A Markov chain trained on the Puppet documentation and H. P. Lovecraft
thedoomthatcametopuppet.tumblr.com
thedoomthatcametopuppet.tumblr.com
Ah, ye olde buffer overflows caused by missing null terminators.
Thank you so much for posting this — I am in stitches.
Recording: http://gollmar.org/media/07-sacra.mp3
That's the sign we should really be looking for!
The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”,
“SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in
this document are to be interpreted as the fantastic mythology of those beingsNow somewhat tempted to run the Ansible docs against the complete text of Ender's Game, but it's hard to beat Lovecraft :)
I'm stealing that one!
“This document is currently being used in production at several large sites,
but there are some experiences and intimations which scar too deeply to
permit of healing, and leave only such an added sensitiveness that memory
reinspires all the original horror.”
Sounds like J2EE.> without this argument, git-lower-submodule --circumvent-index format-patches branches that clone the specified refs
That would be useful. Where can I merge the patch?
I've been meaning to make it into a proper gem and update the README. Perhaps now is the time. Check examples/ in the meantime.
Github project moved to: https://github.com/rickhull/loremarkov
Anyhow, hilarious.
you could probably adjust it to your own stuff easily enough.
[0] https://github.com/gburtini/Learning-Library-for-PHP/blob/ma...
I wrote a Markov text generator in Lisp a few months ago (https://github.com/jl2/markov), and had fun generating text using some old sci-fi books from Project Gutenberg and some documentation we use at work (Fiber Channel and ISCSI specs), but the sentences mine generated weren't as coherent as these.
I'm curious if this is generating thousands of sentences and only posting the best, if it used the basic algorithm and a ton of sample text, or if it's using a more advanced algorithm.
I was planning on making mine use parts of speech to help avoid some of the nonsense sentences, but never got around to it. And it would be harder to find source text to feed into it.
To make it easier, I've got it colorizing output based on the source corpus, so I can see where there's crossover. Then I just generate a wall of text and look for gems where the colors change.
It's an incredibly unsophisticated process
I'll be dropping in here to read any more comments that show up, although it looks like I'm late to the party.