YHK - yet another hacker news clone

Not quite, and its addressed in the post. Caveman was cool, but hand wavy. I've measured the compression of Cablese, determined where it does the most good, and also that it is already baked in to most model family's training, which saves instruction tokens.

I suspect you might be right. My conclusions from the digging are that this kind of compression works best with settled instructions/data for machine to machine talk. For something like OpenClaw (which I use a lot), that might mean the AGENTS.md, TOOLS.md, etc. Compression there would free up the context for the agent.

I can confirm I’ve seen this with Deepseek V4.1, I imagine it’s with other models too.

Would be pretty funny if OpenAI's models are so token efficient now due to hidden caveman prompt.

Cool story bro. Maybe you could engage with the content? I'm an actual person.

Oh crap, if these are the new LLM tells then a lot of people are going to start accusing me of AI writing...

I have a strong tendency of talking about concepts like they're physical objects. A lot of the people I know IRL do too, so it might be a regional thing idk.

>"Minimize ambiguity" has been my go-to instruction as of late when the agent drifts back towards vague terms and lack of specificity.

Anthropic has called the greater category containing this type of writing "mannered prose" https://platform.claude.com/docs/en/build-with-claude/prompt...

If you ask the models to avoid mannered prose (or use their extended prompt), it basically eliminates all of this type of slop writing.

Here's a de-slopped example.

> Write Like It's 1866: LLMs Relearn Telegraphese

> Adding one sentence to a prompt, telling the model to write like a telegram, cut its output tokens by 40–49%. The sentence asks it to drop articles and filler but keep every fact. Models from four different labs then answered questions from that compressed text as accurately as from normal English. So when one model writes something for another model to read, you pay about half as much for the output. This post introduces the Telegraph Test, a benchmark that measures how well a given model does this.

For me the really interesting part was:

> Haven’t we seen LLMs do this already?

> Yes, BabelTele (arXiv, June 2026) demonstrated that LLMs can encode text in compact, non-standard forms — omnilingual word fragments, symbols, emoji — that other models recover with high fidelity (99.5% semantic fidelity at 27.9% of original length, by their metrics), including cross-model transfer, agent memory, and multi-agent communication. It proves the general phenomenon: human readability is not a requirement for model-to-model text.

I remember people testing early GPT-4 (2023?) in similar ways, to compress text, it would emit a string of strange text, Unicode, emojis, but was able to decode the compressed version very reliably.

This seems to cut usage by another ~50%, at the cost of being incomprehensible to humans.

A useful critique, thanks.

I would say that the grader has the same threshold for whatever answer it receives, and is equally harsh on whichever it grades. Any scores above the baseline (1.0) are really claims about parity, rather than better understanding in the compressed format.

The decoder step is a model expanding the cablese to regular text, not having seen the initial question. A separate model instance then reads that regular text and answers. And the result is still at parity with the plaintext record.

Had cablese knocked out information, that wouldn't have been the result, would it?