Micha Gorelick mynameisfiber

## gist:1030133
#include <stdio.h>
#include <functional>

double any_op(double, double, std::function<double(double,double)>);

int main()
{
  std::function<double(double,double)> add = [](double a, double b) -> double { return a+b; };
  std::function<double(double,double)> sub = [](double a, double b) -> double { return a-b; };

## gist:1642403
In [21]: a = np.zeros( (20) )

In [22]: b = np.asarray( [2, 5, 7, 15] )

In [23]: a
Out[23]:
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.])

In [24]: a[b] = 1

## gist:1653539
$ # First we set the correct python path and compilation flags
$ export vi_cv_path_python_plibs="-L/bitly/local/lib/python2.7/config/ -lpython2.7 -lpthread -lutil -Xlinker -export-dynamic"

$ # Configure will all the options we love the most
$ ./configure --with-features=huge --enable-pythoninterp --with-python-config-dir=/bitly/local/lib/python2.7/config/

$ # Make & install
$ make -j & sudo make install

## challenge.py
import re

def check_winner(board,row=4):
    try:
        return re.search(r"([^.|])(\1{%d}|%s)"%(row-1,"|".join(r"(.{%d}\1){%d}"%(len(a[0])+d,row-1) for d in range(-1,2))), "|".join("".join(x) for x in a)).group()[0]
    except:
        return "No one"

if __name__ == "__main__":
    a = [

## imggtr.go
package main

import (
	"bytes"
	"errors"
	"fmt"
	"io"
	"net/http"
	"net/url"
	"path"

## chunk.py
#!/bin/env python
# $ seq 50 | python chunk.py 5 "xargs echo '-> '"
# ->  1 2 3 4 5
# ->  6 7 8 9 10
# ->  11 12 13 14 15
# ->  16 17 18 19 20
# ->  21 22 23 24 25
# ->  26 27 28 29 30
# ->  31 32 33 34 35
# ->  36 37 38 39 40

## gist:2853066
package main

import (
	"crypto/tls"
    "net"
    "net/http"
    "time"
    "fmt"
    "errors"
)

## irc2ps.sh
#!/bin/bash
# Publishes IRC messages from a particular channel to a pubsub

server="irc.wikimedia.org"
channel="#en.wikipedia"
pubsub="http://localhost:8080/pub"

( echo "USER irc2ps * * :irc2ps_bot
NICK irc2ps$RANDOM
JOIN $channel"; cat -) | nc $server 6667 |

## gist:3426140
$ sort -m country.decodes.2012-07-* | grep -v "(null)" | uniq -c | sort -n > country.count
$ cat country.count.norm | column -t -c1 | cut -f1 -d" " | paste -sd+ | bc | xargs -I{} awk '{if ($1/{} > .001) {print $1/{} " " $2}}' country.count.norm | sort -n | column
$ #tl;tr?

## index.html
<!DOCTYPE html>
<!--
    This visualization shows how a distribution gets decayed to uniform
    using a poisson based decay model.  This type of decay is implemented
    in forgettable in order to store time relevant statistical models.

    In this visualization, the blocks on the right (darker color)
    represent the original distribution, while the blocks on the right
    (lighter color) are the decayed values.  The decay is currently
    happening at a constant rate and the visualization can be reset by
	#include <stdio.h>
	#include <functional>

	double any_op(double, double, std::function<double(double,double)>);

	int main()
	{
	std::function<double(double,double)> add = [](double a, double b) -> double { return a+b; };
	std::function<double(double,double)> sub = [](double a, double b) -> double { return a-b; };
	In [21]: a = np.zeros( (20) )

	In [22]: b = np.asarray( [2, 5, 7, 15] )

	In [23]: a
	Out[23]:
	array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
	0., 0., 0., 0., 0., 0., 0.])

	In [24]: a[b] = 1
	$ # First we set the correct python path and compilation flags
	$ export vi_cv_path_python_plibs="-L/bitly/local/lib/python2.7/config/ -lpython2.7 -lpthread -lutil -Xlinker -export-dynamic"

	$ # Configure will all the options we love the most
	$ ./configure --with-features=huge --enable-pythoninterp --with-python-config-dir=/bitly/local/lib/python2.7/config/

	$ # Make & install
	$ make -j & sudo make install
	import re

	def check_winner(board,row=4):
	try:
	return re.search(r"([^.\|])(\1{%d}\|%s)"%(row-1,"\|".join(r"(.{%d}\1){%d}"%(len(a[0])+d,row-1) for d in range(-1,2))), "\|".join("".join(x) for x in a)).group()[0]
	except:
	return "No one"

	if __name__ == "__main__":
	a = [
	package main

	import (
	"bytes"
	"errors"
	"fmt"
	"io"
	"net/http"
	"net/url"
	"path"
	#!/bin/env python
	# $ seq 50 \| python chunk.py 5 "xargs echo '-> '"
	# -> 1 2 3 4 5
	# -> 6 7 8 9 10
	# -> 11 12 13 14 15
	# -> 16 17 18 19 20
	# -> 21 22 23 24 25
	# -> 26 27 28 29 30
	# -> 31 32 33 34 35
	# -> 36 37 38 39 40
	package main

	import (
	"crypto/tls"
	"net"
	"net/http"
	"time"
	"fmt"
	"errors"
	)
	#!/bin/bash
	# Publishes IRC messages from a particular channel to a pubsub

	server="irc.wikimedia.org"
	channel="#en.wikipedia"
	pubsub="http://localhost:8080/pub"

	( echo "USER irc2ps * * :irc2ps_bot
	NICK irc2ps$RANDOM
	JOIN $channel"; cat -) \| nc $server 6667 \|
	$ sort -m country.decodes.2012-07-* \| grep -v "(null)" \| uniq -c \| sort -n > country.count
	$ cat country.count.norm \| column -t -c1 \| cut -f1 -d" " \| paste -sd+ \| bc \| xargs -I{} awk '{if ($1/{} > .001) {print $1/{} " " $2}}' country.count.norm \| sort -n \| column
	$ #tl;tr?
	<!DOCTYPE html>
	<!--
	This visualization shows how a distribution gets decayed to uniform
	using a poisson based decay model. This type of decay is implemented
	in forgettable in order to store time relevant statistical models.

	In this visualization, the blocks on the right (darker color)
	represent the original distribution, while the blocks on the right
	(lighter color) are the decayed values. The decay is currently
	happening at a constant rate and the visualization can be reset by