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.
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.
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... ). 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.
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.
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."
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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[ 3.1 ms ] story [ 25.1 ms ] threadI 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.
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.
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... ). 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.
Any thoughts on how to make a career out of Julia development ( for someone who isn't a scientist but only a developer)?
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.