garrepi

I'd say it's more than tangentially related. The blog post is about Richard Stallman, his life, and his contributions; it seems strange to not acknowledge the recent controversy surrounding him.

Notable mention: the Apple TV app for macOS. Truly one of the worst experiences I've ever had on the desktop.

I didn't realize how strongly line spacing effected code readability for me -- really glad I saw this.

+1. Really love the organization of this

common BSD user vs. common Linux user

I've had success with Ubuntu, Arch, and Neverware on my old plastic Macbooks. All of them were pretty easy to install and I have no major when complaints using them

That's totally fair. I wish Spotify gave us more granularity in the data we ask for, I don't think we need the blanket permissions a user has to grant us (as someone who just deleted their Facebook and soon Google accounts, this is really important to me).

We've reached out to a lot of local artists here in Atlanta and hope to use the platform to recommend and share their content to those interested.

Hand curating playlists is also a big feature we've been toying with. That's where something like Apple Music's hand crafted playlists are usually a lot stronger than Spotify's computed playlists.

I haven't heard of Calmradio, it's exactly what I've been looking for! I've resorted to listening to music through a cheap FM radio to try and break out of the algorithm hell Spotify tends to put me in.

Thanks for sharing!

That's definitely something we're focusing on more now. Like qnsi mentioned, last.fm does a pretty good job of that -- I've been a fan since high school.

We, SameTunes, have an advantage when it comes to calculating a user's affinity for songs, artists and albums. Last.fm ranks your music by tracking how many times you've played (or "scrobbled") a song, songs by an artist, etc. SameTunes uses additional data Spotify publishes (like position in a created playlist, occurrences in your liked music, etc) to build a more well rounded representation of your music taste in the past and present.

There are definitely features we can, and plan to, add to help better portray this data to a user.

Brilliant.

Through the Spotify API we are given locational and positional data on some music objects. Your proposal to structure songs as vectors opens up a lot more flexibility for storing, weighting, and manipulating that data.

The way in which the overlaps are calculated is crucial to our platform, something like feeding the dot product into a weighting matrix would leverage this structure really well.

Your proposal is very similar to how comparisons are currently computed on SameTunes. But, leveraging existing mathematical theorems and restructuring the algorithm with linear algebra in mind would help a ton with some of the noise and normalisation issues we've experienced.

I really appreciate you taking the time to draft that out!