51–60 of 204 posts
Yeah. This is very “using a saw to cut a piece of steak” meme. I feel like most obfuscations can already be thwarted anyway. At this point I just see it as some cargo culting thing that nerds do in their hacker news profiles.
People have noted for many years now that getting your email address scraped isn't a thing. I assume it used to be a thing, and the cost of doing a zero-effort obfuscation is zero, so almost nobody stops.
Email obfuscation was already obsolete, if you were relying on these techniques to prevent scrapers from finding your email you have been doing it wrong for close to a decade and annoying your customers at the same time. Spam filters are really good these days and what they don't catch you can make custom filters for.
So you can feed 50 characters before and after any use of "com" "net" "org" or a dozen other TLDs, and ChatGPT will give you the email addresses. That saves on OpenAI bills.
But then I have another address which I've been similarly careful with but have also used on a few registrations and that gets a bit of spam (not that much though).
Reckon it'll still work a while more yet.
Is it just me or is "obfuscation" like "john [at] company [dot] com" trivially solved with regular expressions? Or even simple search/replace? Are there more advanced techniques for that?
Earlier quoted context omitted.
The grid, of course, is less sure about this compromise.
The grid can be negotiated with if we put in the infrastructure.
No large-scale email scraper has the budget necessary to run the content it scrapes through a LLM. So as far as real-world goes, nothing changes: the .2 cents it would cost to run ChatGPT on a page to extract potentially obfuscated emails would cost magnitudes more than it could ever bring in revenues. Regarding the examples provided, there is nothing there that a simple regex couldn't achieve, so I don't really see…
This is assuming you’re using a really big LLM behind a paid service. There are plenty of smaller open source models. Not sure at what point it’s not “large” but when fine tuned they are capable of matching the largest LLM in performance on narrow tasks. Some of these open source models can even be run on your local machine. It’d be very inexpensive to run thousands of pages through it. https://llm-leaderboard.stream…
Can anyone explain what the ‘think step by step’ magic words referenced in the article are ?
Can anyone explain what the ‘think step by step’ magic words referenced in the article are ?