akaptur

################################################################################
### namedtuple
################################################################################

_class_template = '''\
class {typename}(tuple):
    '{typename}({arg_list})'

    __slots__ = ()

jvns

A lot of these are outright stolen from Edward O'Campo-Gooding's list of questions. I really like his list.

I'm having some trouble paring this down to a manageable list of questions -- I realistically want to know all of these things before starting to work at a company, but it's a lot to ask all at once. My current game plan is to pick 6 before an interview and ask those.

I'd love comments and suggestions about any of these.

I've found questions like "do you have smart people? Can I learn a lot at your company?" to be basically totally useless -- everybody will say "yeah, definitely!" and it's hard to learn anything from them. So I'm trying to make all of these questions pretty concrete -- if a team doesn't have an issue tracker, they don't have an issue tracker.

I'm also mostly not asking about principles, but the way things are -- not "do you think code review is important?", but "Does all code get reviewed?".

jvns

• Geek Feminism: http://geekfeminism.org/
• Philip Guo's writing: http://www.pgbovine.net/writings.htm
• Mel Chua: http://blog.melchua.com/
• Pointers Gone Wild (by Maxime Chevalier): http://pointersgonewild.wordpress.com/ (compiler fun!)
• Sumana's blog: http://www.harihareswara.net/ces.shtml
• Lindsey Kuper's great research blog: http://composition.al/
• Kelly Sommers is so inquisitive and thoughtful and amazing: http://kellabyte.com/
• Selena Deckelmann: http://www.chesnok.com/daily/
• Dan Luu: http://danluu.com/
• http://planet.mozillaopennews.org/

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

indirect

#!/bin/bash

# Set up paths and whatnot
test -e ~/.bashrc && source ~/.bashrc

# We need tmux. Obvs.
if [[ -z `which tmux` ]]; then echo "You need tmux first!"; exit 1; fi

# Named variables are much more flexible
name="$1"

patshaughnessy

Recently someone asked me for online resources about MRI's internal C source
code. Here are a few - if there are more to add please leave a comment! - pat

1. Ruby Hacking Guide - The definitive resource for people who want to learn
the C programming details of how Ruby works internally. Intended for C hackers.
It was just recently translated into English from the original Japanese.
http://ruby-hacking-guide.github.io

2. Various presentations by Koichi Sasada - he often does public presentations
on Ruby internals and they're always fascinating and full of technical details.

macintux

This is not intended to be comprehensive or authoritative, just free online resources I've found valuable while learning more about Erlang.

Similar lists

• 0xAX's list of Erlang bookmarks
• Federico Carrone, Erlang Spawned Shelter
• Ivan Uemlianin's list of resources on various BEAM languages
• David Robakowski's curated list of awesome Erlang libraries, resources and shiny things
• Julius Beckmann's curated list of amazingly awesome Elixir and Erlang libraries, resources and shiny things

Instruction

gigamonkey

Books

Algorithms and Data Structures

The Art of Computer Programming (Knuth)

Programming Pearls (Bentley)

Data Structures and Algorithms (Aho, Hopcroft, Ullman)

ragingwind

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

adamwiggins

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