TFTC: A Bitcoin Podcast - #726: Mapping The Mind Of The Machine with Brian Murray & Paul Itoi

Episode Date: March 14, 2026

Marty sits down with Brian Murray and Paul Itoi to discuss the convergence of AI agents, graph databases as a solution to LLM memory limitations, and Bitcoin's Lightning Network as the native payment rail for the emerging agentic economy. Paul on X: https://x.com/paulitoi Brian on X: https://x.com/murr STACK SATS hat: https://tftcmerch.io/ Our newsletter: https://www.tftc.io/bitcoin-brief/ TFTC Elite (Ad-free & Discord): https://www.tftc.io/#/portal/signup/ Discord: https://discord.gg/VJ2dABShBz Opportunity Cost Extension: https://www.opportunitycost.app/ Shoutout to our sponsors: Bitkey https://bitkey.world/ OPNEXT https://tinyurl.com/tftc2026 Unchained https://unchained.com/tftc/ SLNT https://slnt.com/tftc Salt of the Earth: https://drinksote.com/tftc Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Newsletter tftc.io/bitcoin-brief/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Nostr https://primal.net/tftc Follow Marty Bent: Twitter https://twitter.com/martybent Nostr https://primal.net/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://www.tftc.io/tag/podcasts/

you've had a dynamic where money's become freer than free if you talk about a fed just gone nuts all all the central banks going nuts so it's all acting like safe haven i believe that in a world where central bankers are tripping over themselves to devalue their currency bitcoin wins in the world of fiat currencies bitcoin is the victor i mean that's part of the bull case for bitcoin if you're not paying attention you probably should be i don't know and we're like two months into it i'm like i'm gonna have to set up a new one at some point because this is going to be outdated uh really okay i think that i mean that's the conclusion i'm coming to you can switch out the models and stuff like that but i

think the context memory is the the big problem i think and users like me who aren't as technically competent need to figure out like how do we nail that sounds like a problem you're familiar with sounds familiar yeah right yeah yeah that's what i mean that feels like a good starting point i i think um yeah what would be most helpful i think about just catching up overall on what's happening in the space and talking about the pieces you've heard me talk about it for a long time so um can we talk about the 1031 offsite we're at sometimes yeah um yeah i mean just uh i get a little embarrassed getting up there and showing graph stuff just in front of everyone i see everyone go, Oh God, Paul, another year, the graph guy, the graph man. So, and you get a lot

of grief for graphs on online because they've been tried so many times, but, um, I've worked with Neo4j since 2010, 2011, sometime around then, I think they were just starting out. Uh, one of our technical guys brought it into the company and I hated it cause it just crashed all the time. So, um, but now 15 years later, it's kind of seeing its light of day. So I think you you just have to have all these kind of primitives in your toolbox. And then when the time's right, you pull them out. And so we just think that the memory issue

you just brought up, graph databases just serve as a great scratch pad for that. And it doesn't have to be in a graph database. It can be in Obsidian files. It's just the whole thing is relating one thing to another. But anyway. Well, I think it'd be worthwhile to go into differentiating like LLMs how they work from these these graph this graph

approach because I think you know we were just discussing before we hit record yeah ton of capital time and effort has been put into LLM specifically but I think some would argue yours I think yourself included that LLMs may not be the the best way to go about this problem yeah I think I think people anthropomorphize LLMs a lot, you know, because it's speaking language to you, because you can talk to it, you think that it's actually reasoning and especially when they call it a reasoning model and it does do an amazing job of mimicking logic,

but it does not know why it's saying what it's saying. It's just a statistical output of the next word. Yeah. Yeah. I mean, how do you see it? Do you, when you think of an LLM, Do you think of it as do you find yourself thinking of it as a machine spitting out words? Or do you think of it as, you know, especially when you name a bot or something like that, it really starts to feel like a human or something. No, I definitely don't think it's human.

Any time I interact with our open call, I'm like, OK, what context do I need to feed it to make sure that it gives me the right response? Like, that's what I think most about is like, what do I need to preload this thing with? Maybe we should start with what's everyone running right now? Like, that would be a good thing. You're running a bunch of cool stuff, right? Yeah. I mean, I'm using

Hive, of course, doing some cloud code. But I think the context issue is something that everybody's kind of running into. They're able to just stumble through it or hack their way through it. But I think we're all going to feel this need of something better, like something that's going to be more accurate, give us better answers, help us do the next thing better um so and you're starting to see it like you open x and there's more and more visualizations of graphs i feel like the whole like ecosystem is drawn that direction

um but yeah those are some of the things i've been messing around with yeah it's about you marty i i mean i for the context like interacting with my open claw bot like when we're doing something i i have to be very specific i'm like hey we're going to write the bitcoin brief today i've dropped some stuff in a folder that i've named a specific name i'd say the name of the folder go to my tab the tab is named this like i have to give it like direct direction and i've heard of people leveraging the obsidian bolts to to sort of solve that context issue i haven't dove down that but yeah we're using open claw we use cloud 4.6 and then um combination of code we have codex subagents that we can and we can ping if we need to build something and then 11 labs are the models that

we're using now for voice yeah when you told me about your automation process it was pretty solid about what you do has that stayed the same since then or yeah you want to describe that a little bit or yeah i mean for um like we'll take this podcast this transcript and then we have prompts that we've been iterating on for literally two years at this point um in claude that will that are very long prompts that will take the transcript and then pull certain sections quotes and be able to either um directly quote for a section of the newsletter or um find something that we're talking about to deeper research on it to expand on the topic for another piece of content um and that is for the podcast like post-production it's one of the things we'll do but other things we

have um for clips we'll give it the transcript with timestamps and claude will then uh use our third-party twitter api that we have access to look at what people are sort of talking about what's trending and then it'll go back to our transcript see if we talked about something um during that episode that people are really on top of and then we'll be like all right here's the time stamp of a section of the conversation that you just had that if you clipped it out and put it on twitter it would it would probably get good engagement and are you using n8n for that sort of pro that pipeline anymore or it sounds like you brought up robot so we were okay we vibe coded our own like back-end dashboard that basically replaced n8n um shout out to ed on our

team he's put a lot of effort into that that's pretty cool but uh so when you're done like this episode when you hit finish or record the recording's over does it automatically kick that over to these workflows once we put the files in dropbox it'll start um and then yeah it'll be or you can just if we put it in dropbox you don't want to trigger automatically in the dashboard that we've built you can go like all right ready to run this process that's crazy yeah that's cool i was talking to scott who runs uh um you know scott foreman he runs a video uh lab they do a lot of work for the human resource foundation and he's trying to do a similar automation on the back end so everyone is trying to figure out this uh this process yeah but it is like it does feel

we were joking you came here in a waymo and it was driving on the wrong wrong side of the road yeah i look you know i'm like i don't i stop paying attention to the road when you're in the back of an uber or waymo and i look the car starts like jittering and i look up the waymo is on the wrong side of the road with traffic heading towards me because the austin has all these events going on so there's they were like coning off the streets um so you could tell that somebody took over via tele-op yeah and then finally got me over and then as soon as we were getting over there was a traffic cop who was it was like trying to direct the waymo and it was just this moment i i was like gosh what the hell are we doing here yeah well that's a human directing waymo and then

some poor person sitting probably in the philippines operating the car like a video game on some playstation console from 1988 or something like that yeah it's crazy but it one thing i was thinking on the way over was um and we had a team meeting yesterday we were talking about this i think we all find ourselves thinking about the current state and it's hard to think about the through line, like where things are going to be in a few months. Um, and when I was in this Waymo today, I was thinking about when Waymo first came out or, or self-driving in general, people have been talking about, about this for a long time and dismissing it, but it's like, it's basically here. Um, and of course there's going to be some kinks along the way, but

we've come a long way in a short period of time. And it's pretty hard to imagine what a year from now it's going to look like because things are changing so much um but yeah it's like an amazing time to be alive i mean we don't want to say the phrase but you have to say it gradually then suddenly it is and people just have such a hard time with i have a hard time with it so i mean we're doing coding automation stuff lots of people are but the platform that brian mentioned um the takeoff on it is so real it's so crazy i'll do a demo for you later on but i mean we pull up a voice assistant on our conference calls and you haven't even seen this yet um we nicknamed jamie after uh rogan's assistant um which i don't think anyone's really got on that so i think it's a good

nickname um but you literally just talk and and jamie can read your code it's not just some note-taking assistant where you tell it to you know make a note of this or basically transcript manipulation that's what most of these things are it can read through all of our graph database which is all of our code all of our chats all of our conversations all the context for the conversation so how many times have you been a meeting where you go hey is that the way it really works so you don't remember hey jamie can you look that up and see if the code actually does that and then once you can get past that in a meeting um you just have it's such a flow state because you're not like hey let's figure this out and circle back together again yeah like imagine

right away imagine if we were talking about your agent in this in this chat right now and you're like yeah i really wanted to do whatever and then by the time we're done with the conversation it's just done fixed it i think that's what we all want right yeah i mean you were demoing it to us in san francisco november and it was doing it come a long way yeah yeah well that's the that's the thing as an operator integrating these tools to your point brian like what is the through line to where this is like you don't want to wear like we i tell everybody at our team here like hey this stuff's changing rapidly like something's going to work one week and then something better is come out we're gonna have to like start from scratch to rebuild it and it's just like this

constant iteration of like okay build something quick it works it works better than the thing we built before but oh here's something new like we got to go do that again so that's what like i'm curious like when are we going to get to the point where yeah you don't have to keep doing that well there's a book that really impacted me early it's called second machine age have you heard of this before yes i've read that have you read it it's been a while but yeah it is an old book mit professors i think and they talked about this idea i mean don't read it but i think the main idea is combinatorial tooling so once you make tools that help you make other tools this is when you start to see the fast takeoff and so i think that book made the point of what we're seeing

right now is that the software tools to make software tools is happening so quickly and you can automate workflows you can automate all the stuff that you're talking about and i think that the people who figure this out and get these tools are in a permanent you know we talk about the k shape economy have you heard of that have you talked about that so i was explaining that to someone yesterday it's so true if which line you're on could sort of determine what it's like forever or for a very long time if you're on the down slope of that k then you may not ever make it to the upslope which to me means that we're just at this really critical moment i was talking to the best man at my wedding last night in georgia he works in construction management and i'm going

are you using any of these tools have you heard of any of this nothing so i sent him a video here's how you get started so i think that um just the distribution of this tech is just so uneven right now what do you think we're in a bubble basically yeah that's what i was gonna say what do you think penetration is like 0.1 percent x and you think everybody's like knows everything and then you go to like a family gathering you're like nobody knows anything um no i mean that's been my experience it's it's tough we're sitting here driving around in waymos and experimenting with apple vision pros and playing around with claw bots and um it's it's pretty scary what i think can happen when you diverge people this this distinctly it's going to be tough super tough

well that's again another question on the through line that i have like thinking through this social problem like will it get good enough where yes we're early adopters we're reaping the benefits of this uh massively right now but will it get good enough where it doesn't matter somebody like your best man will be able in a year from now to download an app just do it like that with little learning curve it'll accelerate so quickly i think it'll be way faster than that i mean what's your take on the well okay where are you on the doomer there's like a divergent point here so on the one hand you could say the k-shaped graph which is basically to say that people there's the people who know how to use these tools and the people who don't right on the other hand you

could say all these the tooling is benefiting the creators because the tooling is getting more accessible and easier so then it lowers the bar to creation i hope that's how things come to pass and i think about my kids so i've got four daughters and the older two are old enough now to where i'm like working with them on some things in fact i'm working on this bitcoin project with them i want to tell you guys about but um like our local pool a community pool they have a website where you like go and see the pool schedule and times and order your swim gear and stuff like that it's a piece of crap website. And so my 10 year old, I was like, Hey, why don't we try to fix this website? And the tools are easy enough for her to sit down at the computer and we just like

iterate on it. Um, so if she's able to do these sorts of things, she has the intention and the desire and she has like a point of view on how things should be and she can do it. Um, and that's today. I just imagine a year from now, it's going to be everybody with an idea can go and create their idea. I hope that's what the future looks like. Did you see Replets announcement yesterday, Agent 4? Yeah. I mean, I tried it yesterday. It's really good. I mean, it was really good. You can basically just start with a sentence and then it'll build the slides. So he completely expanded what vibe coding or vibe building is like. You get the code, you get the decks, you get the images.

I mean, a lot of it is stitching it together in a clever way, but you stitch enough of that stuff together and eight-year-olds can create businesses, 10-year-olds. Sup freaks, this rip of TFTC was brought to you by our good friends at BitKey. BitKey makes Bitcoin easy to use and hard to lose. It is a hardware wallet that natively embeds into a two or three multi-sig.

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and it was blowing my mind like how how robust they're the aperture of like what they let the kids do at the school it has me thinking like so i have three boys uh the oldest of which is six right now he's in kindergarten and he's just like a catholic school and i think it's a good school uh kindergarten it's montessori based so they're not really getting too much in the weeds of math and science and stuff like that but i really want to like go in there and be like hey we need to like sort of rejigger how you're teaching these kids with these and you need it in a great ai because this is what they're going to grow up with but they're doing the opposite the guy i did last night his um uh son's friend is going to one of the top boarding schools in the country

we've all heard of and they're just so anti-ai you cannot use it they're not allowing it you don't touch it it's evil imagine teaching that to kids right now i mean you're just it's unbelievably responsible i'm of two minds so marty you haven't met my wife but um paul's met her many times she's great she's awesome uh she's on like a crusade to reduce the amount of screen time for our kids in school we go to public school um which i'm all about i think i think just giving these kids chromebooks and having them sit on a computer all day long is not what young brains should be doing on the other hand i want them to be exposed to these tools and become capable with this technology because it's so cool it's amazing you can make anything so i'm i'm like mixed on

this you know how much to expose young people to like kids to this technology and encourage them to build um but without having them sit in front of a computer screen all day yeah that's why i like the alpha model it's like two hours of intense like how they do integration then beyond that it's like social skills like group projects stuff like that yeah that sounds good especially if we if things happen the way we just described where it's like people can have a conversation and then you end up with a product that other people can use that's a lot different than the hacker spend it you know not sleeping spending all night coding something um i hope that's the the future we find ourselves in yeah well i i think the the focus needs to be on like first

principles logical thinking like clearly you that's what i've come to learn again going back to how i interact with my my agent is again thinking about the context and the direction i'm going to give it so like having to think from first principles okay what's the sort of logical order of instructions i need to give this thing to get the output that i need yep and i think reading comprehension uh is going to be way more important than stem moving forward unfortunately well you write every day so it forces you to clearly think through steps and i think having that cape that muscle fully exercised when you write down it's you know you've heard this term spec driven development you're no longer coding you just you have to clearly articulate what you

want and that's really hard to do when i sit down and try to spec out and the system's asking you questions well you want to select all and then what if you select all then how do you unselect you're just going oh man you're right but forcing you to think through the whole thing is actually where the mental friction takes place so how do you teach that to the next generation yeah like how are you i mean your kids right so it's like how old are they and what would you say to them i mean my oldest is he's just in the process of starting to read but i think never too late marty no but i think reading the classics again like uh getting like i don't know at what age you introduce it like literally socrates played oh like how do you think like how do you logically

like aristotle read the old really old stuff yeah i mean i went to a jesuit high school in that uh and actually like learning latin like we had uh we were we had to take six years of language and of which had to be latin and like you get down to like the etymology of language and the whole goal of our high school is like by the end when you graduate you're going to be able to write competently like you're going to be able to read something and then have an opinion on it and write articulate about it and i feel very fortunate it set me up like you said i write every day but i think that that sort of model is going to be more important um moving forward what survives after ai then i don't know that's the other question like is it going to make us dumber

like you you see people allocating their thinking to the machines and are we going to get find ourselves with a competency crisis a decade two decades from now where god forbid something happens to to the machines and then we don't know how to rebuild anything because you have a generation who never learned the first principles of software or math yeah but but just i i think think most of society has gone that way like when the average person encounters a car engine they're probably I don't know what the hell is going on in here and then you meet someone who knows what's happening and it's extremely impressive and that person who knows what's going on can fix it they can do a lot more things so I hope that that kind of competence gets rewarded in the future I hope

these types of people are better at creating things at advancing things and encourages other to do the same thing and i think from like a a parenting perspective i think that's an important thing to bestow on your kids and um honestly my role model is paul i remember the first time oh do you mind if i tell the beaver story sure okay first time i met paul's younger son um came over to his house first time i went to your house there was a sous vide happening you know I was like, what are you guys cooking? Like, oh, beaver tail. Like, okay, interesting.

And then he goes on to tell me that his son just got, he was like 10 at the time. Yeah, 10. 10-year-old son just got back on a solo hunting trip. He trapped a beaver, brought it back, was sous-viding the beaver tail. So he's cooking for the family. But then before that, he like scanned the beaver

and was making a video game out of the 3D scan of the beaver. He's 10. it's like what and then over the years i've uh come to you homeschooled your kids yeah for a while up until uh high school really and i just i think i've learned that you took a very you and nikki your wife took a very intentional approach to how you brought your kids up and they understand things deeply and now they're super resilient and capable um so i mean i feel like this is a tangent but also pretty important thing for people nowadays is to try and teach people these like yeah i mean i was i mean you guys are in a different phase in terms of upbringing but um i

told i was right about one thing and wrong about another thing i said don't learn to code i don't think that people will be writing these letters long term so i wouldn't say that learning right the letters um is unnecessary because i think that just like learning latin you're not writing latin but learning latin which i did too teaches you the structure underneath and so i really felt like the job of writing code won't be around long that's why i told them when they were that age just don't don't bother worrying about that i also thought they would never have to drive a car so um that took way longer than any of us thought you know i rode in a tesla you know it was driving down the freeway a decade ago and i thought oh we're so close so

i think and now that's basically here so the the question of timing is the one that's the tough part i will say back to our previous point just that it i know this from firsthand that you can automate basically two day with no advancements no change of anything 80 of every job done on a computer so everything that we're all doing here and 80 is is pretty conservative so go into the most you know esoteric job in some law firm and you can automate it consistently to a degree that's better than the human doing it and that's a fact and whatever we do with that fact i'm not sure but you know again my son's friend who's doing finance at a public university and i'm looking at his homework and it's manipulating spreadsheets like oh my god you're paying to have someone test

you on editing an excel spreadsheet and i dropped the files in the folder and said here's this thing called cloud co-work uh go to town you know and um and then i had an uber driver the other day take paying six thousand to eight thousand something like that he saw the way most it's like i have three i've been driving for 15 years i guess i have three years left before this whole job goes away i'm paying six to eight thousand dollars to learn to write qa code for class at night and i went oh buddy don't do that don't do that that's a bad that's the job that's going away now i mean could you turn that into something else and he goes he's sitting there and he just says well what should i do and i went that is a very good question man and i've had to go with

um you know what do you what do you love to do in your free time how do you turn your hobby into something but literally that was when the ride was over and i just said uh it's just stuck with me what do you tell people to do what do you tell your kids to study what do you major in college for do you go to college i mean these are things you know my kids aren't have don't seem to have plans to go to college um which i don't think is a bad thing i just i you know i think they miss out on some of that critical thinking and socialization that the college part the collegiate part of meeting people is super critical what they're teaching you don't use ai to write this essay is absolute crap you know so how do you separate the two and so i think there are you

know whatever these schools are popping up home schools all that stuff i would say you know go i would advocate for that strongly i think that you don't want to have you don't want to be cookie cutter in this next era you don't want to be like everyone else because you're already in the training data well and so are all your friends you're already in the training data like why do you exist well like talk about gradually than suddenly this has been around for like we didn't have an attendance policy so i i didn't get a class and i just used khan academy to teach myself like oh really yeah like i basically took all my khan when was this sal khan i graduated in 2013. so like sal khan covered everything i i studied in college and i was like all right i'll just teach

teach myself and where i wound up working uh during the day and um but yeah i mean to the point gradually and suddenly this has been positive this change has been emerging slowly but now it's hitting a critical tipping point where yeah it's um it's it's here sup freaks up next the bitcoin scaling conference where bitcoin developers meet institutional capital is coming to new york next month join bitcoiners from blockstream chaincode brink and more at the the Can't Miss Summit at the intersection of Bitcoin tech and finance. This conference has one of the best signal-to-noise ratios, and that's why I'll be joining BlockSpace's third annual Bitcoin tech conference

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retirement accounts, and idle capital. if you plan to use this downturn instead of watching it pass go download it at unchained.com slash tftc that's unchained.com slash tftc and i really want to dive into this like graph model versus llm sure 80 of the way there are you confident with that graph because again going back to the llms you can tell i was talking to justin moon about this like the first thing he does whenever he tests out a new llm model is tell me a joke and nine times out of ten the joke's very similar to the previous model yeah and it's basically like it's using that distribution of okay this is what a good joke is and you end up in the middle of the bell curve and you get a

similar joke every time um with the graph model seems like it has way more yeah i mean there's graph pops up a lot so i mean i don't know if you guys want to dive into the details or not but it's i think i was practicing explaining this the other day but there's really um four maybe five pieces so should we go through what you guys think so um and you've seen this slide from i think 2022 is when i wrote this slide so there's basically um people over in debt when chachi bt came out it was like six-year-olds playing soccer this model is going to do everything because you're so blown away by where you previously were versus what chachi bt can give you and so everyone was like these models just going to take over the world remember the thin wrapper

thing. Don't be a thin wrapper because it's going to eat, you know, the model is going to eat everything. No one talks about that anymore. In fact, I think OpenAI bought a workflow company late last year. If the model is going to eat everything, then why do you have all these extras? So in the corner, you do have the models, but I think that they are very good at interpreting human intent and then communicating back out the pieces of logic and words. So I look at it as like the input output to these things. And you can extract a ton of logic from the language models because the sentences contain logic. So you have the models in one corner. And then you have the thing that everyone's excited about right now, which is agents and loops. But any programmer

will tell you, because the more technical you are, the more you look at this and you just go, well, what is an agent doing? It's a loop with tools and language. And you can do crazy stuff When this thing just has the right tools, it can do crazy good, it can do crazy bad. But that's, I think of agents as figure this problem out, iterate, and then get back to me. That's what the agents are really good at. I look at them as like explorers or discoverers. I don't really know what the problem is. You go and do your thing and then tell me what happened.

And those are agents are incredible. So people call it agent harnesses and stuff like that too. So language models, agents. and then the piece that people overlook are the workflows you actually don't want your agent figuring out how it did something yesterday every single day right i don't need it to go and figure out um the way i look at it is if you didn't have google maps and you just had your cars driving around town with no map they will end up at the coffee shop one day but they're just driving and driving and driving once you know how to get to the coffee shop write it down create a list and

execute that list so the workflows help keep the agents on track so don't you know just go off the rails so when you know how to do something that's the workflow cron jobs and those are the kind of things schedulers you'll hear those words so that's that part of it um and then chrome jobs is you would put that in the workflow bucket yeah that's basically triggering the workflow okay and just to just make sure i'm tracking so the agent and the driving around town analogy the agent's loop is just go find a coffee shop and they're just sort of driving aimlessly whereas you give it the workflow that's kind of like the instructions of how to get exactly to or the directions to get to the car once you figured something out got it so input output with the

language models then you've got the agents doing discovery using tools and in language and then you've got your workflow that actually can do very complex long steps and you just when you know what you want you generally want it to go you put it into a workflow that's what you were doing with with any of that. Yeah, or that could be like a Claude skill or something. Could be anything, yeah. Anything that's writing down steps.

Skills can be tools though, too. I'm less familiar with how Claude does the skills. But then the last piece is memory, right? So, and memory right now is in the form of files. So whenever you talk to your Claude bot or whatever you're calling these things is just, it's writing down these Markdown files and it's reading them.

So the analogy people use is that movie Memento where the guy wakes up every morning and doesn't have any memories. He has to read all of his tattoos. So every time the agent goes through the loop, it's actually rereading the files and figuring that out. The problem is, is that you have the language model, the agent loop, and you have text memory.

Those combinations results in the thing you were talking about before. The language model can only have the attention span of so many words. The agent is just spewing through words like crazy, writing down reading and writing down reading and then the more complicated it gets the longer it gets the model can no longer hear all the agent has to say so then what do you do you have to shrink or truncate or compact those are the words you'll hear compaction um and it'll just

arbitrarily take long sentences and turn them into short ones or prop them up there's many different strategies to do that that's when you start to see mistakes so now you have an agent with alzheimer's because it's just missing chunks of his memory so can you do files what everyone's trying to figure out now is are files really the best way to do this right now they're incredible because the models and the agents have really great file searching capabilities using grep and bash tools so um they're really finely tuned to do that but it's almost like the tail wagging the dog hey we're really good at text tools so let's use text for all of our memory it doesn't make sense so what we've explored for years is using graph databases so we started out with um micro

task payments where we pay human beings to do small tasks using lightning right that was our company was founded on that we were taking complex human processes breaking them down into small pieces and then giving them to different human beings around the world and then reassembling the answer later which is exactly what you know these language models are good at and all these systems what you're describing is you're taking your process of of doing a podcast and breaking it up into small pieces giving them the different models and then reassembling it at the end we were doing that with humans from day one and so we were very well primed to then say oh we know to break things up into workflows these skills humans are doing and some are automated and now

just more and more is automated um so we think that memory will end up in a graph structure whether it's a graph database or not and so that's what we've been working on forever and again you're demoing hive to us in yeah in san francisco like comparing it to how people are using these harnesses something like open claw like how would you describe the benefits of leveraging this graph database instead of all these text files yeah um the the graph database can basically construct using a search the pieces of text you need instead of a file the reason why we moved i mean you when you log into airbnb they're not reading a bunch of files to get the listings they have a database right because databases are way more efficient than text files

so somehow we've gone full circle back to pre-database land and going we're now in the 70s going oh let's just use text files right so what do you miss with text files relationships versioning typing like it'd be like i don't want an excel file i just want to put it all into a text file you could but you still lose some stuff you know so that's what we're trying to point out to people is the same things that drove people away from storing all of your information in text files 50 years ago are applied to memory today. So you have to be able to query the pieces

that are relevant in the background. We do so much using workflows, janitor work to clean up the context so that it can be presented in a way, this is what Evan from our team has really excelled at, is pre-rendering the context in the way that the model can actually understand it. So there's a lot of layers.

So you have, say, every recipe you've ever cooked, all the ingredients in text files, but then you don't know how often you've cooked that recipe, right? This is a recipe I've cooked 800 times. Text files aren't great at that, right? Because they have the recipe. They have what you ate in a different text file.

Let's say you're tracking your meals. but those two things aren't linked together so how would you do that you you couldn't count and these things are trivial for databases to do but that's why your open claw um or your agent of any kind might seem dumb because it can't keep those statistics yeah whereas any database and especially a graph database can do all those things for you yeah that makes sense i mean i mean it does and i um we have uh our agent has qmd yeah toby from toby yeah from toby running in his server to sort of solve some of that context not exactly it's a vector database or a rag yeah um i haven't loaded qmd but i was just gonna bring up rag um remember you guys invested in the company too

that does rag and so rag for people is just the the process everybody does rag all the time so the term rag is really retrieving stuff and feeding it to the model the most popular way of doing rag for years has been with a vector database and no one no lay person touches vector databases right we've gone over this too but um it's basically turning text into points points in a space but you can't picture the space because it's not three-dimensional space it's multi-dimensional space and the example i use is if you do back to the recipe analogy would be if you do a keyword search for the word avocado guacamole will not come back those are two totally unrelated spellings but a vector database guacamole and avocado will be very close to

each other in this space because they appear all the time together right yeah um but when you take the word guacamole and take the word avocado one is from a farm bureau about avocado farming and the other one is a recipe or menu in a mexican restaurant those two sources for those two words are completely unrelated but the vector database cannot tell that those two things are unrelated they'll just say they appear together very often so if you use vector databases for rag you get these kind of random combinations it's better than keyword search but um it actually orphans the word from the source of the word so i don't know if that helps or i mean there's a bunch of different examples but um vector databases peaked like crazy about two years ago

and they took a precipitous fall because of this problem it's orphaned information um the best example i've heard and i'm borrowing this from someone else is if you do the berkshire Hathaway annual report, and you're searching for 2023 EBITDA versus 2024, a vector database can easily mistake those two things because it's just one character difference. And the embedding might be very close to each other, but those two numbers are very different meaning if you're trying to plug them into a stock picker. So that's the kind of thing that you get with vector and especially vector with code because code is so similar to each other.

If you search on, if you use vector databases purely for code search, you cast almost like too wide of a net and you get a bunch of false positives. So maybe that's too detailed. No, I think it's great. Does that help? But back to the four corners, we've been living in this memory here. So just to summarize, you've got text memory or you've got database memory. And then once you get into database memory, how do you want to, what kind of database

do you want to use? and vector databases are just we use all three. So it's there's no right or wrong answer. Vector is much faster. But then when you want to know the ground truth, the graph databases are really good for storing and keeping things that you've learned that you want to know for sure. I do care if it's 23 or 24 EBITDA.

Don't just randomly pick one. So that's that's where I feel like all roads are leading to graphs for us was just the most convenient tool. so recap sure lom is like the input output um you've got the agents which are the loops they're out doing stuff discovery you think of them like explorers explorers that's why open clause so fun you're like go crazy you know okay and then to direct them you got workflows yeah and then memory is the fourth point yeah okay um i think there's like we can connect this back to bitcoin pretty soon uh if you you'll allow me so we're in the it feels to me like we're in

this era right now where it's so fun everybody's trying their own thing like it kind of isolated and independent everybody's spending tokens to create their app or whatever it is but we're redoing a lot of work like millions of people independently are spending tokens to do the same thing now what if that thing that they spent tokens on let's say they spent 100 tokens to do to create a transcript of the latest tftc episode um you already do that you spend your 100 tokens doing that you put it on an accessible graph with an l402 in front of it and charge 10 tokens to get that transcription that's where i think all of this independent work that we're all doing which is a lot of fun for everybody i think it starts to connect with each other that is an

incredible point because one of the things we'll do is we'll turn like if i listen to a podcast like hey this was a great podcast i think we should make twitter aware uh aware of it and or x aware of it and a lot of people don't want the clips they prefer to read it and so you just transmute the the content from audio video and ultimately a transcript into like a long yep a long tweet but we are going to youtube sort of spending tokens to download the transcript running it through our prompt but to your point if you had a marketplace where it's like hey who already produced this transcript i'll pay you 10 sats for like a token arbitrage i think there's like this big thing that's this is this is this is very big yeah i think it's big and i

think you want to take a look at it yeah i'll show you yeah all right so this is a graph database and we have workflows uh well first we have a topic so think of this as subreddit and this also leads to the questions of what will remain how do you make money in a post ai world it's not easy like i don't have a great answer for that question so i think short answer is software goes to very cheap everyone's going to have software uh software engineers will percolate for a period but five years really i mean maybe not um so what do you end up with is marketplaces and not just marketplaces but marketplaces for agents and so your agents if you instruct it to save money get my job done but don't burn all my tokens so if you're downloading a transcript or you're

downloading a podcast and transcribing and someone else's you're just everyone is wasting money and you're just burning electricity and tokens for no reason so if there's a source if you instruct your agent uh go to this place and they tend to have transcripts search and then get the transcript back and it'll be cheaper than you doing it yourself cheaper faster better and so the these content graphs can be built by agents or human beings um and then what you can see here is you have i'm using uh shout out to callie's claw interface so i signed up for this it's great and you can see here all i have to do is say search this graph for let's say so claw you has an lsat that i gave it and it's using lightning to go out and retrieve data back from the graph

and paying 10 sats to get that data and then the person who uploaded the transcript you hopefully is getting paid some of that money back for providing that data so if you play this out and there it is coming back so this is everything the graph has on this thing so this is scraping all tweets podcasts um reddit you can put anything you want into the graph but in order to make money back from the graph by putting it up there you have to stake a little money otherwise you get garbage so everyone is talking about how do you have an internet when content goes to zero the cost of content goes to zero, you're just going to get spam, right? So if creating profiles is free and creating content is free, then how the heck is your agent going to find real information or not?

And so you'll have to have these walled gardens. Think of them as a protected subreddit. It could be on K-pop, on fly fishing, on skiing, on machine learning, whatever the topic is. And then you have a community of people and agents building this shared memory together. And then if you instruct your retrieval agent to just get it in the most efficient way, then they'll say, yeah, it'll cost me 10 cents of tokens to get this, or I can spend two cents here. but then you if you upload tftc now you'll be making sats for years to come every time someone retrieves it you get it back this is what um i talked about with adam curry five years ago this was and the way i told him i said hey there's going to be podcasting 2.0 but really that's a stop in the journey this is the final destination of the journey it's all content it's um gated so that the creators make their money without having to have advertising

and this just wasn't going to happen i didn't believe with human beings so we built the first prototype of that podcasting 2.0 and streaming sass with you and um i loved it this is you can feel how this could work but also human beings aren't going to change their habits but now you don't have to convince human beings to change their habits the agents figure it out on their own so people like us who aren't great at marketing myself i'm speaking about myself this could be the golden era you just have to build something better and the agents find it and then once you have this rolling and your graph becomes the most popular graph on that topic honestly it's like open it's like owning beachfront property if you keep the best graph

going it's the most thorough high quality reputation all that stuff the agents will keep coming back if you're down for a week agents are going to look for someone else this is the full information meritocracy but there's pay to put it up there but you earn it back it's like an investment and then you pay to retrieve you have to pay your way um so this really is the value for value for the agent world anyway and but this is where like the human in the loop is like the curator the tastemaker exactly right and that's going to be again going back to what i was saying earlier like understanding logic and like what people need and thinking about humanity in the age of machines like what is our edge that's funny to think about i was one of the many

listeners of that episode with you guys talking about podcasting 2.0 and uh being involved with the Bitcoin and Lightning community for a long time. We've been talking about machine-to-machine payments forever. But I think everybody when, if we're being honest, when we were talking about it, we were thinking about like a refrigerator is going to pay like a Roomba

or something like that. It was like an IoT use case. But really what it's come to be is agent. Machine-to-machine is agentic payments. And so I think this graph concept or what we're talking about here, for me, it was really eye-opening to

it just makes sense like these agents are going to be going around on the internet trying to get information or skills or things to complete a job they're going to be spending tokens and they're economically rational actors agents so if it costs them 100 tokens to do the thing or they can get it for 10 tokens they're just going to go get it for 10 tokens but the as paul was saying the cool opportunity is for people to cultivate these graphs and make sure their their graph is really good and has the highest quality content so the agents keep coming back to them um so yeah beachfront property is pretty if i were to do anything if i were to say to my kids what to do i would say build these graphs on things that you know about my son's super into restoring

toyota land cruisers and he was on uh claude yesterday or two days ago building a toyota maintenance app web app i mean he's just going you can just build this and i said yeah but anyone can build that so what is actually useful collecting every single tip and trick about how to do this building a community and then making it available to humans and making it available to agents if you have those two things and you have the lead this is the k-shaped thing if i try to start the second subreddit on open claw no one's going to come to my subreddit because there's already one and it's already huge um so there's a real advantage to be first mover it's kind of like tftc is this for bitcoin for humans right like the humans who want to learn

about bitcoin come to tftc to learn about it yep what is the tftc for agents it's probably like this graph thing it's like easy to traverse easy to do an economic trade to get the information like agents are probably not listening to ad reads right they're just like going to get the information yeah um so yeah the information curation as like a job i think it's going to be very rewarding because people will focus on the things they're naturally interested in whether it's land cruisers or uh or bitcoin yeah hopefully uh nobody from zero hedge is listening to this but i have like a zero hedge pro subscription and with that you get access to all the banking reports that they have zero you just get access your hedges agent is listening to this

i've been like so i've been running cron jobs and all the banking reports that get uploaded to the zero hedge pro subscribers so like i've got insight like with golden city like all the european banks are saying like every day and like i just wake up to a report from martin the sophisticated marty agent right that uh it's like what are the banks thinking today he's like here's what they're all saying well people will do this with play it forward and i'm saying that i would but people will upload articles from behind a paywall right same way a friend will print it to a pdf and send it to you agents will figure all this stuff out whether you want them to or not yeah you know so the next step i think so build graphs for agents with l402s in front

one shot open claw once i pasted the lsat knew what to do with a 402 response from the web server which is you have a lsat do you have the string of characters i paste it in it goes the next step will be can it buy one on its own and all that stuff there's i think there's actually a real business for selling all four twos where you take bitcoin and turn it into an l42 so in case your wall doesn't support it but i think the big next step is um uh your personal graph so this is your shared you know machine learning graph or your fly fishing graph and think of it like a magazine the old days where you would buy fly fishing magazine and read about what's going on you know the physical magazine so this is like a collection point for all that information for

enthusiasts on any topic of business topic personal topic whatever it is and then i get so sick of going to youtube and twitter to try to keep up with anything i'm interested in surfing we both surf um i want my personal graph to know what i'm interested in give it a budget and just go get me the best surf clips from insta i don't want to go to instagram and youtube i just i want five minutes of great surf clips every day and i want you know surf clips from where my friends live so i see what they're you know yeah that kind of thing so you'll store that on your personal graph and so your graph will be talking to your agent will have your graph and talk to these share graphs and exchanging value back and forth and then it'll know what you know so applying this

to say um the example i use is like the joe rogan podcast because he's a ton of interest that i like i'm a bow hunter i love bow hunting stories from joe rogan there are certain ones he tells too many times so my graph knows that i've heard this bow hunting story so when i listen to joe rogan's podcast i can have it shrink that section of the story down to hey he's telling that same story again that you've already heard 50 times so i can just set to skip it just like i can skip ad reads right well so as you're describing this i'm thinking because like you brought up surfing and uh i just want to get the pinch my salt clips i think uh yes i think those guys are hilarious funny it's like but i don't want to depend on the instagram algo to like

surface it to me or me to have to go exactly find it via search uh pinch my salt how's it funny like a top tier comedian is running a surf podcast yeah this is like so funny and it's uh but it's like to your point like you can get it and this is like another thing that's been a big topic is like how do you get away from the algos exactly and you just create your own by curating what you want that's your that's some sort of personal agent and it's some sort of graph or memory i I think of it as graph is what people have tried to do with obsidian. Remember Rome? This is what, you know, really intense people want how to run their life.

But what if this were more passive? And what if this were just, as you're listening to things, it's collecting what you know, what you don't know. There's a, I think it's called math Academy or something. I don't know if you saw that. Yeah. Third grader doing calculus or speed running six years of math all by doing a skills graph where it knows the skill depends on this skill so if you want to learn there's a google maps for your role exactly vector right exactly the um that's a graph database yeah um and so

if you have my math my math graph would be a tiny fraction of their math graph but i have this node colored and it knows by referencing the shared math graph that the next step for me would be to learn this concept. So I have five minutes and I've told my personal agent, and while I'm waiting for this plane, I want to learn a little math. Give me a math thing. Well, it knows that I'm sitting at a terminal and so I can actually watch a short video. So it could construct a lesson using the video and content and just play me a video in the style that I like, the playback speed, the talking speed, the accent I want, male, female, whatever you want, just generate the content and feed it to me. And the only way to get that is you have to have know what you know,

your agent has to know and store that memory. And then you have to have shared, you know, someone has figured out all these math skills. I don't want to do that. Right. It wouldn't make any sense. And so those two interactions, I think for the first time we're seeing this open claw phenomena really open up the idea that people want this control. They want their version of this which i think is super encouraging yeah well bringing it back to the machine payable web like i bought a 21 co computer back in 2015 uh when pelagi first launched it and we're talking about machine payable web beyond chain bitcoin an idea that was great too early um brian you're on the board of lightning labs like obviously l402 has been around for years now and it's been

funny with the emergence of open clone everybody talking about the agentic economy like oh now we x402 and like all these other payment protocols for agents and as we were talking about like i've had cali justin moon uh matt carollo on in the last month talking about agentic payments specifically and i think massive opportunity for bitcoin but i think it's still unclear to me how this two-sided market emerges like and how the i think demand from bitcoiners is there in terms of we have have bitcoin we're willing to pay for this stuff yeah sats over the lighting network or whatever it may be ultimately the the path to make the payment um but i think that the chicken and egg is like how do we make sure like enough people have like payment gates that are leveraging this

tech yeah a lot to say on this topic so i remember um when elizabeth and lalo first were telling me about L402s. I was like, what? What are you talking about? This was back in the refrigerator paying the Roomba machine-to-machine concept. But it turns out they were very prescient on this point. So you talked with Matt in your last podcast about the concept of 402s. We don't want to replay that. But having a system where you can trade value for resources, where agents can trade value

for resources is going to be a very important part to realize this future that we just described described um i think that there's still a lot of work that needs to happen to make it easier to get up and running like you said the three of us were bitcoiners we are we've made transactions in the lightning network we run nodes etc that's still pretty hard to go from zero to one right now it's pretty complicated. I would encourage people in this ecosystem, and I know Lightning Labs is working on this right now, to make it as easy as possible for the next Vibe coder to integrate L402 payments. So that's not just about putting an L402 gateway in front of some API. They need to be able to spin up a node. They need to be able to manage their channel balances and then liquidity. I still

think there's quite a bit of friction there. I think that's getting solved very quickly. I'm excited for some announcements coming up pretty soon that are going to lower those barriers. But I think if we can make it easier, there's a team called Money Dev Kit, Nick

who's, those guys are doing a lot of good work on this front. But if we can just like lower the barrier for a developer to get going on this, I think well, that's just going to be a massive accelerant for all these things. And the vision that we're talking about with the these interconnected knowledge graphs that that's where we're going but to get there faster we need

to make it easier to just get started in the first place yeah i mean my our clanker he's running a phoenix d server and it was i mean it it was fascinating also that process is to your point like the lightning node management stuff for some reason i had the ldk docs up on my on my screen And I was like, all right, I take these docs and like see if you can spin up an LDK node and it completely like we banged our head for an hour and it completely, completely failed. And I remember I was like, all right, let's think through this logically, like instead of me giving it something when I haven't do research, like what will work best for your

server setup and like, what do you think you could actually do? And then like when did some research came back, it was like, Hey, this Phoenix D server looks, looks good. Like I think I can, we have enough space on our desk. Like it makes sense. it seems like it's easy enough to set up i was like all right go set it up and set up it's like okay it's it's here it's running but we don't have any bitcoin in it that's awesome you need to open up a channel um i was like okay like what's the best way to do that he's like oh i

found this bolts tool where you can send on-chain bitcoin then it will submarine swap and automatically open up a lightning channel with async this is the stuff we need it's got to be like less yeah it was like all right all right get an on-chain address from bolts and tell me where to send it and i was like boom and then it was up and running yep this is where i i just pictured the cat the opening cash app sending bitcoin getting a channel yeah you know jim asked us yesterday how'd you get the inbound liquidity first question yeah so no one's gonna want to deal with any of that you're just gonna have to have it work your agent will have to say do you have this app do you have any bitcoin in it do you have any money go buy some now turn it into a

channel yeah if you want to run one yourself i want to just like so my dream is to be able to let me back up a second so first some a few shout outs like shout out people like uh graham at voltage jesse at amboss the team at albie like a lot of people have been working on the key bits of infrastructure to make this easier we just got to keep going on that front um i can't wait for the moment where someone they're making like viral apps so they see something that happens on x and then they make an app to respond to that thing they saw so i have an example of this that i worked on um which is do you guys remember seeing that that tweet of uh there's a speed reading video it's like you can read something really fast if your eye focuses on

the center letter um so i was like okay how fast can i make an app to do this and a bunch of people did this too but i wanted to like integrate l402 payments so the website's called speedread.fit so you can see it in the wild. But I want to be able to take that idea and have like a production app ready with L402 payment gateways with the right liquidity and everything that's necessary

in under an hour. I think that we're pretty close to that. It took me like a day to get it live because of some of the friction. But I think if we can head in that direction where people have ideas and they can quickly convert those ideas to cool things that people can use,

we're just going to see an explosion of of innovation yeah i wish we had two more hours i know you gotta go soon yeah i think yeah that was great i think um i guess just final thoughts on all this the where we are in terms of approaching the suddenly moment of ai like where this will go by the end of the year and how bitcoin payments actually become an integral part this agentic economy i mean everything takes longer than i think so you know my timelines are are always off but um it's everything that we planned on where speech and money are combined into one protocol and that's what we started with sphinx and we continue to work on we pulled everything back knowing we needed to re-architect everything but speech and money become more

important than ever because when content is infinite then you have to gate it somehow so people start to look for that i think that we'll have uh this concept of staking is going to come up a ton micropayments even nick zabo i think recently retweeted something that was counter to his original criticism of micropayments where people will never dedicate their brain space to micropayments and now the era of you don't have to dedicate your brain space to micropayments allocated to the agent finally here right so lightning l402s staking marketplaces graph databases that act as shared agent memory you can see how this just speed runs this is not stuff that we have to cook up every piece exists right now we're in the assembly phase um and it's

built on i think you know we all agree this is the best money out there it's global um and it doesn't put money into someone's pocket who made up that money right and so i think that uh the agents will be doing their owners a favor by operating on a bitcoin standard yeah i think especially for the listeners of this podcast who are bitcoiners by default it's like why why put your energy into this well you're advancing the core principles of bitcoin if you help build because now we're all kind of contributing to the ecosystem of bitcoin the more sats are flowing around the internet the more bitcoin is successful and the more we're all achieving the bigger picture goal that we care about as people who believe in the power of bitcoin and the reason for its

existence so i think there's like this really neat underlying motivation that everybody in this community has which is not tied to corporate profits it's tied to the mission of satoshi which makes it i think when people are mission driven like that like even if it's sort of deep down in the psyche it makes it more interesting more fun people are more motivated to to see things through so you know like we've been saying it's a really fun time to be out there and i think everybody should be trying things trying to make you have an idea try to make it make it happen um it's never been easier i need to set up a graph yeah it'll help you it'll be fun yeah and uh i'll just reiterate what i said with with matt and i think the one core advantage that all this

bitcoin tech has compared to other payments mechanisms is the interoperability and i think that it's not it doesn't mean it's a foregone conclusion that's going to win but i think it is a massive edge that people need to lean into more it's you don't have to the agents will find the pathways that work the best and bitcoin and lightning have the best pathways it's just purely factually correct you know and so um now's the time and bitcoiners like myself aren't good at marketing so you don't have to be agents will just find you so yeah we can't wait five years and let's not brian thank you for coming on let's harvest yeah thanks for having us thank you thanks marty peace and love freaks thanks thank you for listening to this episode of tftc if you've made

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