Recurse Center, 2014-06-11


Continue with the Haskell tutorials, with additional reading from Real World Haskell.


  • Finished exercises 4 and 5.
    • Learnt about Type classes in Haskell and how to create them and make data types instances of them.
    • Also came across a new algorithm of sieving called the Sieve of Sundaram, which was discovered by a student. Surprisingly, I never heard of it before!


  • A few of us had an interesting discussion about various places people grew up in and visited, and the cultures, etc.
  • Idea: implement a GitHub dashboard for organizations (say, HS batches) that give you different statistics, and interesting graphs. (Use elm?) [Update: Tom is working on HS and GitHub integration. So, that's perfect!]

GitHub Cue: Recommendations for GitHub Repos

If you aren't already aware of it, I'm one of those people who goes around saying, "GitHub is my Facebook". I spend quite a lot of time on GitHub, browsing the work of various people, looking at loads of interesting stuff that people built. I keep jumping between people pages and projects using the Watchers/Watching & Followers/Following pages. This way, I do come across interesting people and projects, but the SNR is too low. I wanted a better way to be able to see stuff, that I find interesting. That's how the idea for this Chromium app โ€“ GitHub Cue โ€“ was born.

@baali and I hacked on this, during the last few days and got it working. It works as follows, (from the README) โ€”

  1. Scrapes all the descriptions of the repositories being watched by the user.
  2. Key terms are extracted from this description text using the Yahoo Term Extractor.
  3. A list of languages is obtained, based on the languages of the repositories, the user if watching.
  4. GitHub searches are performed for a combination of 3 randomly chosen languages and 5 random key terms.
  5. 10 random repositories out of all these, are shown.

This is a very simplistic algorithm, but works decently for my purposes. Ideally, I would've liked to use a Collaborative Filtering algorithm, but I found the data to be too sparse, and the amount of computation to be too much to be done on the fly. I wasn't really interested in pre-computing stuff and putting it onto my server. I settled down to the next best thing I could think of.

I would appreciate any further ideas and suggestions. Thanks!