For others: The original paper is short and very readable - https://projecteuclid.org/journalArticle/Download?urlId=10.1...
The intuition seems to be that computability of a function is actually approximability, and derivatives can be large even when the function is very small, by having the large slope confined to a very small area. So by constructing a function f that contains an uncomputable (but recursively enumerable!) set encoded in the derivative by strategically placing little bumps, but having the bumps grow exponentially smaller, Myhill was able to construct a function that was easily approximable but whose derivative wasn't.
The key detail here is that the bumps grow exponentially smaller in the order the uncomputable set appears in its enumeration. This allows Myhill to construct a sequence of computable functions that (uniformly) approximate f to within 2^(-n). If the bumps weren't ordered in this way, we wouldn't know how far down the sequence to go to approximate f to within 2^(-n). The ordering lets us just look at the first n elements of the uncomputable set.
Why doesn't this allow us to approximate the derivative? Because at any x, we don't know how well we have to approximate f in order to approximate f'(x), so we need knowledge of the entire uncomputable set, which we cannot have.