Someone just open-sourced tokenminimizing (8500 stars on GitHub)

Look at this guys. You can now use Clawude Code while using 60 to 95% fewer tokens. What does this mean? If you're on the pro plan, this means that you might just be able to start building for 20 bucks a month on Claude Code. Guys, this is Headroom. I just found it on GitHub trending. And in today's video, I'm going to go through it quickly and explain what it does and what it is. So you can use this with claw code, codeex, cursor, ader co-pilot, cli and openclaw. And it says here, great fit for you if you want to run AI coding agents daily and want savings without changing your code. Work across multiple agents and want shared memory. Need reversible compression, original, always retrievable by CCR. Skip if you only use a single provider's native compaction and don't need cross agent memory, work in a sandbox environment where local processes can't run. Basically guys, it's similar to Caveman. Um, if you guys are familiar with Caveman, Caveman basically just makes Claude code less verbose, but I've heard for a very long time that Caveman is actually not that effective. This was created by a passion project, not necessarily by like a company or whatever. Obviously, I don't know for sure about Headroom, but Headroom does seem um like a little bit more professional, I would say. And it's got a lot of stars. It's currently number one trending on GitHub as of today. So let's talk about it. So what is headroom? Headroom is the context compression layer for AI agents. Uses 60 to 95% fewer tokens. Um MCP mainly library tool use. Basically it just kind of reduces the amount of tokens used during those processes. Same answers a fraction of the tokens. Headroom compresses everything your AI agent reads. Tool output logs. rag chunks, files, and conversation history before it reaches the LLM. Something I noticed yesterday was I said hi to Claude code and it used 22,000 tokens as an input uh to say hi back, right? Which is absolutely absurd. So, this is kind of something I've been looking at for a while. I will test Headroom for myself, but I just thought I'd talk about it today. So, one engine, five ways to drop it in library. So you can use it as a library, you can use it as a proxy, you can use it as an agent wrap which is what they recommend I believe. So you just write headroom wrap claude in one command. MCP server as well. Cross agent memory and headroom learn. I would personally use it like this. Um you could either use proxy or library as well. It runs locally. Your data stays on your machine. Okay, that's pretty cool. The right compressor for every content type. So content router detects the content type and selects the right compressor automatically. Smart crush shower code compressor compress base compresses JSON source code ASL or pros cache aligner stabilizes prefixes so provider KV caches actually hit keeping latency and cost down. I'm not going to pretend I know what that means. CCR reversible compression stores originals locally. The LLM calls headroom retrieve only if it needs them. Okay, so that kind of makes sense. I do understand what that means. So the original prompt is sent uh is is stored locally. It's compressed and then Claude only gets the uh headroom retrieve. Okay, that actually makes sense. Token savings where it counts. So code search, which is like the big one, right? You've probably done this before where you said like look at my code. It really does way too much, right? Research. So they're saying that code search 100 results goes from 17,000 to 1,400 tokens, which is really good. SR incident debugging 65,000 to 5,000. GitHub issue triage 54,000 to 14,000 cob base exploration doesn't go down that much but still does go down. So if cost is the main thing here right or token maxing or whatever the hell it's called um right this could be a really really good way to do this. So what they're saying here is the benchmark hasn't dropped right so it's the exact same on benchmarks um which is obviously really really important. You don't want to lose quality wraps the agents you already use. So claude codeex cursor ader co-pilot cli openclaw like I mentioned at the beginning. All you do is headroom install and then uh headroom wrap claude. Is it for you? Great fit. We went through this before. Um I wouldn't necessarily agree with this. I think the main reason to use this is to save tokens, right? Basically without changing the code quality. All context local reversible. So headroom these are the other types of tools that do the same thing right and it says the headroom is all context tools rag logs files history are local and reversible install is just pip install headroom AI all mpm install headroom AI pick your mode headroom wrap claude this is probably what I'm going to be testing and then compress everything before it hits the LLM basically so really really interesting It seems to be doing very very well on GitHub. So, it's definitely a project to keep your eye on. I will leave a link to Headroom's GitHub in the description of this video. I just thought this would be an interesting video just to show people the kind of better way to do things over Caveman. I was very, very skeptical of Caveman when it first came out. So, I'm glad that this tool now exists. With all that being said, guys, thank you so much for watching. Just quick shout out to our sponsor, me. This is my website, incomestreamsurfer.com. I was just working on things yesterday and basically I came up with this new offer. Um, so you just click build your plan. Let's say you need a website. Everything is as transparent as possible here. So these are the prices that you'll be paying. Marketing website 2,500. Admin dashboard AI system 2500. Let's say you already have a website though. Um, do you want to work with me? So for €500 a month, I will be your tech guy. any website changes that you need on your website. Plus, we'll be adding content to your website every single month. That is not just blogs. Let's say you're running an e-commerce. We'll be adding product categories that we think will rank on Google or rank on LLMs. The way that we do that is Semrush and Claude Code together to find really really interesting keywords and also your search console. And then we basically create the best possible product categories that we can think of. Let's say you want to work with me €5 a month. 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