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1) Make a download dir to store the node source and download it.

    mkdir downloads
    cd downloads
    git clone https://github.com/joyent/node.git
    Find the latest version

2) List all of the tags in the repository, and check out the most recent.
    git tag
    git checkout v0.9.9

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import scrapy

class StackOverflowSpider(scrapy.Spider):
	name = 'stackoverflow'
	start_urls = ['http://stackoverflow.com/questions?sort=votes']

	def parse(self, response):

		for href in response.css('.question-summary h3 a::attr(href)'):

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# source: http://multithreaded.stitchfix.com/blog/2016/01/13/market-watch/
# https://github.com/klarsen1/MarketMatching

# from terminal command line:
    sudo apt-get install libcurl4-openssl-dev

# from R command line:
# install CausalImpact package:

    install.packages("devtools")

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# How to build your own NN classifier in r
# source: http://www.r-bloggers.com/build-your-own-neural-network-classifier-in-r/
# reference: http://junma5.weebly.com/data-blog/build-your-own-neural-network-classifier-in-r

# project:
# 1) build simple NN with 2 fully-connected layers
# 2) use NN to classify a dataset of 4-class 2D images & visualize decision boundary.
# 3) train NN with MNIST dataset

# ref: stanford CS23 source: http://cs231n.github.io/

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import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import requests
import bs4
import zipcode
import threading
import re
import json
import time

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import dash
import dash_core_components as dcc 
import dash_html_components as html 

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

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Original notes by James Clear

• Do the math yourself. Too many people take numbers from unreliable sources.

• There are 4 levels of learning: Awareness, Awkwardness, Application, Assimilation

• Customer-Solution Profit: Know your customers incredibly well and create a solution specifically for them.

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# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
import time
import sys

try:
    import annoy
except ImportError:
    print("The package 'annoy' is required to run this example.")

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from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
import numpy as np
from math import sqrt


def cagr(start_value: float, end_value: float, years: int):
    return (end_value / start_value) ** (1.0 / years) - 1

def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
    diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array

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{"status"=>1, "complete"=>1, "list"=>{"3492948982"=>{"item_id"=>"3492948982", "resolved_id"=>"3492948982", "given_url"=>"https://www.practicalecommerce.com/tag/video-tools", "given_title"=>"", "favorite"=>"0", "status"=>"0", "time_added"=>"1638225235", "time_updated"=>"1654562695", "time_read"=>"0", "time_favorited"=>"0", "sort_id"=>0, "resolved_title"=>"Video Tools", "resolved_url"=>"https://www.practicalecommerce.com/tag/video-tools", "excerpt"=>"Video offers merchants a range of ways to engage customers and prospects. Here is a list of new and updated tools from video platforms and social media applications. There are tools for promoting products and producing shoppable videos and as well as for editing, live-streaming, and monetizing.", "is_article"=>"1", "is_index"=>"0", "has_video"=>"0", "has_image"=>"0", "word_count"=>"55", "lang"=>"en", "tags"=>{"ecommerce"=>{"item_id"=>"3492948982", "tag"=>"ecommerce"}, "prodmgmt"=>{"item_id"=>"3492948982", "tag"=>"prodmgmt"}, "tools"=>{"item_id"=>"3492948982", "tag"