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syntaxhacker / mp3.js
Created February 25, 2019 19:53 — forked from dtrce/mp3.js
streaming mp3 using nodejs
var http = require('http'),
fileSystem = require('fs'),
path = require('path')
util = require('util');
http.createServer(function(request, response) {
var filePath = 'path_to_file.mp3';
var stat = fileSystem.statSync(filePath);
response.writeHead(200, {
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syntaxhacker / cheetah-inst.sh
Created November 26, 2018 06:28 — forked from rogerdudler/cheetah-inst.sh
Cheetah Installation
wget http://pypi.python.org/packages/source/C/Cheetah/Cheetah-2.4.4.tar.gz
tar xvfz Cheetah-2.4.4.tar.gz
cd Cheetah-2.4.4
sudo python setup.py install
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syntaxhacker / stdc++.h
Created November 2, 2018 11:21 — forked from velicast/stdc++.h
Linux GCC 4.8.0 /bits/stdc++.h header definition.
// C++ includes used for precompiling -*- C++ -*-
// Copyright (C) 2003-2013 Free Software Foundation, Inc.
//
// This file is part of the GNU ISO C++ Library. This library is free
// software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the
// Free Software Foundation; either version 3, or (at your option)
// any later version.
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syntaxhacker / node_cheerio_scraping.js
Last active September 5, 2018 13:25 — forked from bradtraversy/node_cheerio_scraping.js
Simple example to scrape some posts and put into a CSV file using Node & Cheerio
const request = require('request');
const cheerio = require('cheerio');
const fs = require('fs');
const writeStream = fs.createWriteStream('post.csv');
// Write Headers
writeStream.write(`Title,Link,Date \n`);
request('http://codedemos.com/sampleblog', (error, response, html) => {
if (!error && response.statusCode == 200) {
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syntaxhacker / iris-tensorflow-js.js
Created September 2, 2018 18:19 — forked from learncodeacademy/iris-tensorflow-js.js
Solving Iris with Tensorflow.js and Iris JSON dataset
import * as tf from "@tensorflow/tfjs"
import "@tensorflow/tfjs-node"
import iris from "./iris.json"
import irisTesting from "./iris-testing.json"
// convert/setup our data
const trainingData = tf.tensor2d(iris.map(item => [
item.sepal_length, item.sepal_width, item.petal_length, item.petal_width,
]))
const outputData = tf.tensor2d(iris.map(item => [