Are your AI tools burning through tokens faster than ever? You're not alone — and in this episode I'm sharing the framework that's changed how I manage knowledge and query AI at scale.

I'm Mike from Lone Wolf Unleashed — I help solo founders build business systems so they can switch off sooner and live larger. Today I'm walking through Andrej Karpathy's wiki methodology, how to implement it in Obsidian, and why it reduces AI token consumption by up to 85% compared to traditional approaches.

I also cover how I've applied this directly to Lone Wolf Unleashed — building a target operating model, setting up agent teams in Paperclip, and designing a content production architecture that closes the gap between production and distribution for a solo operator.

If you're hitting your AI limits, spending too much on token-heavy workflows, or just looking for a smarter way to manage your business knowledge — this one's for you.

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Chapters

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00:00 — Introduction: Token limits and why this matters for solo founders

00:43 — How Andrej Karpathy's wiki methodology works

02:50 — Setting up a wiki ingest workflow in Obsidian

03:33 — Why the wiki is 80–85% more token efficient

04:38 — Visualising knowledge connections with Obsidian's graph view

05:56 — Replacing a team of analysts as a solo operator

06:43 — Target operating models and the 5Ps framework

07:50 — Introducing Paperclip and automated content production

10:13 — Building an AI Business Analyst assistant

13:00 — What this means for your business and your life

14:21 — Resources and wrap-up

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RESOURCES

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Wiki resources and setup guide: https://lonewolfunleashed.com/resources

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CONNECT

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Website: https://lonewolfunleashed.com

Email Mike: mike@lonewolfunleashed.com

Mentioned in this episode:

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Check out the "Websites Made Simple" podcast with Holly Christie at https://websitesmadesimple.co.uk/

This podcast is part of the Podknows Podcasting ICN Network

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Day.

My name is Mike from Lone Wolf Unleashed and today we're going to be talking about what to do with these token limits in our AI tools and maybe some ways that we can start to capture information and very efficiently query information that are in our repositories.

And this is relevant for the solo founder because when we go to get the information out of our heads, it has to go somewhere.

And when we need to go and query our knowledge base, we want to be able to do that in a very cost effective way.

So I'm going to take you through that and that's going to include Karpathy's wiki methodology.

That was.

There's a lot of talk about it this week, so we're going to go through that.

So strap yourself in.

Here we go.

This past week I have hit my Claude limits the fastest I ever have.

And there has been some chatter around about how Opus particularly has been degrading.

And there's some challenges there around how many tokens are being consumed to do the same tasks that we used to do.

And I have certainly found that, yes, I have been working on some other things that have consumed more tokens, but not my normal activity has been, has not really changed that much.

And I, I have seen that I've been going through the tokens more at a higher rate.

So something that's got me thinking is that because I was already on a max plan, I was sort of, I was spoiling myself, I was keeping myself in Opus a lot because a lot of what I do is a lot of analysis work.

However, I've started to break down the different tasks I'm doing into different models.

And part of why that is is because I've started to hit these limits and I don't really want to have to spend a lot more money every month to try to maintain my AI use.

So something that came out this week was the AI guy, Andrej Karpathy, same guy, same guy that coined the term vibe coding.

And what he's done is he's been working on how to effectively query large information sets with very specific information, have the AI retrieved that in a very efficient way.

So what he's done is he's created a wiki type setup in Obsidian that you can do and the tweets are there that you can go and refer to.

And I'm going to have a resource on my website that can help you install this in your own ecosystem.

Basically, when you have a knowledge source, let's say I do this a lot with YouTube now is.

I'll look at the YouTube, I'll pull down the transcript, has a lot of the information there.

It has the embed of the YouTube thing so I can watch it again.

I can pull this via the Obsidian web clipping browser extension.

And so when I use that, it will insert a new note with that content into my wiki inbox.

I can use a command in Claude that says wiki ingest.

And it will ingest that article, it will create a summary, it will create stubs.

Stubs are the references to other files of similar topics that might be referenced throughout.

And then it is basically there for me to use.

There's another command which is wikilent, which is basically going through and seeing what stubs you have that might have a lot of incoming references.

And you can sort of build up your knowledge ecosystem over time.

Why is this useful?

Well, at the moment, a lot of people traditionally in organizations will build up really big procedures and they'll have all their information and their processes and things, and there'll be a whole bunch of files that you have to start to manage.

What this does is it allows the AI to be able to go in, organize all of your information and to build those connections between different places, regardless of where in your ecosystem they are stored, and allows it to do that automatically.

We don't have to sit there and figure out which text needs to link to which part of the ecosystem.

And it allows us to basically create a space for us to query the AI against the knowledge base.

So it'll go in, it'll look at the index, it will see if there's anything in the index, it will look at our summary pages, and then if it needs to go deeper, it will then go and hit the main pages of those summary pages for more information.

This is so much more token efficient.

It's about 80 to 85% more token efficient than before.

Why is that?

It's more token efficient because it doesn't have to read the entire markdown page every time it needs to retrieve information.

You're basically creating a filter or a funnel for it to go down, or reverse funnel in this point, query a little bit, get some information, get some direction, query to the next layer, get some more information, query the next layer, and it goes down.

That's excellent because we can take our resources, we can take our information, we can look at our procedure pages, we can ingest them, we can codify them.

It will store them in different domain files.

It's, it's all set up there so it can be easily stored and queried.

Now, the joys of doing this in Obsidian is I can now look at all the different stubs, the different connections between the different notes, and I can pull up my graph view and I can see how the different topics and sources and articles are all related to each other.

Now, this is an absolute game changer because previously, let's say five years ago when I was in an enterprise role, a lot of manual work from a team of analysts would go into a process management system, for example.

Now, all those connections had to be made manually.

And to be able to get visibility on things you would have to go through, you'd have to drill down, you'd have to find the right file in the right place.

You don't have to do that anymore.

I can do this solo for an entire organization now.

I don't need a team of analysts, I don't need to manually make all those connections anymore.

So being able to have a visibility or visualization tool to help us understand the connections between different tools and notes of the ecosystem is very, very helpful.

It helps us really literally connect the dots on how things within your business are connected.

And this leads us into the next thing that I've been working on.

My work, I feel like, has accelerated a ridiculous amount recently.

And this is how we actually form up target operating models.

Now, I know this is a little bit of a pivot away from wikis and things like that.

However, if we zoom out of your business and we come up with what I call my 5Ps framework, the profile.

The profile is basically a very high level view of your business and it is basically what you end up with is a target operating model.

Now, the target operating model basically paints a picture of what your organization looks like, what the key players in that organization are, who the key users are, key processes, systems, stuff like that, all on a page.

So I've just done this for Lone Wolf Unleashed, for the social components of it, for the community and program parts of it.

And basically what I'm able to do now is codify what that target operating model is.

And I can now insert that into an agent team to plan out how I can automate as much of that end to end as possible.

Now, why is this important?

Well, if I take that target operating model and I create an architecture spec, I can now feed this into my wiki and I can know exactly where I'm up to.

I can see how decisions that I've made in the past can affect what I might be doing in the future.

I can See how I'm going to break down this plan about how to make this particular state become true.

It is amazing.

It is simply amazing.

And the things that I'm going to do now is what I've started already is I've started to build this into a tool called Paperclip.

Now, Paperclip was brought out last month and I'm going to be talking a lot more about this tool in future episodes because I think it's so, so powerful about how we can set up solo businesses to deliver epic amount of value.

What I've done is I've fed it the target operating model and description and architecture and I've set up agent roles to be able to help us build this out.

What we're going to be able to do is have agents go out to APIs, we're going to be able to take a podcast episode, we're going to have it automatically clip, we're going to have it automatically generate the, the descriptions and all that sort of stuff.

That's amazing because an amazing amount of work goes into managing all that type of content.

And so what we're going to end up with is a state where the, the arbitrage that exchange, that direction, that middleman between production of content and distribution is that gap is going to be closed.

The time that it will take and the time that it takes to manage that piece is going to be dramatically reduced.

Now, what does this mean?

It means that I, as a solo operator, am now able to go out and I can have more meaningful conversations with my prospects.

It means that I can focus more on the community building side of things rather than the delivery side or the content side, the marketing side.

That's an amazing result that I want to be able to get myself towards because it will mean that I don't have to keep going out and hiring people to do stuff like that.

Their time can be freed up to do other things and my time certainly can be freed up to do other things.

So.

And again, is this all coming back into that wiki space?

The wiki space can be used for any big piece of work, any persistent knowledge.

And so if I've got a spec or a build that I'm going through, if I've got general procedures that I need to look at or a strategy, I can see how now all of those things connect together and I can see how I'm on track.

What this has led me to think now is the participants in my program have said, hey Mike, you're really, really good at translating between what the business needs and how we actually go implementing that in it.

And I see this with a lot of clients is there's often a mismatch in the languages that both sides of that equation use.

And so how I sit in that equation is a translator between each.

Hey, when the business user says they do this, this is what they mean and this is what it can look like in the platform.

And so what I've done with that is there's even a lot of big businesses out there that say, hey, we don't really have the budget available for a ba.

And I can see how their systems operate.

They definitely have not invested in business analysis because the platforms don't talk to each other, their users are very unhappy with them, their technology department isn't delivering very well and it's because they don't have the information that they need.

Right?

So everyone in that scenario has been set up to fail.

And so what I've done now is I've gone, how do I make this better?

Better?

I can make this better by codifying exactly the things that I need to do to deliver results.

How do I create a digital AI BA assistant that a subject matter expert can sit alongside and it can prompt and it can create requirements documents and it can do workshop plans with the right questions to ask and, and all those things, all of that will have an inbuilt wiki which you can use to track the requirements, the constraints and the questions and things, the outstanding items all within there.

So this is why it's so powerful, is basically you can create under any application within the knowledge ecosystem as a knowledge worker, and you can create an agent team that utilizes a wiki automatically to be able to deliver you more results and still be able to tell you exactly where things are up to.

So what does this mean?

There's been so much I've covered in the last 15 minutes.

What does this mean?

I would like you to go away this week and I would like to think about some processes that you're using your knowledge for and how AI might be able to speed up the outcomes of what that looks like.

What we're really looking to do is we're trying to level up our use of AI to manage these administrative burdensome tasks so that we can focus more on the really the things that matter.

What are the things that matter?

Relationships, client conversations, asking good questions, networking.

All right, what are the things that allows us to do outside of the business?

Family time, time with your spouse, time with your partner, time with friends, more time exercising the really meaty stuff that you know that you want to do more of, but you're working too much to do right now.

So have that in mind.

This is possible.

We are in a space now where this is possible.

We want to be able to be in a space where we can do this efficiently.

Right.

We're getting into the to the crux of this now.

AI models are getting more expensive to use.

We need to be token efficient and we need to be able to start to leverage this technology that is in front of us now to be able to free up our time.

And being able to do this will mean that we can remain as solo businesses or we don't.

It means that we don't need to go out and try to get more and more people to do these really laborious admin tasks.

I want you to to go away and think about that.

The wiki stuff.

There's going to be some resources on our website.

You can go and check out lonewolfunleash.com resources.

Go check that out.

Thank you so much for joining me today.

I really appreciate your have been doing a million things but you decided to hang out with me and figure out how to use AI more efficiently this week alongside your knowledge, resources and your wikis.

Thank you so much and I'll see you next week.