Pragam pmg007

`vs` Questions

http://stackoverflow.com/questions/804115 (rebase vs merge).
https://www.atlassian.com/git/tutorials/merging-vs-rebasing (rebase vs merge)
https://www.atlassian.com/git/tutorials/undoing-changes/ (reset vs checkout vs revert)
http://stackoverflow.com/questions/2221658 (HEAD^ vs HEAD~) (See git rev-parse)
http://stackoverflow.com/questions/292357 (pull vs fetch)
http://stackoverflow.com/questions/39651 (stash vs branch)
http://stackoverflow.com/questions/8358035 (reset vs checkout vs revert)
http://stackoverflow.com/questions/5798930 (git reset vs git rm --cached)

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

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

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

Title

<Additional information about your API call. Try to use verbs that match both request type (fetching vs modifying) and plurality (one vs multiple).>

URL

<The URL Structure (path only, no root url)>
Method:

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

	Below are the Big O performance of common functions of different Java Collections.


	List \| Add \| Remove \| Get \| Contains \| Next \| Data Structure
	---------------------\|------\|--------\|------\|----------\|------\|---------------
	ArrayList \| O(1) \| O(n) \| O(1) \| O(n) \| O(1) \| Array
	LinkedList \| O(1) \| O(1) \| O(n) \| O(n) \| O(1) \| Linked List
	CopyOnWriteArrayList \| O(n) \| O(n) \| O(1) \| O(n) \| O(1) \| Array