Golang

Introduction If you have an AWS account in Free Tier, you have (updated: March, 13th 2018) 750 hours/month to run EC2 (small ones) in your VPC. You also have a lot of other resources, such as AWS Lambda functions (I wrote about them here and here) and CloudWatch Events. In this article, I talk about smart resources handling and some trick - actually, not so smart XD - I setup to take the best from the services. Attention!!! Picture Spoiler ...

Introduction Recently I worked with AWS Lambda and API Gateway to extend my set of personal APIs and collect information from several sources. I wrote an article on that (if you want to have a look). In this article I will talk about the AWS Step Functions service that enable create finite states machines to easy coordinate the components of distributed applications and microservices using visual workflows. Why AWS Step Functions? Because they let me create a tool to gather movie titles in teather, search for reviews about each of them and make a basic sentiment analysis over the review to help me decide what’s worth watching at teather and what’s worth waiting for on Netflix :D More in general, with AWS Step Functions, you can build applications made of individual components that each perform a discrete function: this lets you scale and change applications quickly. Step Functions is a reliable way to coordinate components and step through the functions of your application. They provides a graphical console to arrange and visualize the components of your application as a series of steps. This makes it simple to build and run multistep applications. Step Functions automatically triggers and tracks each step, and retries when there are errors, so your application executes in order and as expected. Step Functions logs the state of each step, so when things do go wrong, you can diagnose and debug problems quickly. ...

Introduction In this article I will talk about my experience with AWS Lambda + API Gateway, GoLang (of course) and Grafana to build a sentiment analysis tool over customizable topics. Who should you read this post? Don’t know, maybe a CIO, a CTO, a CEO, a generic Chief or a MasterChef, for sure an AWS and GoLang fan like me. First of all: to better understand how to use Elasticsearch, read my previous post Elasticsearch over My home Network Attached Storage: it’s not so exciting as it seems, but you will have a general idea about what is Elasticsearch and how can you use it. Second: if you don’t know about AWS Lambda, study it. I personally believe that it represents one of the most interesting services currently offered by AWS: as they state, AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume and there is no charge when your code is not running. The amazing thing is that with a Free Tier trial you have 1 milions requests for free - O.O - to run code of any type of application or backend service - all with zero administration: you just upload your code - unfortunately the online editor for GoLang is not supported yet - and AWS Lambda1 takes care of everything required to run and scale your code with high availability. You can even set up your code to automatically trigger from other AWS services - as I have done with API Gateway - or call it directly from any web or mobile app. And…last but definetly not the least, why I’m writing this post!? Because starting from 15 January 2018, AWS Lambda support GoLang!!! ...

Introduction In the last months, I worked a lot with GoLang on several projects. Although I’m certainly not an expert, there are several things that I really appreciate about this language: first, it has a clear and simple syntax, and more than once I noticed that the style of the Github developers is very close to the style used in old C programs. From a theoretical point of view, GoLang seems to take the best of all worlds: there is the power of high-level languages, made simple by clear rules - even if sometime they are a little bit binding - that can impose a solid logic to the code. There is the simplicity of the imperative style, made of primitive types with the size in bits in their name, but without the boredom of manipulating strings as array of characters. However, two really useful and interesting features in my opinion are the goroutine and the channels. ...

History We can date the birth of artificial neural networks in 1958, with the introduction of Perceptron 1 by Frank Rosenblatt. It was the first algorithm created to reproduce the biological neuron. Conceptually, the easier perceptron that you might think of is made of a single neuron: when it’s exposed to a stimulus, it provides a binary response, just as would a biological neuron. This model differs greatly from the neural network involving billions of neurons in a biological brain. Shortly after his birth, the researchers showed the world the problems of Perceptron: in fact, it was quickly proved that perceptrons could not be trained to recognize many classes of input patterns. To get a more powerful network, it was necessary to take advantage of multiple level of units and create a multilayers perceptron, with more intermediates neurons used to solve linearly separable2 subproblems, whose outputs were combined together by the final level to provide a concrete response to original input problem. Even though the Perceptron was just a simple but severely limited binary classifier, it introduced a great innovation: the idea to simulate the basic computational unit of a complex biological system that exists in nature. ...

Recipe for a good meal A few months ago I started working with Golang to a proof-of-concept project using the amazing goa package (thank you atosatto for your advise): I omit the praise, I would only say that - imho - Golang it’s a very funny language to use for many reasons (stay tuned, I will write about Golang and my favourite packages). However, in this post I want to share a little piece of my experience about this project: the main ingredients for this recipe are Golang (in particular the goa package) and Docker - with some piece of Angular 4, nginx, and minor stuff. I excluded the storage part so the two elements of the boilerplate I will talk about live in separate container - you can of course orchestrate with k8… ok, whatever you want :P ...