Amazon recently made a new Progressive Response API available for Alexa Skills. This API allows a user to hear an immediate sound or other audio feedback when invoking an Alexa Skill, while more complex or resource intensive tasks are used to build out the remainder of the Skill. This post walks through how to use this new API in .NET and C#, and how to include it in an Alexa Skill.
This post outlines the buildout of an Alexa Skill using .NET Core, deployment using AWS Beanstalk, and the integration of a number of AWS Services including ElastiCache, Relational Database Service, and S3.
Last week, I received very exciting news that my Alexa Skill had good engagement statistics for the month of September 2017, and as a result Amazon is sending a reward check for the Skill’s performance statistics. $111.46 for September’s Skill Performance… not bad! For reference and transparency to anyone who may find this helpful, this post outlines several of the performance statistics for my Grammar Tool Skill.
This article outlines the steps to building an Alexa Skill using .NET 4.5, MVC 5, and C# and deploying and hosting it on Microsoft Azure. This post walks through the background of the Alexa Skill referenced in this post, setting up a local Visual Studio development environment with Alexa, creating an intent schema, developing skill logic, and deployment of the Alexa Skill to Microsoft Azure.
In the first three posts of this series, we have made it through the outline of graphs, the integration of the google matrix distance api, and the setup of the min-heap data structure, all for the focus of this application, which is the implementation of Prim’s algorithm for finding the Minimum Spanning Tree. This implementation, like all of the examples so far, is completed using Ruby.
This post addresses an overview and technical implementation of the heap data structure (in Ruby), which will be used as an important part of Prim’s algorithm for finding the MST, which is used in the demo application.
After discussing the high level applications of graphs, we start here by implementing our graph. The first three components of the graph are modeled in the graph, node, and edge classes seen below.
This post is the start of a four-part series covering the buildout of an application which showcases an example of creating and editing graphs and calculating their minimum spanning trees in a visual manner.
Part 1 of this post outlines the demo application used throughout this tutorial to showcase graphs, minimum spanning trees, and the integration with the Google Maps Distance Matrix API. This post covers high level examples of graphs, and the real world applications of a Minimum Spanning Tree (MST).
This post and tutorial gives background into how to build a basic create/read/update/delete (CRUD) application using Ruby/Rails. The focus of this post is how to set up a file attachment system and put it into production, using the fantastic resources contributed to the the rails and open source community from thoughtbot, Heroku, and Amazon. This post explores the specifics of thoughtbot’s paperclip gem, Heroku’s application hosting platform, and Amazon Web Services’ S3 for attachment/image hosting.
The tutorial below gives background and insight into the technical details behind the development and deployment of PDFs. One of the biggest challenges comes in the deployment and hosting of PDFs and including any custom fonts in the final output. This tutorial uses the following resources:
Framework: Ruby on Rails
Gems: wicked_pdf & wkhtmltopdf