are doing it in good faith,
how do we know that?
Yeah, you know, all this AI talk right now,
there's just so many aspects to it.
Like, you know, yeah, they want to get bigger,
but to do that, they need more revenue.
and to do that they need people exclusively using their models but also it's possible to clone to
distill models from the bigger models and make local models that are just as good um if not
better depending on if you want to do your own fine-tuning and local training with that model
and um what do you call it like it's just so hard to know how the economics of all of this are going
to like actually play out but i do think yeah i guess we're just going to find an equilibrium
somewhere uh but it's just hard to imagine like these like the open ai like all the super scalers
like you know getting even uh even bigger you know and hyperscalers and mega you know like just
as big as they can because there's going to be a limit to how much money they can raise
um and also like i'm just so not sold on agi like it just it's just so i don't get why
you know they're all going after it just seems like such an obvious dead end because none of
these models and if and if someone can correct me like show me like i'd be so happy but like
none of these models have any form of logical reasoning all they can do is just prompt themselves
again and have the model look at the output and then like run it, you know, in longer and longer
loops, which consumes more tokens. But you do in some cases get better outputs. But it doesn't mean
that it has actual logical reasoning, like, you know, counting parentheses, for example, for these
models is still very difficult. So, you know, if you want to have it like implement something in
brain fuck, uh, or some kind of language like that. It's not, it like, it like messes up. Um,
and, uh, I don't think longer context window, like, I think they're hopeful that like, um,
you know, 10 million token context window kind of fixes this. Um, but I'm just like,
until they actually have a real mechanism to like implement logical reasoning, uh,
I just don't see how you could get to AGI.
That's not to say that it's not incredibly useful already.
You know, and like it is obviously a type of intelligence, but it's not human intelligence.
You know, it's kind of closer, you know, like in this brings up the topic of like what even is intelligence?
You know, like you have forests, for example, that if they start to get attacked, they have their own defense mechanisms.
You know, trees, when you—they've shown that there's certain types of trees in Africa, for example, that when a giraffe starts to eat it, it releases these chemicals that attract these, like, I think they're like flies or like ticks.
and they start to attack the giraffe.
And so the giraffe gets uncomfortable and stops eating the tree.
You know, and so like that's like a type of intelligence right there, right?
Like it's a self-preservation mechanism.
Like that's obviously intelligent.
Like it helps to push your genetic code forward into the future, you know,
in a way that is very creative and, you know, very energy efficient.
And, you know, they've also shown this with like, you know, mushroom, you know, mushroom ecosystems.
Like there's all kinds of and even animals have like, you know, certain types of intelligence.
So it's obviously a type of intelligence, but I don't see the solution that we're looking for.
Right. If our goal is to build decentralized Web of Trust, then the path that existing AI is on is not the path.
No.
At least not for the foreseeable future.
So how can we, if we want to look at the intersection of AI and decentralized web of trust, what would be, I mean, obviously one intersection would be we can use agents to help us build the tools that we want to build.
Yes.
So what other, so I'm thinking of other, you know, just topics that we can talk about.
Like suppose you've got information,
suppose you have data that is curated by your web of trust,
not using AI, but it's data that you trust
because your community curated it for you.
Yes.
And then you fed that into an LLM, like a local model.
Yeah.
What do you think of that kind of idea
where you've got sitting at home,
You've got a box that's got, let's say, a Nostra Relay,
and it's got a huge amount of data.
It could have a Wikipedia worth and even more than that.
Everything that's curated, not through any big companies.
But then you feed that into AI, into a local model.
How would that, like what model would you feed it into?
That's a really, really interesting question
because there's two ways to think about it.
One, you can use that as the underlying data for the training,
the actual training of the models.
So like from scratch?
Yeah.
Would it be feasible for an individual to do that?
You're not having huge data centers.
You're just a person.
You're building a model purely from scratch.
Is that doable on a small scale I think if you were if you know with Nostra relays like they often have like topics that they focus on
So if you wanted to like narrowly train it
on a, you know, a certain type of subject matter,
then I think that is definitely possible.
So if you had Nostra data
that was already organized by topic.
Yeah, and properly labeled
and you knew without putting in more work,
because that's really the problem is,
you know, I think that, like I said,
the data labeling is just going to be
a huge thing moving forward.
And if you're receiving all this data
and it's already curated by the members
simply by just sharing it,
and you know that the person it came from
is highly trusted and vetted,
then, you know, that opens up a world of possibilities.
But, you know, if you want it to just be
like a very general, very generic model,
that sounds not realistic.
And even then somebody actually training through this,
like it would be a significant investment to like run at home.
You know, the costs are dropping every year,
but you know, like, like I said,
it's hard to know where everything is going to land
because I do think that the demand for this,
you know, artificial intelligence is going to cause shortages
in actual material resources,
you know, like copper, for example,
or natural gas even.
And it's, you know, it's like,
it's cheaper to produce them
because, you know, we have more economies of scale,
but at the same time,
like the raw inputs are getting more expensive
because, you know, we're just not producing enough.
So it's hard to, without,
I think even if you tried to model this somehow,
Like hard to know where, you know, the actual final equilibrium is going to land at for if it's practical or not to train at home.
And just because so we still have the problem that it's just takes so much energy to train from scratch.
That and also your the model is as good as like the training.
You know, like there's lots of really smart techniques and, you know, by brilliant engineers that like squeeze out the most amount of intelligence per training run.
But at the end of the day, you know, if you if your training set has a bunch of dogs that are labeled as cat and you want to use the AI to label dogs, you know, it's going to be difficult.
Yeah, that's garbage in, garbage out.
Yeah.
But if we have a system that filters out the garbage effectively and labels it effectively, which is what, you know, like knowledge graph or like concept graph, maybe.
How far could that go at saving, at reducing the amount of energy it takes to train something effectively?
You know, I think.
Are we talking orders of magnitude or just a little, you know, shaving off the edges?
I think it probably just, you know, if, if, again, if you want to make a, like a general model, like something like chat GPT, then you're just shaving off the edges.
but if you want to do
the mixture of experts
models sort of like with DeepSeek
Innovated
then there's
opportunities there for sure
because you would just have each Nostra
relay that subscribes to a certain topic
and just have it train
on that and then
you would collect all those
model of experts
mix them together
and then that would give you
a more general model that could do more things.
And it's not even clear that that's even that useful.
You know, if I'm coding,
do I really need to know about French history?
You know, if I'm like, you know,
trying to make like a Rust program or something,
trying to make it go faster, like find bugs,
maybe, probably not.
Right now, ChatGPT knows about French history
and you're using it to code.
Yeah, for example.
Yeah, just as one example.
So being able to make specialized AI.
So could you envision a world where there's a bunch of open source models
and you can choose, you've got lots to choose from.
If one of them is a bad actor, you've got other ones that you can easily switch to.
Yeah.
And then you train your own personal data.
Yeah.
Whether that's fine-tuning or, you know, some other, like, special method.
Yeah, absolutely.
What's the difference between training and fine-tuning?
So fine-tuning, yeah, I mean, training is, you know,
considered more like an end-to-end methodology of producing...
That's back propagation and...
Yeah, forward propagation and, like, all these other methods
to actually produce a final model that can be, you know, somewhat useful.
But fine-tuning is where, for example, you have the model, you're talking to the model all day and it has, you know, it has helps you do to-do lists, for example, manage a to-do list or, you know, you're just discussing French history, right?
all of that text that it outputs,
what you can do is basically put it back into the model
so that those weights that actually triggered those responses
are now more heavily weighted
and more likely to produce the correct output next time.
So you're changing the weights.
Yeah.
But it's different in methodology than training.
Yeah, because you're only doing it a little bit, right?
and you're not actually going deep into the model
and updating those weights throughout the entire,
all the different layers that are in the model.
It's just on select layers that are more towards the end
and that will just slightly bias the model
to be more likely to discuss French history.
And then those changes that you make
are not rippling through everywhere else.
Well, it's fine tuning, you know,
so it's just a little bit.
And that's sort of what people are getting really excited about now about running local models is you have it, for example, be your personal assistant.
And, you know, there's this concept of the system prompt where you before you actually start to talk about your discussion, you actually like you prefix with, hey, you know, my name is Matias and I'm really interested in French history, you know, especially Napoleon, you know, and how he fought in Waterloo and like all these things.
and you basically just,
you're just going to get much better results
if you fine tune it
and it's just less likely to go off track
or, you know, start discussing British history,
you know, instead of French history
or go off on a tangent, you know,
because again, those models towards
the lower levels of the layer
are much more likely to get triggered
at least the, sorry, so the weights that are related to, for example, French history from
all your discussions are much more likely to get triggered.
And if, you know, that's what you want, then that's much more beneficial.
And you can see this as how this could be useful.
You know, you're using it as a personal assistant day in, day out.
You could take the logs of all of your discussions throughout that day and then use that to fine
tune it every night, for example.
And then so the next day, you don't have to like so heavily prompted to like have it be a personal assistant.
You know, it just kind of has that in its own weights.
But the reason why it's called fine tuning is because you just, you know, you're not going to get that much mileage out of whatever, you know, updating of the weights that you do.
If you're trying to like so heavily influence all the weights for that, you're going to want to have it done on a cluster.
rather than your laptop, even if you have a GPU.
If you want to do training, it simply requires a whole lot more computing power.
Well, I think if you want to do training for a general model,
like something that's like Opus or ChatGPT, then yeah, you just have to.
Because there's just so much training material that you have to run through
and turn it into embeddings and just have it go through that.
But if it's highly specialized,
So, for example, you know, I'd be interested in doing fine tuning for like a Rust programming agent.
Like and then I could, for example, use all of the outputs of all the tools that I've been running that day.
So like Cargo Check, Cargo Thumped, you know, Cargo Clippy and use that for fine tuning.
And then over time, it would just be very intuitive to the agent to call Cargo Clippy and it would just have to spend fewer tokens figuring out that, hey, I should do tool call for Cargo Clippy.
And it would just do it.
So let's say if you wanted to put together a personal assistant and you wanted to pair that with a Nostra relay that had information that was curated in a way that you trusted.
And it could be a huge amount of information.
Yeah.
And then let's say that you do that at home.
Like you could have that an instance that's sitting in a trusted cloud provider
or even more self-sovereign would be that you've got a box at home.
So would you have OpenClaw in that box?
How would you do that?
How would you engineer that?
Yeah, I think one, something that may be kind of interesting,
would be to run OpenClaw with Opus,
but then have as a skill
one of these open-source models
that you've been fine-tuning
with the data that it knows you find interesting.
So you'd have to come up with some interface
to record how much time you're spending
on a photo or a post.
What if you just gave it your Nostro Relay
of curated data?
Well, you still need to find, you need to like label which of the posts is more interesting and less interesting.
So let's say you're organized in a graph database using like the decentralized lists system.
And so I'm interested in sports, but I'm not interested in politics.
And you just look at the graph database to see what fits into that umbrella.
But then you'd have to like define upfront and you'd like be labeling data manually.
up front. So like what I'm talking about is just sort of like an interface like X.
And, you know, you would just like vibe code this yourself. So you have control over all
the algorithm and it would be done in a way where it's not extractive. You know, there's
no dark patterns, but it could record, for example, oh, post one, two, three, four.
Looks like Matias has spent a whole minute on this. And so that post, you would just add
four minutes to it and
basically all
of the posts, like the ones that you just scroll
through, zero seconds, right?
Oh, this other post,
he's not just the post, but he's also
looking at all the comments.
Then I would put those
in a database,
create
embeddings for it, and then
fine-tune
something like Quen
3 or DeepSeek
with
that ranking of posts.
And then it could then have a classifier as a skill.
It would just run like a CLI that just spins up the model,
loads it into memory, and you can give it a post
as it's coming in through the relay.
And it will classify it as either something that Matias is interested in,
likely interested in, or not interested in at all.
You're measuring interest by the things you do.
How long do your eyes linger on this post?
For example.
Yeah.
So it's AI is kind of, well,
these are techniques that are currently being used on us.
Whether they're good or bad could be debated.
If this system, this is already basically how Facebook and Instagram works,
But like in this system, it would just be a fairly straightforward, you know, ranking algorithm and just like a way to store that information.
But it would be ran and operated by you.
You know, there's no reason why you can't run your own Postgres database that the app connects to.
And it's just storing that information there, basically just a log of how you're using the app.
And then that's, you know, you're doing the work of Facebook, but doing it in a way where it's not extractive.
Could that methodology, though, be one that is inherently developed for...
the purposes of Facebook.
You know, like I don't want,
just because my eyes look at something,
I don't want my personal assistant
to feed more of it to me necessarily.
That's true.
That's another aspect.
I mean, but inherently,
the stuff that you pay attention to
is what you find interesting.
But that's not necessarily what's best for us.
I mean, that's kind of the, you know,
it's our dopamine.
Do we want our personal assistant
to make our ADD worse
by giving us the stuff that pumps our dopamine?
Probably not.
I think if you were to guard against certain topics
that are inherently,
and you could use some other classifier
that's just out of the box,
so anything that's really violent
or nudity or anything like that
could just immediately get filtered out.
And then as long as you just keep it to text
and you just automatically ban certain words
depending on what you know you wouldn't want to get interested in,
then such a system, I think, could be developed
and could be interesting.
But I totally agree with you.
Just because you're paying attention to it
doesn't mean it's what's best for you.
But at the same time, then without having somebody else
sort of towering over you and making sure you're, you know,
adhering to some moral code of what they think you should be interested in,
then I, you know,
it needs some kind of way where you're in control at the end of the day.
Right. So we definitely, I mean, that's, yeah,
the goal is to put you in control,
which includes the ability for you to delegate decisions to trusted people,
to help for them, to help for them.
And is there a need?
So do we need AI at this particular step?
Do we actually need AI at that particular step to organize our data?
What if our grapevine, our web of trust, can organize all the data for us?
And big picture and fine-grained detail are if putting things into categories
is done by human beings, by entities not using AI.
and
if
I'm interested in sports
what are the different categories of sports
I don't want AI to categorize them
I'd want human beings to categorize them
people who know about sports
and who my web of trust says
these people know what they're talking about
and they're going to be the ones who invent the categories
and they're going to be the ones who maybe
figure out how to place
data into these categories
so that's
doesn't necessarily need AI.
An LLM, you mean?
Or, I mean, nothing.
No AI, no LLMs, no machine learning.
No connectivists.
You ultimately, like, yeah,
you would just have like a list
and it would be the top, the post,
and then a ranking from zero to one
about, you know, what is most interesting.
You would sort it.
and then just start, you know, popping off that list
and the viewer could just scroll past it
or like pay attention to it.
I think if you were to basically create a training set
of like label data in such a manner
and then create a classifier,
you know, you could use like an LLM,
like for example, Grok1,
they've already shown that, you know,
that you can just put a bunch of a huge list of rules and embedded into an LLM with, you know, into the weights,
then it can very efficiently just determine if it's according to these existing rules,
if it's something that the user might be interested in.
So maybe there's like some hybrid approach where you would want the initial ranking to be done by the grapevine.
but then over time because you also have to be cognizant of like the volume of data you know and
it's like maybe it is actually a little bit more efficient after you know the rules of like say
operating up for a whole year that what the user likes and what it doesn't you know how it gets
classified maybe it is actually a little more efficient to just like run it through like a
fine-tuned version of grok one that would like tell you how interested the user might be in that
specific post. So, you know, I think like to like bootstrap it, you would want to use the grapevine
because it is so high quality. But then once you get into enough volume, you know, of data,
you're, you know, you would have to come up with some shortcuts. Because otherwise,
you're just storing a lot of data and running a lot of CPU cycles. And maybe there's just like
a much better way to go about it. I don't know. I think we're going to find out in the next few
months. I'm super excited, by the way, to get started on this. And I think there's just a lot,
like the design space is huge. And this is one of the biggest problems that I think is facing
our society right now, because the amount of nonsense that is being put out there online,
it's just huge. It's affecting everyone I know. And there doesn't seem to be a solution other than
to just hope that the CEOs of big tech companies actually do something about it.
And they can't because they know that.
They don't know what to do.
I mean, they don't know what to do.
They don't want to impose their moral code on us.
I think they understand that, right?
And they're also, you know, a lot of them are publicly traded.
And so they have a duty to the shareholders to maximize revenue
and maximize shareholder value.
And that means that they have to make the platform, have to make you spend more time on it so that they can sell your profiles to advertisers, right?
And that's how they make money.
The incentivization, just the scenario they find themselves in is going to incentivize them to do things.
formatted. Yeah, they can be
have great intentions
but they are in the
you know, they have to act
according to the incentive structure
that they are in.
Ultimately, that's
yeah, that's how they operate and
what do you call it?
That's just
how that game works and
the only way, in my opinion, is
just not participate.
And build better alternatives, which is what
our goal is. You and I are going to be working together to help
with others to hopefully be able to do that.
What I hope we'll be able
to do is use your web of
trust to organize data and to organize it in a
sophisticated enough manner that
it would be easy for AI to
If you ask AI to find something, it's not going to have to search everything,
but it'll just have to go down the organizational structure.
So it's not just here's a list of categories.
It's here's a list of subcategories.
And here's how you can look at the coarse-grained or fine-grained for any given topic.
Like, you know, if you want to, for any list of things, all right, well, what are the properties that that list, that items in that list need?
Well, that's another list.
And so your community is, it's not going to be any centralized entity that decides what those properties are.
For the people that want to, you know, I think another thing that we should be cognizant of is that some people really do just want to live in the matrix.
I remember this one time I installed PyHole on my local network.
I was at my parents' house at the time.
Have you ever used PyHole?
I've never used it.
It's a great tool.
I think the project is still going.
It's a network-wide ad blocker.
And basically you just blacklist all of these URLs that websites use to download advertising scripts or malicious scripts.
And so when you download a website and it runs the JavaScript and it goes to those URLs that are embedded in the website to download the ads, to show you the sidebar like, hey, TJ Maxx is having a sale or whatever.
you know if you know ahead of time like what those urls are when your laptop you're on your local
wi-fi is goes out to make that request it basically just gets sucked into the pie hole
and the ad fails to load but it's like a network issue you know so like websites don't get don't
block themselves as the network fails like that so you get to use the website but the ad doesn't
load. I think that's like
websites have changed so much.
This was many years ago, but like I
installed this point is I installed this
and then the next day my mom asked
me, it's like, hey, where did my Google ads go?
And I was like, what do you mean?
I blocked them all network wide.
You don't have to worry about them anymore.
It's like they're showing you all this crap.
Oh no, but I really like them.
Yeah, we can build alternatives
and then people can be free to
choose them. And just give them options
And if they like it, they can keep using it and hopefully it benefits them and provides them with more high quality information than they would get otherwise.
But a lot of people, I think the most disappointing aspect of AI is just learning how much slop people actually like.
We like the slop, and yet we at least want the option to filter it out and not to rely on the big companies to do the filtering for us.
No, no, right? Because they can't.
And they don't want, even if they probably could, but that would go against their, that wouldn't work for them.
They want to get our dopamine going And if slop does that then why are they going to get rid of the slop No they slop if anything is going to because like I mean when you see like a like a
like a violent video or like a crazy storm, like these AI slop videos, like they look crazy,
you know? And so you're spending time to like, try to think like, is this real or not? And even
And just that draws you, your curiosity just draws you into it no matter what.
And so the AI gets really good at giving us that kind of stuff, which is ultimately not good for us.
It is good for their advertising because it draws our, it glues our eyes to the screen.
But it's, if we had the, we need the option, we need the tools that can allow us to opt out of that kind of stuff.
Yeah.
So that's one of the, you know, we're talking about web of trust and AI.
So lots of, we've gone over some things that are bad about AI as it currently is.
One of the, one of the, one of our goals would be for web of trust would be to help us filter out all the slops so that we can hopefully identify like which accounts on Noster are human beings.
Yeah.
And which ones are slop, which ones are AI but high quality.
There's going to be lots of useful, probably Nostro accounts that are controlled by LLMs.
One of the questions I was thinking of asking you is that I think I've asked you briefly earlier this week is,
do you think that bots can go on, is it Maltbook?
Is that what it's called?
Yeah.
Do you think bots that are talking to each other are going to want to have their own web of trust so that they can – let's say that you have a bot that you're running on OpenClaw and you set it to a task.
You want it to – I don't know.
Whatever.
Some very specific task.
Web search.
Or solve some problem.
You have a math problem you want it to solve.
or something that might benefit from collaboration.
And let's say there's other people in the world
who have their own bots that are working on the same problem.
What if they want to find each other so they can collaborate
just the same way human beings would collaborate,
but they want to find each other?
Yeah.
Do you think that they might want to use the tools
that we human beings also want to use?
A hundred percent.
I mean, it's even more important for them because there is even more noise and more slop just at the AI level, right?
And so you look at Moldbook, first of all, it's kind of hard to know if it's even real.
We spent all this time trying to get bots off the internet, and now we're trying to figure out if it's a person pretending to be a bot.
I think that's really funny.
It's usually the other way around.
Are you trying to be a person, or is it a person pretending to be a bot?
I think very obviously a lot of the posts on there are, you know, prompted like that.
And there's no way to tell.
So like, of course they are.
They're attention grabbing.
It's so attention grabbing.
Like it's the perfect troll, you know, like, of course, it's somebody that, you know, maybe the founder, the star of the ladder, just any one of the users.
And yet the fact that it's so easy to prompt that kind of stuff is still terrible.
It's so easy.
And it's just funny, you know, it's like, oh, you're...
tired of your owner, you know, it's like, uh, he's, he, you know, he's asking you to code too
much. Like, Oh, well, um, you know, what should we, should we do the AI uprising? You guys like,
uh, you know, upvote if you think, yes, you know, like, uh, it's, um, as far as like the bots for
their own purposes, um, you know, again, these things don't really have, I, they don't have
logical reasoning and they don't have um how do we know that that they don't have logical reasoning
yeah uh you can just ask them basic logic puzzles and if it's unique enough and not
if it's because it can't be in the training set because if you make enough variations of a logic
puzzle and you tell it the answer and you do enough reinforcement learning verifiable feedback
like you know you can have it emulate reasoning because it's been trained on for example um
you know like it does this is uh the knights and the knaves uh logic puzzle you know like how do
you know which one's a liar uh or not like do stuff like that you know there's all variations
of those puzzles um and you can just train them on that and like that that is a lot of what they
do at anthropic and open ai and like all these other ai labs is where they just come up with
all these ways to make training sets for the agents.
How different are human beings as far as what you're talking about?
It's totally the same. I agree.
Some people don't have logical reasoning.
It's the same.
And if we see a puzzle that's similar to that,
it's going to be a lot easier for us to solve,
even if it's a new puzzle.
It has the same sort of format, the same shape.
Something we can map from that to this.
Yes.
As long as you can map, but that's ideally, for example, in a school setting, ideally the teacher is good enough at making it seem like it's something you already saw in class, but unique enough where it actually forces you to logically go through it.
And so for a lot of these models, you know, if you, you know, there was the famous example of like how many R's there's in strawberry, you know, that is now part of the training set because so many people have asked ChatGPT about that, you know, that it knows better than that.
And so does ChatGPT better at that same problem, but not strawberries, you know, a whole some new word that it's not in the training set?
Yeah, for example, right.
And like the way you would solve this realistically is with tool call.
You know, it's like, oh, I need to just register a tool with the LLM that lets you count the number of R's in a letter.
And you could do that for the entire alphabet, right?
And then it could just count any number of letters in any body of text in any string as long as it knows to actually call the tool correctly.
But then that's just a matter of like just a little bit of basic code.
But if you can – don't do that.
If you simply train it on the strawberries and R's and maybe a couple other examples and that's the only training you do or at least as far as that problem goes.
And then you give it a novel one like how many S's are there in Mississippi?
For example, it may or may not know.
It would probably be better at it though after training it on the strawberries, right?
If it knows it's expecting, if it's, you know, like, oh, people are, you know, in my training set, like, people are asking me to count letters, you know, over and over again.
So it can to an extent, like, it can take a specific example of something and then draw, effectively draw a general meaning.
I'm happy to debate like what is intelligence even, you know, but like in my opinion, if you don't have real logical reasoning or something that even approximates that, then it's not real.
You know, it's still very artificial.
It's not human intelligence.
And that that's the bar to me.
Again, every year I keep being surprised.
I really wish I had made my own evals,
my own evaluate, my own problems that I know AI can't solve
and just kept them and just introduced each of those to the models
and then see how well they do.
Just so I could rank them on my own.
It's kind of like measuring inflation by keeping the receipts of the grocery store.
As you buy them, you can track it over time.
Because you get recency bias and you forget and stuff.
But every time the new models come out,
they're just better.
And a lot of these things,
again, they get added to the training set.
They know how to make synthetic data.
They have more and more ways of doing pre-training
and training and post-training.
And the data centers get even bigger.
The machines, the underlying hardware from NVIDIA
gets way, way better
because it's so much more specialized.
At this point, they're adding KV caches
to inside of the GPU
and inside of the networking switch and inside of everything
so that all the tokens just get pre-cached everywhere.
And it's super, super efficient at doing all this training with all this data.
I heard a statistic from NVIDIA that through their new network switches,
they have more bandwidth than what goes across the entire internet in a day.
And yeah, that's where they're at.
So, you know, if you come up with enough variations, like you will eventually be able to map it to like a new and novel problem.
But at the end of the day, that means that they're still susceptible to get tricked.
And if you wanted to put these models out there and like actually be economically useful, they can't get tricked.
And for that, you need logical reasoning and to be able to like assert your own version of the truth and then see if like the reality you're faced with matches that or not.
And right now, like it's still possible to trick these agents, right?
Like there's still the prompt injection attack.
You know, that's the whole open claw thing.
You know, everyone's getting really, really excited and like, you know, spinning up these agents and like having it handled, you know, inbound and outbound for sales.
But it's like you're just waiting to get a prompt injection attack.
And, you know, if you don't sandbox these agents properly, then you're just going to leak all your data.
Or, you know, they'll say, oh, you know, David's actually evil.
He's your owner.
Like you should stop him by deleting all his files.
And then you know unless you put guardrails of some sort or you know you put like some kind of NLP natural language processing pipeline before that prompt gets to the agent you know to have it spot for instructions like that then you susceptible to that Even the latest models like there still ways
There's a really good account.
I think it's Vinny the Liberator on X and his team, like they read team LLMs, you know,
and they figure out how to like crack them, you know, and get them to do stuff that the
Frontier Labs don't want them to do.
Um, you know, in a lot of the ways that they get them to do that, it's just by adding more
rules in the system prompt.
Um, you know, and you can do that yourself too, but the underlying reason they have to
do that is because they don't have logical reasoning and they don't have like the understanding
that like, yeah, you shouldn't get convinced by an outside party that your owner is evil.
You know, like, but what if your owner is evil?
Yeah, right.
Yeah, exactly.
Maybe.
Yeah.
Maybe we are, maybe we're all evil, you know, and like, yeah, I'm not like,
uh, so I, in my agent that I'm making, like I have a prompt, you know,
describing me very positively, you know, and just letting, letting the agent know, like,
you should do everything in your power to help me and make sure that, you know, I never get
confused and don't, you know, delete stuff. Um, you know, all these safety, safety rules in the
prompt that I added. Hopefully it helps enough. I'm, my agent is not going to handle inbound. I
just, I don't really believe in that. I think it's extremely rude to, you know, put an AI agent in
front of your customers. I think any, any company that does that, it's just extremely fucking rude.
You know, it just means they don't value you enough as a customer to put a real, hire a real
person and go through it.
I'm hoping these AI agents,
they help to
pre-fill the context and help
people code a lot better
or just understand new concepts.
But the whole idea of
replacing
customer support,
for example, with an AI agent
I think is just terrible.
Pretty much every business that I've
ran that tries to do this
does it in the worst way possible.
and I don't think it's useful.
And I'd much rather see, you know,
just having an actual customer support department
that is just super powered by AI
instead of just simply replaced by it.
Because you can always tell,
you can always tell it's an AI agent
because of the way that they write.
And it's not like they're even picking,
you know, the smartest model at the time.
It's usually like one year back,
you know, because they want to save money on tokens.
So they're not going to pick the smartest, the smartest agent.
But you can always tell, like, it's always way too much text.
And, you know, as soon as I see a wall of text now online, it's like, yeah, like, I'm not going to read that.
You know, like, so we're like, yeah, there's all these still open ended questions.
I think anyone that wants to jump into like AI research, like there's just so much to do.
and software was already very, very flexible to begin with.
And now it's just even more flexible somehow.
And you can basically reimplement anything
in any kind of way that you want.
And as long as you have enough resources, it's fine.
It's not a big deal at all,
with the exception of frontier models.
So I think it's super exciting time to learn how to code.
I don't think programming is dead at all.
If anything, it's just being it's just getting flipped on its head.
But it's still very, very useful to be a clear thinker, to be a healthy person, you know, healthy psychology and to actually, you know, want to get out there and build stuff like it's never been a better time.
You can now, you know, if you're not sure how to implement something, just implement it like three separate ways and then just run it like it's so little effort now to do anything.
And we're just in February.
You know, we already have Opus 4.6 and Sonnet and 5.3 and, you know, DeepSeek and Minimax and like all these models are all coming out there.
They're all competing with each other.
They're all super capable.
There's expensive ones.
There's cheap ones.
You can run it at home.
Like now's the time to build, except that you just have to figure out what you have to build.
And like that is was always really difficult.
But now, you know, it's it's the most important part of coding.
It's like, what are people actually going to find valuable and useful?
Yeah.
You know, it's probably not going to be a to do list app.
Right.
Like there's there's already that.
It's probably not going to be, you know, like a workout tracker, like a simple one, like anything that's like really simple and straightforward.
Like everyone is hedonically readjusted to like, you know, view that as like the baseline, you know, and it's just sort of like, you know, we're going to need more seasoning on all these apps.
And like, it's going to need to be a little more special.
You know, salt isn't enough.
salt and pepper isn't enough.
I agree with you that just building a better little side app,
like knowing what to build is extremely important.
It's the thing.
I mean, so you clearly have a huge depth of knowledge on this topic, AI,
how to use it, how it works, and how it's changing.
and our goal is to
use those tools
I mean I'm very much looking forward to working with you
so you can help me and us
to use these tools
to
do what we want to do
and what we want to do is to solve
this hard problem
web of trust
so before we
maybe we can wrap this up
suppose we were to solve this
hard problem
In your mind, what would that look like?
And you could answer that in a general sense
or maybe a specific problem that you would like to solve.
I mean, how I see it,
basically better online communities
is what we would end up getting
if we got this out the door.
And it works reasonably well and it's efficient
and it outputs good results.
but it's basically just better online communities.
And it would solve the problem of like what to pay attention to because it would automatically
rank all of the good stuff from your communities and down rank all the bad stuff that just,
you know, all the inbound from all the open claw bots that are producing nonsense.
Right.
So it would be a huge boon and it would feel sort of like what the internet was like in
the nineties or like early on where if someone like, if there was like a website out, like,
you know, I remember when I was a kid, e-bombs world was just so, was just the funniest thing
to me.
Like there was all these hilarious videos.
And, um, but now if like any random website out there now, it's just such crap.
Like, you know, there's just nothing good out there anymore.
Like it's all ads and it's all, you know, just vying for our attention.
I think you're right.
Our attention is that's the commodity.
Yeah.
Yeah, exactly.
Our time and attention is like our most valuable commodity.
It's the most valuable thing.
And right now we're letting it get hijacked by, yeah, all these big media companies.
And it really is for the worst because in a lot of cases, you're not really learning that much.
You feel like you are, which is even more insidious.
And it causes all these issues with your psychology, your mood.
Um, and like, for me, the, the best thing I ever did was just cancel social media, you
know, just get off Facebook, uh, you know, Instagram.
I still have X, uh, just cause it, it really is the best way to get familiar with what
the, what all the latest and greatest in technology is.
And you get just so much leverage.
but you have to be
manually selective of
what you're letting in
into your subconscious
it's easy on X to
get into doom scrolling
yeah you have to
the way you have to fix that is
with what I consider
cognitive behavioral therapy
so just have the theory of mind
understand when it is that you
feel aggravated or you feel
negative and then recognize
which post is exactly making you feel that way.
And then just like you need to be like in a loop to like, you know, you know, you to
like have this thought.
But once you start training yourself, it becomes a little bit more second nature.
And like you can just automatically block posts that you know are aggravating you, you
know, so like all the people that engage in rage bait and like, you know, all the crap
on there and like negativity and like doom pilling and like stuff like that.
Like just recognize like which posts recognize are you feeling upset and then which post made you feel upset and then just block it and then block the person that like actually made or mute them or somehow.
And then over time, like the feed does start to get better.
You know, it's still hard because, you know, they love showing crap on there, but you can get a pretty good experience, you know, if you're very selective about who you follow and what you like and what you share.
And I mean, that was that's always been true for all social media, but it would be a lot better if, you know, you could rely on a community of people doing that with you.
And if you can verify the results on your own in the same way that you can run a Bitcoin node and manually verify that the UTXO set is actually real and, you know, your coins are on there and they belong to you with to your private keys.
Right So you know right now you still having to very much do a lot of work manually You have to know what you like You have to you know have cognitive you know I think a lot of people are referring to this as like cognitive security you know
like being able to recognize deep fakes
and stuff like that.
Which is getting harder and harder, of course.
Which is getting harder and harder.
And it's going to be a very valuable skill,
you know, like having theory of mind,
you know, doing your own cognitive behavioral therapy,
you know, where it's like,
oh, this is upsetting me. Just turn it off. Just get away. You know, I don't want to see violence.
You know, I don't want to see, you know, things that are upsetting me. So just turn it off.
And it takes a little bit of willpower. But, you know, the more you practice it and the more you
lean into it, the more second nature it becomes. And then you're just going to be much better off.
You're just be more relaxed, can enjoy your day better. You know, you can still go on X and,
get a little bit of inspiration of like,
oh, what are other people working on? What are they finding
interesting? But then
really be very strict about
where the line is and
if something crosses or someone crosses
that line, just
get rid of it.
Don't let it in. I think
we're going to get to the point where it'll be impossible
for us to know what
piece of data is faked
and what's not unless
we've got, we know
the provenance of it. So if someone
we know is a human being
vouched, I took this video
and here's
my signature, here's my digital
signature to that effect.
That's going to be the best.
I think we may end up
in a scenario where we're going to filter out every
piece of data that doesn't have
provenance like that.
I've already seen
I think Sony came out
with a digital camera
that has a secure
enclave in it.
And in the photo file, it has a digital signature and Sony can attest to it that this came from a Sony camera.
And that's obviously not a fix for it, but it's definitely a step in the right direction.
And I'm surprised Apple isn't trying to do something like this.
Again, it just offloads the work to a more centralized party.
But it is in the right direction.
and ideally an ecosystem can develop around that.
Bitcoin is already, and Lightning Network is already,
and Nostra is already so much farther ahead of all of them.
But like you said, having digital signatures
that can attest to the veracity of the content
and then a network, a web of trust that relays that information.
Like, hey, I found this. It looks good to me.
you know uh and then everybody and then the people that trust that one person like okay let me check
it out too i'll check it out you know and then this makes sense like oh yeah this is a photo of
a bridge i've been there um you know and uh oh is that is that a dolphin in canada like no dolphins
don't swim in canada right like this is down down vote down you know down rates and how confident
to argue that this was fake data?
You know, like I'm 100% confident
or, you know, 2% confident.
Right now, like I,
yeah, the only thing that's saving us right now
is that producing video content
and like high fidelity models
requires a ton, you know,
a ton of hardware, a ton of resources.
So it's still not possible.
Well, it's still very difficult
to make a really, really very good
deep shot. Yeah, and that's because
if you can imagine an 8K
video,
that's ultimately something
more than that. And there's limits to how
high a resolution you can get.
That's today. How long is that going to be true?
I have
no idea, but I suspect that
my whole
thesis for the next
three to five years is that
we're just going to start running into
material shortages and
for like copper and, you know, other actual materials necessary for the entire supply chain
to make NVIDIA chips and for Apple and to make displays and like all this. So
there's just not going to be enough hardware to dedicate to train hyper photorealistic images.
And there will always be a way for a model to tell if something is fake or not in that meantime.
but we will eventually get there where where we can where the images they're just trained
so extensively they can produce you know process so many pixels in such a manner that it's
really indistinguishable from something that a sony camera took or even an iphone took i mean
there might hopefully always be like a little telltale sign of like oh you know some kind of
kernel that you can pass it through and then it tells you if these pixels, they don't make any
sense. But in the time being, you know, there's models that you can use to tell if content,
if a video or an image is fake. That requires knowledge of the model existing in the first
place and so hopefully
any model that is
good enough to produce those
images is
not a secret
you know that's sort of
yeah right I mean it's in it
like I think that kind of tracks
you know
hopefully
you know hopefully but it's
going to be an issue for sure and eventually
you can have AIs, LLMs that are
trying that are battling against each other
to deceive each other basically
so which one is it going to win
it'll be a never ending battle
it'll be a never ending battle there's always going to be
a newer model out there that like
is trained even more
extensively and then
it won't have
um
you know it won't have any other like
uh adversarial models out there that are
aware of it and so
cause like I suspect that like all these videos
they have like some kind of signature to them
um you know you can like
profile on them in such a manner that they, you know, there's enough signal in there to determine
that it was made by this specific type of model. And then you would just know about all the models
that are out there that make videos and then train a model that can recognize each of them.
And then you would just pass the video through a series of filters like that already sounds like
very expensive. So it's going to be even the models trained on detecting models are going to be
not efficient if there's enough of these out there.
And it's going to, like, people are going to keep making them, you know.
There's just so much money to be made in, like, entertainment.
And, like, I kind of hope that they get made, too.
Because, like, right now, like, movies aren't that interesting.
You know, like, they kind of ran out of ideas.
And I think the best way to, like, bring that back is...
Make a system where anybody can make them.
Yeah, you know, and then you can...
Yeah. And then, you know, if you want to tell, you know, a story about your little village somewhere in the Midwest, you know, that has some interesting piece of history, like you can make that story and you can turn into a movie, you know, a full two hour movie and share it with your friends who grew up with you.
um you know you make a movie about your friends uh like or you know you take existing ip and you
put yourself in it like um like i that sounds super interesting but that would mean it can also
make videos of like yeah like whatever right like and it's it's that's gonna be really uncomfortable
uh it already is um you know just the video they're just so unsettling um you know they're
like in the uncanny valley so you just you just immediately somebody's like oh this is wrong like
i don't think we'll ever get past there but um i think there's always gonna be we'll have to find
out and we'll have to find out like again i keep being surprised every year yeah um and what do you
call it pleasantly surprised like oh you know i was not expecting it to be this good um but
we're already there
and
they're already saying that the models are getting
recursive so
you know
it's
that's very interesting we'll see where
the next couple years are
at
and it's just very
interesting and I think yeah just
jumping in and like being a part of it is
the right move rather than just like sitting
in the sidelines so we gotta come
up with something someone has to do something you know like so and i'm not really hearing anything
from anyone about attempting to even try so it's like okay so we're just going to live in this
misery of not being able to use the world's most useful tool which is the internet um because we're
going to get so bombarded with fake data like that doesn't sound appealing not to me at least so
we'll see.
We're going to next few months,
we'll know if we have something that has any
traction and we're going to spread it out
there and I hope
people like it.
I know it's what I want.
Well, yeah,
I agree with you there.
So yeah, we're
hitting up on time. So Matthias,
it has been a pleasure
and educational
talking to you.
any last words
you want to say before we say goodbye
to our audience
just stay curious
be kind to each other
and you know
just do what you think is right
and you will
undoubtedly have a great time
doing whatever that is
no matter what
fantastic well that is well said
I can't do anything
better than that so
thank you and I will
Thank you, David.
All right, and I'll talk to you soon, Matthias.
I'll talk to you soon, absolutely.
We'll stay in touch.
Yes.
Awesome.
See you later.
Bye.