A done-for-you setup that cuts AI coding agent token usage across Claude Code, Codex, Cursor, Windsurf, Copilot, and similar tools.
Added Jul 17, 2026
Developers and small technical teams are adopting multiple AI coding agents, but token usage rises quickly as models get larger and workflows become more agent-heavy. The pain is not abstract AI cost management; it is the day-to-day waste from verbose outputs, oversized context, repeated prompts, and unoptimized agent setups. Buyers want to keep using powerful models without burning through subscriptions, API? credits, or usage limits.
Offer a productized token optimization audit and implementation service for AI coding workflows. The service installs and configures prompt rules, compression tools, output-length policies, agent profiles, and measurement scripts across the buyer's existing tools. Delivery includes a before-and-after benchmark, a reusable token minimization playbook, and a maintenance checkup for new agents or model changes.
Frontier coding models are becoming more useful but more token-intensive, and many teams are stacking several agent tools at once. The signals show active experimentation with open-source token reducers and strong interest in practical setup help rather than theory.
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Everyone is building AI coding agents, but almost nobody is solving the real pain: how do you run multiple agents in one place without burning tokens like crazy? That’s the gap. I’m building a multi-agent coding control plane with built-in token optimization think Conductor-style orchestration + TokenShift-style cost control. ⚙️💸 If you’ve ever felt the pain of too many tabs, too many agents, too many tokens, and too much debugging, this is for you. Would you actually use a single UI for this? 🤔
What I'm looking at doing is switching to just one of the higher plans, but obviously, you know, can only have one. So, the question is, will that affect what the work we do and teach inside the AirProft Boarding is using if you just pick one? And the answer here is that you can use whatever agent you prefer. So, everything that we teach inside the community is very interchangeable. Whether you're, for example, setting up an agent operating system with Hermes or Claude or ChatGPT or all of them is very, very flexible because they all essentially work the same way. The other thing that I'd recommend is checking out our token minimization playbook, which I'll link to below. This is great for just reducing the amount of tokens you use with any system. And that way, if you're using these Frontier models, you can reduce the amount of tokens, which means that you get more out of the models that you pay for. So, if we go to the token minimization playbook here, we have a full video tutorial and a step-by-step guide. And if you're wondering, okay, like, how do you reduce the amount of tokens that you use? There's several systems for this. So, for example, some really good open source projects are Headroom, Caveman, you've got RTK, and these basically all suppress the amount of tokens you use with different systems. So, Caveman reduces the amount of output tokens, Headroom compresses the actual tokens you use, and then you've got RTK as well, which is similar for compression. And so, those systems allow you to reduce the amount of tokens so you can get more out of the model. So, you can get potentially a lot more out of systems like, for example, Cable 5 or anything else like that. Now, you can also see something that's really cool about this community is that people are sharing one that build it. So, someone the other day actually requested a way to automate LinkedIn follow-ups, and then Sheena shared how to do it, gave an example of how it runs inside her own customized agent OS from our regional setup, and then actually guides everyone through how to use it with some tips. And that's the thing I love about this community is like everyone's just sharing a lot of value that you wouldn't be able to share publicly because there's no filter on like who's using it and who's an expert and who's not inside a normal open group. So, this is fantastic.
sharing all the stuff you're working on it, it can be literally life-changing. So the reason our agent OS is so good is because we've got a community of people who are like, we need to improve this. We need to add that. We need to change this. We need to remove that, right? And so so many people are building this, giving us feedback and then helping us improve it. And this is why I personally answer the questions every single day, because it's just like the best way to build a great community where we all win together and we build something amazing. I mean, for example, Sheena, they actually turned their agent OS into a powerful system that just self-improves for lead generation. And there's so many cool use cases for this. So it's great to see these sort of shares and people building amazing stuff. Now Elsa was asking, you know, what's the best way and the best use cases for using Fable 5 and then also reducing the amount of tokens that you use as well. So for us, we are using it to build new features into the agent OS. So for example, like the system with Hermes and Asteros wouldn't have been possible without the Fable 5 update because it's just like so powerful, but so neatly organized and great UI and everything else. So just adding new features inside the agent OS is my big focus with Fable 5. And then for anyone who's watching this, you know, I'd recommend that you write down everything that you're working on, spend just 10 minutes on it, write down in three to four steps what the workflow is, and then ask Fable 5 to automate it or to build a tool that always it for you. So for example, you could build that into the agent OS. We did that with keyword research and with SEO content creation. And then for reducing the amount of tokens that you use, I would recommend three things. So there's three open source projects that are free and also reduce the amount of tokens that Fable 5 will use. So they are Caveman, Headroom, and Ponytail. Ponytail is great for coding. Headroom compresses the amount of tokens you use. And then Headroom also compresses that. Caveman also reduces the amount of output tokens because it basically just responds like a caveman. So it's very blunt and straight to the point. Also, a lot of cool people like share or some stuff like you can see right here, like free tools and
And it's like Caveman mode is now live in the session, which means it's going to reply to me in a very short, brief way to reduce the amount of output tokens he uses. So if you have a look before and after, with a normal agent, it would give you a long response like that with a lot of fluff. With Caveman, it would be much shorter. With Ponytail, it reduces the amount of code it creates. And then with RTK, this just filters the amount of tokens that it uses as well. So these are all like good ways to reduce the amount of tokens you use to be way more efficient. And then you can plug them all into your agent to reduce the amount of tokens you're using. And this is actually part of something we call the Token Minimizer Playbook, especially when you're using Fable 5. This is a great way to get more out of Fable 5. So, for example, this week we've been testing Fable 5 relentlessly with loads of different tests, 47 different tasks. But we use that all on the subscription of Claude because we were minimizing the amount of tokens we use. We're creating like pretty crazy stuff here, as you can see. Like full real-world games, as you can see. So if you want to get more out of your plans, out of your subscriptions, and bear in mind, like the bigger the context window of each of these models gets, the more tokens you're going to use. So when you can be more efficient with these playbooks, it's just going to help you save a lot more. So that's basically it. They're free to try as well, so you can test them out, see what you think. If you want to get a full machine, I mean, this is part of the Agent OS setup. So if you're wondering what the Agent OS is, it's an agent operating system where you can plug in Claude, OpenClaw, you can add Hermes, you can add memory systems inside there. And a lot of people say, like, won't this use a lot of tokens? But we use it with our existing CLI plans and free APIs, simply because we have smarter playbooks in place to reduce the amount of tokens we use across everything that we do. So if you want to get that, it's inside my AI Profit Boredom community. Link in the comments description, or go to the AIProfitBoredom. com. Inside the community, you can ask questions, and I answer them personally with a video tutorial every single day. Inside the classroom, you can get access to all my best trainings, including new daily updates.
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