I agree. Typing in some code and trying to compile it is experimentation. Trying to fix the error in your code is exploration. Discarding the compiler's output as getting in the way means ignoring very, very precious advice.
There is a good reason why the ML community took Python as the favorite language overall.
On the other hand heavyweight type systems that demand preemptively writing code to cover cases that won't happen and/or aren't important, including cases that make sense but aren't needed yet, can waste time actual exploration and experimentation, compared to writing only useful code and being sometimes surprised by runtime errors when the program accidentally attempts something not implemented yet.
On the third hand "exploration and experimentation" can suffice for a proof of concept, not for production code: tests and automated checks are necessary to be confident about your program, and even rudimentary tools like Python type annotations and type checkers can be useful.