npinsker

use std::cmp;
use std::collections::HashMap;
use std::env;
use std::fs;
use std::fs::File;
use std::hash::Hash;
use std::io::Write;
use std::time::SystemTime;

const POWERS_OF_THREE: [u8; 5] = [1, 3, 9, 27, 81];

dhh

ruby '2.7.1'

gem 'rails', github: 'rails/rails'
gem 'tzinfo-data', '>= 1.2016.7'  # Don't rely on OSX/Linux timezone data

# Action Text
gem 'actiontext', github: 'basecamp/actiontext', ref: 'okra'
gem 'okra', github: 'basecamp/okra'

# Drivers

0xASK

Interledger Testnet

Welcome!

It’s awesome that you’re interested in building on the Interledger Protocol (ILP). Here are some resources to get you started and if you have any questions feel free to ask questions over the Interledger gitter chat.

What is the Interledger Protocol (ILP)?

andyrbell

# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

lmakarov

'use strict';
exports.handler = (event, context, callback) => {

    // Get request and request headers
    const request = event.Records[0].cf.request;
    const headers = request.headers;

    // Configure authentication
    const authUser = 'user';
    const authPass = 'pass';

alysonla

-- Active issues
-- Count of total active issues in the specified time frame
-- Source: githubarchive public data set via Google BigQuery http://githubarchive.org/

SELECT
	COUNT(DISTINCT JSON_EXTRACT_SCALAR(events.payload, '$.issue.id')) AS events_issue_count
FROM (SELECT * FROM TABLE_DATE_RANGE([githubarchive:day.],TIMESTAMP('2015-09-01'),TIMESTAMP('2016-08-31')))
 AS events

-- 10,723,492 active issues

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

rgrove

Can't share the complete code because the app's closed source and still in stealth mode, but here's how I'm using React Router and Redux in a large app with server rendering and code splitting on routes.

Server

1. Wildcard Express route configures a Redux store for each request and makes an addReducers() callback available to the getComponents() method of each React Router route. Each route is responsible for adding any Redux reducers it needs when it's loaded. (This isn't really necessary on the

NameFILIP

document.querySelector('.player-video-wrapper').addEventListener('click', function() {
  document.querySelector('.player-play-pause').dispatchEvent(new MouseEvent('click'));
});

bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen