> So, you'd think that something that pretends to be “Artificial Intelligence” would use the most efficient way of using our data for training purposes, right? Clone the repos, walk every commit. Done.
If repos like this were 10%+ of what they crawled, having a special case for clone-able repos would be smart, but if you're crawling everything, you're not going to do an efficiency tweak for each special case that has a more parse-able option.
I guess GitHub is in a similar bunch of sources, it should be also more efficient to crawl by cloning.
Anyway, isn’t it the whole sales pitch that it generates tailored solutions fast?
This tick about "if AI smart how come crawler dumb" is in most complaints I've read about AI crawlers and I've started to find it pretty annoying. The crawlers might be written using AI but they're evidently not actually running AI inference over the pages they get back--besides being able to tell this from the behavior, if this is pretraining input, that's enormous scale, so it'd mean a large increase in effective training cost. Naively assume inference costs are equal to pretraining costs (probably not true but maybe right order-of-magnitude) and it's a doubling.
This ends up tacitly turning a very legitimate complaint (ill-behaved crawlers) into a justification for head-in-the-sand AI denialism.
There's nothing "denialist" about recognizing the utter stupidity of systems that are being mislabeled as "AI". You judge a tool by its results, and the results have been very poor indeed. The only heads in the sand are those whose owners continually refuse to recognize the proofs before their very eyes that there's zero intelligence here.
That's head-in-the-sand stuff. AI is certainly very capable of being dumb (as are humans). But:
> “The problem was in need of a new real idea, which this new result seems to provide,” says James Maynard, a mathematician at the University of Oxford. “It seems that the AI has made a genuinely interesting mathematical contribution.”
Nobody a decade ago would have said "oh yeah solving a bunch of open problems in research mathematics, and finding a bunch of zero days in Chrome and Firefox, and winning literature prizes, are things that don't require intelligence."
>AI is certainly very capable of being dumb (as are humans).
I wonder how exactly the average scraper got to be so inefficient on kernel.org.
Did someone prompt a SotA model to write the most generic scraper possible?
Did someone prompt an old local model on their laptop to write a kernel.org scraper?
Perhaps no LLMs were involved in the first place. Seems to me there isn't much relation between how good a random scraper is and how usable/effective Mythos/Sol's outputs can be.
If repos like this were 10%+ of what they crawled, having a special case for clone-able repos would be smart, but if you're crawling everything, you're not going to do an efficiency tweak for each special case that has a more parse-able option.