π in Julia is not just a Float64 constant. It's evaluated to the accuracy required of the type you're using! https://julialang.org/blog/2017/03/piday
Related Posts
Really cute approach to reporting type errors: when there's a type error, show an example of a runtime error that the type check has prevented!
Data-Driven Techniques for Type Error Diagnosis https://escholarship.org/uc/item/59s4h4pv
Playing with optional type signatures in Python, I realise that the return type is the most important to me.
I'd much rather have a function with only a return type instead of a function with only parameter types. It's often quick to add too.
Bootstrapping a language can be immensely satisfying.
I've added the ability to define stub types in the Garden stdlib and suddenly I don't need to special-case Int or String! They're just normal type declarations.