i-am-tom

{-# LANGUAGE AllowAmbiguousTypes #-}
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE FlexibleInstances #-}
{-# LANGUAGE FunctionalDependencies #-}
{-# LANGUAGE GADTs #-}
{-# LANGUAGE KindSignatures #-}
{-# LANGUAGE PolyKinds #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE TypeApplications #-}

mpickering

How to test your package with ghc-8.6.1

1. Setup the binary cache -

cachix use mpickering

For only GHC

porglezomp

import Data.Vect

-- `minus` is saturating subtraction, so this works like we want it to
eq_max : (n, k : Nat) -> maximum k n = plus (n `minus` k) k
eq_max  n     Z    = rewrite minusZeroRight n in rewrite plusZeroRightNeutral n in Refl
eq_max  Z    (S _) = Refl
eq_max (S n) (S k) = rewrite sym $ plusSuccRightSucc (n `minus` k) k in rewrite eq_max n k in Refl

-- The type here says "the result is" padded to (maximum k n), and is padding plus the original
leftPad : (x : a) -> (n : Nat) -> (xs : Vect k a)

Gabriella439

import Numeric.Natural (Natural)

import qualified Data.Semigroup

-- | @fibonacci n@ computes the @nth@ fibonacci number efficiently using infinite
-- precision integer arithmetic
-- 
-- Try @fibonacci 1000000@
fibonacci :: Natural -> Natural
fibonacci n = x01 (Data.Semigroup.mtimesDefault n m)

VictorTaelin

This is a term that performs modulo 32 addition on two 32-bit binary numbers efficiently on the abstract algorithm.

On this example, I compute 279739872 + 496122620 = 775862492.

binSize= 32
binZero= (binSize r.a.b.c.(a r) a.b.c.c)
binSucc= (binSize r.x.a.b.c.(x b x.(a (r x)) c) a.a)
binFold= x.a.b.c.(binSize r.x.(x x.f.(a (f x)) x.f.(b (f x)) f.c r) a.c x)
binToNat= (binSize r.n.x.(x x.f.(f x) x.f.(add n (f x)) f.0 (r (mul 2 n))) n.x.0 1)

taktoa

# Add this file to your /etc/nixos/configuration.nix `imports = [ ... ];` attribute.
#
# After running `nixos-rebuild switch`, `systemctl --user start keybase-gui.service`
# can be used to start the Keybase GUI.
#
# Not sure if it's just my tiling window manager, but there is a bit of wonkiness
# with the tray icon. Other than that it works perfectly (as of 2017/11/22).

{ pkgs, ... }:

thumphries

{-# LANGUAGE TemplateHaskell #-}
module Lens where

import Control.Lens
import Data.Monoid (First, (<>))

data FooBar =
     Foo (Either Int Bool)
   | Bar (Maybe Bool)

chrisdone

{-# LANGUAGE OverloadedStrings #-}
{-# OPTIONS_GHC -Wall #-}

-- Set your font to a monospace font which makes this character the same as the line-height: │
--
-- Otherwise, you'll see an ugly gap between connected lines if the
-- line-height of the font is high.
--
-- Example fonts:
--

i-am-tom

module Main where

-- | JSON is an incredibly simple format. Even its lists are untyped.
-- | As with all languages, functional programming encourages us to
-- | make a domain-specific language (or DSL) to capture the "ideas"
-- | of the language, which we can then use to talk about its content.

-- | In this little snippet, we'll build a JSON DSL, transform it into
-- | a recursive structure, and then use that result to generate some

EdOverflow

GitHub for Bug Bounty Hunters

GitHub repositories can disclose all sorts of potentially valuable information for bug bounty hunters. The targets do not always have to be open source for there to be issues. Organization members and their open source projects can sometimes accidentally expose information that could be used against the target company. in this article I will give you a brief overview that should help you get started targeting GitHub repositories for vulnerabilities and for general recon.

Mass Cloning

You can just do your research on github.com, but I would suggest cloning all the target's repositories so that you can run your tests locally. I would highly recommend @mazen160's GitHubCloner. Just run the script and you should be good to go.

$ python githubcloner.py --org organization -o /tmp/output