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How to Reduce GPT-6 Astra AI Tokens by 80% (FREE) — Transcript · YTtoTranscript

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How to Reduce GPT-6 Astra AI Tokens by 80% (FREE)

Julian Goldie SEO · 2,463 words · 12 min read · EN-ORIG

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Below is the complete, readable transcript of How to Reduce GPT-6 Astra AI Tokens by 80% (FREE) by Julian Goldie SEO on YouTube. Read the full text, copy any part you need, or generate a transcript for any video with our free tool.

G6 Astra is pretty amazing, but one of the biggest issues with it right now is that it uses up a lot of tokens. A lot of people are actually like afraid of using it, especially on the higher level, simply because it it uses so many tokens, it can absolutely rinse them. So, today we're going to talk about some

of the best ways to reduce the amount of tokens that you use with your AI agents like Jeep 6 Astro. And you can apply this to Hermes, you can apply this to Claw, to whatever you prefer. But the main point here is that you can save a lot of tokens and you can get more out

of your existing plan with cheap 6 Astro. With that, let's get straight into exactly how to do this. I want to kick this off with some of the best token minimization playbooks I've found for reducing the amount of tokens you use with AI agents. So, one of the first ones that you can use is something

called Headroom. Headroom is basically a way of compressing the tool outputs to reduce the amount of tokens you use. And for coding agents, you use about 20% less. So imagine, for example, if you have Astra for 5 weeks, well, you would get an extra week out of it because you've got that extra 20%. Now, if

you're wondering, okay, how do you use Headroom with something like, you know, for example, Codeex? What you can do here is you can grab the GitHub details like so. Then you can go over to chat GBT and you can just plug this in, right? And you can say, okay, use headroom when you're coding for me

inside uh codeex. And so you can just install each of these different playbooks I'm running through today using the system. And these are all like free and open source available on head uh on on GitHub, available to install. And very quickly you can start reducing the amount of tokens you use. So this is

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like a context compression layer for AI agents. And what this means it basically uses less input tokens so that when you're coding with your AI agents, it's more efficient. It's using less tokens and therefore you reduce the output as well. This is also really good not just with for example Guptics Astro, but if

you understand how to use any of these different AI agents and these token minimization playbooks. The cool thing about it is you can apply this to Hermes. You can apply this to Claude. And bear in mind like you know Astra 6 is great now, but what about when Fable 6 comes out? What about when for example

the next open source project comes out? like things are constantly changing, but the playbooks and the systems you can use to reduce the amount of tokens inside each of these um you know those don't change and so you can apply this to whatever you learn which is a super useful skill to use. The next thing that

we can use is something called RTK RTK. So basically this reduces LLM token consumption by up to 60 to 90% on common developer commands. So again, it's a way of just reducing the amount of output your AI agent reads. Now, one thing to note here is like when you hear like 60 or 90%, like test it yourself, see what

you think, see if it's useful for you or not. But the point here is that you you you can test these things out and then you can reduce the amount of tokens you use, right? And [snorts] so you can see here the RTK cuts up to about 90% of the bash output your agent reads. And this

is not the same as like reducing your total amount of tokens by 90%. But it just makes it more efficient. Even if you reduce the amount of tokens to use by 10% here and then 20% for example of headroom and that sort of thing like together they all stack up and they all

reduce the amount of tokens to use which is pretty cool here. So here's an example. So again we can go to GitHub here. We can set up a new chat and we can say okay can you use this for coding? Now not every token minimization playbook you use is going to work directly with codec. So some will, some

won't, but we can test them like this. The other thing that I'd recommend here is that whenever you're coding with or any of these agents and you or for example these GitHub repositories, just check them yourself. Get codeex to scan them. Don't install anything that you don't understand. So read through it first. Make sure you're comfortable with

it and then you can keep going from there. So my point here is like you want to check this stuff out because for example, if anyone can create this, anyone can upload it to GitHub and you want to just make sure you're comfortable with it. Even if you don't use this system, well, the great thing

about this is like you can actually take the principles from this and you can ask codeex to implement it if that makes sense. So you could look at how this actually works and go over to codeex and say hey based on how this operates could you create your own playbook for this or

could you adopt this approach whenever you're coding out and you don't even have to install the GitHub repository if you don't want to as well. Okay so you can see here that we've got RTK uh ready and installed on the Mac now and we have headroom as well. Right now with Headroom, we have to restart codeex to

start loading the new tools. But in the space of like 2 minutes, we've installed two different playbooks that are going to help us save a lot of time when it comes to reducing our token usage. Fantastic. [snorts] The next thing that we have is something called Caveman. So Caveman, what it does is it reduces the

amount of output tokens we use inside our AI agents. Now you can see that in the actual description, it says claw to code. It says claw code skill. Does that mean it only applies to clawed code? No. Like any AI agent that you're talking to, sometimes they can be a little bit verbose. Sometimes they can talk too

much when you're uh reach, you know, when you're using them. And so this reduces the amount that they talk. How? By making it talk like a caveman. Why is that important? Because it reduces the amount of information it gives you when it's responding to you. So it's more direct, but also it reduces the amount

of tokens cuz every time it replies to you, so for example, we're using asterics over here. every time it's replying to us and it's given us like a long paragraph or it's giving us loads of details inside the response that actually uses up more tokens and it uses up our our usage. And so how do we

reduce the amount of tokens? We can reduce the output tokens from the agent itself. How do we install it? Right? All we do is we grab the GitHub like so. Then we're going to go to chat. You'll see a common thread here and you click on install this plus start using this. And what we can also do if you want to

test any of these out you can actually say to codeex once you've installed this. So once you've installed this can you test it out yourself and just show me if there's actually a difference in the before and after on a task. Tell me the difference in terms of a percentage. And so you can ask it to test each of

these skills and see if it actually makes a difference. Cuz I know some people comment they be like oh this you know caveman doesn't do anything or RTK doesn't produce anything. Test out yourself. Test all this stuff out yourself. Right? That's what I recommend to everyone watching stuff like this. And then you can see we've got RTK,

we've got Headroom, Caveman is now being installed. Pretty simple and easy. And the final token minimization system that I wanted to show you is Ponytail. Now, this is great if you're coding. If you don't do any coding with Astro 6, number one, you are missing out. But number two, uh Ponytail is really good for

this, but it's specifically for code. So, what it essentially does is it makes your AI agent think like a lazy senior developer. Why is that important? It's important because basically it's going to write minimal code. It's going to be very efficient in the code it writes. There's going to be less fluff inside

the code which means that it performs better. But also this means it's going to be faster when it replies to you. It's going to use up less code which means less tokens and it's cheaper to use your AI agents. And so this is really useful because when you combine all these different systems together,

you have a very very efficient chat system that's ready to go. The other thing that you can do here and you've got a few options is you can actually switch between the different models that you have depending on the task. So option number five once you've installed each of these and we can install

Ponytail like this. So I'm going to say let's grab the GitHub repo from Ponytail. Now I'm going to go over to codeex like so. Can you install this and use this when you're coding out projects? So, we've got those four playbooks installed like so. And we are testing out Caveman as you can see here.

And actually, this is really interesting. So, some people again, they're going to say, "Well, Caveman doesn't work or uh RTK doesn't work, blah blah blah." You can see an example right here where we've tested it where we've got Chat Chubity to test it directly for us. And caveman used 54.9% fewer output tokens than the normal

answer and 35% fewer than answer concisely, but it kept all the recommended fixes. So the quality of what you're building and what you're coding and what you're creating with Astra 6 doesn't drop, but the amount of tokens you get out of Astra 6 increase significantly. So that's what this system is all about. And you can see the

comparison right here. So this is normal versus answer concisely versus Kayman. 54% reduction in output tokens. Not 54% overall but 54% in output tokens which is pretty cool. So these are the systems that you can use and then again the final one is that you can actually switch here. So let's say for example

you are not coding something out you just need a quick answer inside chat GPT. Well if you wanted to save your tokens on the GP6 Astra plan then you can switch over to something like GPT 5.6. You don't have to use GP6 Astra on every single thing. You could also switch down to astral light, right?

Light would use less tokens than medium and medium would use less tokens than extra high. So these are all dials that you can change as well. The final thing that I was going to say is that you can get it to orchestrate stuff for you. So you could have for example Astro 6 as

the brain when you're coding out with it or when you're building stuff. So Astra 6 can be the brain and then that can delegate tasks to harder working cheaper APIs. So I'll give you an example. You could use Astra 6 as the main brain and then that could delegate to something like Claude or to something like Grock

for the work where you don't really need a Frontier model or you don't you're not in a rush. You don't need to get that done quickly but you do want to save the amount of tokens you use on Astra. So, those are like what's that uh five different options for reducing the amount of tokens. And you know,

altogether when I've tested this out with Claude Code in the past, uh it reduced the amount of tokens in total across everything that we did by about 80%. Which was pretty wild. Uh so, you know, it's it's a it's a great way even if you reduce the amount of tokens by 20%. That means you squeeze a lot more

out of Astra and that sort of thing. So, if you want to get our full playbook on exactly how to use it step by step, we've got that inside the AR profitable boardroom inside our token minimization playbook as you can see right here. Uh, it's got links to all the open source tokens. And also, we've got a full

step-by-step guide on different dials that you can change inside the system as well to reduce the amount of tokens you use with any AI agent. And that's inside the airoff boardroom link in the comments description or just go to the airoff.com. Inside the community, you can post questions and I personally answer them quite often with a video

tutorial if it's a relevant um question. And quite often we'll just break everything down step by step as well as you can see right here. Then inside the classroom, you can get access to all my best trainings, my best systems. We got the agent operating system inside here too. All my best playbooks and we update

this all the time. As you can see right here, we have a full 1hour course on how to use G6 Astra as well just to help you as much as we can. And you can personally direct message me. Plus, you get access to all of my best trainings. As you can see, we also have four weekly

coaching calls inside the AirPuff volume. So, you can ask questions live on a call, um, share your screen, meet other members, and then inside the map, you can meet people in your local area who are building out with AI agents like you. Feel free to get this link in the comments description or just go to the

aprof.com. I swear.