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[assumes everything is installed]
cd ruby_rails [ie, wherever you want to keep all your rails projects]
rails new_project [create the project]
ruby new_project/script/server [wow - your new app is up!]
[ctrl-c to kill it]
[in new_project dir, do]
git init
git add .
git status
@chitchcock
chitchcock / 20111011_SteveYeggeGooglePlatformRant.md
Created October 12, 2011 15:53
Stevey's Google Platforms Rant

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

@diegodurs
diegodurs / build_new_rails_app.md
Created September 11, 2012 09:24
Build new Rails App

Generate new app

Don't forget -T -d postgresql. One day create a template and builder

  • rails new AppName -T -d postgresql

Use rspec, haml, coffee, scss

  • rails generate rspec:install In application.rb:
@emeeks
emeeks / README.md
Last active March 25, 2024 07:56 — forked from mbostock/.block
An online tool for interactive teaching of network visualization and representation principles.

The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.

Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.

It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.

Now with Clustering Coefficients!

Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.

@nootanghimire
nootanghimire / postfix-eval-new.cpp
Last active May 29, 2018 05:11
Stack/Queue Implementation in C++ and Application in Expression Evaluations
/******************************************************
* @author Nootan Ghimire <nootan.ghimire@gmail.com>
* @file postfix-eval.cpp
* @desc Evaluation of Multi-Digit Postfix Expression
*****************************************************/
// C++ Includes
#include <iostream>
#include <cctype>
#include <cstdlib>
@dwayne
dwayne / ch1.md
Created January 17, 2014 10:25
My notes from the book "Ruby on Rails Tutorial by Michael Hartl".
@karpathy
karpathy / min-char-rnn.py
Last active April 16, 2024 18:25
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)
@baraldilorenzo
baraldilorenzo / readme.md
Last active November 21, 2023 22:41
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@toboqus
toboqus / btree.cpp
Created November 3, 2015 08:53
Binary tree implementation in c++
#include <iostream>
using namespace std;
struct node{
int value;
node *left;
node *right;
};