What if you don't know what you want it to do yet? Maybe you have a new API or library you're working with and the documentation is lacking. Maybe you're expl

What if you don't know what you want it to do yet? Maybe you have a new API or library you're working with and the documentation is lacking. Maybe you're exploring some dataset trying to figure out what kind of information can be gleaned from it. Maybe it takes 3 minutes to get it into the state where it failed, so instead of starting over every change you just want to modify the one function over and over. Doing it in a repl like jupyter has you run the actual code many times as you're writing it so ensure things don't break before you ever save the file. Of course thats not an excuse to skip tests, but if I was going to pick one, I would choose running the code as I write it, instead of testing it after the fact.