[dmit]

It's always been baffling to me that certain problem domains with extremely slow feedback loops have standardized on using dynamically typed languages. Machine learning and Python. Video game scripting and Lua. A lot of scientific programming as well.

How did that happen? Did the trend start because the work was done by domain specialists who were not necessarily expert programmers, and C was too unergonomic for non-experts to use? Is that the reason a Skyrim modder can't get immediate feedback from a compiler in 2020 - because 25 years ago game devs thought that documenting a C API was harder than embedding a scripting language in the game engine? Or perhaps because evaluating scripts was more secure than loading third-party DLLs and exposing the game's innards to them?

Edit: also, if every CSV file came with a schema, that would be great. Even if it says that every column is of type Option<Any> - at least then I know what to expect.

[75dvtwin]

I think it is more that 'expert programmers' where told to leave 'domain-specific' bits for power-users to deal with.

Therefore,

- game scene development in games, are in scripts. - Equity derivatives and swap contracts are in Excel - scientific modeling is in python

I also agree with your other though that, dynamic loading enables 're-use' of the core framework, so those frameworks/engines become marketable technology assets so to speak.

The programmers, then in some instances, created DSLs to then auto-generate performant framework-specific code from the DSL-written user specs.

But those, usually, are quite limited and also some power users migrated to be descent (not the expert engine/core-level, perhaps) programmers