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The underlying question is "how to make ML mainstream" which is what the (broad) ML community has been trying to do for decades with limited success. Among other things we have tried
- standards (SML, Haskell 98) with multiple implementations
- optimizing compilers (OCaml, MLTon)
- education (first language, data structures, books)
- (killer) applications
- popular virtual machines (Java, CLR) to reuse their code base
- web (Caml as browser extension language in MMM, Caml to JavaScript compilation)
and many more
Agreed that many of these were successful research projects, not specifically meant to take over the world (of programming languages).
The result is two folds
- a technical success : check the code written by the INRIA teams, almost everyone uses Caml but those with very specific needs (Java rewriting systems, Prolog, high performance Grobner basis, etc).
- a mainstream failure : limited industrial usage besides a couple of companies
Diego Olivier