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Comment by los_pantalones | Hacker News Reader

Yea this is pretty much the norm for Julia deployments. You have to be really wise about how you architect the use of Julia for production. In a lot of common use cases, in my opinion, it's business infeasible. Unfortunately it can even be "passion project" infeasible. For serious compute projects or research - its nothing short of amazing.

I think it stands a chance but this side of things hasn't been nourished. I hope developers who have these kinds of experiences and share them are heard.

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I learnt the hard way that you can architect Julia apps the wrong way. I think my biggest frustration is how easy it is to make mistakes that cause type unstable code.

From real experience and will probably get in trouble for saying this: I worked with Julia developers who are scientists. They write software like they write scripts. So everything we had was absolutely not production worthy but running in production. The whole company was built on stilts like this. I still wonder how it actually worked and never came crashing down.

Also, personal gripe and absolutely not the fault of Julia creators: I hate 1-based indexing.

There is a wealth of startups out there takings small hordes of junior devs out of non-CS grads. Learning on the go, they become proper devs or get into business a few year later. Projects get out there by pure force of will.

They are like the E. Coli of devs, cheap to make experiment with, and very adaptable to any environment.

This is true. Biotech/Physics/Math grads getting into the ad-tech/SaaS grind kind of makes me sad though. Under-utlization of talent. No doubt there are interesting challenges in literally every field, just that scientists are good at doing science - Design of Experiments, Theorizing stuff, grokking mathematics that would make me faint, etc. and the same people fucking around with webpack/JS tooling, learning how IAM works or spending their time on fixing CI/CD pipelines is depressing. We need some sort of a division of labor here.

My situation is slightly different from what you describe, but yeah, we have scientists and data scientists involved in writing Python packages, setting up CI/CD pipelines, dealing with auth and frontends for APIs. There's talk of dividing labor up a bit better so that we can focus on science and data work, but there's hardly enough engineering staff as it is and they're largely not much better from a software engineering perspective (and half of them hardly know any Python, which our non-engineers have 100% standardized on). It's kind of a mess right now.

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