Judging from my experimentation with local models:

You can use a small coding model to produce working code with a deterministic workflow (ex: state machine) if you carefully prune the context and filter down what it can do per iteration. Instead of letting it "reason" through an ever growing history, you give it distinct piecemeal steps with tailored context.

I think this can be generalized to:

Anything that can be built from small, well understood pieces and can be validated and fixed step by step. Then the challenge becomes designing these workflows and automating them.

(I'm not there yet, but one thing I have in mind might be a hybrid approach where the planning is produced by a more expensive model. The output it has to produce are data driven state machines or behavior trees (so they can be validated deterministically). Then it offloads the grunt work to a small, local model. When it's done, the work gets checked etc.)

For the end user who just installs the app it's probably all the same. It's not a technical document.

For the user it's just important that the small grimlin that sits in the Ente app is not as smart as the grimlin that sits in the Claude app.

If you go to the product website: https://claude.com/product/claude-code

> Use Claude Code where you work

> Desktop Termianl IDE WEb and iOS Slack

Not that it is important any way ¯\_(ツ)_/¯