ananthrk

Your blockchain must have all of the following properties:

It's a merkle tree, or a construct with equivalent properties.
There is no single point of trust or authority; nodes are operated by different parties.
Multiple 'forks' of the blockchain may exist - that is, nodes may disagree on what the full sequence of blocks looks like.
In the case of such a fork, there must exist a deterministic consensus algorithm of some sort to decide what the "real" blockchain looks like (ie. which fork is "correct").
The consensus algorithm must be executable with only the information contained in the blockchain (or its forks), and no external input (eg. no decisionmaking from a centralized 'trust node').

If your blockchain is missing any of the above properties, it is not a blockchain, it is just a ledger.

Make it real

Ideas are cheap. Make a prototype, sketch a CLI session, draw a wireframe. Discuss around concrete examples, not hand-waving abstractions. Don't say you did something, provide a URL that proves it.

Ship it

Nothing is real until it's being used by a real user. This doesn't mean you make a prototype in the morning and blog about it in the evening. It means you find one person you believe your product will help and try to get them to use it.

Do it with style

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

awesome graphql great for learning more about GraphQL as a technology.
graphql schema design at scale @xuorig's excellent talk, he has a book coming out soon too
shopify graphql schema design tutorial
Lessons of 4 years of GraphQL @leebyron touches on schema design and tooling here
Fighting legacy codebases with GraphQL and Rails good discussion on some tooling around performance here by @nettofarah

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. While it's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.

###Dataflow Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. While it's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.

###Dataflow Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

	import sys


	#
	# ParserInput
	#
	# This class represents the input data and the current
	# position in the data.
	#
	# Brief note about 'max_position':