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

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[–] [email protected] 58 points 5 months ago (2 children)

Wow. This is pretty impressive since you usually only see these kinds of things from big tech companies and their stuff is definitely NOT privacy friendly.

Can you provide more detail on how it works and how it is different than what big tech is doing?

[–] Aurelius 54 points 5 months ago* (last edited 5 months ago) (1 children)

Thanks a bunch! It took me a while to craft the solution to make sure it was both effective + private. I was originally inspired by Canopy. They built a news aggregator with private & personalized posts a few years back and the idea sat in my head.

To answer your question(s), there are quite a few signals that big tech uses to recommend content. Not all of them are privacy invasive (or at least they don't HAVE to be). My approach was to do thorough research on the different signals used by big tech to make their recommendation engines, and just build ones that 1.) were possible given fediverse API limitations and 2.) private. I had to craft some novel approaches to make it work but I'm pretty happy with the outcome!

One of the biggest differences between the "big tech" approach and Quiblr's is that most big tech does not keep data simply on your device. They store it in datacenters to build large social-webs to essentially cluster users (and push more relevant ads).

But I was able utilize many of the other signals used by big tech (e.g. communities you engage with, metadata of content you read, dwell time, post/comment/vote activity) and I designed it to work offline with no servers.

Edit: grammar

[–] [email protected] 4 points 5 months ago

How does it make the decision to recommend one post over another using the data it collects? Also does it treat all that data differently when ranking posts?

btw it feels really well polished so nice job.

~Anti~ ~Commercial-AI~ ~license~ ~(CC~ ~BY-NC-SA~ ~4.0)~

[–] [email protected] 13 points 5 months ago

Yep I will be happy to see that too