This is actually a call for research from YC-funded OpenAI:
> Spam the Spammers
> Investigate the use of language models to remove the profit from spamming.
> Spammers generate a huge amount of undesirable traffic and attention. Their emails are merely annoying for most people, but a small fraction of users fall into their trap. Spammers receive responses from users extremely infrequently. Therefore, they manually reply to each email.
> The task is to build a bot that automatically replies to spam emails. Such a bot shouldn't be easy to detect, which could be achieved by use of a powerful language model.
Could be an extension from Graham's A Plan for Spam, which basically called for a DDoS on spam servers:
> As I mentioned in Will Filters Kill Spam?, following all the urls in a spam would have an amusing side-effect. If popular email clients did this in order to filter spam, the spammer's servers would take a serious pounding. The more I think about this, the better an idea it seems. This isn't just amusing; it would be hard to imagine a more perfectly targeted counterattack on spammers.
> So I'd like to suggest an additional feature to those working on spam filters: a "punish" mode which, if turned on, would spider every url in a suspected spam n times, where n could be set by the user.
Considering that a lot of spam is sent via botnets, it would not take much for spammers to either protect themselves by hosting their pages on botnets, or use it to cause others to run a DDOS for them.
> So I'd like to suggest an additional feature to those working on spam filters: a "punish" mode which, if turned on, would spider every url in a suspected spam n times, where n could be set by the user.
EDIT: But note that the problem is the same if you reply to the e-mails. If this is automated and enough people do it, then sending spam with fake from addresses becomes an effective way of attacking peoples mail accounts. I've seen this happen first hand by a spammer that used incendiary content to trigger manual responses by sending out what claimed to be an ad for child porn sent in the name of an anti-spam activist. It was scarily effective, even though in this case it required getting people to take manual action (in this case bad enough that he needed police protection as a result of a range of credible threats)
Why is it a call for research? A simple n-gram model would be enough. It only needs to be almost grammatical, the spammers can't read all the messages anyways, if they want to fight that they need another machine classifier.
I came across these articles a while ago and decided to check my filtered messages just out of curiosity. I hardly ever use fb anymore so I didn't expect to find anything. Lo and behold though, there was a two year old message from my best friend from elementary school who I had lost touch with forever ago. Lesson learned: don't trust Facebook with anything important, they'll fuck it up even if it's one of their core features.
It seems to have become even more cumbersome and confusing in the recent times. FB does not indicate that there may be message requests or filtered messages (even from "friends") waiting to be examined. It seems pointless when you see a message very late and wonder how it could even be missed. FB's message system cannot be trusted with the lack of notifications about these.
It's a low effort meme-like post that adds nothing to the article or discussion. Those are frowned upon at HN.
It's not impossible to make joke-y replies to posts and receive upvotes, but it takes a lot of practice (or authority).
If a 100 people would post one-liner memes then the comment section would be no fun to read. It used to be a strength of HN to have an incredibly resourceful comment section with good debate. It was not uncommon for the author of a programming language to chime in on a discussion about said language.
> The most important principle on HN, though, is to make thoughtful comments. Thoughtful in both senses: civil and substantial.
> The test for substance is a lot like it is for links. Does your comment teach us anything? There are two ways to do that: by pointing out some consideration that hadn't previously been mentioned, and by giving more information about the topic, perhaps from personal experience. Whereas comments like "LOL!" or worse still, "That's retarded!" teach us nothing.
Edit: I see that your account was created 1499 days ago. This may make my reply seem silly (also coming from a newly created account). It was a sincere attempt at answering your downvoting concerns though.
I know this. More than a silly post it was pointing out the similarity between the thanks Obama meme and the Pauli effect. Yet something like this made it to the front page.
I think if you actually made clear that you were mentioning the meme in order to make a comparison, as opposed to "merely" invoking it, the comment would have been received better.
This is an interesting response, because it reveals a few things:
- Linkedin was not aware of the size of the 2012 breach.
- Linkedin did not use preventive measures one would usually do after a significant breach (They only now issued a password reset for accounts older than 2012).
It seems like they also botched the 2012 post-hack evaluation.
I wonder if their security engineer(s) could be held personally liable. Someone has advertised him/herself as a security engineer, while completely botching the password scheme (unsalted Sha-1), and leaving massive holes in the post-evaluation of the breach.
For anyone to be liable, there'd have to be a criminal or civil suit against LinkedIn, neither of which has happened or seems likely to happen. Maybe a class action lawsuit, but I don't think there's a precedent for an individual employee being held personally liable in a class action suit.
Also I really doubt there was a single employee you could place the blame on.
"Learning To Learn Using Gradient Descent" by Hochreiter et al.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.32...