Hacker News Reader

> I'm a bit confused about their so-called improvements to video recommendation quality and bot detection.

Let's not dismiss the possibility that they're just lying. I don't think they're trustworthy enough to be given the benefit of the doubt.

They're just running most of the bots in-house on the GPU supercompute cluster now, and billing Putin for it directly, now that the middleman Yevgeny Prigozhin is out of the picture.

> I've seen a lot of sentiment from people that they see more bots, hate speech, and irrelevant content on their timelines. Maybe what I'm hearing is just anecdotal evidence or stories in a bubble?

1. Note that they do not unambiguously define bots; it's "bots and content scrapers" lumped together:

> Blocked bots and content scrapers at a rate +37% greater than 2022. On average, we prevent more than 1M bots signup attacks each day and we’ve reduced DM spam by 95%.

2. I suspect it's a matter of signal-to-noise. Yes, the absolute bot count could be down, but how does it compare to the number of humans using X/Twitter and the volume of content which they are contributing each day? I've tried to find reliable statistics on this, but to no avail. My anecdotal experience is that fewer people I've followed are still regularly using X/Twitter since the Musk acquisition. Some are over at Post.News, some at Threads, some have shifted to Mastodon. It's a very fragmented experience now.

Sometimes I click a link to twitter, get log-in-walled or see the head of a tweet thread that's missing the rest of the thread. I wonder if they measure that as a successful "scrape bot blocked"?

> that they see more bots

My go-to here are the scams advertising support for metamask problems. Those still appear every day and are trivial to identify. I'm not sure I consider Twitter having a bot detection success until trivial issues like that are solved. They're still below the level of "text match and auto ban" solutions.

70000 hard coded references to the data centre in Sacramento...

At Twitter scale, 700 quick hacks would be excusable, 7000 iffy, 70000 just makes the old engineering team look like bozos.

Maybe it's embellished?

>They have a GPU supercompute cluster?? It seems like they have the capability to do training and inference with state-of-the-art algorithms at massive scales then. Why have Twitter's recommendations and ad revenue (even before the acquisition) been so poor then?

In general, assuming you are in some sort of decent state better ML doesn't make your revenue 50% better. It makes it 5% better per year.

> I've seen a lot of sentiment from people that they see more bots, hate speech, and irrelevant content on their timelines. Maybe what I'm hearing is just anecdotal evidence or stories in a bubble?

TBF, this is my year-over-year experience on all social media platforms.

love how those comments in [2] have aged haha