Looked at your repo, even starred. On the surface, I like your approach a bit better. It looks like your idea sits at the space between semantic search and compressing tokens. I was into semantic search before, but mostly trying to vectorize codebase instead of tree sitter and couldn’t make the semantic search work for me. Thanks for sharing!

That's already better than RTK because you measure task accuracy AND savings! So I'm more confident in this one than in the RTK/caveman/ponytail stuff.

There are still two things that bother me:

1) I don't really know when tilth is called, how it works kind of. Does the model itself select it when it needs it? Do you need to instruct the model to use it?

2) If the model itself chooses to use it, I'd like to have a benchmark of non regressions on tasks where tilth isn't helping, to ensure you made the model + harness + tools as a whole better rather than more specialized ; or be upfront about more specialized and have more details when to use/when to not use.

Very cool. I'll probably switch away from AFT to this. Can you add tree-sitter-bash?