joyrexus

Nice answer on stackoverflow to the question of when to use one or the other content-types for POSTing data, viz. application/x-www-form-urlencoded and multipart/form-data.

“The moral of the story is, if you have binary (non-alphanumeric) data (or a significantly sized payload) to transmit, use multipart/form-data. Otherwise, use application/x-www-form-urlencoded.”

---

Matt Bridges' answer in full:

The MIME types you mention are the two Content-Type headers for HTTP POST requests that user-agents (browsers) must support. The purpose of both of those types of requests is to send a list of name/value pairs to the server. Depending on the type and amount of data being transmitted, one of the methods will be more efficient than the other. To understand why, you have to look at what each is doing

yoavniran

The Ultimate Unit Testing Cheat-sheet

---

• Sinon Chai
• Chai
• Sinon

carlhoerberg

var amqp = require('amqplib/callback_api');

// if the connection is closed or fails to be established at all, we will reconnect
var amqpConn = null;
function start() {
  amqp.connect(process.env.CLOUDAMQP_URL + "?heartbeat=60", function(err, conn) {
    if (err) {
      console.error("[AMQP]", err.message);
      return setTimeout(start, 1000);
    }

apolloclark

AWS CLI Cheatsheet

http://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html https://www.youtube.com/watch?v=_wiGpBQGCjU

Setup

alexaleluia12

#!/bin/bash

# to get the list of files to exclude
# ls -al | grep ^- | egrep "^[a-z-]+x " | grep -v .sh$ | cut -d' ' -f 9 > arq_exc

# then

for i in $(cat arq_exc); do
  rm - $i
done

alexaleluia12

// I want to share a tricky thing that happened with me
// * I was programming in C style using feature of C++ (on g++)
// I have this structure

typedef struct {
    vector<float *> * lst_vertices;
    vector<int *> * lst_faces;
    float referencia[3];// o ponto sobre o qual vai ser realizadas as transformacoes
} Obj3d;

alexaleluia12

# Can I use finditer ?
# Yes

# ...

def generate_tokens(pat, text):
    scanner = pat.finditer(text)
        for i in scanner:
            yield Token(i.lastgroup, i.group())
    

alexaleluia12

# font
# http://chimera.labs.oreilly.com/books/1230000000393/ch02.html#_writing_a_simple_recursive_descent_parser

"""
grammar

    expr ::= expr + term
         |   expr - term
         |   term

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

ncopa

FROM python:2.7-alpine
MAINTAINER Nick Janetakis <[email protected]>

ENV INSTALL_PATH /bsawf
RUN mkdir -p $INSTALL_PATH

WORKDIR $INSTALL_PATH

COPY requirements.txt requirements.txt
RUN apk add --no-cache --virtual .build-deps \