First, briefly, re unix philosophy: Beets creates opportunities for other features. Whether those features are implemented in beets, a plugin, or in another program working in concert, I have no opinion. Calliope exists for now, as well as stuff like Confuse that got spun out of beets.
I honestly thought I wrote the OP and it was bumped.
I’ve written similar things, and have similar pain points and suggestions to you. (In the link, other people including adrian have their own ideas on a roadmap, circa May 2020 i.e. pre-1.5.0.)
I really go into detail at the link.
I have no idea what drives people to contribute to open source projects. My model is: some number of users are drawn to the project, and some percentage of them have the desire / expertise to contribute. There are probably additional variables like how constructive the community is when a new user makes a suggestion. But, this is getting complicated.
If I’m right, then evangelizing beets, and tweaking features to make them more useful to novices, would lead to more developers on the project. I understand beets more now, but when I started, I was just making documentation suggestions or finding obscure parts of the source code that didn’t make sense. Those suggestions didn’t take much knowledge of beets.
To put a finer point on it, I don’t care if beets acquires users. But I care a lot about how it acquires contributors. Code, docs, feedback, it’s all important, and beets doesn’t have enough right now. Acquiring users is probably the only way to acquire contributors for an open source project without some company paying contributors. (Ex. Google and Chromium, which is the base for Chrome)
It seems to me that this is low-hanging fruit for beets since it already has most of the information.
Indeed, most other tools for the job are going to stumble on the complexities of a music library. Only a few tools like beets, Musicbrainz / Metabrainz, Picard are going to handle all the edge cases well.
The number of new tools that have come up in this area is not surprising.
All of your links have paid plans, so making a 100% free version would be good.
Generate smart(er) playlists:
Your suggestion is good but not ambitious enough! There’s a whole world out there for algorithmic playlists and I’m not aware of open source tools doing anything interesting with all this data! We have to walk before we run, and that’s where Calliope comes in. Extensions to that can perhaps get into the machine learning side of things.
Here is a more ambitious example, pretty easy in the field of statistics. But it’s purely conceptual for now. We may not have enough data and data processing to make it interesting. Ex. it could say that The Beach Boys and The Beatles are totally different by its algorithm, when they’re really quite similar.
The Metabrainz / ListenBrainz people are really interested in discovery and user-acquisition type things, so there is room to collaborate with them too. But I do see beets as a piece of it.