Where they more exhaustive or more frequent?

I think what's really going on is that bots expose how underpowered web servers has gotten in recent years. In the 2000s, even poorly-architected PHP sites tended to serve about 200 requests per second, with 1000+ being common for static sites. I remember when Node.js came out and claimed that it could serve more like 100,000 RPS due to its cooperative threading model. But today sites have a remarkable slowness to them, running many hundreds or thousands of database queries due to ORMs and N+1 problems, so that response times can be 500 ms or more and even 1000 simultaneous users stresses servers.

What went wrong is that nobody solved stuff like Russian doll caching in a general way separate from the programming language and database. We should have had ways to make dependency graphs using Etag headers as keys with real cache invalidation of dependent data. I went down that rabbit hole 10 years ago using touch events in Laravel with callbacks to handle cache invalidation when class model data was saved to the database. Also a query cache using Redis which I think might have been handled better at the database level anyway. After that experience, I can honestly say that cache invalidation is so difficult to get right that it's effectively an open problem. Meaning that programmings should use a package instead of rolling it by hand, and it should be a major concern from the start (along with sharding by user id or using something like Firebase).

Don't get me started on how the web should have been a P2P content-addressable memory anyway. Nearly everything should be available from a nearby edge peer, similarly to BitTorrent. But nobody bothered to solve how to make that work with HTTPS/SSL. I suspect that has to do with early flaws in the browser security model where the whole page has to be behind HTTPS or warnings appear. So it was never clear what was personally identifiable information (PII) or merely public data being served over HTTPS. To really solve that, we probably need real trust networks and maybe even zero-knowledge proofs.

Since these problems are so challenging to fix, and big companies can't be bothered to do it since they pulled the ladder up behind them, we're probably stuck with banal "are you human" challenge screens for the foreseeable future.

It probably seems daunting but to be honest this feels like a weekend's work at this point with LLM assistance. Not to be glib!

The thing is, people were scraping and monetizing other people's websites long before the current LLM fad. The difference is the magnitude of the problem.

> And I can see why maintainers of sites like this, or other free but incredibly useful resources might start to get irate at that.

Those who object to the scraping fall into several camps, but the biggest complaint I am hearing is that it increases both maintenance costs and time. In other words: it sucks when people are using your work in a manner that you find offensive, but it goes beyond that by doing actual harm.

I feel the same way too. But guess what, all the code you did not publish gets into the training corpus anyway (when you gave Codex or whatever full read access to your filesystem).

AI solves exactly the meta-problem you describe: "I had a problem and needed to write a one-off doo-dad utility program to solve it." Now you can do something with your time besides writing pointless one-off doo-dads.

As for monetizing the training data, (a) it cost hundreds of millions of dollars to generate the weights, so why begrudge the companies that made the investment and did the research necessary to make it happen?; and (b) rest assured, whatever your doo-dad does, an open-weight model like GLM 5.2 can generate it for free using your own hardware.

So you don't have to pay anyone in that case. Well, except nVidia, I guess. Point granted there.