Research software engineering requiring high performance with limited resources, matrix operations and serious number crunching like simulations or machine learning
Julia delivers high performance and a unified workflow for scientific computing, but requires initial patience to learn.
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now I m full on on rust, julia and python.
Hacker News • HackerNews • Use case: General programming (current primary language)
If you need to deal with matrices Julia's built-in support for that kind of stuff is the best out of any language I've ever seen. It's like having first-class numpy arrays without installing any third party packages, and the syntax is even more convenient than Python.
Hacker News • HackerNews • Use case: matrix operations, interactive data inspection, graphing, debugging, and serious number crunching like simulations or machine learning
Julia with some initial patience does deliver a better developer experience in this scenario than the equivalent Python experience for me partly because I do not need to play vectorizarion games, and I can do everything in one integrated environment.
Hacker News • HackerNews • Use case: Research software engineering requiring high performance with limited resources.
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