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@llj098
llj098 / Makefile
Created December 23, 2010 01:36
a sample tcp server runs in kernel
obj-m += tcp_svr_sample.o
all:
make -C /lib/modules/$(shell uname -r)/build M=$(PWD) modules
clean:
make -C /lib/modules/$(shell uname -r)/build M=$(PWD) clea
@nthj
nthj / password.rb
Created June 6, 2011 04:15
Password value object for MongoMapper documents
# In a MongoMapper::Document,
# simply add
# key :password, Password
# your password will be automatically hashed,
# and you can compare plain-text passwords with the user's hashed password, ruby style:
# User.first.password == '1234'
# => true
#
# Also, prevents passwords from being leaked onto the console or error messages:
# User.first.password
@panicsteve
panicsteve / gist:1641705
Created January 19, 2012 18:26
Form letter template for acquired startups
Dear soon-to-be-former user,
We've got some fantastic news! Well, it's great news for us anyway. You, on
the other hand, are fucked.
We've just been acquired by:
[ ] Facebook
[ ] Google
[ ] Twitter
@erikh
erikh / hack.sh
Created March 31, 2012 07:02 — forked from DAddYE/hack.sh
OSX For Hackers
#!/usr/bin/env sh
##
# This is script with usefull tips taken from:
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx
#
# install it:
# curl -sL https://raw.github.com/gist/2108403/hack.sh | sh
#
@jboner
jboner / latency.txt
Last active June 1, 2024 18:05
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

@aras-p
aras-p / preprocessor_fun.h
Last active May 28, 2024 05:15
Things to commit just before leaving your job
// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,
@john2x
john2x / 00_destructuring.md
Last active May 29, 2024 00:25
Clojure Destructuring Tutorial and Cheat Sheet

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

[
{ "name": "Shanghai", "lat": 31.22222, "lng": 121.45806 },
{ "name": "Buenos Aires", "lat": -34.61315, "lng": -58.37723 },
{ "name": "Mumbai", "lat": 19.07283, "lng": 72.88261 },
{ "name": "Mexico City", "lat": 19.42847, "lng": -99.12766 },
{ "name": "Beijing", "lat": 39.9075, "lng": 116.39723 },
{ "name": "Karachi", "lat": 24.9056, "lng": 67.0822 },
{ "name": "Istanbul", "lat": 41.01384, "lng": 28.94966 },
{ "name": "Tianjin", "lat": 39.14222, "lng": 117.17667 },
{ "name": "Guangzhou", "lat": 23.11667, "lng": 113.25 },
@karpathy
karpathy / min-char-rnn.py
Last active May 30, 2024 23:33
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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)