zlacker

>>Scramb+(OP)
Developed by Jordan Hubbard of NVIDIA (and FreeBSD).

My understanding/experience is that LLM performance in a language scales with how well the language is represented in the training data.

From that assumption, we might expect LLMs to actually do better with an existing language for which more training code is available, even if that language is more complex and seems like it should be “harder” to understand.

>>thorum+ci
A lot of this depends on your workflow. A language with great typing, type checking and good compiler errors will work better in a loop than one with a large surface overhead and syntax complexity, even if it's well represented. This is the instinct behind, e.g. https://github.com/toon-format/toon, a json alternative format. They test LLM accuracy with the format against JSON, (and are generally slightly ahead of JSON).

Additionally just the ability to put an entire language into context for an LLM - a single document explaining everything - is also likely to close the gap.

I was skimming some nano files and while I can't say I loved how it looked, it did look extremely clear. Likely a benefit.