copumpkin

module Prime where

open import Coinduction

open import Data.Empty
open import Data.Nat
open import Data.Nat.Properties
open import Data.Nat.Divisibility
open import Data.Fin hiding (pred; _+_; _<_; _≤_; compare)
open import Data.Fin.Props hiding (_≟_)

thoughtpolice

-- Inspired by the writings of Edward Kmett, Edward Yang and Gabriel Gonzalez
-- concerning free monads and the codensity transformation.
--
-- http://comonad.com/reader/2011/free-monads-for-less/
-- http://blog.ezyang.com/2012/01/problem-set-the-codensity-transformation/
-- http://www.haskellforall.com/2012/06/you-could-have-invented-free-monads.html
--
{-# LANGUAGE RankNTypes #-}
{-# LANGUAGE MultiParamTypeClasses #-}
{-# LANGUAGE FlexibleInstances #-}

ymasory

Companies hiring Scala developers in the Bay Area.
Created in response to a thread on scala-base.

My favorites:
- CloudPhysics
- Wordnik

Unbiased list:
- 10Gen
- Audax Health

swannodette

;; list comprehensions are often used as a poor man's Prolog
;; consider the following, it has only one solution 
;; [1 1 1 1 1 1 1 1 1 1] yet we actually consider 10^10 possibilities

(for [a (range 1 11)
      b (range 1 11)
      c (range 1 11)
      d (range 1 11)
      e (range 1 11)
      f (range 1 11)

romainguinot

--- checkgmail	2013-02-28 14:24:44.614055113 +0100
+++ checkgmail.current	2013-03-01 22:22:30.211693526 +0100
@@ -33,13 +33,18 @@
 
 # global variables (can't be set global in the BEGIN block)
 my ($version, $silent, $nocrypt, $update, $notsexy, $profile, $disable_monitors_check, 
-		$private, $cookies, $popup_size, $hosted_tmp, $show_popup_delay, 
-		$popup_persistence, $usekwallet, $libsexy, $nologin, $mailno, $debug);
+		$private, $cookies, $two_factor_auth_switch, $popup_size, $hosted_tmp, $show_popup_delay,
+		$popup_persistence, $use_kwallet, $libsexy, $nologin, $mailno, $debug);

NathanHowell

{-# OPTIONS_GHC -fno-warn-orphans #-}
{-# LANGUAGE BangPatterns #-}
{-# LANGUAGE DeriveFunctor #-}
{-# LANGUAGE DeriveFoldable #-}
{-# LANGUAGE DeriveTraversable #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE StandaloneDeriving #-}

macintux

• https://ferd.ca/a-distributed-systems-reading-list.html
• http://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/
• https://github.com/palvaro/CMPS290S-Winter16/blob/master/readings.md
• http://muratbuffalo.blogspot.com/2015/12/my-distributed-systems-seminars-reading.html
• http://christophermeiklejohn.com/distributed/systems/2013/07/12/readings-in-distributed-systems.html
• http://michaelrbernste.in/2013/11/06/distributed-systems-archaeology-works-cited.html
• http://rxin.github.io/db-readings/
• http://research.microsoft.com/en-us/um/people/lamport/pubs/pubs.html
• http://pdos.csail.mit.edu/dsrg/papers/
• http://scalingsystems.com/2011/09/07/reading-list-for-distributed-systems/

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

michaelochurch

Real estate (like oil in the short term, cf. "oil shocks" of the 1970s) is extremely supply-inelastic, 
such that a 1% loss of supply might cause prices to go up *a lot* more than 1%-- possibly 10%, 20%, 
even 2x. The result of this is that a destruction of real estate supply will actually increase the 
*total* market value: a cataclysmic loss of 5% of NYC's real estate would drive prices through the roof 
and make the price level look "healthy" (from the buyer-centric perspective common in the US when it 
comes to housing). But no value was created! Much was destroyed. 

The 2000s run-up in NYC's housing costs was driven largely by speculation after 9/11. While we 
*actually* had 12+ subsequent years with no major domestic terror attacks, there were a large number of 
(esp. foreign) speculators who expected future attacks in major cities and bought lots of RE in 

david-christiansen

module FizzBuzzC

%default total

-- Dependently typed FizzBuzz, constructively

-- A number is fizzy if it is evenly divisible by 3
data Fizzy : Nat -> Type where
  ZeroFizzy : Fizzy 0
  Fizz : Fizzy n -> Fizzy (3 + n)