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This means that when using polynomial features, the data must be normalized to lie in an interval. It can be done using min-max scaling, computing empirical quantiles, or passing the feature through a sigmoid. But we should avoid the use of polynomials on raw un-normalized features.
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This paragraph has nothing to do with numerics. It is about the fact that continuous functions can not be approximated globally by polynomials. So you need to restrict to intervals for reasons of mathematical theory. This is totally unrelated to the numerical issues, which are nowhere even acknowledged.
The author does not address the single most important reason why high degrees of polynomials are dangerous. Which is pretty insane to be honest, obviously to be honest you have to at least mention why people tell you to be cautious about high degree polynomials AND point out why your examples circumvent the problem. Anything else is extremely dishonest and misleading.
That is pretty obvious. Consider that every polynomial grows arbitrarily large, but not every continuous function does.