I hope people realize that tools like caveman are mostly joke/prank projects - almost the entirety of the context spent is in file reads (for input) and reasoning (in output), you will barely save even 1% with such a tool, and might actually confuse the model more or have it reason for more tokens because it'll have to formulate its respone in the way that satisfies the requirements.

> I hope people realize that tools like caveman are mostly joke/prank projects

This seems to be a common thread in the LLM ecosystem; someone starts a project for shits and giggles, makes it public, most people get the joke, others think it's serious, author eventually tries to turn the joke project into a VC-funded business, some people are standing watching with the jaws open, the world moves on.

While the caveman stuff is obviously not serious, there is a lot of legit research in this area.

Which means yes, you can actually influence this quite a bit. Read the paper “Compressed Chain of Thought” for example, it shows it’s really easy to make significant reductions in reasoning tokens without affecting output quality.

There is not too much research into this (about 5 papers in total), but with that it’s possible to reduce output tokens by about 60%. Given that output is an incredibly significant part of the total costs, this is important.

https://arxiv.org/abs/2412.13171

All LLMs also effectively work by ”larping” a role. You steer it towards larping a caveman and well.. let’s just say they weren’t known for their high iq

Exactly. The model is exquisitely sensitive to language. The idea that you would encourage it to think like a caveman to save a few tokens is hilarious but extremely counter-productive if you care about the quality of its reasoning.

This specific form may be a joke, but token conscious work is becoming more and more relevant.. Look at https://github.com/AgusRdz/chop

And

https://github.com/toon-format/toon

I believe tools like graphify cut down the tokens in thinking dramatically. It makes a knowledge graph and dumps it into markdown that is honestly awesome. Then it has stubs that pretend to be some tools like grep that read from the knowledge graph first so it does less work. Easy to setup and use too. I like it.

https://graphify.net/

Someone should make an MCP that parses every non-code file before it hits claude to turn it into caveman talk

I hesitated 100% when i saw caveman gaining steam, changing something like this absolutely changes the behaviour of the models responses, simply including like a "lmao" or something casual in any reply will change the tone entirely into a more relaxed style like ya whatever type mode.

I think a lot of people echo my same criticism, I would assume that the major LLM providers are the actual winners of that repo getting popular as well, for the same reason you stated.

> you will barely save even 1% with such a tool

For the end user, this doesnt make a huge impact, in fact it potentially hurts if it means that you are getting less serious replies from the model itself. However as with any minor change across a ton of users, this is significant savings for the providers.

I still think just keeping the model capable of easily finding what it needs without having to comb through a lot of files for no reason, is the best current method to save tokens. it takes some upfront tokens potentially if you are delegating that work to the agent to keep those navigation files up to date, but it pays dividends when future sessions your context window is smaller and only the proper portions of the project need to be loaded into that window.

Output tokens are more expensive

They are indeed impractical in agentic coding.

However in deep research-like products you can have a pass with LLM to compress web page text into caveman speak, thus hugely compressing tokens.

Help me understand: I get that the file reading can be a lot. But I also expand the box to see its “reasoning” and there’s a ton of natural language going on there.

We started out with oobabooga, so caveman is the next logical evolution on the road to AGI.

I wonder if you can have it reason in caveman

You really think the 33k people that starred a 40 line markdown file realize that?

I mean we had a shoe company pivot to AI and raise their stock value by 300%, how can we even know anymore