Karpathy's Obsidian RAG + Claude Code = CHEAT CODE

---
title: "Karpathy's Obsidian RAG + Claude Code = CHEAT CODE"
video_id: OSZdFnQmgRw
source: https://www.youtube.com/watch?v=OSZdFnQmgRw
date: 2026-05-06
type: transcript
---

# Karpathy's Obsidian RAG + Claude Code = CHEAT CODE

[Źródło](https://www.youtube.com/watch?v=OSZdFnQmgRw)

## Transkrypcja

Andre Carpathy just gave us the keys to
his personal Obsidian Rag system. And I
put Rag in air quotes because this
Obsidian power knowledge base has no
vector database, no embeddings, and no
complicated retrieval process. Yet, it
solves the exact same problem that these
more complicated rag structures claim to
do, which is allow our large language
model to handle large amounts of
documents and answer questions and
gather accurate information about them.
And the best part about this Obsidian
powered system is that it is very
lightweight. It's essentially free and
it is the perfect middle ground for a
solo operator or a small team. So today
I'm going to show you how Carpathy's
Obsidian knowledge system works, how to
set it up yourself, and how it differs
between traditional rag systems so you
know if this is the right option for
you. So the process by which we are
going to create this obsidianpowered
knowledge system was laid out yesterday
in a pretty comprehensive Twitter post
by Andre Karpathy. Now, the big takeaway
from this post is that we are able to
create large language model knowledge
bases that essentially act in the same
way as something like light rag or rag
anything or any other graph rag system
with obsidian. And we're able to do so
in a rather simple manner by just having
a clever structure to our file system
and how we actually ingest data. And the
end result is that I am able to ingest a
pretty significant amount of data and
documents into my Obsidian vault and use
cloud code to ask questions about it to
figure out connections between different
things. Aka the exact same thing you
would do with a traditional rag system,
but with none of the overhead and a way
simpler setup. And as Andre lays out,
the setup looks something like this.
First, we have data ingestion. We are
bringing in articles. We're bringing in
papers. We're bringing in repos from the
internet or from wherever and we're
bringing it into a raw directory inside
of our Obsidian vault. This is
essentially the staging area before it
gets turned into a wiki. We as the human
being in this interaction are able to
see all of this happening via Obsidian.
Obsidian for all intents and purposes is
our front end. Here is where I can see
where all the documents are laid out.
Here's where I can read all the wiks.
So, it isn't sort of abstracted away in
a black box like it isn't a rag system.
It's kind of hard even in a graph rag
setup like light rag to actually go
inside of here and really see
everything. I mean I can but as cool as
this looks this isn't you know very
efficient and from there you just do a
Q&A via something like cloud code and
like Andre laid out here he expected
that he would have to reach out for
something like rag but the large
language model has been pretty good
about automaintaining index files and
brief summary of all the documents it
reads and this is something we are going
to be able to do too with a pretty
simple cloudmd file which I will be
giving you and you will be able to find
that cla md as well as a written guide
that comes with a bunch of prompts
inside head of my free Chase AI
community. There will be a link to that
in the description of this video. And
speaking of Chase AI, and you knew this
was coming, quick plug for my Cloud Code
Masterass. Just released this a couple
weeks ago, and it is the number one
place to go from zero to AI dev,
especially if you do not come from a
technical background. You can find a
link to this in the pinned comment. So,
make sure to check this out if you're
serious about learning this tool. Now,
before we jump into the specifics of how
to set up this Obsidian system for
yourself, let's go over the actual file
structure because this is important to
understand how data is coming into our
vault and then getting turned into
wikis. So, the Obsidian vault is where
everything lives. As you'll see, if
you've never used it before, when you
download Obsidian, you are going to
designate a specific folder as the
vault. In my case, it is quite literally
called the vault. That's where
everything in Obsidian lives. As a
subfolder of the vault, we are going to
have the raw folder. The raw folder is
where all of our research gets dumped.
Anything we want to manually include in
these wikis gets put. This is
essentially the staging folder. So, this
is where all the raw data is going to be
held. This can be markdown files. This
can be PDFs. And I'm going to show you
how to use the Obsidian Clipper to
essentially turn any web page into a
markdown file that gets sent to the raw
folder automatically. We will have
another subfolder that is the wiki
folder. So what the large language model
is going to do, what cloud code will do
for us is on demand or you could have it
even be a skill or have it be automated
is we are going to point it at the raw
folder and say hey I want you to create
a wiki about whatever subject you've
been gathering information about. From
there it will then create a wiki about
that. So you can see we have three
different wiks here. One for AI agents,
one for rag systems and one for content
creation. Now in in between the wiki
folder and these sub wiki folders is the
master index markdown. This is
essentially just a list of all of the
different wiks that have been created
because the idea is when you this is you
when you talk to claude code all right
that's cloud code over there and say hey
I want to learn more about AI agents can
you ask you know I want to ask questions
about my wiki well what is it going to
do? Well, it's going to go to the vault
because you're probably already in
there. It's then going to go to the wiki
folder. It's going to go to the master
index folder and say, "Hey, what wikies
have we created?" Oh, he wants to know
about rag systems. Okay, goes down to
rag and the wiki folders themselves have
index files which break down all the
additional content. So, what Obsidian
gives us and what this file structure
gives us is a very clear path to find
information even if we have a ton of it
floating around. And this helps claude
code because it's not going to have a
ton of issues finding the data. We're
not going to run a million tool calls to
see what's in our file structure, but it
also helps you because it's very clear
where to go. For example, over here on
the left is my Obsidian folder. I'm in
the Obsidian UI, and we'll go through
the download here in a second. But if I
want to see a wiki, what do I do? I just
go to wiki. I have a master index which
lays down everything in there. Right
now, it's just three things, but if
there were 3,000, it still wouldn't be
too difficult. And then from there, you
know, I can click on it. It takes me to
the index of that specific wiki. And
then I can look at different stuff
inside of there. It's that simple. And
it's that simple for AI, too, which is
why we're able to use essentially just a
markdown file structure to somewhat
mimic a rag system. So, while that
theory is cool, now let's go into how to
actually set this up for yourself. First
and foremost, you're going to need to
download Obsidian. You're just going to
head to obsidian.md,
hit download now, go through the wizard.
It's completely free and you're going to
designate some folder as the vault. Just
create one, call it the vault. Makes it
easy for me and that'll probably work
for you. After we create the vault, we
now need to set up this file structure
inside of it. The easiest way to do that
is with Claude Code. Simply open up
Claude Code in the vault. So that's the
directory I'm in. And you're going to
give it a prompt telling it to create
this file structure. Now, luckily for
you, I already created the prompt. So
you can just copy this thing and paste
it in the cloud code. Now, if you're
like me and you've already been using
Obsidian for a bit, you probably have a
bunch of folders already in there. So,
maybe you don't want to call it RAW.
Maybe you want to call it something
else. The whole point of it is you just
need to designate some folder is, like I
said, sort of the holding area or the
staging area for where all this
information is going to get dumped until
it gets turned into a wiki. So, adjust
as needed. Now, the next thing we want
to do is create a cloud.md file.
Personal assistant type projects, things
like this that are very markdown heavy,
claw.mds are perfect for. And this
claw.md file is breaking down the
knowledgebased rules as well as how to
essentially traverse it. So again that
we aren't wasting tokens when we ask
questions. Again I have this entire
claw.md template prompt you can use this
claw.md file is also telling claude how
to structure these markdown files. So
it's very easy to traverse files with
this wiki links format. Now let's talk
about how we can bring things into this
raw folder. How we can get data into our
system in the first place. Well, a super
easy way to do this is with the Obsidian
Web Clipper. So, I will put a link to
this in the school or you can go to
obsidian.mmd/clipper.
And this is just a Chrome extension
which makes it super easy to turn a web
page into data into a markdown file.
Now, the one issue with this web clipper
is it's going to struggle with images.
It's just not even going to bring them
in. It'll have them as a link. But I
want to be able to see the images from
these documents I ingest inside of
Obsidian. So, what do we do? Well, we
are going to use an Obsidian community
skill or Obsidian community plugin to
help with this. So, one of the cool
things about Obsidian is the community
plugins. There's thousands of them. So,
if you're inside of Obsidian, I'm inside
the desktop app right now. If I come
down here and I hit this little gear,
I'm going to go to community plugins.
I'm going to go to browse. And then
you're going to search for local images
plus. You're going to download it,
install it, and turn it on. Make sure
it's enabled. You can confirm it's
enabled by heading to your community
plugins tab and seeing this little tab
turned on. Now, if we use the Obsidian
Web Clipper, and I can see that over
here as an extension, you can see what
happens. It immediately pulls
everything. And if I hit add to
Obsidian, I can see this entire article,
including the images. Now, there is one
thing we need to set up inside of the
web clipper, and that's making sure it
actually pulls it into the raw folder
automatically. I don't want to have to
manually do that. You're just going to
head to the options on your web clipper.
I just rightclicked it. And then over
here on the left where it says default,
I created my own new template, but you
can stick on the default if you want.
Where it says location
and note location right here, you're
want you're going want to change that
from clippings to raw. And that will
make sure when you use the web clipper,
it automatically goes into the raw
folder. So now with the Obsidian Web
Clipper extension and the images
community plugin, we can now turn any
web page on the internet into a markdown
file that will be used for our wiki. But
that is just one data funnel. That's a
manual one. We can have Claude Code do a
bunch of heavy lifting, too. So let's
say I was trying to create a wiki about
Claude Code skills. So I told Claude
Code, let's create a wiki about claude
code skills. I already included some
info in the raw folder, what we pulled
in via the web clipper. go conduct your
own research and bring in the relevant
raw MD files to generate that wiki. So
what is it going to do? It's going to go
on the internet use its standard web
search and it's going to create its own
wiki about claude code skills. So what
you see is that this raw folder this
whole raw pipeline that's more for you.
That's for when you mainly want to put
in some information. Now you can have
cloud code do that as well but cloud
code is also smart enough to essentially
take the research figure out what's
relevant itself and just create the wiki
directly. This raw folder is really for
you the human being to have some level
of organization. And here's what cloud
code came back with. So it created the
cloud code skills wiki. We see here in
the master index that it's referenced
here. If I click on it, this then brings
us to the index of claude code skills.
And right now it has four articles. So
here's the skills overview article. You
can see it links to websites and it also
links to different articles within our
obsidian vault. So if I click on skill
ecosystem, here's more stuff. If I click
on top skills, right? So on and so
forth. There's a very clear pathway from
one article to another and how these
things relate. Which means when you ask
cloud code questions about these
articles in these subjects, it's easy
and cheap for it to answer questions
about them. Which then brings us to the
obvious question. Do we need rag at all?
You know, we look at something like this
light rag setup. You watch my last few
videos with light rag and rag anything.
and seeing how simple the setup with
Obsidian, you're probably like, "Well,
why would I ever even bother with these
more complicated setups at all?" And the
truth is, if you're a solo dev, a solo
operator, or a small team that isn't
dealing with thousands of documents, the
answer probably is Obsidian makes more
sense for you. It's lightweight, and you
really don't need Rag. These large
language models, these harnesses like
Cloud Code are good enough for your use
case. And we can sit here and get in the
weeds about the differences between the
obsidian rag and true rag. But the truth
is the big thing is scale, right? Are we
trying to scale to millions of documents
or are we not? Because at a certain
scale, it's going to be cheaper and
faster to use a proper rag system no
matter how good cloud code is at
navigating this MD file document network
you've created. But this isn't a
question you necessarily need to have
the exact answer to right away. Why
wouldn't you just start with something
like obsidian? And if it's clear your
scale goes well beyond the bounds of
what this thing can handle, then just
move into rag. I think people get really
caught up in like answering this
question when it's like just try it out.
Just experiment. It's not costing you
anything to use some sort of rag system,
rag system like obsidian. And if it
doesn't work, it doesn't work. Fine.
Then go use light rag instead. People
want to sit here as they inevitably will
in the comments and like argue this back
and forth. Just try it. And I think the
answer will be pretty clear at a certain
point when you need to move to a true
rag system. But the nice thing with this
is is again most people don't need a
real rag system. They just don't, right?
Even if they're in a small business team
situation. So having a proper, you know,
orchestrated system like the subsidian
knowledge base, I think is a huge boon
to the majority of people. So I hope
this breakdown was useful to you.
Definitely check out Andre's post about
this. He goes into a fair amount of
detail. Make sure to check out the free
Chase AI school. There's a link to that
in the description that has all the
prompts and a written breakdown of how
to actually do this if you got confused
at any part. And as always, take a look
at Chase AI Plus if you want to get your
hands on that master class. Besides
that, let me know what you thought and
I'll see you