Beyond Awesome Lists

What is an Awesome list?

What Makes Awesome Lists so Successful?

  1. Easy to start (Start a repository with a single README.md file)
  2. Easy to contribute to (Edit a single line in a markdown file)
  3. Fills a need (Every developer searches for new code tools)
  4. Builds a community (They have a very large number of contributors. People come back to check the list from time to time. People send links to it to others.)

What do Awesome Lists Not do Well?

  1. How popular each code project is?
  2. Is the rate this new code project is growing in popularity unusual relative to similar code projects in the past?
  3. What code projects are used by other code projects within the domain space as a dependency and therefore you might want to learn about?
  4. What code projects share dependencies and probably do related things?
  5. What code projects share contributors?
  6. What organizations own the most code projects in that domain/problem/solution space?

Lawerence Livermore National Laboratory’s Software Catalog’s Explore Pages

Images from the Explore Section of the website.

Additional Questions that Could be Answered that Would “Nudge” Developers

The tech stack to get “Community Level View” from “Curated List”

  1. An Awesome List scrapper to pull out GitHub repository links. [No work done on this yet]
  2. A GitHub Actions script to do number 2 above and popular the input_list.json file of all the repositories to be visualized. This could be scheduled to check for updates on some interval. [No work done on this yet]
  3. Documentation for how to easily add new or update existing visualizations in the explore section pages, such that changes can be easily integrated into already deployed forks. [No work done on this yet]
  4. Remove more of the list specific content in HTML and Markdown files and have it instead be populated programmatically from the key:value pairs in the _config.yml file in order to get the initial deployment down to a couple hours from several days. [This is 50% complete.]

How You Can Contribute

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Geology, Space, Maps, Machine-Learning, Data Visualization. Traded figuring things out for building new things.

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Justin Gosses

Justin Gosses

Geology, Space, Maps, Machine-Learning, Data Visualization. Traded figuring things out for building new things.

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