JuliusBrussee/caveman

what is the difference between caveman and a system prompt "you are a problem-solving first agent, less tokens"?

hehehe. 你会整文言文 gpt不一定会。

I’m a bit skeptical about the 文言文.skill direction.

I checked the current caveman skill definition, and it explicitly says , so at least today the mode is intentionally scoped to English. That said, this looks more like a product/design boundary than proof that the underlying style cannot work in other languages.

My guess is that removing that English-only constraint and testing the behavior would be a reasonable first step. But without actual experiments, I wouldn’t treat that as a confirmed explanation yet.

> Love this idea! For Chinese, there's actually a centuries-old "compression language" already built-in: **Classical Chinese (文言文)**.
>
> Modern Chinese: "这个函数的作用是将用户输入的数据进行验证，确保格式正确后再存入数据库" (30 chars) Classical Chinese: "此函校用户所入，格式合则存库" (14 chars) — **2.4x compression, same meaning**
>
> It's basically the OG caveman-speak — humans already figured out maximum compression 2000 years ago 🪨
>
> I built a skill that does exactly this for Chinese: **[文言文.skill](https://github.com/voidborne-d/wenyanwen-skill)** — makes AI respond in Classical Chinese to save tokens, with a local MCP server that translates back to modern Chinese at zero token cost.
>
> The translation layer is the key difference: caveman English is readable as-is, but Classical Chinese isn't readable for most modern Chinese speakers. So the MCP server handles the decompression locally (rule engine + dictionary, pure Python, no API calls).
>
> Would be happy to collaborate on a multilingual compression framework that inc...

Love this idea! For Chinese, there's actually a centuries-old "compression language" already built-in: **Classical Chinese (文言文)**.

Modern Chinese: "这个函数的作用是将用户输入的数据进行验证，确保格式正确后再存入数据库" (30 chars)
Classical Chinese: "此函校用户所入，格式合则存库" (14 chars) — **2.4x compression, same meaning**

It's basically the OG caveman-speak — humans already figured out maximum compression 2000 years ago 🪨

I built a skill that does exactly this for Chinese: **[文言文.skill](https://github.com/voidborne-d/wenyanwen-skill)** — makes AI respond in Classical Chinese to save tokens, with a local MCP server that translates back to modern Chinese at zero token cost.

The translation layer is the key difference: caveman English is readable as-is, but Classical Chinese isn't readable for most modern Chinese speakers. So the MCP server handles the decompression locally (rule engine + dictionary, pure Python, no API calls).

Would be happy to collaborate on a multilingual compression framework that includes both approaches ...