do you expect the model to train on synthetic data or do you expect to grow a userbase that will generate organic training data?

> One of the biggest issues with symbols is that, to a human, matching parentheses is relatively easy, but the models struggle with it.

Great point. I find it near trivial to close parens but llms seem to struggle with the lisps ive played with because of this counting issue. To the point where ive not been working with them as much. typescript and functional js as other commentors note is usually smooth sailing.

> do you expect the model to train on synthetic data or do you expect to grow a userbase that will generate organic training data?

Both, essentially, I expect the code examples to grow organically but I expect most of them to come from LLMs, after all, that's the point of the language. I basically expect there to be a step function in effectiveness when the language has been ingested by the models, but they're already plenty decent-ish right now at it.

The most fascinating thing to me, generating the whole thing, has been that the LLMs are really, really good at iterating in a tight loop by updating the interpreter with new syntax, updating the stdlib with that new syntax, building some small extension to try using it, and then surfacing the need for a new builtin or primitive to start the cycle over.

I'm also leaning heavily on Chatgpt-5.2's insanely good math skills, and the language I'm building is very math heavy - it's essentially a distant cousin to Idris or any of the other dependently-typed theorem proving languages.