partiallyblind

Things I like to do while visiting Copenhagen

• Copenhagen Street Food: big indoor street food place in Paper Island. Tons of options, but the duck fat fries from Copper and Wheat are amazing.

• Musiksmag: decent little cafe / bar / record shop in Nørrebro.

• Ruby: great cocktail bar.

• Kalaset: fun place for brunch. Close to:

dougireton

Company Requirements

I'm definitely not looking for work. However to provide some helpful guidance for hiring like-minded engineers, I would only consider working for a company if it met these requirements:

1. Fewer than 250 employees.
2. Concrete, measurable plan to increase the number of women and minorities in engineering roles.
3. Commitment to using and contributing to open source.
4. Collaborative, friendly atmosphere where pair programming is encouraged.
5. Meaningful work with clear linkage between work and company goals.
6. Demonstrated commitment to ethical business practices, e.g. B corp certification.

nickstenning

package main

import (
	"encoding/json"
	"errors"
	"fmt"
	"io/ioutil"
	"log"
	"net/http"
	_ "net/http/pprof"

philandstuff

Scale Summit 2014

Intro, MBS

• Chatham House Rule, so no attribution of ideas to people or companies

ideas for sessions

• bootstrapping environments (without object stores)
• service discovery
• removing spofs

jtopper

        config.vm.provider :vmware_fusion do |vm|

            vdiskmanager = '/Applications/VMware\ Fusion.app/Contents/Library/vmware-vdiskmanager'

            dir = "#{ENV['HOME']}/vagrant-additional-disk"

            unless File.directory?( dir )
                Dir.mkdir dir
            end

norm

Accept no meetings days

I operate a policy of having days in which I try hard to not have meetings. If you're reading this, chances are you've looked at my calendar and are wondering what kind of meeting takes all day and happens so frequently. It's not an actual meeting, it is a reservation of the day.

When you're operating on the maker's schedule, meetings are a disaster. A single meeting can blow a whole afternoon, by breaking it into two pieces each too small to do anything hard in.

— Maker's Schedule, Manager's Schedule by Paul Graham

If you still need to book a meeting with me on a reserved day, go ahead. I'll accept it if I think it's more important than having time for coding and thinking and solving hard problems.

richardjpope

# This is a very basic script to backup oyster card data to a scraperwiki vault
# Notes:
# 1) You need an oyster card that has been registered on tfl.gov.uk
# 2) This script requires you to enter your username and password (this about what that means before progressing, and do so at your own risk)
# 3) This script should be run in a PRIVATE SCRAPERWIKI VAULT ONLY https://scraperwiki.com/pricing/ not a public scraper, or the world will know your password

import scraperwiki
import mechanize
import lxml.html
from lxml.etree import tostring

npryce

#!/bin/bash

# Usage: merge-to-subdir source-repo destination-repo subdir
# 
# Merges the history of source-repo into destination-repo as the
# history of the subdirectory subdir.
# 
# source-repo can be local or remote.
# destination-repo must be local to the machine.
# subdir can be a relative path, in which case intermediate

npryce

If you're coming to the Property-Based TDD As If You Meant It Workshop, you will need to bring a laptop with your favourite programming environment, a property-based testing library and, depending on the language, a test framework to run the property-based-tests.

Any other languages or suggestions? Comment below.

.NET (C#, F#, VB)

• FsCheck

Python:

mattb

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/