When imperfect systems are good: Bluesky's lossy timelines

So, let's say I follow 4k people in the example and have a 50% drop rate. It seems a bit weird that if all (4k - 1) accounts I follow end up posting nothing in a day, that I STILL have a 50% chance that I won't see the 1 account that posts in a day. It seems to me that the algorithm should consider my feed's age (or the post freshness of my followers). Am I overthinking?

This feels like an edge case.

The "reasonable limit" is likely set based on experimentation, and thus on how much people post on average and the load it generates (so the real number is unlikely to be exactly "2000", IMHO).

If you follow a lot of people, how likely it is that their posting pattern is so different from the average? The more people you follow, the less likely that is.

So while you can end up in such situation in theory, it would need to be a very unusual (and rare) case.

Yeah, this seems concerning to me. Maybe now as the platform is new this isn't much of an issue. But as accounts go inactive people will naturally collect "dead" accounts that they are still following. On Facebook it isn't uncommon of to have old accounts of sociable people naturally collect thousands of friends.

It seems that what they are trying to measure is "busy timelines" and it seems bag they could probably measure that more directly. For example what is the number of posts in the timeline over theast 24h? It seems that it should be fairly easy to use this as the metric for calculating drop rate.

I think the 'law of large numbers' says that it's very unlikely for you to follow 4k and have _none_ of them posting. You could artificially construct a counter-example by finding 4k open but silent accounts, but that's silly.

The other workaround is: follow everyone. Write some code to get what you want out of the jetstream event feed. https://docs.bsky.app/blog/jetstream