[Mehdi2277]

I normally care about reproducible rng results and do try to seed all rngs used. There are lots of applications where randomness is used but you still want repeatable behavior. ML experiments is one situation where randomness is common, but is also intended to be repeatable.

I think python standard library/several of data science library rngs are platform independent.

[tptacek]

This is not that kind of RNG; this is the system secure random number generator. Secure random numbers aren't seeded.

[SAI_Peregrinus]

I wouldn't say the lack of a seed is the difference between a CSPRNG and a PRNG. That's more the difference between a CSPRNG and a stream cipher, where the "seed" is called a "key" and "IV".

PRNGs (Pseudo Random Number Generators) are predictable. CSPRNGs (Cryptographically Secure Pseudo Random Number Generators) aren't (if they're working). HWRNGs (Hardware Random Number Generators) that aren't debiased via a CSPRNG or similar produce nonuniform output (not suitable for cryptography or most other uses directly). TRNGs (True Random Number Generators) might not exist in this universe (deterministic interpretations of quantum mechanics are consistent with observation), it's safer to assume they don't and avoid the term entirely.

[fault1]

I think you are talking about a psuedo-rng.

[akerl_]

Basically every RNG you interact with on general purpose computers is a pseudo random number generator. The kernel RNG being discussed here is a CSPRNG, as was the one it replaced.