> Who cares about DRY? You don't have to be the one updating the same long conditional in four different files - the AI will just do it for you! Right?

One pattern I've really been enjoying lately has to do with a language called haxe. It's designed to be compiled into other languages (java, python, others). There's this extension called reflaxe which lets you make mini compilers for compiling haxe into pretty much anything.

So if I have anything with duplicate structure... like maybe I want the CLI subcommands to resemble the http API.

    $ foo bar --baz 6 # produces similar output as HTTP GET /foo/bar?baz=6

...and `docs/generated/foo.md#bar` should have documents both the CLI and the http usage. Then I have the LLM maintain a haxe source of truth and then have it compile that source of truth to the other stuff.

Previously I was using OpenAPI spec's and generators for this, but they wound up feeling like a black box that I ended up debugging all the time. The reflaxe setup is much more generic. So you end up with this mountain of generated code, but unlike LLM-generated code it's obviously generated (in .../generated/thingy.py or whatever) and a comment at the top of the file says where to look to learn how it's generated). I'm generating docs this way, accessor functions for database tables and SQL for making those tables, matching clients and servers in different languages... it replaces a lot of purpose built tools with just one, and LLMs are pretty good at it.

So even though the repo is large, the parts of it that are authoritative remain small. I find LLM's manage this boundary much better because it's so crisp an in their face. But they have to be told to do this, otherwise they'll just create context-size problems that they'll later struggle with.

Of course you could do this with yaml files or some such, but unlike yaml, haxe has a type system, you can write tests in it, so the agent can notice fundamental flaws before the generation happens... with yaml those flaws would be propagated into the generated code before they'd get noticed.