@along_the_road what’s the alternative scenario here?
You could push to remove some public information from common crawl. How do you identify what public data is _unintentionally_ public?
Assume we solve that problem. Now the open datasets and models developed on them are weaker. They’re specifically weaker at identifying children as things that exist in the world. Do we want that? What if it reduces the performance of cars’ emergency breaking systems? CSAM filters? Family photo organization?
“These were mostly family photos uploaded to personal and parenting blogs […] as well as stills from YouTube videos"
So… people posted photos of their kids on public websites, common crawl scraped them, LAION-5B cleaned it up for training, and now there are models. This doesn’t seem evil to me… digital commons working as intended.
If anyone is surprised, the fault lies with the UX around “private URL” sharing, not devs using Common Crawl
@abaci @mojo 100% worth it for me.
I switched from Google to #DDG to #Kagi. Even if the results were exactly the same, Kagi is FAST. The whole experience is very snappy.
Beyond that:
- All your DDG bangs work, and you can add custom bangs
- They have some neat AI summarization features.
- You can manually boost/penalize/block domains
- Lenses focus your search on particular kinds of sites
@kent_eh exactly.
The alternative is “if you want your content to be private, share it privately.”
If you transmit your content to anyone who sends you a GET request, you lose control of that content. The recipient has the bits.
It would be nice to extend the core technology to better reflect your intent. Perhaps embedding license metadata in the images, the way LICENSE.txt travels with source code. That’s still quite weak, as we saw with Do Not Track.