davoclavo

emacs --daemon to run in the background. emacsclient.emacs24 <filename/dirname> to open in terminal

NOTE: "M-m and SPC can be used interchangeably".

• Undo - C-/
• Redo - C-?
• Change case: 1. Camel Case : M-c 2. Upper Case : M-u

3. Lower Case : M-l

lalyos

There is no official alpine Docker image for the arm platform. The onec i found are not taking advantage of the much quicker Fastly CDN basedpackage mirror option

build

Here are steps to rebuild the images on a rpi:

git clone https://github.com/gliderlabs/docker-alpine.git docker-alpine-gliderlabs
cd docker-alpine-gliderlabs/

hongster

package main

import (
	"fmt"
	"net"
	"time"
	"bufio"
)

func handleConnection(conn net.Conn) {

adewale

{
  "statements": [
    {
      "source": {
        "web": {
          "site": "https://applinkingexperiment.appspot.com."
        }
      },
      "relation": "delegate_permission/common.handle_all_urls",
      "target": {

jzelinskie

package main

import (
	"log"
	"time"

	"golang.org/x/net/context"
	"google.golang.org/grpc"

	pb "github.com/jzelinskie/grpc/simple"

alphamu

public class NetworkHelper extends Fragment {
  public static final String TAG = "NetworkHelper";
  public static final String CHECK_INTERNET = "network_connection";

  private Activity mActivity;
  AlertDialog mAlertDialog = null;

  private BroadcastReceiver onNotice = new BroadcastReceiver() {

    @Override

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

cbeyls

package be.digitalia.common.widgets;

import android.content.Context;
import android.os.SystemClock;
import android.util.AttributeSet;
import android.view.View;
import android.widget.ProgressBar;

/**
 * ContentLoadingProgressBar implements a ProgressBar that waits a minimum time to be

brandt

# Optimal bloom filter size and number of hashes

# Tips:
# 1. One byte per item in the input set gives about a 2% false positive rate.
# 2. The optimal number of hash functions is ~0.7x the number of bits per item.
# 3. The number of hashes dominates performance.

# Expected number of items in the collection
# n = (m * ln(2))/k;
n = 300_000

ngauthier

// keepDoingSomething will keep trying to doSomething() until either
// we get a result from doSomething() or the timeout expires
func keepDoingSomething() (bool, error) {
	timeout := time.After(5 * time.Second)
	tick := time.Tick(500 * time.Millisecond)
	// Keep trying until we're timed out or got a result or got an error
	for {
		select {
		// Got a timeout! fail with a timeout error
		case <-timeout: