[nottorp]

> Instead of optimizing for engagement and ad revenue, these systems are optimized for user utility.

Are they, or instead they will help keeping you in your comfort cage?

Comfort cage is better than engagement cage ofc, but maybe we should step out of it once in a while.

> During the RSS reader era, I was exposed to a lot of curated tech and design content and it helped me really develop taste and knowledge in these areas.

Curated by humans with which you didn't always agree, right?

[janalsncm]

> Are they, or instead they will help keeping you in your comfort cage?

I’ve been paying close attention to what YouTube shorts/tiktok do. They don’t just show you the same genre or topic or even set of topics. They are constantly in an explore-exploit pattern. Constantly trying to figure out the next thing that’ll keep your attention, show you a bunch of that content, then on to the next thing. Each interest cluster builds towards a peak then tapers off.

So it’s not like if you see baking videos it’ll keep you in that comfort zone forever.

[nottorp]

But you're describing the engagement cage, while I'm just pointing that you need to be careful not to escape from it just to get trapped inside the comfort cage.

[Karrot_Kream]

Is there a rigorous definition of "engagement cage" or is the term just HN-tuned engagement bait? (:

Fundamentally content discovery is always an explore-exploit loop. What you tune the loop with is what makes it useful for any given purpose.

[nottorp]

> Fundamentally content discovery is always an explore-exploit loop

Is it? Or only when you do it for profit?

[Karrot_Kream]

Huh? "Explore" means to show the user new content. "Exploit" is to show more of the same content. You can say the HN algorithm does both as well: the front page shows a variety of topics allowing you to explore but the upvotes "exploit" shared interest by bumping it higher in page rankings.

[muzani]

YT Music is a good algorithm for exploring music. You click a song and the playlist after that song is a mix of similar music and music you like to hear. There's also a button which lets you decide if you want to discover different music or something fresher based on your taste.

Twitter is an example of being entirely in the comfort cage - it links you with people who you agree with, even going out of the way to create these bubbles.

Meta seems to end up with a rage cage. If you criticize say, videos on how to become a billionaire, it would show more billionaire videos.

HN used to be the default. A forum where everyone is in the same cage. People criticize HN culture and thoughts, but sometimes it's just people being shown a side of the world they're not used to.

[jackdawed]

That's the core challenge in designing a system like this. Echo chambers and comfort cages emerge from recommendation algorithms, and before that, from lazy curation.

If you have control over the recommendation system, you could deliberately feed it contrarian and diverse sources. Or you could choose to be very constrained. Back in RSS days, if you were lazy about it, your taste/knowledge was dependent on other people's curation and biases.

Progress happens through trends anyway. Like in 2010s, there was just a lot of Rails content. Same with flat design. It wasn't really group think, it just seemed to happen out of collective focus and necessity. Everyone else was talking/doing this so if you wanted to be a participant, you have to speak the language.

My original principle when I was using Google Reader was I didn't really know enough to have strong opinions on tech or design, so I'll follow people who seem to have strong opinions. Over time I started to understand what was good design, even if it wasn't something I liked. The rate of taste development was also faster for visual design because you could just quickly scan through an image, vs with code/writing you'd have to read it.

I did something interesting with my Last.fm data once. I've been tracking my music since 2009. Instead of getting recommendations based on my preferences, I could generate a list of artists that had no or little overlap with my current library. It was pure exploration vs exploitation music recommendation. The problem was once your tastes get diverse enough, it's hard to avoid overlaps.