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There are so many great GIFs out there and I want to have copies of them. Twitter makes that harder than it should be by converting them to MP4 and not providing access to the source material. To make it easier, I made a bash pipeline that takes a tweet URL and a filename, extracts the MP4 from that tweet and uses ffmpeg to convert back to GIF.
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script for updating dynamic DNS records on he.net (hurricane electric)
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Configuration for ProPublica’s Tor hidden service proxy.
Note (December 16, 2021): These example files haven't been updated since 2016. In either 2019 or 2020, our onion domain was changed to a longer v3 onion address (p53lf57qovyuvwsc6xnrppyply3vtqm7l6pcobkmyqsiofyeznfu5uqd.onion). The examples below don't reflect this, but the configuration portions remain accurate regarding how we currently serve the onion site. (Tor Browser dropped support for v2 addresses, such as propub3r6espa33w.onion, in the second half of 2021.)
These files contain the base configuration for ProPublica’s Tor hidden service mirror.
Of note:
We're using the nginx "subs_filter" and "headers more" modules to allow us to rewrite content and update headers, so that we can convert clearnet links into onion links, where possible.
Last active
May 25, 2025 19:05— forked from kaleb/XDG.vim
vim XDG Base Directory support
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Hofstadter on Lisp: Atoms and Lists, re-printed in Metamagical Themas.
Hofstadter on Lisp
In the mid-80s, while reading through my roommate's collection of Scientific American back issues, I encountered this introduction to Lisp written by Douglas Hofstadter. I found it very charming at the time, and provide it here (somewhat illegally) for the edification of a new generation of Lispers.
In a testament to the timelessness of Lisp, you can still run all the examples below in emacs if you install these aliases:
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I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real