Yes, Haskell's choices are often overwhelming. Think of Ruby On Rails, where the community has a well-worn playbook and everyone knows the game. Then compare Haskell, which hits designers with an overwhelming menu of choices, and the community still hasn't picked the winners.
Glancing at r/haskell, people often ask for help in choosing web frameworks, SQL libraries, effect systems and monad transformers, regex libraries, text datatypes, lens packages and so on. Simple Haskell and Boring Haskell tried eliminating those problems but the community ignored their pleas, occasionally dismissing the idea with frank negativity.
> what abandonware and a tiny community have to do with cognitive load -- I agree they're bad, I just don't see the connection to cognitive load.)
Our due diligence on 3rd party libraries investigates how active a library is, which includes github submission frequency, online discussions, blog posts, security fix responsiveness, courseware, etc. Activity runs from extremely high (like pytorch) to graveyard code written long ago by graduate students and researchers. Between those endpoints, the big middle is often murky and requires lots of contingency analysis, given that we're delivering real systems to clients and they must stay absolutely safe and happy. All that analysis is brain-deadening, non-productive cognitive load.
Obviously none of this is unique to Haskell, but it's fair to say that other platforms provide more standardized design conventions, and for my needs, a quicker path to success.
Glancing at r/haskell, people often ask for help in choosing web frameworks, SQL libraries, effect systems and monad transformers, regex libraries, text datatypes, lens packages and so on. Simple Haskell and Boring Haskell tried eliminating those problems but the community ignored their pleas, occasionally dismissing the idea with frank negativity.
> what abandonware and a tiny community have to do with cognitive load -- I agree they're bad, I just don't see the connection to cognitive load.)
Our due diligence on 3rd party libraries investigates how active a library is, which includes github submission frequency, online discussions, blog posts, security fix responsiveness, courseware, etc. Activity runs from extremely high (like pytorch) to graveyard code written long ago by graduate students and researchers. Between those endpoints, the big middle is often murky and requires lots of contingency analysis, given that we're delivering real systems to clients and they must stay absolutely safe and happy. All that analysis is brain-deadening, non-productive cognitive load.
Obviously none of this is unique to Haskell, but it's fair to say that other platforms provide more standardized design conventions, and for my needs, a quicker path to success.