I think it can very well be applied to the Threadiverse.

• Sin #1: The First-Move Problem - Doesn’t really applies for the Threadiverse, because the instances (at least for me) do feel genuinely different, with a different culture, etc., which is one of the most exciting things for me here
• Sin #2: Navigation Inconsistency - Basically the same here.
• Sin #3: Remote Interaction Hell - Also the same here, right?
• Sin #4: Private Mentions Aren’t Really DM’s - Same here, right?
• Sin #5: The Phantom Social Graph - There definitely are synchronization issues on Lemmy, too (see the australian instance, which, I think has a latency of a week of so per post :D). Otherwise because the Threadiverse is still rather small, it seems to work mostly fine.
• Sin #6: The Discovery Problem - Much better on Lemmy. The algorithms are both transparent and make the threadiverse feel alive even though it has much less user.
• Sin #7: Basically doesn’t apply here, because you don’t follow users, but can be applied for communities. And multiple of the same communities on different instances are a big problem of the Threadiverse. Also abandoned communities. PyFed solves this with topics and Lemmy also has an upcoming feature for this in v1.0 I think.

I think the most pressing issue is sin#7 if applied to communities.

In an abstract sense, I see the Threadiverse as inversion of Mastodon: instead of posting messages to a personal account, which tags may be interesting to you to discover other similar content, in the Threadiverse, users post to hashtags and who posted them is only secondary important to you, but may be used to discover more content by the same account.

Cool. Well, the feedback until now was rather lukewarm. But that’s fine, I’m now going more in a P2P-direction. It would be cool to have a way for everybody to participate in the training of big AI models in case HuggingFace enshittifies

Yeah thats a good point. Also given that nodes could be fairly far apart from one another, this could become a serious problem.

Currently the nodes only recommend music (and are not really good at it tbh). But theoretically, it could be all kinds of machine learning problems (then again, there is the issue with scaling and quality of the training results).

Thanks :)