I'm not even using the features beyond the recipes yet, but i'm already very happy that i can migrate my recipes from google docs to over there

I do use VSCode too, but mostly for quick scripting or non-programming projects. and even then i installed a bunch of extensions to make it more like Pycharm.

I wish you could be a bit more specific though, you can't set which commands you want to auto-accept in detail.

As for GitMCP: I think the url fetching tool of the docs it does is not great, but the code searching tool is quite good. Regardless, i remain open to alternatives, not stuck to this yet.

I used it recently to do a major refactor and upgrade to MLFlow version 3.0. Their documentation is a horrid mess right now, but the MCP server made it a breeze because i could just query the assistant to browse their codebase. Would have taken me hours extra myself.

Long answer: Is the metric for people with subject-matter knowledge? Then (Weighted)RMSSE, or the MASE alternative for a median forecast. WRMSSE is is very nice, it can deal with zeroes, is scale-invariant and symmetrical in penalizing under/over-forecasting.

The above metrics are completely uninterpretable to people outside of the forecasting sphere though. For those cases i tend to just stick with raw errors; if a percentage metric is really necessary then a Weighted MAPE/RMSE, the weighing is still graspable for most, and it doesn't explode with zeroes.

I've also been exploring FVA (Forecast Value Added), compared against a second decent forecast. FVA is very intuitive, if your base-measures are reliable at least. Aside from that i always look at forecast plots. It's tedious but they often tell you a lot that gets lost in the numbers.

RMSLE i havent used much. From what i read it looks interesting, though more for very specific scenarios (many outliers, high variance, nonlinear data?)

- Forward filling missing short periods of missing values. Why keep this in when you explictly mention this is not normal? Either remove it all or don't impute anything

- Claiming superiority over classic models and then not mentioning any in the results table

- Or let's not forget, the cardinal sin of using MAPE as an evaluation metric

One personal favorite of mine is __all__ for use in __init__.py files. It specifies which items are imported whenever uses from x import *. Especially useful when you have other people working on your codebase with the tendency to always import everything, which is rarely a good idea.

Can see some finetuning after generation being required, but assuming you know your own codebase that's not an issue anyway.

Ruff for me meant i could turn 4 pre-commit hooks (which you have to configure to be compatible with each other too) into just 1, and i no longer dread the "run Pylint and take a coffee break" moment.

I jumped ship to UV recently. Though i was skeptical at first i don't regret it. It makes dependency management less of a chore, and just something i can quickly do now. Switching from Poetry was easy for me too, only package i had issues with was pytorch, but that just required some different toml syntax.

Also, when saying "Nixtla" do they mean TimeGPT or one of their other libraries? Because the former definitely supports exogenous regressors, while they say it doesn't.

I personally prefer Darts because it's very user-friendly, and the devs are responsive,