Transcription
I'm going to break down how you can set up Claude so you never hit a token limit again. The process is broken down into three parts that you can copy. Quick wins, system upgrades, and nuclear enhancements.
But before we get to those fixes, you need to understand how AI token consumption works because a lot of people get this wrong. There are three key terms to understand. Tokens, the model you're using, and your compute budget.
A token is essentially how much text the model has to process. An AI model is what impacts how much compute is used per token. Better models require more compute per token. And your compute budget is how much compute is budgeted to your account. So Claude Pro Max, these subscriptions all have an allocated budget you can draw down from. When you run out of tokens or hit a limit, that really has to do with the total compute associated with your account, not necessarily the amount of tokens that you consume.
So with this foundational understanding, if we don't want to spend more money to increase our compute budget, there are two variables you can play with. The tokens we use and the model we use. A formula I'm going to be referencing throughout this video is compute budget used equals tokens consumed times model used. Every fix we cover in this video will optimize one of these two variables.
So the first part, part one, which is quick wins, is all about token consumption. To identify some of these simple quick wins, the best thing to do is to audit your system. At the end of the day, you can't solve a problem if you're not sure what's causing it. So, if you open Claude Code and you type /usage, you'll see a breakdown of how many tokens you've used, plus a section called what's using your limits, which is essentially where your tokens are going. This is Claude's way of helping you identify your problems.
Now, for me, and for a lot of you, you'll likely see two different issues that jump out. The first for me is that 65% of my usage ran above 150K context. And number two is that 58% came from sub Asianheavy usage. And we'll cover sub aents later, but for this part, we'll focus on optimizing our context. Context is essentially all of the additional information you provide to Claude to get a better response. The more context, the more tokens you use, the quicker you reach your limit.
So to fix this, you need to set some new habits. So the first quick win is fix your contextual habits. As conversations progress, your context window will quickly fill up. To show that, on the left, I type /context in a new chat. And then on the right is my best Bart Simpson impersonation where I sent a long message which had nothing but adding context a bunch of different times. And you can see just with this one message that my context more than doubled which means that my token usage essentially doubled. Now this is with one message. You can imagine how bad this gets over time.
Some good habits to maintain your context is first whenever you're switching tasks run /clear or start a new chat. The second is try and consistently work on projects versus coming back after 5 minutes. So if you're waiting 10 minutes before responding, you don't get that caching benefit. The third is that you can actually adjust your effort. So no matter what model you use, there is a default effort you can set. Simply put, the higher the effort, the more compute required per task. To adjust yours, click the bottom right corner on claw desktop and then it might say low, medium, high, and then you can drag it to whatever effort you want. The fourth is a mid habit. As your context window fills up, let's say 60%, and you can see that in the bottom right corner, the circular icon, you type /compact, which will compress everything into a short summary to help you manage your context. And if you are using clawed code directly in terminal, write /status line so that you can see your context directly on screen.
So that first quick win is just improving your habits with all the strategies I just mentioned. The second is you need to run a contextual cleanup. If you start a new conversation and then then type /context in a completely fresh chat. This will show you everything that gets preloaded automatically to every conversation you have with Claude. So for me, you'll see that I've already filled up 58.8K of my context window before typing a single word. The higher this number is, the higher your minimum token consumption will be for every new conversation. And so we can actually clean this up so that we're not starting from such a high token count.
Here's a prompt to help you do it. But going through it, the first part of the prompt will review any of your unused MCPS and delete them. If you want to do this manually, you can type /mcp and you'll see a full list of what you currently have set up. Next, it will clean up your skills. Every time you start a new session, your skills and their descriptions get loaded into context. So, if you have unused skills, just delete them. Or if your skill descriptions are extremely long, just shorten them. This part of the prompt will help you do exactly that. And then third, this will revise your Claude MD file. This file gets reread every message for the entire conversation. So if you have a 4,000 token claude MD, you start every conversation 4,000 tokens deep. The best practice in terms how to structure your claude MD is you want it to tell Claude how to interact with your specific project. You don't necessarily want it to have extensive documentation. And a rule of thumb from anthropic docs directly is that you want to keep your Claude MD under 200 lines. Anything longer than that and you're paying a tax on every single message. So that'll clean up your context. So you're starting at a lower point.
The third quick win is reduce your output tokens. The more words that Claude says to you, the more tokens you spend, right? These are the response. These are the output tokens. And this is generally a relatively small amount of your overall consumption, but this is a quick win that I absolutely love. Just paste this or add this to your Claude MD. Be concise with all of your responses. Or you can go a step further and install a plugin that I love called Caveman. It makes Claude speak like a caveman. And so you can actually see on screen the before Caveman and after caveman response.
Those three quick wins are all foundational things that you need to do. But the next section are system upgrades that can make your system up to 60 to 90% more efficient. But before we get to that, one thing I'll spend a lot of tokens on unnecessarily is trying to make decks for my clients. Which brings us to today's video sponsor, Bolt, and specifically their new product, Bolt Slides, which has changed how people make decks going forward. To use it, just go to bolt.new, new click slide deck and type something like build me a five-page deck about AI automation I can build for a client builds a real responsive web app styled and structured in seconds or you can connect it directly to clawed code and have it generate a deck for you without ever leaving the terminal and so yes bolt can make a deck very quickly but that's not why I like it you guys know how much I stress the importance of using AI to improve the quality of your outputs not just do things faster and Bolt Slides enhances the deck creation process in three ways first is you can embed whatever you want into your slides. For example, a live chart with updating data or a clickable diagram. Honestly, how bangers would it be if you had a product showing real-time usage and it's counting as you present? Now, this really unlocks a world of possibilities. Now, the second way is it looks native everywhere. So, whether you're on a projector, a phone, a tablet, it doesn't matter because these slides are a native web app. One of the things that I personally hated when pitching brands like Google and Amazon at my last startup was whenever I shared a deck, I'd have to say, "Make sure to open this on desktop." And that was just annoying because if they viewed it on their phone, it would look terrible. But now with Bold Slides, you can view it wherever, however. And the third is speed of iteration allows you to visualize decks as part of the creative process. Typically, when I'd make a sales or an investor deck, it started with a bulletoint list. And then I take that bulletoint list and then visualize it. And at that point, I'd often realize the concept didn't actually work. But by creating a visualization of these concepts earlier, you're able to streamline the entire process. Now, if you want access to Bolt's new feature, click the link below and differentiate yourself with the next generation of slide decks.
Part two, system upgrades. So, the first upgrade to your system is compress inputs before AI sees them. So, every piece of text that you provide Claude consumes tokens. So, in an ideal world, you would compress the text. So, it's only sharing with Claude the text that actually matters. To help you visualize this, imagine you're working on a report for a client and you want Claude to review specific changes. If you only made changes on the first page, would it make sense to share the entire 10-page report to Claude? No, it wouldn't. But Claude does this by default. So, what you could do instead is use Claude hooks to pre-process these files to be more efficient with tokens. This is pulled directly from anthropic docs and it tells you to do exactly this. It says offload processes to hooks and skills. And then it gives an example of reading a file and only pulling the information that has a prefix text error. Luckily, we don't have to figure out how to do this because there are some gigab brains who have helped us for free. There's a free open source tool called RTK that will help make the text that's shared with Claude significantly more concise.
To visualize where it sits, normally Claude will read the report. It'll reread the changes to check the work and then the full raw output is dumped into Claude. And so, let's say this process takes about 15,000 tokens. After RTK, Claude will edit the report. It'll reread the change to check work. RTK then uses deterministic logic to clean up the text, removes repeated text, boilerplate formatting noise, compresses the text, and then shares that with Claude. And that would end up being about 1,800 tokens. It's quite genius, and it works for both technical and non-technical tasks. I ran some tests on my computer across 13 commands, and I was able to save 92% of my tokens. Now, RTK claims 60 to 90%, and the exact savings depend really on what you're doing. But to set this up, you literally can just paste their repo and then say set up RTK on my project. So it runs automatically in the background. Hit enter and then it'll go through the process.
The second system upgrade is leverage sub aents with reduced models. Let's go back to the equation we had earlier. Compute budget used equals tokens consumed times model used. Thus far we focused on tokens consumed. But what about the model that we actually use? The reality is if you can get a task done with ha coup instead of fable, you save 90% of your compute budget. So the general rule of thumb in your brain is if AI could solve this task a year ago, you don't need a frontier model to solve it. Scraping, summarizing, formatting, fetching files, none of that needs the frontier models that are getting released. This framework is what I call using the minimum viable model.
So how can we do this in practice without overengineering our system and pulling our hair out? What we do is we predefine the model we use inside clawed skills. Skills are predefined tasks that you use over and over again. So once you define the minimum viable model for that skill, it will use that model for all future runs. And when setting it up, there are two variables you can play with, context and model. Model, you specify the model that you want to use for this specific skill and then the context. If you don't need to have contextual information, you can say context fork, which will create a new thread for that skill to work on. In turn, reducing the contextual information that's needed. If you do need previous info, you'll just leave that part blank in the skill itself. Here's a table on how to optimize skills based on specific needs. If you're still struggling to visualize why to do this, think of it like a team of lawyers. You want the partner at the law firm leading the case. But because his hourly is so expensive, you want junior lawyers doing a lot of the simple grunt work. It's the same idea here. And to find where this applies in your own setup, here's a prompt.
Now, that's just one of the ways to upgrade your skills to be more efficient. But the next upgrade is one of my favorites. Upgrade number three, move your workflows into script driven skills. After you've updated your skills to use a specific model, there is one more step. This one improves quality, consistency, and speed while also reducing token consumption. So, a lot like RTK enhancement that uses computer logic to compress text. You can use computer logic or scripts to complete tasks that you're currently using AI for. So, a script will run the same way every time, cause zero tokens, and never hallucinates. So, in an ideal world, we want to use AI for judgment, and then scripts for everything else that's repeatable. This way, AI doesn't have to refigure out something every time you do it. Here's a prompt that enhances your skills to leverage scripts and flags any skill your project is missing.
Now, before we get to part three, which walks through nuclear enhancements. If this is your first video of mine, welcome to the channel. But if this is your second or more, you know the drill. This is our anti-Slap agreement. The visuals, the testing, the hours of research that went into this video. This is entirely built for humans, not for these token gobblers. Okay? So, all that I ask is that you subscribe as part of this agreement. to help this content reach more people so that I can just keep doing this. Also, every video I give a Claude Max subscription away. This video's winner is Chad N5N5X, who's building a complete AI operating system. Shout out Chad. You are a legend. Now, if you want to enter the next giveaway, comment below with what you're building or any recent issues you've run into. And every video you comment on is another entry.
Part three, nuclear enhancements. These are four big changes listed in the order that you should do them. And the last one is going viral right now, but I'll explain why I don't necessarily think it's worth your time. Nuclear enhancement one, route specific work to codecs. The reality is that certain models are more efficient than other models, but that also extends outside the model layer into the harness layer. The tool that's actually orchestrating using AI. So when you prompt Claude code, the logic that interacts with the AI models is the harness. So, Codex ecosystem, OpenAI's product compared to Claude, on certain tasks, it burns 4x less tokens than Claude does. And the reason for this, to simplify it, is that Claude is built to be thorough. It rereads, it verifies, it thinks before it acts, and you pay tokens for every step along the way. Whereas for Codex, it's built to be surgical. Get in, make the edit, get out. So, to use this to your advantage, you can intertwine Codeex and Claude to get the most out of your subscriptions. The simplest way to do it, and my favorite, is there's a Claude code plugin for Codeex. You can install that, then have Claude route any tokenheavy execution task to codeex. This prompt will help you install that plugin, set it up, and then update your Claude MD to tell Claude to actually do this.
Nuclear enhancement number two is use images instead of text. This one sounds fake, but it's so awesome that I had to include it. So, Claude processes images at a different rate than it process a text. So, you can convert text into images and then submit that to Claude to become more efficient with your tokens. Now, there's a lot going on there, but to visualize it, imagine you had a massive page of text with over 10,000 tokens. And then an image with that same text, but in image form. In order for Claude to actually process this image, it only takes 3,000 to 4,000 tokens, resulting in a 60 to 70% token reduction. These are based on claims from PXpipe, the open- source tool on GitHub, that will help you do exactly this. Now, there are some trade-offs, like it won't read the text perfectly, and there is a nonzero chance that Anthropic patches this in the future, but I found this so damn interesting that I just had to include it.
Nuclear enhancement number three, swap the engine out entirely. Most people aren't aware of this, but clawed code is the harness, and the model that's inside of it and is actually used can be swapped. So, for example, you could use clawed code and only use OpenAI or other open- source models. Now, to actually do this, it's pretty straightforward. There are some environment variables that Claude code will reference to know which model to use and by default it goes to Claude's models. Now, two potential options that are considering looking into are ZI's GLM plan and then the DeepS plan. Based on research for these on a compute per dollar spent, these can provide you with more capacity than Claude's plans. There are trade-offs, right? Deepseek is Chinese model. You may be concerned with your data privacy and also when you're using these tools, you are getting slightly worse models. Here's a prompt you can use to go down this rabbit hole and learn a lot more. And as part of that, it'll build you an implementation plan if you want to eject out of the anthropic ecosystem. This prompt is also designed to help you identify current providers as offers are constantly changing.
And the fourth nuclear enhancement is running your own model locally. This is an enhancement that I eventually will fully believe in. It's beautiful, right? You can run your own models locally and not rely on external data providers. So, for example, I have this Mac Mini at the corner here. can't see it, but I could run a model on it and then route all of my requests directly to that instead of clawed. So everything is staying within my office. And now there are pros and cons to this. Some of the pros is you can essentially have limitless tokens. All I have to do is pay for the power to keep the hardware running. Next is that you own all of your data. If data privacy is a concern, this means nothing is exposed to external service providers. Third, you own the entire AI stack. You aren't relying on anyone elsewhere. And this concept of running a model locally is why we'll never actually be able to ban AI entirely.
Now, the cons of this first, my Mac Mini and then 99% of consumer hardware can't actually run any of the top tier open- source models. And in order for you to get to those models, you're going to have to spend over $10,000. Next is that none of the frontier models like Fable are accessible to download locally, which means you're sacrificing quality in the short term. And then finally, you have to set up a computer server farm. And as someone who turns his phone on airplane mode at night, I would not want all of that EMF radiation near me. Jokes aside with the EMF radiation, but you have to set up the server farm and you have to manage it. That's work. So long-term, I do fully expect local models to be a thing as hardware gets better and the open- source models get better as well. And at that point, it may 100% be worth it. But right now, my recommendation for you is you can play around with local models. It's valuable for you to know that it exists, but I just wouldn't go all in on this. So, please don't go and buy expensive hardware. Maybe one day, just not today.
So, with that being said, let's speedrun the changes that you need to make today to get more out of your system. So, the first quick win is fix your contextual habits. Run /cle every time you switch tasks. Work in focus block so you can keep 90% cash discount and set your effort to match the task. And then use /compact when you get to 60% full on your context. The second quick win is run the cleanup prompt. Disconnect MCPs you don't use. Archive unused skills and shorten their descriptions. and turn your claw MD into a directory instead of a document. And then make sure your Claude MD is less than 200 characters. The third is cut your output tokens. Add be concise to your Claude MD. Use the Kman plugin or update your Claude MD to tell Claude to be concise with all their responses.
Then for the system upgrades, install RTK so every command output gets pressed before Claude reads it. This could reduce your token input by 60 to 90%. Second, enhance your skills so Grunt work runs on minimum viable models instead of your most expensive ones. The third system update is turn every repeatable step into a script inside the skill. Computer code causes zero tokens to run.
Then the nuclear enhancements. First, route any tokenheavy execution to codecs with the plug-in. This is where you could have two subscriptions with two different budgets and more total firepower. The second is use images instead of text. Keep an eye on this one. The third is that you can swap the engine out entirely if you want to work with different model providers. And the fourth, as I mentioned, you can run your own local model if you really want to.
Now, once you apply this, you'll be able to build all day long instead of having to wait every 5 hours for your limits to reset. And if you like this video, you will love this video where I walk through my exact setup to leverage Clawude skills to build 10 times faster. This builds on a lot of what I covered in the skill optimization section of this video, and you will love that. I'll see you over there. Peace.