YouTube has been showing me a ton of channels with low Hundreds of views recently, and they’re very to my taste. Ymmv but they’re doing well at exactly what you’re complaining about in my experience
Yeah I've noticed that YouTube almost always recommends me one or two videos with less than 100 views now. Must be a relatively recent change. The videos are often pretty relevant and pretty good, which is a bit depressing. Seeing people put a lot of work into videos that nobody watches (by YouTube standards) is sad. Though that is the reality of most channels. Maybe this change will help them.
I have a conjecture, they're using you as a barometer to see how you react to it compared to people with similar profiles
I've seen a couple that go on to get decent viewcounts, but I don't care enough to keep tabs on anything in particular
Then again, I do have 10 hour a day watch time average, my data is vast, but highly skewed
That sounds severe and so unlike what I’ve experienced. But some amount of noise in vision is normal, you just don’t notice it unless you focus on it, or have a background that really makes it apparently. Light is inherently noisy. If you look at a blank sheet of paper and all you see is #FFFFFF, you’re not paying attention.
One theory I've heard is that we normally filter out the noise but in some people that's not happening and thus they experience VSS. Likewise for tinnitus.
Except that Stack Overflow’s CEO, in this very article, says that it’s a violation of the Creative Commons license to train an LLM on their answers. So what he’s actually proposing is very unclear.
> When AI companies sell their models to customers, they “are unable to attribute each and every one of the community members whose questions and answers were used to train the model, thereby breaching the Creative Commons license,” Chandrasekar says.
> Except that Stack Overflow’s CEO, in this very article, says that it’s a violation of the Creative Commons license to train an LLM on their answers.
Yes, because it's a license violation — "If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original". That includes derived data products, like AI models, built using the content.
That seems rather debatable. While I don't think the overall use case favors fair use, due to the commercial nature of most of the end products, the fact that such use is clearly transformative is definitely a positive factor on the side of LLM creators:
> A key consideration in later fair use cases is the extent to which the use is transformative. In the 1994 decision Campbell v. Acuff-Rose Music Inc,[13] the U.S. Supreme Court held that when the purpose of the use is transformative, this makes the first factor more likely to favor fair use.[14] Before the Campbell decision, federal Judge Pierre Leval argued that transformativeness is central to the fair use analysis in his 1990 article, Toward a Fair Use Standard.[11] Blanch v. Koons is another example of a fair use case that focused on transformativeness. In 2006, Jeff Koons used a photograph taken by commercial photographer Andrea Blanch in a collage painting.[15] Koons appropriated a central portion of an advertisement she had been commissioned to shoot for a magazine. Koons prevailed in part because his use was found transformative under the first fair use factor.
SO content is dual licensed to SO, giving them the right to "commercially exploit" it. That means they can relicense under terms that also allow commercial exploitation.
