sunburn.nvim VS youtube-cue

A while back I read about the Oklab color space, and long story short I decided I wanted to create my own Neovim coloscheme. That led to sunburn.nvim[1], which aims to take advantage of the hue and brightness uniformity that Oklab provides.

At first I was using lush.nvim to build sunburn.nvim, but quickly it became a hassle to only be able to specify colors via RGB or HSL. My initial thought was a PR to add Oklab support to lush, but that framework does so much that it was hard to see where to start. So I ended up writing polychrome.nvim[2], which is a dead simple micro framework in comparison to lush.nvim, but does enough to take care of all the boilerplate, and supports a bunch of color spaces (which are converted to RGB on the fly).

I also wanted push notifications for when certain RSS feeds I follow were updated, because I suck at remembering to check in on things or check an RSS feed app. But I didn't want to pay for IFTTT or other bespoke solutions, so I wrote notifeed[3]. It's designed to run as a service on a server, and then check all your feeds at predetermined intervals and send the necessary webhooks based on your configuration. Feeds and clients are configured via the CLI and stored in a SQLite DB for simplicity.

[1] https://github.com/loganswartz/sunburn.nvim

> CLI: I wanted to download songs from youtube, but they were often stitched as complete albums - so I wrote a youtube-cue generator that generates cuesheets that can then be used to split and tag the yt-dlp downloaded audio file. (https://github.com/captn3m0/youtube-cue)

Thanks for this! I need to do some testing, this might automate the last manual step of my own script for converting YT mixes into distinct tracks. The problem I faced is that often the timestamps are not in the description, but instead in a comment, sometimes not even the pinned/top voted comment. That is why I paste it in via stdin for now.

As this fits the thread topic, a short description of this script. I enjoy YT mixes and wanted to listen to them in my car. I can use an USB stick with media files and playlists which are displayed decently by the infotainment system. I wrote a script that takes in a YT URL (or anything supported by yt-dlp), downloads & converts it to mp3, splits the mp3 file based on a list of timestamps, recognizes (tries to anyway) the songs via SongRec [0], tags & names the files correctly and finally generates an M3U playlist in the format recognized by my car. I use song recognition instead of parsing out the names from the timestamped list as the format of Artist - Title is nearly always slightly different. It was easier to use SongRec instead and get everything I need for tagging with >90% hit rate.

The heavy lifting is done by calling out to yt-dlp, ffmpeg and SongRec. I just glued them together with Python. I like your approach of a do one thing well and might add youtube-cue to the toolset.

[0] https://github.com/marin-m/SongRec

Beets is amazing and comes with great defaults. I wrote code recently to generate CUE sheets from YouTube mixes[0] and beet imports it nicely and easily.

[0]: https://github.com/captn3m0/youtube-cue There is a bash snippet in readme to show the Beets integration.