In a previous post, I did some experiments with gRPC, protocol buffer and Terraform. The idea was to transform the “Terraform” cli tool into a micro-service thanks to gRPC. This post is the second part of the experiment. I will go deeper in the code and see if it is possible to create a brand new utility, without hacking Terraform. The idea is to import some packages that compose the binary and create my own service based on gRPC.

This post is a little different from the last ones. As usual, the introduction tries to be open, but it quickly goes deeper into a go implementation. Some explanations may be tricky from time to times and therefore not very clear. As usual, do not hesitate to send me any comment via this blog or via twitter @owulveryck. TL;DR: This is a step-by-step example that turns a golang cli utility into a webservice powered by gRPC and protobuf.

Imagine a CCTV at home that would trigger an alert when it detects a movement. Ok, this is easy. Imagine a CCTV that would trigger an alert when it detects a human (and not the cat). A little bit trickier. Now imagine a CCTV that would trigger an alert when it sees someone who is not from the family… Disclaimer: This article will not cover everything. I may post a second article later (or not).

Google has recently announced its new service called “Google Cloud Video Intelligence”. The purpose of this service is to offer tagging and annotations of digital videos. I will try this service on a trailer of a French parody. This movie is made of several scenes taken from erotic movies of the seventies. Why this parody? because it is fun because it is composed of a lot of different scenes because it is short (so it won’t cost me a lot) because, as it is related to erotic of the seventies, I am curious about the result!

TL;DR: Thank you for passing by. This article is, as usual, geek oriented. However, if you are not a geek, and/or you are in a hurry, you can jump to the conclusion: Any real application? During the month of may, I have had the chance to attend to the Google Next event in London and the dotAI in Paris. In both conferences I learned a lot about machine learning. What those great speakers have taught me is that you should not reinvent the wheel in AI.

Part I: Agility 2003 I have discovered the notion of extreme programming more than 15 years ago. My job was to integrate and to develop pieces of code in Java for the IBM Websphere Business Integration server. We were a small team with light programming skills. A part of our job was to operate the software, the other part was to develop. It was in 2003. We were trying hard to stick to the specific framework we developed.

In the last post I have played with GraphQL. The next step is to actually query the data and display it. In this post I will use react (from Facebook) and the Apollo GraphQL client. Tooling React I won’t give in here an introduction of the language because you may find on the web thousands of very good tutorials and advocacy articles. Anyway I will explain briefly why I have chosen React.