I am glad that others saved the source code elsewhere and kept it alive. How does deDRM_tools by noDRM avoid takedown due to piracy? I use that on a regular basis, and I am afraid that it might be taken down someday, and surprised that it is alive for so long. How has it stayed alive for so long?

Thank you for your wonderful work. Would you consider integrating Mastodon at some time?

That would be absolutely amazing! I look forward to an open source version, regardless of whether it is listed on F-Droid or not.

Many thanks for this!

I would also prefer a short press to collapse child comments.

That’s awesome! Thanks

He was good enough!

The window can be a good starting point. The other details can be worked out mid flight.

There was a previous discussion here, about how a feature similar to multi-reddit can be created in fediverse.

https://mander.xyz/comment/271745

I think that if each community can be associated with some tags, then users can just subscribe to the tags, and follow feeds from all related communities. Some more details can be added to this idea, for example, posts can inherit the tags of a community by default, and the poster can customize the tags for each post. Other users can report some tags if they are incorrect. Each community and user can have a tag health, which would be high if their post tags are not reported too much, and then those posts would be shown to users who subscribed to those tags.

Maybe this can create a feature similar to multi-reddit.

Duck Tales.

https://youtu.be/V8yr3vTgTok

I think that it fair.

Even after they connect, a user needs to subscribe to topics of their interest. It would be burdensome for a user to subscribe to the same topic multiple times on multiple servers, because everything is fragmented.

I propose two ways to create super communities:

Proposal 1) Tag and tag health)

A) Community tags: Here each community is associated with a set of tags (for example, the community ‘Earth’ can have tags #geology #geography #climate_change). When users post original content to these communities, they will be automatically suggested to add these default tags, but they can remove some tags or add other tags.

B) Tag health: A user Alice who consumes the content can view these posts, upvote some posts, downvote some posts, and can also report an inappropriate tag. Based on these reports, a numeric value can be ascribed to the health of each tag in that community. If the tag #climate_change is reported in a post, then that tag health would reduce.

For example, the community ‘Earth’ can have tags #geology (90% health), #geography (80% health), #climate_change (40% health).

C) Super-Community: A user Bob can create super communities based on community tags and tag health.

He can create a super-community ‘#geology’ which would lookup ‘Earth’ and pull the appropriately tagged posts from there. These tags are considered healthy, but Bob can set his own health threshold (say he sets it at 70% health).

He can also create a super-community ‘#climate_change’. This will not lookup posts from ‘Earth’, not even the appropriately tagged posts, because a lot of these posts were reported to be bad.

D) Tag Algebra: A user Charlie can create a super-community with multiple tags. For example, he can create (#novels OR #light_novels) EXCEPT (#tragedy AND #drama).

E) User Interface: When user David tries to create a super-community, he would simply enter the relevant tag(s). An advanced customization option would be hidden by default, but the user can expand it if he wishes. Upon expanding, he can see the default health threshold, and can modify it if he wishes. He can also see a list of all communities with that tag, along with their tag health, and a toggle button which automatically turns on or off based on the health threshold. The user can also manually override the automation, and specifically set one community to be on or off.

F) Conclusion: Unlike a central repository of super-communities, this approach can dynamically add new good communities. This approach can also remove old communities which have been abandoned by their moderators, when their tag health has deteriorated.

Proposal 2) Machine Learning Classification)

This is an extension of the first proposal of tag and tag health. Here we consider the following problem:

A) Problem) Suppose a community considers itself to be a neutral #news community. However, they have an unknown bias (capitalist, socialist, communist, etc). This bias is not reflected in the community tag or the post tag. However, users of one bias group would be dissatisfied to see posts of a different bias group.

Users outside the community cannot set unofficial tags or unofficial descriptions for the community since that can be abused. Then how would users create super-communities in a satisfactory manner?

B) Solution) Tag subgroup) Communities that share the same tag (say #news) would dedicate some of their server computing power for appropriately grouping themselves.

Suppose there are communities News1, News2, News3. Each community (say News1) would observe whether its own users upvote/downvote content from other communities (News2 and News3). Based on this, it would establish a positive link strength or a negative link strength with other communities.

In addition, moderators can also add a section called suggested similar communities, and dissimilar communities.

Based on these link strengths, all communities with the same tag (or tag algebra) can be grouped into multiple groups. When a super-community is created with this tag (or tag algebra), then the communities are grouped together if they have positive link strength with each other. However, if some child community has high negative link strength with other communities, then they would be classified into two or more groups, such that each group has positively linked child communities.

C) User interface) When user Emily tries to create super-community #news, it will get automatically created if the child communities do not have too much negative link strength with each other. However, if some news communities have a different theme from other news communities (negative link strength), then they would be auto-grouped into two or more groups.

Emily would see an advanced setting which is auto-expanded, and it will show two or more different groups. Each group would have a snippet that shows a couple of highly upvoted posts. Based on these snippets, Emily can choose any one group, or she can also choose any/all combination of these groups.

D) Conclusion) We can avoid the problem of duplicate communities with conflicting themes on multiple server instances.