onelinecode VS stdlib

You can find the all the utility functions from this series at github.com/martinkr/onelinecode The library is also published to npm as @onelinecode for your convenience.

From integrating BLAS bindings and optimizing special mathematical functions to enhancing support for boolean arrays and improving our interactive REPL experience, their work strengthened the foundation of stdlib and paved the way for even greater advancements. Beyond just code, their contributions sparked deeper engagement within our community, leading to over 2,000 pull requests from more than 100 contributors and 3,000+ new commits to stdlib since February 2024.

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.

A brief overview about stdlib. stdlib is a standard library for numerical and scientific computation for use in web browsers and in server-side runtimes supporting JavaScript. The library provides high-performance and rigorously tested APIs for data manipulation and transformation, mathematics, statistics, linear algebra, pseudorandom number generation, array programming, and a whole lot more.

For more details on how we author Node-API native add-ons and leverage macros and various utilities for simplifying the add-on creation process, the best place to start is by browsing stdlib source code. For the examples explored in this post, we've brushed aside some of the complexity in ensuring cross-platform configuration portability (looking at you Windows!) and in specifying compiler options for optimizing compiled code. For those interested in learning more, you'll find many more examples throughout the codebase, and, if you have questions, don't be afraid to stop by and say hi! 👋

Numpy is a library - node.js has plenty of them, what is missing? There is stdlib package that offers optimized math functions, for example.

Websites do the same thing as 15 years ago? Not true, websites can now exceed desktop apps. Was their figma 15 years ago?

20 times more complex - this comes with doing more. 15 years ago there was no CI/CD.

Write CSS - don’t have to given Sass, components, and bootstrap.

Performance is worse - look at v8 benchmarks, look at webgl and Wasm. The browser itself can run much faster today, and people are doing more with it.

JS barebones - just one package. https://github.com/stdlib-js/stdlib. - and the node ecosystem is a feature not a bug.

Look elsewhere - every other UI framework we’ve tried before has been worse in terms of compatibility, functionality, flexibility, and available prebuilt tooling.

two of the best PRNG libs for JS/TS: https://github.com/transitive-bullshit/random and https://github.com/stdlib-js/stdlib

Scientific calculations - like what? There is a stlib package for JS where CPU hungry parts are written in C.

lodash and lodash-es for browser are good, check out also stdlib