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

john2x

Clojure Destructuring Tutorial and Cheat Sheet

(Related blog post)

Simply put, destructuring in Clojure is a way extract values from a datastructure and bind them to symbols, without having to explicitly traverse the datstructure. It allows for elegant and concise Clojure code.

Vectors and Sequences

max-mapper

davatron5000

Backstory: I decided to crowdsource static site generator recommendations, so the following are actual real world suggested-to-me results. I then took those and sorted them by language/server and, just for a decent relative metric, their Github Watcher count. If you want a heap of other projects (including other languages like Haskell and Python) Nanoc has the mother of all site generator lists. If you recommend another one, by all means add a comment.

Ruby

• Jekyll 5,676
• Octopress 2,696
• Middleman 685
• NestaCMS 664
• Statis 509
• Nanoc 355

mbostock

Collections

each(array)

Underscore example:

_.each([1, 2, 3], function(num) { alert(num); });

tmcw

This is a non-technical reading list for technical people.

• How to Program Independent Games
• Kill Math (and Don't Kill Math)
• Move Fast and Don't Break Things
• Launch & The Web Is a Customer Service Medium by Paul Ford
• _why at Art & Code
• Repetitive Process

This is a list of software you should read like a novel.

mplewis

Flask + uWSGI + nginx Primer

I've been having trouble with serving a Flask app via uWSGI and nginx, so I thought I'd put together some of the basics to help out others.

How this shit works

• Flask is managed by uWSGI.
• uWSGI talks to nginx.

kwmsmith

#cython:boundscheck=False
#cython:wraparound=False

import numpy as np
from cython.parallel cimport prange
from libc.math cimport sqrt

cdef inline double dotp(int i, int j, int N, double[:, ::1] X) nogil:
    cdef:
        int k

ptaoussanis

(comment ; Fun with transducers, v2

  ;; Still haven't found a brief + approachable overview of Clojure 1.7's new
  ;; transducers in the particular way I would have preferred myself - so here goes:

  ;;;; Definitions
  ;; Looking at the `reduce` docstring, we can define a 'reducing-fn' as:
  (fn reducing-fn ([]) ([accumulation next-input])) -> new-accumulation
  ;; (The `[]` arity is actually optional; it's only used when calling
  ;; `reduce` w/o an init-accumulator).

jneen

; it's a bit cumbersome to set up and there's the unfortunate need to ignore the tag
; in the individual methods, but this allows you to leave the interpretation of open 
; variants, well, *open* for extension by multimethod.

; dispatch off the first argument, which will be the tag
(defmethod command-multi (fn [tag & data] tag))

; the first argument to the *method* is still the tag
(defmulti command-multi :print [_ val] (println val))
(defmulti command-multi :read [_ fname] (slurp fname))