In this talk, I will first introduce the post-model-selection inference setting, that has recently been subject to intensive investigation. In the case of Gaussian linear regression, I will review the post-model-selection confidence intervals suggested by Berk et al (2013). These intervals are meant to cover model-dependent regression coefficients, that depend on the selected set of variables. I will present some personal contributions on an adaptation of these confidence intervals to the case where the targets of inference are linear predictors. Then, I will present an extension of these confidence intervals to non-Gaussian and non-linear settings. The suggested more general intervals will be supported by asymptotic results and numerical comparisons with other intervals recently suggested in the literature.