You're right, there is lots of confusion in the topic and not all texts on regression get it right. Theoretically, linear regression depends only on a normally distributed outcome variable, the predictors don't have to be normally distributed. Practically speaking even that requirement can mostly be ignored: OLS is fairly insensitive to non-normal outcomes. Violations of the assumptions of regression usually don't affect the estimates much, they mainly affect the uncertainty around those parameter estimates -- the standard errors. This too is easily solved: use bootstrapping to calculate the standard errors.

In short: you can't just throw linear regression at anything and expect to get reasonable results, but it's pretty damn close.

Even that normality is not required; see, for instance, https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem.

Good catch. Outcome (or error, which boils down to the same thing) can't just be anything either, though, e.g. a common pattern is that the variance in an outcome increases when the predictor increases (e.g. a machine behaves more erratically when it spins faster) and that can mess with estimates.

Also some features of regression and hypothesis testing are actually fairly robust to non-normality, particularly with large sample sizes.

Look at normal qq-plot to measure normality