GraphQL is the new black

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Prelude GraphQL is an open-source data query and manipulation language for APIs and a runtime for fulfilling queries with existing data. GraphQL was developed internally by Facebook in 2012 before being publicly released in 2015 because - believe me or not - it was quite tricky for them dealing with their schema. It allows clients to define the structure of the data required, and exactly the same structure of the data is returned from the server, therefore preventing excessively large amounts of data from being returned.

A Golang Turing machine library

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Preamble In 1962, Hungarian mathematician Tibor Radó introduced the Busy Beaver competition for Turing machines: in a class of machines, find one which halts after the greatest number of steps when started on the empty input. Even if it could seem trivial, the Busy Beaver competition has implications in computability theory, the halting problem, and complexity theory. I decided to use GoLang to implement a Turing machine library and accomplish three goals: first, having a Turing Machine model to play with for learning purpose; second, learning how to use interfaces and the factory pattern, other then testing package to test my code and let it be more flexible for future enhancement (at least I hope!

My first UniKernel image for sequence prediction

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Introduction Predicting the next item of a sequence over a finite alphabet has important applications in many domains. Since I always wanted to implemented something like that, while I was looking for an interesting approach I found this interesting idea based on tree. And you don’t deal with trees since a lot, be prepared because as usual it seams simple, but it not. Moreover, since I like Golang and I always wanted to try UniK, I decided to implement my version of the CPT using Golang and use this exercise as a source to build my first unikernel image.

How my Elman network learnt to count

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Introduction This is actually a sort of back-to-the-future post because it’s related to something I completed one year ago: I built this Elman network and it learnt to count. What I shame, I forgot it, now it’s kind of its first birthday so let’s celebrate :D This is Elman, the best in class in adding int32 numbers. For everybody who already knows what I will talk about (what?!), here’s the Github repo.

Go Erlangen!

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A little tool for a small city Here we are!! I recently moved to Germany to join the adidas - platform engineering team. This is a really small piece of GOde (:P) I wrote for Erlangen (my new place) citizens ^^ Needs You will only need an movie api key. You can get one quite easily by going here Scope You would simply like to be informed about new OV movies on air in cinemas from your shell…here we go!

AWS Free Tier, Docker and Jenkins: smart resources handling with CloudWatch Events and Slack

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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.

Node.js, DynamoDB, and AWS Step Functions to collect <em>sentimented</em> movie reviews

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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?

AWS Lambda, GoLang and Grafana to perform sentiment analysis for your company / business

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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.

GoLang vs Python: deep dive into the concurrency

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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.

Build a multilayer perceptron with Golang

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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.