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I'm not sure, but this seems a bit like how Julia specialize functions based on type of arguments? Or maybe it's the inverse - as Julia creates specialized functions for you (eg add can take numbers, but will be specialized for both int32 and int64 and execute via appropriate machine instructions).

In fact, I think Julia is a great example of taking some good parts of scheme and building a more conventional (in terms of syntax anyway) language on top.

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Yes, Julia has some of it. But you're still required to specify the template parameters of a type (unless I'm mistaken). Whereas what I'm talking about is that any value of a data type could be compile time known. For example, some or all of the dimensions of an nd-array, as well some or all values of said nd-array.

Julia has explicit parameterization, but will also interprocedurally propagate known field values at compile time if known (which happens a lot more because our compile time is later), even if they weren't explicitly parameterized. Since this is so useful (e.g. as you say for dimensions of nd arrays - particularly in machine learning), there's been some talk of adding explicit mechanisms to control the implicit specialization also.

Ah, that's good to know. This sounds exactly like what I'm looking for. Thanks will read up on this in the docs!

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