# set http proxy
export http_proxy=http://PROXYHOST:PROXYPORT
# set http proxy with user and password
export http_proxy=http://USERNAME:PASSWORD@PROXYHOST:PROXYPORT
# set http proxy with user and password (with special characters)
# Find all tables and when they were last vacuumed/analyzed, either manually or automatically | |
SELECT relname, | |
last_vacuum, | |
last_autovacuum, | |
last_analyze, | |
last_autoanalyze | |
FROM pg_stat_all_tables | |
WHERE schemaname = 'public' | |
ORDER BY last_vacuum DESC; |
#!/bin/bash | |
sudo apt-get update && sudo apt-get dist-upgrade -y | |
sudo apt-get install git | |
git clone https://github.com/jakedahn/dotfiles | |
cd dotfiles | |
./scripts/atom-kali.sh |
5uper5ecret3@ |
I use Namecheap.com as a registrar, and they resale SSL Certs from a number of other companies, including Comodo.
These are the steps I went through to set up an SSL cert.
// What system are we connected to? | |
systeminfo | findstr /B /C:"OS Name" /C:"OS Version" | |
// Get the hostname and username (if available) | |
hostname | |
echo %username% | |
// Get users | |
net users | |
net user [username] |
wat |
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
{ | |
"binary_file_patterns": | |
[ | |
"*.db", | |
"*.sqlite", | |
"*.sqlite3", | |
"*.jpg", | |
"*.jpeg", | |
"*.png", | |
"*.gif", |