Comment by DonHopkins

What about the cost of the millions of tokens you have to spend to prompt the LLM to understand your bespoke language with manuals and tutorials and examples and stack overflow discussions and the source code to the compiler, added to every single prompt, that it totally forgets after each iteration?

It already knows python and javascript and markdown and yaml extremely well, so it requires zero tokens to teach it those languages, and doesn't need to be completely taught a new language it's never seen before from the ground up each prompt.

You are treating token count as the only bottleneck, rather than comprehension fidelity.

Context window management is a real problem, and designing for generation is a good instinct, but you need to design for what LLMs are already good at, not design a new syntax they have to learn.

jaggederest's opposite approach (full English words, locque) is actually more aligned with how LLMs work -- they're trained on English and understand English-like constructs deeply.

noosphr's comment is devastating: "Short symbols cause collisions with other tokens in the LLMs vocabulary." The @ in @ GET /users/:id activates Python decorator associations, shell patterns, email patterns, and more. The semantic noise may outweigh the token savings.

Perl's obsessive fetish for compact syntax, sigils, punctuation, performative TMTOWTDI one-liners, to the point of looking like line noise, is why it's so terribly designed and no longer relevant or interesting for LLM comprehension and generation.

I think the ideal syntax for LLM language understanding and generation are markdown and yaml, with some python, javascript, and preferably typescript thrown in.

As much as I have always preferred json to yaml, it is inarguably better for LLMs. It beats json for llms because it avoids entropy collapse, has less syntax, leaves more tokens and energy for solving problems instead of parsing and generating syntax! Plus, it has comments, which are a game changer for comprehension, in both directions.

https://x.com/__sunil_kumar_/status/1916926342882594948

>sunil kumar: Changing my model's tool calling interface from JSON to YAML had surprising side effects.

>Entropy collapse is one of the biggest issues with GRPO. I've learned that small changes to one's environment can have massive impacts on performance. Surprisingly, changing from JSON to YAML massively improved generation entropy stability, yielding much stronger performance.

>Forcing a small model to generate properly structured JSON massively constrains the model's ability to search and reason.

YAML Jazz:

https://github.com/SimHacker/moollm/blob/main/skills/yaml-ja...

YAML Jazz: Why Comments Beat Compression

The GlyphLang approach treats token count as THE bottleneck. Wrong. Comprehension fidelity is the bottleneck.

The LLM already knows YAML from training. Zero tokens to teach it. Your novel syntax costs millions of tokens per context window in docs, examples, and corrections.

Why YAML beats JSON for LLMs:

Sunil Kumar (Groundlight AI) switched from JSON to YAML for tool calling and found it "massively improved generation entropy stability."

  "Forcing a small model to generate properly structured JSON 
   massively constrains the model's ability to search and reason."

JSON pain:

  Strict bracket matching {}[]
  Mandatory commas everywhere  
  Quote escaping \"
  NO COMMENTS ALLOWED
  Rigid syntax = entropy collapse

YAML wins:

  Indentation IS structure
  Minimal delimiters
  Comments preserved
  Flexible = entropy preserved

The killer feature: comments are data.

  timeout: 30  # generous because API is flaky on Mondays
  retries: 3   # based on observed failure patterns

The LLM reads those comments. Acts on them. JSON strips this context entirely.

On symbol collision: noosphr nails it. Short symbols like @ activate Python decorators, shell patterns, email patterns simultaneously. The semantic noise may exceed the token savings.

Perl's syntax fetish is why it's irrelevant for LLM generation. Dense punctuation is anti-optimized for how transformers tokenize and reason.

The ideal LLM syntax: markdown, yaml, typescript. Languages it already knows cold.