heri

# quarantine algorithm for subreddit
# see https://medium.com/product-dot-io/reddit-empowering-the-community-5c0ce2c266e2
def quarantine(number_of_reports, number_of_content)
    # number of content is number of comments and submissions with positive upvotes
    s = number_of_reports - Math.log10(number_of_content) 
    
    order = Math.log10([s.abs, 1].max)
    
    sign = if s > 0

heri

# community ads ranking for subreddit
# see https://medium.com/product-dot-io/reddit-empowering-the-community-5c0ce2c266e2

def user_ad_ranking(ad, ups, downs)
    # difference between upvotes and downvotes for user-submitted ad
    # coeff is a business constant to be determined
    s = ups + coeff * Math.log10(ad.budget * 10) - downs
    order = Math.log10([s.abs, 1].max)
    sign = if s > 0
        1

heri

# Callback hell :/
Yelp.get("restaurants").then do |response|
  PlacesTasks.sort(response.json[:places]}) do |response|
    Place.review(response, {ranking: -1}) do |response|
      puts "All finished"
    end
  end
end

# More readable asynchronous calls with Promises 

heri

# Saves a comment by current user to an event
def save_event_comment
  model._created_at = Time.now
  model.save!.then do
    flash._notices << "Thanks for adding a comment"
    new_event_comment
  end.fail do |err|
    flash._notices << err.to_s
  end
end

heri

<!-- Using a top level navigation component -->
<:nav href="/about">About</:nav>

<!-- Rendered as -->
<:Nav>
  <li class="{{ if active_tab? }}active{{ end }}">
    <a href="{{ attrs.href }}">{{ yield }}</a>
  </li>

heri

<h1>Let's chat</h1>
<ul>
  {{ page._messages.each do |message| }}
    <li>{{ message._name }}</li>
  {{ end }}
</ul>

<form e-submit="add_message">
  <label>Todo</label>
  <input type="text" value="{{ page._new_message }}" />

heri

def add_message
  page._message << { name: page._new_message }
  page._new_todo = ''
end

heri

  jQuery.getJSON('/cart.js', function(cart) {
  items = cart.items;
  var results = items.filter(x => x.id === 6223106801703);
  if ((results.length == 0) && (items.length > 0)) {
    jQuery.post('/cart/add.js', {
      quantity: 1,
      id: 6223106801703  
    });
    location.reload();

heri

# A basic end-to-end autonomous car uses 2d images to infer throttle and steering. However, speed and acceleration can change this. If the car is already speeding, throttle should decrease. If the car already has speed, then steering should be proportionally less
# One way to improve this is with hardware, by adding a RPM speed sensor (optical or magnetic), a GPS, an IMU or a combination. This is used as an input to the model
# If one does not have a speed sensor, another way is to update the model so it takes into account the last frames, and by using Kera's 3d convolution methods. Reference paper to read : https://arxiv.org/pdf/1412.0767.pdf
# This can slow down inference and result in lower fps

img_in3D = Input(shape=(3, 120, 160, 3), name='img_in') # First layer, input layer, takes 3 Shapes comes from camera.py resolution, RGB
x = img_in3D x = Cropping3D(cropping = ((0, 0),(60, 0),(0, 0)))(x) # Depending on your camera angle. Most cameras are pointed downards so the top X pixels can be safely cropped
x =

heri

var my_city = "Montreal";
var getJSON = require("async-get-json"); 
var geLatLng = await getJSON("https://api.opencagedata.com/geocode/v1/json?key=50adaa7181d24a7581a62fd3d9c0a2e0&q=" + my_city);
var latLng = geLatLng.results[0].geometry["lng"] + "," + geLatLng.results[0].geometry["lat"];