iconara

// this is an attempt to create a synchronous InputStream from a call to
// S3AsyncClient#getObject using a blocking queue.
//
// the purpose is to be able to make many S3 operations asynchronously, but
// at the same time be able to pass off some results to threads and into
// code that expects InputStream or Reader, like a Commons CSV.

public class InputStreamResponseTransformer extends InputStream implements AsyncResponseTransformer<GetObjectResponse, InputStream>, Subscriber<ByteBuffer> {
    private static final ByteBuffer END_MARKER = ByteBuffer.allocate(0);

ericflo

var UndoMixin = {
  getInitialState: function() {
    return {
      undo: []
    };
  },

  handleUndo: function() {
    if (this.state.undo.length === 0) {
      return;

joestump

San Francisco by Foot

• You can start anywhere in the Castro, Mission (home of the burrito), or downtown. I'd recommend starting at Chow at Market & Church or The Pork Store on 16th at Valencia. If you wanted to go upscale on the weekend, Maverick at 17th and Mission is a fantastic brunch. Another option is to start in Chinatown (just north of Union Squre) for dim sum.
• From Castro take one of the N, J, etc. trains (every light rail train route in the city intersects at Church and Market) down to the Powell Street station. From the Mission you can take BART a couple of stops up to Powell Street.
• Once at Powell Street hop on the Powell Street trolley. A quintessential SF experience.
• Take the trolley up through the city where it will eventually stop at Lombard Street, which is known as the most crooked street in the world. Walk down the hill, take a few pictures and laugh at the tourists trying to drive down the damn thing.
• Once at the bottom of the hill, turn left. You'll be walking towards the w

freeformz

A slightly updated version of this doc is here on my website.

Why I Like Go

I visited with PagerDuty yesterday for a little Friday beer and pizza. While there I got started talking about Go. I was asked by Alex, their CEO, why I liked it. Several other people have asked me the same question recently, so I figured it was worth posting.

Goroutines

The first 1/2 of Go's concurrency story. Lightweight, concurrent function execution. You can spawn tons of these if needed and the Go runtime multiplexes them onto the configured number of CPUs/Threads as needed. They start with a super small stack that can grow (and shrink) via dynamic allocation (and freeing). They are as simple as go f(x), where f() is a function.

matthewwithanm

from django.db import models
from imagekit import ImageSpec
from imagekit.models import ImageSpecField
from imagekit.utils import get_field_info
from imagekit import register
import os


class TvSpec(ImageSpec):
    suffix = '-tv'

robgolding

class FilterMixin(object):
    """
    View mixin which provides filtering for ListView.
    """
    filter_url_kwarg = 'filter'
    default_filter_param = None

    def get_default_filter_param(self):
        if self.default_filter_param is None:
            raise ImproperlyConfigured(

srid

// A simple sub command parser based on the flag package
package subcommand

import (
	"flag"
	"fmt"
	"os"
)

type subCommand interface {

piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = [email protected]:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

robgolding

class LoginRequiredMixin(object):
    """
    View mixin which requires that the user is authenticated.
    """
    @method_decorator(login_required)
    def dispatch(self, request, *args, **kwargs):
        return super(LoginRequiredMixin, self).dispatch(
            self, request, *args, **kwargs)

nf

var cluster = require('cluster');
var http = require('http');
var numCPUs = require('os').cpus().length;

if (cluster.isMaster) {
  // Fork workers.
  for (var i = 0; i < numCPUs; i++) {
    cluster.fork();
  }
  cluster.on('exit', function(worker, code, signal) {