The way I keep an eye on model performance is alternating between models for code review. It shows how much the model understands the codebase without disrupting my workflow.

Hopefully the desktop story is going improve as Canonical is now leading the Flutter desktop side.

It’s crazy not to have privacy reading a magazine.

## 1A Rule name

Some prose explaining the rules liking to official documentation.

``` if municipality and inhabitants > 10000 then functionA else functionB ```

Then a trivial parser would extract the rules, the DSL was then handled by Lark[1]. So pretty simple, but it made collaborating with experts easier as simulated results would also output some markdown they could read.

1. https://lark-parser.readthedocs.io/en/stable/

Once I built a little domain-specific language for them, that was tested against old jobs to see if they contradicted the past; it was a nifty project and since then I am convinced that DSLs are underrated as a way to encode expertise.

https://uncloud.run/docs/guides/deployments/rolling-deployme...

I believe the largest Linux Desktop initiative in France is GendBuntu[1] for the National Gendarmerie

[1]https://en.wikipedia.org/wiki/GendBuntu

- initially it wasn't working, plenty of parent/child relationships problems like described in the post

- so I designed a thin a wrapper and used sealed classes for events instead of dynamic spans + some light documentation

It took me like a day to implement tracing on the existing codebase, and for new features it works out of the box using the documentation.

At the end of the day, leveraging typing + documentation dramatically constrains LLMs to do a better job