nsmalch
Joined 13 years ago
Joined 13 years ago
http://www.theguardian.com/technology/2015/sep/11/gchq-passw...
If NLG upsets you, you can always settle for a Markov chain and some atmospheric noise to seed the random values
So to give an example, any natural language phrase like:
"I took a walk in a park" is easier to crack.
I am certain there is somebody coding an infinite-monkey-type bruter to crack diceware as we speak:
"Your password must contain the seventh circle of hell, and a Taco Emoji"
MY only issue with that approach is people re-inventing the wheel. There is more code on Github than one could imagine, and not enough people evangelizing for Less-lines-of-code.
Of course it may take a programmer two decades to realize this, but what's the phrase:
"In the beginner's mind there are many possibilities, in the expert's mind there are few"
One thing though: Greptweet has an archive somewhere with a huge trove of tweets that users of the service have searched for, and were thus logged and kept. (Some even go over the 3200 limit). It's a massive Tarball, so set aside time to download it and parse out boring/noisy links.
A lot of HN links are tech-press posts which consist of hearsay and merely proxy the thoughts of others. The recent changes in HN with regards to more academia-style posts is refreshing.
I like to use Twitter to analyze HN datasets. It's mostly limited to links, because that's what I'm after mostly.
https://twitter.com/newsycombinator https://twitter.com/HackerNews .. And a few other accounts. Try to avoid Bitly wrapped links.
Use something like Greptweet to harvest the tweets and parse out any noise.