For those that don't know its also built upon OTP, the erlang vm that makes concurrency and queues a trivial problem in my opinion.
Absolutely wonderful ecosystem.
I've been wanting to make Gleam my primary language, but I fear LLMs have frozen programming language advancement and adoption for anything past 2021.
But I am hopeful that Gleam has slid just under the closing door and LLMs will get up to speed on it fast.
Why would that be the case? Many models have knowledge cutoffs in this calendar year. Furthermore I’ve found that LLMs are generally pretty good at picking up new (or just obscure) languages as long as you have a few examples. As wide and varied as programming languages are, syntactically and ideologically they can only be so different.
Because LLMs make it that much faster to develop software, any potential advantage you may get from adopting a very niche language is overshadowed by the fact that you can't use it with an LLM. This makes it that much harder for your new language to gain traction. If your new language doesn't gain enough traction, it'll never end up in LLM datasets, so programmers are never going to pick it up.
If this does appear to become a problem, is it not hard to apply the same RLHF infrastructure that's used to get LLMs effective at writing syntactically-correct code that accomplishes sets of goals in existing programming languages to new ones.
That would make sense if LLMs understood the domains and the concepts. They don't. They need a lot of training data to "map" the "knowledge transfer".
Personal anecdote: Claude stopped writing Java-like Elixir only some time around summer this year (Elixir is 13 years old), and is still incapable of writing "modern HEEX" which changed some of the templaring syntax in Phoenix almost two years ago.