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#! /bin/sh
if test $# = 0 ; then
    echo "Usage: $0 <sleep interval like 15m>"
    exit 1
fi

while true
do
    echo "Randomizing MAC now"
    spoof-mac randomize en0

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--[[ Usage

Windows:
    lua factorio-recipe-parser.lua "C:/Apps/Factorio/data/base/prototypes/recipe/"
Steam on macOS:
    lua factorio-recipe-parser.lua ~/"Library/Application Support/Steam/steamapps/common/Factorio/factorio.app/Contents/"

NB: json.lua is from https://gist.github.com/tylerneylon/59f4bcf316be525b30ab
]]--

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# tmp: move files to /tmp instead of removing them outright.
# /tmp is cleaned up on reboot or so, so there is time to rectify mistakes.
#
# Usage: tmp [files or directories...]

# batch "do stuff to a file" files...
function batch() {
    p="$1"
    shift
    for x in "$@"; do

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'''
Warning: Turns out parsy is very slow :(

Usage:
    import strace_grammar as grammar
    for line in strace_output:
        # not interested in this strace metadata:
        if line.startswith('strace:'):
            continue

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#! /usr/bin/env bash
set -eu
city=helsinki

for type in restaurant sport culture; do
    for page in {0..5000}; do # limit to 5000 queries on failure.
        url="https://search.edenred.fi/affiliates?page=${page}&count=30&city=${city}&type=${type}"
        file=edenred_${type}_${city}_page_${page}.json

        curl -fL "$url" > "$file"

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![Adblock Plus 1.1]
! NB: Also add https://gitlab.com/isaakm/Custom-Prebake/raw/master/filterlist.txt

! 04/03/2018, 13:08:43 https://sway.com/
sway.com###msccBanner

! 04/03/2018, 14:01:30 https://lumo.fi/
lumo.fi###cookie-disclaimer

! 11/03/2018, 12:04:06 https://pretix.eu/about/en/

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#! /usr/bin/env python
'''Create a simple RSS feed for a podcast

Usage:  python create_feed.py *.m4a
Assumes .m4a audio files. Edit MIME type if needed.
Depends on feedgen:  pip install feedgen
'''
import sys
from feedgen.feed import FeedGenerator

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# This creates a pre-emptible VM on Google Cloud with a
# more or less NVIDIA approved configuration and drivers
# using the public NVIDIA GPU Cloud (NGC) image on GCP.
# The VM has nvidia-docker installed so anything like
# this will work out of the box:
#
#   $ docker run --runtime=nvidia --rm paperspace/fastai:cuda9_pytorch0.3.1
#
# None of the public scripts and info had working configuration anywhere.
# Even the "official" sample scripts don't have working default configuration:

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#! /usr/bin/env python3
# Usage: ndjson_to_csv <files... or stdin> > output.csv
# NB: Assumes that each line is a simple JSON object with no nested arrays or objects
import csv
import json
import sys
import fileinput
from collections import namedtuple, OrderedDict

lines = fileinput.input()

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#
# pip install zstandard
#
# The zstandard bindings are a little off, compared to gzip, etc.
# So small tricks like this are needed to fully decompress a file in-memory:

dctx = zstd.ZstdDecompressor()
data = b''.join(dctx.read_to_iter(open('foo.zst', 'rb')))