svaksha

using HTTPClient: HTTPC
using JSON

# Fill in your city name and GitHub API token
const MEETUP_LOCATION = lowercase("Seattle")
const API_TOKEN = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
# Pages of search results to examine. Warning - you WILL hit the rate limit
const NUM_PAGES = 1

typealias Field Union{AbstractString,Void}

rossant

Problems with HDF5

• Corruption: not sure why, sometimes files get corrupted during a session and users lose all their work, either automatic or manual, which may correspond to days of computer time or, worse, human time. Corruption is more likely to happen because libhdf5 is a very complex piece of software, and a crash or sudden kill is likely to corrupt the file completely. This would be much rarer with flat binary or text files, at least you'd be able to recover part of the data.

• Not possible to delete arrays, but that might be fixed in the future (not today though...).

• Various bugs with strings on Windows and h5py: users may need to downgrade h5py in order to use their files, otherwise a nasty segfault occurs. Not a good sign...

• There is a single implementation of HDF5 in the world, so we depend critically on it. It is almost impossible to contribute on such a complex piece of code since it is really low-level (in C). There are bugs and performance issues with it and there is nothing we can

svaksha

function query() {
  var 
    total = 0, shown = 0,
    // HN is done with very unsemantic classes.
    job_list = Array.prototype.slice.call(document.querySelectorAll('.c5a,.cae,.c00,.c9c,.cdd,.c73,.c88')),
    query_list = Array.prototype.slice.call(arguments);
    
  // This traverses up the dom stack trying to find a match of a specific class
  function up_to(node, klass) {
    if (node.className === klass) {

svaksha

@doc doc"""
    Takes a 2d array where each column is a pattern which maps to a row in
    the resulting dataframe. Each pattern index is its own column in 
    the dataframe.
""" ->
function DataFrame(data::Array{Float64, 2})
    m = data'
    result = DataFrame()
    
    for i in 1:size(data, 1)

rofinn

@doc doc"""
    Takes a 2d array where each column is a pattern which maps to a row in
    the resulting dataframe. Each pattern index is its own column in 
    the dataframe.
""" ->
function DataFrame(data::Array{Float64, 2})
    m = data'
    result = DataFrame()
    
    for i in 1:size(data, 1)

dlo

# vim: set ft=python :

from __future__ import print_function

import json
import sys
import datetime

from redis import StrictRedis as Redis

lbenet

jiahao

#Working version
using JSON
auth_token= #fill in to access Github API

function getcontribs(owner, repo, auth_token)
    #Download information about contributors
    page, authors=0, {}
    while true #Download every page
        page += 1
        url="https://api.github.com/repos/$owner/$repo/contributors?page=$(page)&access_token=$(auth_token)"

svaksha

#Python/pypy
import math
def smallestdivisall(n):
    for i in xrange(1, math.factorial(n)+1):
        for j in xrange(1, n+1):
            if i % j != 0:
                break
            elif j == n:
                return i
                

ivandrofly

Unicode table - List of most common Unicode characters *
* This summary list contains about 2000 characters for most common ocidental/latin languages and most printable symbols but not chinese, japanese, arab, archaic and some unprintable.
Contains character codes in HEX (hexadecimal), decimal number, name/description and corresponding printable symbol.
What is Unicode?

Unicode is a standard created to define letters of all languages ​​and characters such as punctuation and technical symbols. Today, UNICODE (UTF-8) is the most used character set encoding (used by almost 70% of websites, in 2013). The second most used character set is ISO-8859-1 (about 20% of websites), but this old encoding format is being replaced by Unicode.
How to identify the Unicode number for a character?

Type or paste a character: