
Is Julia Just a Bubble? - 01jedi
Either Julia is not mentioned or it&#x27;s dubbed as the language of the future. What&#x27;s your opinion?
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ChrisRackauckas
The Python, R, and MATLAB package ecosystem is weak in a lot of areas which
Julia has strengths (dynamical systems, structured numerical linear algebra,
random matrix theory, etc.), which is why it has taken hold in a lot of
disciplines that need these features. Because of that, I don't see it going
away any time soon because there are not equivalents in these other languages
for people to migrate to.

What you see from that though is that there is a growing set of scientific
fields (systems biology, some areas of particle physics, numerical analysis
research, etc.) that are really embracing Julia in a big way. Those fields are
not as big as machine learning, so some metrics may see that as a loss, but in
reality, having a strong core of dedicated users who happen to develop the
core packages for their smaller fields is not a bad place to be. At the end of
the day, scientists will use whatever language has the packages developed for
them. That's why MATLAB and Mathematica are still mainstays: high level open
source software ecosystems never really reviled the support actual
mathematical languages have in areas like differential equations (here's a
benchmark that shows MATLAB is still orders of magnitude ahead of things like
SciPy:
[https://benchmarks.juliadiffeq.org/html/MultiLanguage/wrappe...](https://benchmarks.juliadiffeq.org/html/MultiLanguage/wrapper_packages.html)
). Julia seems to be the first language with package developers taking the
"(non machine learning + non data scientist) technical computing scientist who
stayed on MATLAB+Fortran because of scientific tooling" seriously, and the
ability to be fast enough to write those codes in Julia is the key that binds
that all together.

There are still enough web developers in the world that even if a language
gets every scientist to use it, then it will still not make more than a blip
in the ratings. However, scientists and mathematicians are loyal to their
tools, loyal enough to still be majority MATLAB and Fortran, so any inroads
means staying power for many many years.

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01jedi
The last paragraph first line made me realize why Julia isn't on top of the
list.

Any thoughts on how to make a career out of Julia development ( for someone
who isn't a scientist but only a developer)?

~~~
cbkeller
If I were to ever hire a developer, it'd probably be for work in Julia, but I
don't have nearly enough grants for that yet :)

Coming from academia, I have no entrepreneurial sense -- but if you know
enough math to make use of optimization packages like JuMP, where Julia
excels, those skills would seem to me very transferable to commercial
operations research.

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tony-allan
Julia has carved out a place for itself. It is a new language that has come a
long way in the last seven or so years.

It seems to be a natural successor to Fortran for scientific computing and
with a growing list of libraries, a good fit for data science and other
related disciplines.

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bryanrasmussen
I don't know if I would consider languages as having bubbles.

I am interested in learning Julia, but most languages that have succeeded have
been C like in syntax to allow the greatest migration of programmers I believe
(this does not mean that there are not successful non-C languages just that
there is a large system of C languages that seem to get picked up over the
years)

I think this is because some people think jumping between languages with
similar syntax is easier than completely different syntax, so that is a built
in base willing to jump if you provide them what they need, and a built in
base resistant to jumping if you don't provide.

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elisharobinson
people like stability one of the main selling points is that python2 is
relevant for the last 20 years. I expect more steam to pickup after julia 1.0
as go did.

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tavert
Search HN who's hiring threads for companies looking for it. In December's
thread I see one, and it's telling that their listing says "teach us all how
to deliver more robust software."

