cross-posted from: https://lemmy.world/post/16102424

Hi all,

Quiblr now has personalized post feeds for Lemmy!

I haven’t seen a “recommended feed” feature anywhere else in the fediverse but I thought I would take a crack at building it!

My goal was to make a privacy-focused recommendation engine that tailors your experience based on the content you interact with. None of the data leaves your device. You don’t even need to log in for it to work

  • You can turn it off or tune your feed in the settings
  • Each post now also includes a show me more/less button

I would LOVE feedback from folks if you get a chance to try it out!

This was really fun to build so let me know if there are any questions!

PS: Let me know if someone else has built this feature for the fediverse - then I will change the title to not claim “the first” lol

  • GolfNovemberUniform@lemmy.ml
    20·
    6 months ago

    So it’s fully local right? That’s a really awesome system then. I’d probably recommend encrypting the data but that’s not really necessary

      • GolfNovemberUniform@lemmy.ml
        15·
        6 months ago

        I don’t understand why nobody did it in the past. It makes the experience so much better for normal users. I hope this becomes mainstream. Btw does it have any automatic analytics cache cleaning or something like that (so it doesn’t grow 20 gigs of it in a year)? If so, make sure it cleans it periodically and not immediately when a piece of data becomes unneeded. On low end devices frequent write/delete cycles can be an issue

        • ScratchEnglish
          8·
          6 months ago

          I can’t go into too much detail, I was working for a recognisable company who was dabbling in the Fediverse with a heavy lean towards empowering the users.

          This was high on my list of demands. Along with fine grain controls for what metrics you opt into and what notifications you receive.

          Project got gutted early this year.

        • Aurelius@lemmy.worldOP
          5·
          6 months ago

          I appreciate the kind words. And yes, I included a mechanism to constantly refresh and clean up recommendations so that it doesn’t use too much memory