What do you even charge for that? We might never make a repository of human made content with no Ai postings in it ever again. Seems like selling the golden goose to me

It's already public information. The point isn't to extract rents, it's to remove the incentive for server-melting mass scraping.

Substantially higher loads than Twitter gets today were not "melting the servers" until Musk summarily fired most of the engineers, stopped paying data center (etc.) bills, and then started demanding miscellaneous code changes on tight deadlines with few if any people left who understood the consequences or how to debug resulting problems.

In other words, the root problem is incompetent management, not any technical issue.

Don't worry though, the legal system is still coming for Musk, and he will be forced to cough up the additional billions (?) he has unlawfully cheated out of a wide assortment of counterparties in violation of his various contracts. And as employee attrition continues, whatever technical problems Twitter has today will only get worse, with or without "scraping".

Scraping has a different load pattern than ordinary use because of caching. Frequently accessed data gets served out of caches and CDNs. Infrequently accessed data results in cache misses that generate (expensive) database queries. Most data is infrequently accessed but scraping accesses everything, so it's disproportionately resource intensive. Then the infrequently accessed data displaces frequently accessed data in the cache, making it even worse.

In theory, wouldn’t continuous scraping by AI farms et al put a log of this infrequent data into cache though?

Caches are only so large. Expanding them doesn't buy you much, and increases costs greatly.

The key benefit to a cache is that a small set of content accounts for a large set of traffic. This can be staggeringly effective with even a very limited amount of caching.

Your options are:

1. Maintain the same cache size. This means your origin servers get far more requests, and that you perform far more cache evictions. Both run "hotter" and are less efficient.

2. Increase the cache size. Problem here is that you're moving a lot of low-yield data to the cache. On average it's ... only requested once, so you're paying for far more storage, you're not reducing traffic by much (everything still has to be served from origin), and your costs just went up a lot.

3. Throttle traffic. The sensible place to do this IMO would be for traffic from the caching layer to the origin servers, and preferably for requesting clients which are making an abnormally large set of non-cached object requests. Serve the legitimate traffic reasonably quickly, but trickle out cold results to high-demand clients slowly. I don't know to what extent caching systems already incorporate this, though I suspect at least some of this is implemented.

4. Provide an alternate archival interface. This is its own separately maintained and networked store, might have regulated or metered access (perhaps through an API), might also serve out specific content on a schedule (e.g., X blocks or Y timespan of data are available at specific times, perhaps over multipath protocols), to help manage caching. Alternatively, partner with a specific datacentre provider to serve the data within given facilities, reducing backbone-transit costs and limitations.

5. Drop-ship data on request. The "stationwagon full of data tapes" solution.

6. Provide access to representative samples of data. LLM AI apparently likes to eat everything it can get its hands on, but for many purposes, selectively-sampled data may be sufficient for statistical analysis, trendspotting, and even much security analysis. Random sampling is, through another lens, an unbiased method for discarding data to avoid information overload.

Twitter feels more stable today, with less spam, than one year ago. There's of course parts that have been deliberately shut down, but that's not an argument about the core product.

Pandemic lock downs are 99% over. People are getting back outside and returning to office. These effects have little to do with Twitter's specific actions.

I see more spam these days, particularly coming from accounts that paid for the blue check mark. IIRC, Musk said that paid verification would make things better since scammers wouldn't dare pay for it (I would find where he said this but I hit the 600 tweet limit), but given how lax their verification standards are, it seems to be a boon to scammers, much the same way that Let's Encrypt let anyone get a free TLS cert at the cost of destroying the perceived legitimacy that came with having HTTPS in front of your domain.

(And IMO, that perceived legitimacy was unfounded for both HTTPS and the blue check before both were easy to get, it's just that the bar had to drop to the floor for most people to realize how little it meant.)

I mean, it’s clear the Musk overcorrected. The fact that managers were asked to name their best employees, only to then be fired and replaced by them, or that musk purposefully avoided legal obligations to pay out severance/health insurance payments (I forget the exact name)/other severance, and that the site has had multiple technical issues that make it feel like there’s no review/QA process all show that he doesn’t know what he’s doing.

He got laughed out of a Twitter call thing with lead engineers in the industry for saying he wanted to “rewrite the entire stack” and not having a definition for what he meant.

Doomed or not, Musk is terrible at just about everything he does and Twitter is no exception

I think this action from Twitter showed that it isn't public information. It is pretty much twitter's to do whatever they want with it.

I think that’s always been known, but the tacit agreement between users and Twitter has always been “I’ll post my content and anyone can see it, if they want to engage they make an account”. From a business perspective this feels like a big negative to me for Twitter. I’ve followed several links the last few days and been prompted to login, and nothing about those links felt valuable enough to do so.

Just because it is published doesn't mean authors don't retain rights on it. None of that content is public-domain.

It's about $1 per thousand tweets and access to 0.3% of the total volume. I think the subscription is 50M "new" tweets each month? There are other providers who continually scrape Twitter and sell their back catalogue.

https://www.wired.com/story/twitter-data-api-prices-out-near...

Researchers are complaining that it's far too high for academic grants. Probably true, but that's no different from other obscenely priced subscriptions like access to satellite imagery (can easily be $1k for a single image which you have no right to distribute). I'm less convinced that it's impossible for them to do research with 50 million tweets a month, or with what data there is available. Most researchers can't afford any of the AI SAAS company subscriptions anyway. Data labelling platforms - without the workers - can cost 10-20k a year. I spoke to one company that wouldn't get out of bed for a contract less than 100k. Most offer a free tier a la Matlab in the hope that students will spin out companies and then sign up. I don't have an opinion on what archival tweets should cost, but I do think it's an opportunity to explore more efficient analyses.

> We might never make a repository of human made content with no Ai postings in it ever again.

Wow, never thought of it that way before. Kinda hit me hard for some reason.

Honestly I think that's why reddit is closing itself up too. Everyone sitting on a website like this might be sitting on a Ai training goldmine that can never be replicated.

Too little too late. Anything pre-ChatGPT is already scrapped, packaged and mirrored around the Internet; anything post ChatGPT launch is increasingly mixed up with LLM-generated output. And it's not that the most recent data has any extra value. You don't need most recent knowledge to train LLMs. They're not good for reproducing facts anyway. Training up their "cognitive abilities" doesn't need fresh data, it needs just human-generated data.

Precisely, which brings us back around to the question: why are social media companies really doing this?

I think "AI is takin' ooor contents!" is a convenient excuse to tighten the screws further. Having a Boogeyman in the form of technology that's already under worried discussion by press and politicians is a great way to convince users how super-super-serious the problem must be, and to blow a dog whistle at other companies to indicate they should so the same.

It's no coincidence that the first two companies to do this so actively and recently are both overvalued, not profitable, and don't actually directly produce any of the content on their platforms.

One that's slowly ageing away though.

Synthetic data fed into training isn't necessarily a bad thing. It can produce great results in many cases.

I've seen that work with self-driving cars. Simulating driving data is actually better since you can introduce black swan events that might not happen often in real world.

It doesn't matter all that much. Smaller but better data is better for training than a large, but garbage dataset.

Do you think twitter has no AI postings?

Are you really sure it's legal? In theory it's not different from providing the same information from API or website... but do people working in law think so?

Twitter purchased Gnip years ago, and it's a reseller of social media data. Companies that want all the public tweets, nicely formatted and with proper licensing, can just buy the data from Twitter directly.

I'm assuming their terms give them permission to redistribute everybody's tweets, since that's kind of the whole site. I don't know why they'd restrict themselves to doing it over the internet and not the mail, but do you have any reason to think that to be the case?

We're talking about Elon Musk. I'd be surprised if he gave a shit.

So, I'd just made that suggestion myself a few moments ago.

That said, there are concerns with data aggregation, as patterns and trends become visible which aren't clear in small-sample or live-stream (that is, available in near-time to its creation) data. And the creators of corpora such as Twitter, Facebook YouTube, TikTok, etc., might well have reason to be concerned.

This isn't idle or uninformed. I've done data analysis in the past on what were for the time considered to be large datasets. I've been analyzing HN front-page activity for the past month or so, which is interesting. I've found it somewhat concerning when looking at individual user data, though, here being the submitter of front-page items. It's possible to look at patterns over time (who does and does not make submissions on specific days of the week?) or across sites (what accounts heavily contribute to specific website submissions?). In the latter case, I'd been told by someone (in the context of discussing my project) of an alt identity they have on HN, and could see that the alternate was also strongly represented among submitters of a specific site.

Yes, the information is public. Yes, anyone with a couple of days to burn downloading the front-page archive could do similar analysis. And yes, there's far more intrusive data analytics being done as we speak at vastly greater scale, precision, and insights. That doesn't make me any more comfortable taking a deep dive into that space.

It's one thing to be in public amongst throngs or a crowd, with incidental encounters leaving little trace. It's another to be followed, tracked, and recorded in minute detail, and more, for that to occur for large populations. Not a hypothetical, mind, but present-day reality.

The fact that incidental conversations and sharings of experiences are now centralised, recorded, analyzed, identified, and shared amongst myriad groups with a wide range of interests is a growing concern. The notion of "publishing" used to involve a very deliberate process of crafting and memoising a message, then distributing it through specific channels. Today, we publish our lives through incidental data smog, utterly without our awareness or involvement for the most part. And often in jurisdictions and societies with few or no protections, or regard for human and civil rights, let alone a strong personal privacy tradition.

As I've said many times in many variants of this discussion, scale matters, and present scale is utterly unprecedented.

This is a legitimate concern, but whether the people doing the analysis get the data via scraping vs. a box of hard drives is pretty irrelevant to it. To actually solve it you would need the data to not be public.

One of the things you could do is reduce the granularity. So instead of showing that someone posted at 1:23:45 PM on Saturday, July 1, 2023, you show that they posted the week of June 25, 2023. Then you're not going to be doing much time of day or day of week analysis because you don't have that anymore.

Yes, once the data are out there ... it's difficult to do much.

Though I've thought for quite some time that making the trade and transaction of such data illegal might help a lot.

Otherwise ... what I see many people falling into the trap of is thinking of their discussions amongst friends online as equivalent, say, to a discussion in a public space such as a park or cafe --- possibly overheard by bystanders, but not broadcast to the world.

In fact there is both a recording and distribution modality attached to online discussions that's utterly different to such spoken conversations, and those also give rise to the capability to aggregate and correlate information from many sources.

Socially, legally, psychologically, legislatively, and even technically, we're ill-equipped to deal with this.

Fuzzing and randomising data can help, but has been shown to be stubbornly prone to de-fuzzing and de-randomising, especially where it can be correlated to other signals, either unfuzzed or differently-fuzzed.