personal-wiki/Nate TrascriptObsidian + Karpathy Just 10x’d Claude Code Projects.txt

What you're looking at right here is 36


of my most recent YouTube videos


organized into an actual knowledge


system that makes sense. And in today's


video, I'm going to show you how you can


set this up in 5 minutes. It's super


super easy. You can see here how we have


these different nodes and different


patterns emerging. And as we zoom in, we


can see what each of these little dots


represents. So, for example, this is one


of my videos, $10,000 aentic workflows.


We can see it's got some tags. It's got


the video link. It's got the raw file.


And it gives an explanation of what this


video is about and what the takeaways


are. And the coolest part is I can


follow the back links to get where I


want. There's a backlink for the WAT


framework. There's a backlink for Claude


Code. There's a backlink for all these


different tools I mentioned like


Perplexity, Visual Studio Code, Nano


Banana, Naden N. It also has techniques


like the WT framework or bypass


permissions mode or human review


checkpoint. So, as this continues to


fill up, we can start to see patterns


and relationships between every tool or


every skill or every MCP server that I


might have talked about in a YouTube


video. And I can just query it in a


really efficient way now that we have


this actual system set up. And the crazy


part is I said, "Hey, Cloud Code, go


grab the transcripts from my recent


videos and organize everything. I


literally didn't have to do any manual


relationship building here. It just


figured it all out on its own." And then


right here, I have a much smaller one,


but this is more of my personal brain.


So this is stuff going on in my personal


life. This is stuff going on with, you


know, UpAI or my YouTube channel or my


different businesses and my employees


and our quarter 2 initiatives and things


like that. This is more of my own second


brain. So I've got one second brain here


and then I've got one basically YouTube


knowledge system and I could combine


these or I could keep them separate and


I can just keep building more knowledge


systems and plug them all into other AI


agents that I need to have this context.


It's just super cool. So Andre Carpathy


just released this little post about LLM


knowledge bases and explaining what he's


been doing with them. And in just a


matter of few days, it got a ton of


traction on X. So let's do a quick


breakdown and then I'm going to show you


guys how you can get this set up in


basically 5 minutes. It's way more


simple than you may think. Something


I've been finding very useful recently


is using LLM to build personal knowledge


bases for various topics of research


interest. So there's different stages.


The first part is data ingest. He puts


in basically source documents. So he


basically takes a PDF and puts it into


Cloud Code and then Cloud Code does the


rest. He uses Obsidian as the IDE. So


this is nothing really too


game-changing. Obsidian just lets you


visually see your markdown files. But


for example, this Obsidian project right


here with all this YouTube transcript


stuff that actually lives right here.


This is the exact same thing. Here are


the raw YouTube transcripts. And here's


that wiki that I showed you guys with


the different um folders for what Cloud


Code did with my YouTube transcripts.


And then there's a Q&A phase where you


basically can ask questions about


YouTube or about the research and it can


look through the entire wiki in a much


more efficient way and it can give you


answers that are super intelligent. He


said here, "I thought that I had to


reach for fancy rag, but the LLM has


been pretty good about automaintaining


index files and brief summaries of all


documents and it reads all the important


related data fairly easily at this small


scale." So right now he's doing about


100 articles and about half a million


words. So there's a few other things


that we'll cover later, but the TLDDR is


you give raw data to cloud code. It


compares it, it organizes it, and then


it puts it into the right spots with


relationships, and then you can query it


about anything. And it can help you


identify where there's gaps in that node


or in that, you know, relationship, and


it can go do research and fill in the


gaps. All right. So why is this a big


deal? Because normal AI chats are


ephemeral, meaning the knowledge


disappears after the conversation. But


this method, using Karpathy's LLM wiki,


makes knowledge compound like interest


in a bank. People on X are calling it a


game changanger because it finally makes


AI feel like a tireless colleague who


actually remembers everything and it


stays organized. It's also super simple.


It will take you five minutes to set up.


I'll show you guys. You don't need a


fancy vector database embeddings or


complex infrastructure. It's literally


just a folder with markdown files.


That's it. You literally just have a


vault up top. So in this example, it's


called my wiki. You've got a raw folder


where you put all of the stuff. And then


you've got a wiki folder, which is what


the LLM takes from your raw and puts it


into the wiki. So in here you have all


the wiki pages which it will create but


then you also have an index and you have


a log. So for example in my YouTube


transcripts vault here is the index. You


can see that I have all these different


tools which I could obviously click on


and it would take me right to that page


or after that I have all the different


techniques agent teams sub agents


permission modes the WAT framework and


then we've got different concepts MCP


servers rag vibe coding we've got all


these different sources which are you


know the YouTube videos and then when I


have people or when I have comparisons


they will be put in here in the index


and then we also have a log which is the


operation history so in this case in the


YouTube project the log isn't huge cuz I


only ran one huge batch of the initial


36 YouTube videos, but now every time I


have one, I say, "Hey, can you go ahead


and ingest the new YouTube video into


the wiki and then we'll see every single


time we update this." And then, of


course, you need your claw. MD to


explain how the project works and how to


search through things and how to, you


know, update things. It's also a big


deal from a cost perspective, token


efficiency, and long-term value. One X


user turned 383 scattered files and over


a 100 meeting transcripts into a compact


wiki and dropped token usage by 95% when


querying with Claude. And obviously


token management and efficiency is a


huge conversation right now and will


always be. The other thing that's really


cool about this is there's not really


like a GitHub repo you go copy or


there's not a complicated setup. You


literally just say hey cloud code read


this idea from Andre Karpathy and


implement it. And people on X are now


talking about like this is how 2026 AI


agentic software and products will be


made. You just give it a highle idea and


it goes and builds it out. And Karpathy


even said, "Hey, you know, I left this


prompt vague so that you guys can


customize it." And I'll show you the


ways in my two different vaults right


now that it changed things a little bit


based on the context and understanding


of what the project is actually for.


Okay, so this was the original tweet I


just showed you guys and then he


followed up and said, "Hey, this one


went viral. So here is the idea in a


gist format." So if you open this up,


this is basically just another


explanation of the core idea of how this


works and why the architecture,


indexing, all this kind of stuff. And by


the way, this is the part where he says,


"Hey, this is left vague so that you can


hack it and customize it to your own


project." So we're going to come right


back to this in a sec, but the first


prerec that we're going to do, it's not


necessary, but I like to have a nice


little front end to see the


relationships, is we're going to go to


Obsidian and download it. So, if you


just go to obsidian.mmd, you can see


this is the completely free tool and


you're going to go ahead and download


it. So, just for your operating system,


download this and then open up the


wizard and open up the app. So, when you


open up the app, it'll look like this.


And what we're going to do here is we're


going to create a new vault. So, down


here, you can see I have Herk Brain and


I have YouTube transcripts. I'll just


make it a little bigger. I'm going to go


to manage vaults. I'm going to create a


new one. And now, we just have to give


this a name. So, I'm just going to call


this one demo vault. and you're going to


choose a location where you want to put


this. So, I'm just throwing this on my


desktop and I'm going to go ahead and


create this vault. Then, what you're


going to do is go to wherever you like


to run Cloud Code. So, in this case, I'm


doing it in VS Code. And I open up that


folder. So, demo vault. We get an


Obsidian and then we get a welcome.md.


So, I'm going to open up Claude. So, I'm


going to do it in my terminal. I'm going


to run Claude. And lately, I've been


liking using my terminal better for


Claude. I like to do it inside of VS


Code, but the reason is just because I


like to see the status line and I have,


you know, a little bit more


functionality. So, anyways, now that we


have Cloud Code open, here's what we're


going to do. We're going to go back over


to the LLM wiki thing that we got from


Andre Carpathy. We're going to copy all


of this and we're going to go back into


Cloud Code and then just paste it in


there. So, that is the prompt from


Carpathy that's going to build out


everything we need. And then before we


send that off, we're dropping this in


which you guys can screenshot and then


just throw into yours. But I'm saying


you are now my LLM wiki agent. Implement


this exact idea file as my complete


second brain. Guide me step by step.


Create the cloudmd schema blah blah


blah. So this is just telling it what it


needs to do with this idea that we just


got from kpathy. So anyways, on the


right we have this cloud code running


and on the left we have our obsidian


vault and you can see it just created


those two folders. So it created the raw


and it created the wiki as you can see.


Now, by default, it threw in four


folders. It threw in analysis, concepts,


entities, and sources. Once we start to


populate stuff, we can talk to it to see


if that's actually the way we want to do


it or not. Because it's interesting in


my personal kind of second brain, the


wiki is literally just markdown files.


There's no structure to it. And in some


cases, that's good. Carpathy actually


said, "Sometimes I like to keep it


really simple and really flat, which


means like no subfolders and not a bunch


of over organizing." But then you guys


did see in my YouTube transcript one,


there were different subfolders. And I


think that in this case it actually


makes more sense. But you can see that


it went ahead and it created a claw.md.


It created an index and a log and then a


few different folders in our wiki. But


now it's saying, "Hey, let's go ahead


and try it out. Drop in your first


source into the raw folder and tell me


to ingest it." Okay, so I'm at this


website called AI2027. If you guys


haven't read this before, it's kind of


an interesting read. So go check it out.


And now let's say I want to get this


into my vault. What I could do is just


copy the whole page, right? And it might


just come through a little weird. or we


can just get an Obsidian extension which


lets us basically take articles right


from the web and just put it right into


our vault. Super easy. So search for


this extension called Obsidian Web


Clipper. You would go ahead and add this


to Chrome. So then when you're in the


article that you want, you basically


just click on your extensions, you open


up Obsidian Web Clipper, and then you


can just chuck it into your vault. And


then right here, you're going to want to


set this to RAW because this is the


actual folder that it's going to put it


in. So you can go ahead and click add to


Obsidian. Open Obsidian. And then now


you can see in my raw section we have


this AI 2027 source with the title the


source and it's not super super


populated yet because the LLM in cloud


code is going to do that. So here is our


file. I'm going to open up cloud code


and say awesome. I just threw in an


article called AI 2027 into the raw. Can


you please go ahead and ingest that? It


might ask you some questions. It might


also be helpful to before you start


ingesting stuff say hey by the way this


project is specifically for my second


brain. So, personal things, business


things, whatever. Or this is just a


research project. This is where I'm


going to chuck you all the articles and


all the things that I want to learn


about and all the things that I know.


So, there's different ways that you can


set up the project as you saw with mine.


One for YouTube, one for just personal


second brain. So, now what it's doing is


it's going to read through this article


and then it's going to figure out where


should I chuck everything into the wiki.


It's not just going to create one MD


file for this. It might create five or


it might create 10. And there's going to


be relationships between each of the


different sections that it creates. So,


it's kind of doing its own method of


chunking. Now, one thing I want to call


out real quick is with this extension,


if you go here and you open up the


options for it, you can see that you can


actually change where by default the


folders are dropped, which is in the


location section. By default, it'll be


going to a place called clippings, but


just go ahead and change that to raw.


Okay. So, here it came back with all


these questions, right? It said, "Here


are my key takeaways from this article,


blah blah blah." And now it'll ask you,


"What do you want to emphasize from this


article? What's your focus? How granular


do you want to be? what's your plan? So,


I'm just going to say I want this to be


extremely thorough. This is my passion


looking at where AI is going to go. Um,


and this whole project, by the way, that


you're setting up in this vault is


basically just going to be my place to


dump in research about AI. So, help me


keep all that organized so that I can


query it and that I can, you know, keep


my thoughts related. So, that's just a


quick example of what it might look like


for you to give it some more context to


continuously build your project. So, I'm


going to switch over over here to the


graph view because I think it it'll be


interesting to see as it is starting to


go through and create those different


wiki files. It's going to go ahead and


it's going to create all those


relationships and we'll be able to watch


it in real time. All right, so it's


creating all of the wiki pages now and


you can see that it said it's going to


make about 25 because there's so much


stuff going on in the original AI 2027


article. Okay, so our first one just


popped in here and there a second one


just came through and now you can


understand you're starting to see where


do you have hubs or where do you just


have little individual nodes? So this is


a major hub. Someone named Eli, someone


named Thomas, Daniel, and you can see


all the different relationships here


with things like AI governance with


things like OpenBrain, superhuman coder.


Okay, so that ingest took about 10


minutes. So sometimes you have to be a


little patient with, you know, it


reading through everything and


organizing everything, but it does a lot


of heavy lifting, of course. When I


uploaded the 36 YouTube transcripts in


batch, it took about 14 minutes. So it


kind of just depends, but it created 23


wiki pages. We have the source. We have


six people, five organizations and one


AI systems page, different concepts, so


technical alignment and geopolitical and


then an analysis and then it asks some


questions about it so that it can help


make the relationships and make the


structure even better. Now let's just


open this one up a little bit deeper and


see what it actually did in here with


this stuff. So we have this is the


source with all the main relationships.


So as we start to add other articles, we


will see other big kind of like nodes


and maybe in some cases we'll have


relationships between like compute


scaling with different articles that we


upload as well. So let's just see if I


click into the main source, we can see


the tags that it got. We can see the


authors and we can click around. So


here's a link to OpenAI. Okay, what's


OpenAI? Here's references in AI 2027.


Here's some other connections with


OpenAI like modelsp spec. Okay, we're in


model spec. Let's take a look. We can


see other things about modelsp spec. And


we could also go to how the LLM


psychology model works. So this is just


super super cool all the relationships


that we get. And once again, all of this


stuff that we're looking at was derived


from one article and automatically


organized and related. So the question


now is like what do we do from here? Do


we query it inside of this environment?


Do we query it from somewhere else? And


that's completely up to the way that you


want to use this. So for example, with


my YouTube project, I'm probably just


going to keep this here. And whenever I


want to ask questions about YouTube or


if I want to turn this into like a


website, I can just do that from here.


Or if I need to, I can point a different


project at this folder since


everything's here and it can crawl


through the wiki, it can read the index


and it knows how this stuff works


because you can give it the clawmd so it


understands the project as well. So for


example, in this one which is just my


second brain where we have all of the


different things about like I drop in my


meeting recordings, I drop in, you know,


ClickUp channels, summaries and things


like that. This is something that I want


to use in my executive assistant. So


what I did in my executive assistant


here called Herk 2, if I go to this


cloud.MD, MD you can see that we have a


wiki path. So whenever you need to read


things about me and my business that you


don't have already, you would basically


go to my herkbrain vault. You would go


to that directory and then you would


read through the wiki. You can read the


hot cache which I'll explain in just a


sec. You can read the index. You can


read the domain subindex and then you


can also just search through everything


here. And I said don't read from the


wiki unless you actually need it. Here


are some things that you might do that


you don't need to go read the wiki for.


And all of this is my business


knowledge. Now, if you guys remember, if


you watched my video on setting up an


executive assistant, I used to do this


with context files inside of this


project. And when I changed over to this


method, I actually saw a reduction in


tokens that I was actually calling in


this project. So, the thing about the


hot cache, right, I didn't actually have


this in my YouTube one. So, if I go to


YouTube, you can see there's no hot


cache. But, if I go to the herk brain in


the wiki, you can see there's a hot.mmd


right here. And this is basically just a


cache of like 500 words or 500


characters that it saves, which is like


what is the most recent thing that Nate


just gave me or that we talked about. In


the context of my executive assistant,


this is really helpful. You know, it


might save me from having to crawl


different wiki pages. But in something


like the YouTube transcript project, I


don't really need a hot cache. So,


another thing that I alluded to but


didn't really cover was the idea of


linting. So Karpathi says that he runs


some LLM health checks over the wiki to


find inconsistent data, impute missing


data with web searches, find interesting


connections for new article candidates,


things like that. So it basically helps


you run a lint, you know, every day,


every week, whenever you want, which


helps make sure that everything is


scalable and structured in the right


way. And it might even come back and


say, "Hey, I don't fully understand


this. Can you give me some more info or


can you grab some more articles that


might help me out here?" So now the


final question about this that I wanted


to cover is like does this kill semantic


search rag? And the answer is no, but


kind of yes. And it all depends on the


goal of the project and the goal of the


context, how much context you have. So


here's a really quick chart that I had


my cloud code make. I was in my Herk


brain where I dumped in a bunch of


information about Karpathy's LLM


knowledge and I just said, "Hey, can you


please explain Karpathy knowledge as


simple as possible, keep it super


concise, and um compare it to typical


semantic search rag." So, it found


Carpathy's idea. Instead of a database,


you just give the LM well organized


markdown files and it compares it here


to the actual semantic search rag. So,


actually, I might as well just read it


off from here. So it finds it by reading


indexes and follows links rather than


using similarity search. So we're


getting a deeper understanding of


relationships because they're links


rather than just saying, "Hey, these


chunks seem similar." As far as


infrastructure, it is literally just


markdown. So like I said, you don't even


need the obsidian. You just need these


markdown files. Whereas with semantic


search, you need an embedding model. You


need a vector database and a chunking


pipeline. The cost over here is


basically free. Your only cost is going


to be tokens. Whereas over here, you


might have ongoing compute and storage.


And for maintenance, you just run a


lint. You clean up things. You add more


articles. You give it more context


rather than having to re-mbed when


things change. But right now, the


weakness of course with the uh LLM


knowledge wiki is that it doesn't scale


huge across enterprises, right? Because


it's just a bunch of files. Um and that


is where the cost will probably get more


and more expensive than going to


something like standard semantic search


or knowledger graph or light rag or


whatever other tool is out there for


that. So here you can see if you have


hundreds of pages with good indexes,


you're fine with wiki graph. But if you


were getting up to the millions of


documents, then you're going to want to


actually do more of a traditional rag


pipeline, at least for now with how the


current models are and everything we


know right now in April 2026. So that is


going to do it for today. I hope you


guys learned something new or enjoyed


the video. And if you did, please give


it a like. It helps me out a ton. Now,


after this video, if you're interested


in learning how you can create your own


sort of executive assistant and then


plug it into this Obsidian vault, then


definitely check out this video up here


where I go over how I built my executive


assistant and the way that you should be


thinking about it. So hopefully I'll see


you guys over there, but if not, I'll


see you in the next