Los Alamos, New Mexico Updated 2025-07-16
Video 1.
This is why we moved here by Syd and Macky (2022)
Source.
Ming dynasty Updated 2025-07-16
New York Updated 2025-07-16
Chinese culture Updated 2025-07-16
Bibliography:
Concurrent Versions System Updated 2025-07-16
It is said, that once upon a time, programmers used CSV and collaborated on SourceForge, and that everyone was happy.
These days, are however, long gone in the mists of time as of 2020, and beyond Ciro Santilli's programming birth.
Except for hardware developers of course. The are still happily using Perforce and Tcl, and shall never lose their innocence. Blessed be their souls. Amen.
C# Updated 2025-07-16
Mitchell and Webb Updated 2025-07-16
They do have some really good ones.
It is interesting that in different episodes they often switch the dominant/passive roles, so it's not fixed as in Laurel and Hardy.
Video 1.
Are we the Baddies? by Mitchell and Webb
. Source. See also: cirosantilli.com/china-dictatorship/nazi.
Video 2.
Discoverer by Mitchell and Webb
. Source. Makes fun of the many terrible naming choices British navigators have made while discovering/rediscovering new lands.
Video 3.
Homeopathic A&E by Mitchell and Webb
. Source.
Perforce Updated 2025-07-16
Theria Updated 2025-07-16
Every mammal except the weird monotremes, i.e. marsupials and the placentalia.
The name is completely random, "wild beast". Are platypuses not "wild beasts"? They have a freaking poison!!
X-ray crystallography Updated 2025-07-16
One of its main applications is to determine the 3D structure of proteins.
Sometimes you are not able to crystallize the proteins however, and the method cannot be used.
Crystallizing is not simple because:
  • you need a considerable amount of the protein
  • sometimes it only crystallizes if you add some extra small chemical that stabilizes it
Cryogenic electron microscopy can sometimes determine the structures of proteins that failed crystallization.
Some generic Micropython examples most of which work on this board can be found at: Section "MicroPython example".
Pico W specific examples are under: rpi-pico-w/upython.
The examples can be run as described at Program Raspberry Pi Pico W with MicroPython.
We've noticed that often when there is a hit range:
  • there is only one IP for each domain
  • there is a range of about 20-30 of those
and that this does not seem to be that common. Let's see if that is a reasonable fingerprint or not.
Note that although this is the most common case, we have found multiple hits that viewdns.info maps to the same IP.
First we create a table u (unique) that only have domains which are the only domain for an IP, let's see by how much that lowers the 191 M total unique domains:
time sqlite3 u.sqlite 'create table t (d text, i text)'
time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where d not like '%.%.%' group by i having count(distinct d) = 1"
The not like '%.%.%' removes subdomains from the counts so that CGI comms are still included, and distinct in count(distinct is because we have multiple entries at different timestamps for some of the hits.
Let's start with the 208 subset to see how it goes:
time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where i glob '208.*' and d not like '%.%.%' and (d like '%.com' or d like '%.net') group by i having count(distinct d) = 1"
OK, after we fixed bugs with the above we are down to 4 million lines with unique domain/IP pairs and which contains all of the original hits! Almost certainly more are to be found!
This data is so valuable that we've decided to upload it to: archive.org/details/2013-dns-census-a-novirt.csv Format:
8,chrisjmcgregor.com
11,80end.com
28,fine5.net
38,bestarabictv.com
49,xy005.com
50,cmsasoccer.com
80,museemontpellier.net
100,newtiger.com
108,lps-promptservice.com
111,bridesmaiddressesshow.com
The numbers of the first column are the IPs as a 32-bit integer representation, which is more useful to search for ranges in.
To make a histogram with the distribution of the single hostname IPs:
#!/usr/bin/env bash
bin=$((2**24))
sqlite3 2013-dns-census-a-novirt.sqlite -cmd '.mode csv' >2013-dns-census-a-novirt-hist.csv <<EOF
select i, sum(cnt) from (
  select floor(i/${bin}) as i,
         count(*) as cnt
    from t
    group by 1
  union
  select *, 0 as cnt from generate_series(0, 255)
)
group by i
EOF
gnuplot \
  -e 'set terminal svg size 1200, 800' \
  -e 'set output "2013-dns-census-a-novirt-hist.svg"' \
  -e 'set datafile separator ","' \
  -e 'set tics scale 0' \
  -e 'unset key' \
  -e 'set xrange[0:255]' \
  -e 'set title "Counts of IPs with a single hostname"' \
  -e 'set xlabel "IPv4 first byte"' \
  -e 'set ylabel "count"' \
  -e 'plot "2013-dns-census-a-novirt-hist.csv" using 1:2:1 with labels' \
;
Which gives the following useless noise, there is basically no pattern:
https://raw.githubusercontent.com/cirosantilli/media/master/cia-2010-covert-communication-websites/2013-dns-census-a-novirt-hist.svg

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