JRuby VS NumPy

Another way to look at it based on coming across it in enterprise:

How did he build something adopted by so many enterprises?

It does some things at scale very well and has been afforded the performance improvements of very smart people for 30y.

It’s not to say the language isn’t verbose, one of my favourite features was the ability to write code in other languages right inside the a Java app pretty well in-line by using the JVM, thanks to JSR-223.

It was possible to write Ruby or Python code via Jruby or Jython and run it in the JVM.

https://www.jython.org/

https://www.jruby.org/

https://docs.oracle.com/javase/8/docs/technotes/guides/scrip...

As someone who has looked at Shoes several times but never dove in, it's confusing how Shoes 4 has been the "preview version" of Shoes for, like, a decade or more. It made me actively avoid getting invested in Shoes 3 (the release promoted on the linked website) because Shoes 4 requires JRuby and I am happy with CRuby (the Ruby interpreter most people think of when they hear "Ruby").

https://github.com/shoes/shoes4/

http://www.rubydoc.info/github/shoes/shoes4

No disrespect to the developers but to me it feels like taking over a GUI toolkit created "to teach programming to everyone" (to quote the Shoes 4 readme) and making it depend upon a super-complicated enterprise-focused Ruby was sort of Missing The Point™ in a huge way.

Heck I couldn't even switch to JRuby if I wanted to because I <3 Ractors and JRuby still lacks CRuby 3.0 feature parity: https://github.com/jruby/jruby/issues/7459

__callee__ now properly returns the name under which a method was called, which will be the new name in the case of aliased methods. #2305, #7702

Issue tracker: https://github.com/jruby/jruby/issues

rdoc has been updated to 6.3.3 to fix all known CVEs. (#7396, #7404)

Altering the visibility of an included module method no longer changes what super method gets called. (#7240, #7343, #7344, #7356)

It looks like the readme is copy pasta from jruby: https://github.com/jruby/jruby

Homepage: https://www.jruby.org/

As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.

I hit a similar issue in 2017 which is still the case today: Python's builtin `random.shuffle` destroys numpy arrays passed into it [0]. This is apparently a design limitation within numpy and cannot be detected or fixed, so it still stands today.

[0] https://github.com/numpy/numpy/issues/10215

The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.

In my testing, I found that when checking out 500 commits sequentially from the numpy repository, disabling this feature required 13.8 seconds to complete on average across 10 runs. Enabling this feature took on average 11.2 seconds across 10 runs. Not an astounding difference in testing, but if core.fsmonitor can save me 2.6 seconds per 500 commits, on a project with 37,775 commits that could add up to a time savings of 211.54 seconds, or 3 minutes and 32 seconds! More testing on my end needs to be done if this feature scales linearly, but for now I will keep it on and use version 1 of the tool.

Readers of this blog who are familiar with LAPACK are likely to not be intimately familiar with the wild world of web technologies. For those coming from the world of numerical and scientific computation and have familiarity with the scientific Python ecosystem, the easiest way to think of stdlib is as an open source scientific computing library in the mold of NumPy and SciPy. It provides multi-dimensional array data structures and associated routines for mathematics, statistics, and linear algebra, but uses JavaScript, rather than Python, as its primary scripting language. As such, stdlib is laser-focused on the web ecosystem and its application development paradigms. This focus necessitates some interesting design and project architecture decisions, which make stdlib rather unique when compared to more traditional libraries designed for numerical computation.

numpy

This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays.