| alias accio=wget | |
| alias avadaKedavra='rm -f' | |
| alias imperio=sudo | |
| alias priorIncantato='echo `history |tail -n2 |head -n1` | sed "s/[0-9]* //"' | |
| alias stupefy='sleep 5' | |
| alias wingardiumLeviosa=mv | |
| alias sonorus='set -v' | |
| alias quietus='set +v' |
A quick guide to write a very very simple "ECHO" style module to redis and load it. It's not really useful of course, but the idea is to illustrate how little boilerplate it takes.
Step 1: open your favorite editor and write/paste the following code in a file called module.c
#include "redismodule.h"
/* ECHO <string> - Echo back a string sent from the client */
int EchoCommand(RedisModuleCtx *ctx, RedisModuleString **argv, int argc) {Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
| --- | |
| title: "Visualizing the Clinton Email Network in R" | |
| author: "hrbrmstr" | |
| date: "`r Sys.Date()`" | |
| output: html_document | |
| --- | |
| ```{r include=FALSE} | |
| knitr::opts_chunk$set( | |
| collapse=TRUE, | |
| comment="#>", |
| # Download latest archlinux bootstrap package, see https://www.archlinux.org/download/ | |
| wget 'ftp://ftp.nluug.nl/pub/os/Linux/distr/archlinux/iso/latest/archlinux-bootstrap-*-x86_64.tar.gz' | |
| # Make sure you'll have enough entropy for pacman-key later. | |
| apt-get install haveged | |
| # Install the arch bootstrap image in a tmpfs. | |
| mount -t tmpfs none /mnt | |
| cd /mnt | |
| tar xvf ~/archlinux-bootstrap-*-x86_64.tar.gz --strip-components=1 |
The Federal Aviation Administration is posting PDFs of the Section 333 exemptions that it grants, i.e. the exemptions for operators who want to fly drones commercially before the FAA finishes its rulemaking. A journalist wanted to look for exemptions granted to operators in a given U.S. state. But the FAA doesn't appear to have an easy-to-read data file to use and doesn't otherwise list exemptions by location of operator.
However, since their exemptions page is just one giant HTML table for listing the PDFs, we can just use wget to fetch all the PDFs, run pdftotext on each file, and then [grep](https://medium.com/@rualthanzauva/grep-was-a-private-command-of-m
| # You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine | |
| # This is how I upload my new Sol Trader builds (http://soltrader.net) | |
| # Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash | |
| S3KEY="my aws key" | |
| S3SECRET="my aws secret" # pass these in | |
| function putS3 | |
| { | |
| path=$1 |
2015-01-29 Unofficial Relay FAQ
Compilation of questions and answers about Relay from React.js Conf.
Disclaimer: I work on Relay at Facebook. Relay is a complex system on which we're iterating aggressively. I'll do my best here to provide accurate, useful answers, but the details are subject to change. I may also be wrong. Feedback and additional questions are welcome.
Relay is a new framework from Facebook that provides data-fetching functionality for React applications. It was announced at React.js Conf (January 2015).
| #!/usr/bin/env python | |
| """ | |
| MSN ApplicationId patcher for pidgin/bitlbee/whatever | |
| (because you're too lazy to rebuild the whole thing.) | |
| Usage examples: | |
| python patch.py /usr/sbin/bitlbee | |
| python patch.py /usr/lib/purple-2/libmsn.so |
