isaacs

echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc
. ~/.bashrc
mkdir ~/local
mkdir ~/node-latest-install
cd ~/node-latest-install
curl http://nodejs.org/dist/node-latest.tar.gz | tar xz --strip-components=1
./configure --prefix=~/local
make install # ok, fine, this step probably takes more than 30 seconds...
curl https://www.npmjs.org/install.sh | sh

loisaidasam

# One liner for counting unique IP addresses from nginx logs
# Feel free to comment with better ideas - I'm sure it's not the best way of doing this (I'm no awk ninja!)
#
# Sample output:
# 
# $ cat example.com.access.log | awk -F " " '{a[$1]++ } END { for (b in a) { print b, "\t", a[b] } }'
# 66.65.145.220 49
# 92.63.28.68 126

cat example.com.access.log | awk -F " " '{a[$1]++ } END  { for (b in a) { print b, "\t", a[b] } }'

hayajo

ChangeLog を支える英語

ChangeLog を書く際によく使われる英語をまとめました。

ほとんど引用です。

基本形

Hypercubed

DocPad: rsync Deploy Script

• Place deploy.sh in {docpad folder}/bin/
• Create (or edit) a .env file in your docpad folder with the following values:

#!/bin/bash
DEPLOY_SOURCE_DIR="out/"
DEPLOY_DEST_DIR="~/public_html/"
DEPLOY_SERVER=deploy-server-name

whatnickcodes

// Create Base64 Object
var Base64={_keyStr:"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=",encode:function(e){var t="";var n,r,i,s,o,u,a;var f=0;e=Base64._utf8_encode(e);while(f<e.length){n=e.charCodeAt(f++);r=e.charCodeAt(f++);i=e.charCodeAt(f++);s=n>>2;o=(n&3)<<4|r>>4;u=(r&15)<<2|i>>6;a=i&63;if(isNaN(r)){u=a=64}else if(isNaN(i)){a=64}t=t+this._keyStr.charAt(s)+this._keyStr.charAt(o)+this._keyStr.charAt(u)+this._keyStr.charAt(a)}return t},decode:function(e){var t="";var n,r,i;var s,o,u,a;var f=0;e=e.replace(/[^A-Za-z0-9\+\/\=]/g,"");while(f<e.length){s=this._keyStr.indexOf(e.charAt(f++));o=this._keyStr.indexOf(e.charAt(f++));u=this._keyStr.indexOf(e.charAt(f++));a=this._keyStr.indexOf(e.charAt(f++));n=s<<2|o>>4;r=(o&15)<<4|u>>2;i=(u&3)<<6|a;t=t+String.fromCharCode(n);if(u!=64){t=t+String.fromCharCode(r)}if(a!=64){t=t+String.fromCharCode(i)}}t=Base64._utf8_decode(t);return t},_utf8_encode:function(e){e=e.replace(/\r\n/g,"\n");var t="";for(var n=0;n<e.length;n++){var r=e.charCodeAt(n);if(r

megane9988

amazonlinux + nginx + php + mysql + WordPress + Git

ec2 インスタンス取得

• t2.micro
• Security Group は ssh と http を any で通るようにする
  • 可能ならssh は固定IPだけで通るようにする
• 鍵は、すでにあればそれでも、新たに制作してもOK

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

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

pydevops

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

ogazitt

package auth

import (
	"encoding/json"
	"fmt"
	"io"
	"io/ioutil"
	"net"
	"net/http"
	"net/url"

hrishioa

// Requires the gpt library from https://github.com/hrishioa/socrate and the progress bar library.
// Created by Hrishi Olickel ([email protected]) (@hrishioa). Reach out if you have trouble running this.

import { ThunkQueue } from '../../utils/simplethrottler';
import {
  AcceptedModels,
  Messages,
  askChatGPT,
  getMessagesTokenCount,
  getProperJSONFromGPT,