[oldmanhorton]

> when the author is an AI and the consumer is a compiler, and no human needs to read the output at all.

This seems like a big bet on the assumption that fully autonomous codegen without humans in the loop is imminent if not already present - frankly, I hope you are wrong.

Even if that comes to pass in some cases, I also find it hard to believe that an LLM will ever be able to generate code in any new language at the same level with which it can generate stack overflow-shaped JavaScript and python, because it’ll never have as robust of a training set for new languages.

[measurablefunc]

I recently wrote a simple interpreter for a stack based virtual machine for a Firefox extension to do some basic runtime programming b/c extensions can't generate & evaluate JavaScript at runtime. None of the consumer AIs could generate any code for the stack VM of any moderate complexity even though the language specification could fit on a single page.

We don't have real AI & no one is anywhere near anything that can consistently generate code of moderate complexity w/o bugs or accidental issues like deleting files during basic data processing (something I ran into recently while writing a local semantic search engine for some of my PDFs using open source neural networks).

[sieve]

I am building an assembler+compiler+VM for a python-like statically typed language with monomorphized generics and Erlang-style concurrency. Claude Sonnet/Kimi/Gemini Pro (and even ChatGPT on occasion) are able to handle the task reasonably well because I give them specs for the VM that have been written and rewritten 200+ times to remove any ambiguities and make things as clear as possible.

I go subsystem by subsystem.

Writing the interpreter for a stack vm is as simple as it gets.