GitHub - bookwyrm-social/bookwyrm: Social reading and reviewing, decentralized with ActivityPub
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Social reading and reviewing, decentralized with ActivityPub - GitHub - bookwyrm-social/bookwyrm: Social reading and reviewing, decentralized with ActivityPub

Goodreads is perhaps the best example of enshittification imo. It’s only good now as a way to track your reading lists.

I tried bookwyrm today and it feels quite polished already, like giving you a guided tour of it’s features. Hopefully it takes off as well similar to mastodon and lemmy.

@[email protected]
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192Y

I’ve only ever used Goodreads to track books I’ve read. What was good about it in the past?

@[email protected]
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32Y

I’m curious about this too. I thought that’s what Goodreads was for, tracking your books.

@[email protected]
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22Y

I also use it for:

  1. check ratings and reviews.
  2. jump from a book to a series
  3. check if/how book is translated to another language
  4. rarely check book lists

And, TBH. I do not see benefits of making booksread federated. But, probably, I miss something important.

Turkey_Titty_city
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14
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2Y

discussion and recommendations.

but now it’s mostly shit, like anything that is remotely social media. crazy power users and bad faith actors are all over the place.

I stopped using it a few years ago because people who harass me based on my reviews, esp if it was a critical review of a popular book.

I read that in 2012 a bunch of authors (names included Anne Rice, Kiera Cass and Carroll Bryant) made this website called Stop the Goodreads Bullies, a harassment site disguised as an anti-bullying campaign. Goodreads failure to protect its users/ bowing down to them by changing its policy to say that reviews about author’s behaviour was off-topic caused people to migrate to booklikes. Were you part of that migration?

FaceDeer
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182Y

Once upon a time it held a lot promise for book recommendations. You wouldn’t simply look for books that had high star ratings, you’d be shown books that other people who had the same profile of likes/dislikes had enjoyed. They had all the data necessary to build an awesome recommendation engine.

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