commit 5e004e4de5c1da6888c302607e6556b05b354320
Author: James Halliday
Date: Sat May 17 23:15:29 2014 +0200

One of the most common objections I've heard about embracing modularity and favoring libraries that do a single thing well is that it can be difficult and time-consuming to find packages for each piece of functionality you might need for a given task.

This is certainly true at first, but over time and with practice, it is less and less of a problem as you train up your own heuristics and develop a broad working memory of useful packages and authors who tend to produce useful code that suits your own aesthetic preference.

With a bit of training and practice, you will be skimming npm search results at great speed in no time!

my heuristic

Here's my own internal heuristic for evaluating npm packages:

  • I can install it with npm

  • code snippet on the readme using require() - from a quick glance I should see how to integrate the library into what I'm presently working on

  • has a very clear, narrow idea about scope and purpose

  • knows when to delegate to other libraries - doesn't try to do too many things itself

  • written or maintained by authors whose opinions about software scope, modularity, and interfaces I generally agree with (often a faster shortcut than reading the code/docs very closely)

  • inspecting which modules depend on the library I'm evaluating - this is baked into the package page for modules published to npm

When a project tries to do too many things, parts of it will invariably become neglected as the maintenance burden is unsustainable. The more things a project tries to do, the easier it is to be completely wrong about some assumption and this can also lead to abandonment because it's very difficult to revisit assumptions later.

The best, longest-lasting libraries are small pieces of code that are very tricky to write, but can be easily verified. Highly mathematical tend to be very well represented in this category, like the gamma function or an ecosystem of highly decoupled matrix manipulation modules such as ndarray.

When a library is embedded in an ecosystem of other libraries in a thoroughly decoupled way, a mutual dynamic results where the main library doesn't need to inflate its scope but gets enough attention to find subtle bugs while the dependent libraries can offer excellent interoperability and fit into a larger informal organizational structure.

not too important

Here are some things that aren't very important:

  • number of stars/forks - often this is a reverse signal because projects with overly-broad scope tend to get much more attention, but also tend to flame out and become abandoned later because they take too much effort to maintain over a long period of time. However! Some libraries are genuinely mistakes but it took writing the library to figure that out.

  • activity - at a certain point, some libraries are finished and will work as long as the ecosystem around them continues to function. Other libraries do require constant upkeep because they attack a moving problem but it's important to recognize which category of module you're dealing with when judging staleness.

  • a slick web page - this is very often (but not always) a sign of a library that put all of its time into slick marketing but has overly-broad scope. It is sometimes the case that solid modules also have good web pages but don't be tricked by a fancy web page where a solid readme on github would do just as good for a job.

The main crux of this blog post first appeared as a reddit comment.

git clone