gaugau3000

x0nu11byt3

ELF Format Cheatsheet

Introduction

Executable and Linkable Format (ELF), is the default binary format on Linux-based systems.

Compilation

mjf

⚠️ WARNING
The ordering of listed projects or documents is random and has no connection to relevance or maturity levels!

Postgres

Extensions

Extension	Description
pgaudit	Postgres auditing extension

HarryR

from functools import reduce
import binascii
from os import urandom
from py_ecc.bn128 import *
from sha3 import keccak_256

"""
Implements BLS signatture aggregation as described at:

	https://crypto.stanford.edu/~dabo/pubs/papers/BLSmultisig.html

recmo

contract MerkleVerifier {

    function hash_leaf(uint256 value)
        internal pure
        returns (bytes32 hash)
    {
        return bytes32(value);
    }

    function hash_node(bytes32 left, bytes32 right)

BjornvdLaan

pragma solidity ^0.4.14;

/*
Example of how to verify BLS signatures and BGLS aggregate signatures in Ethereum.

Signatures are generated using https://github.com/Project-Arda/bgls
Code is based on https://github.com/jstoxrocky/zksnarks_example
*/

contract BLSExample {

vasanthk

System Design Cheatsheet

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

Basic Steps

1. 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?

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

dideler

Note: I'm currently taking a break from this course to focus on my studies so I can finally graduate

Table of Contents

1. Overview
2. Introduction
3. Interactive Start
4. Linux and Server-Side Javascript
5. Linux Command Line
6. Linux Development Environment