When should we discretize/bin independent variables/features and when should not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?