As cURL was celebrating its 25th birthday, I was reading Daniel Stenberg’s story behind the project, and discovered a neat little feature I hadn’t heard of before: the --json flag! Daniel even blogged about it when it landed in cURL 7.82.0 last year.

So what’s so cool about it? If you’re like me, you’re used to post some JSON data with the following verbose approach:

curl --data '{"msg": "hello"}' \
    --header "Content-Type: application/json" \
    --header "Accept: application/json" \
    https://example.com

You have to pass the data, and also pass headers to specify the content-type. You can make it slightly shorter with the one-letter flags:

You’ve certainly interacted with webhooks at some point: with a Github commit webhook, for Slack or Dialogflow chatbots, for being notified of Stripe payments, or when you receive an SMS via Twilio. The concept is fairly well known, but there are some roadblocks along the way, whether you implement a webhook handler (the URL being called) or a webhook backend (the service notifying URLs). It’s not necessarily as trivial as it may first seem. As I’ve been interested in Web APIs for a long time, I decided to look into this topic a bit more, by working on a new talk.

At GR8Conf Europe last year, I talked  about how to take advantage of the Google Cloud machine learning APIs  using Apache Groovy.

With Groovy, you can call the Vision API that recognises what’s in your pictures, or reads text.

You can invoke the Natural Language API to understand the structure of your text.

With the Speech-To-Text API, you can get transcriptions of what’s been said in an audio stream, or with Text-To-Spech, you can also generate human-like voices from your own text.

Powerful machine learning APIs are at your fingertips if you’re developing with Google Cloud Platform, as client libraries are available for various programming languages. Today, we’re investigating the Cloud Vision API and its Java SDK, using the Apache Groovy programming language—a multi-faceted language for the Java platform that aims to improve developer productivity thanks to a concise, familiar and easy to learn syntax.

At GR8Conf Europe, in Denmark, the conference dedicated to the Apache Groovy ecosystem, I spoke about the machine learning APIs provided by Google Cloud Platform: Vision, Natural Language, Translate, and Speech (both recognition and synthesis). Since it’s a groovy conference, we presented samples and demos using a pretty Groovy language. I wanted to share the underlying examples with a wider audience, so here’s the first of a series of blog posts covering the demos I presented. I’ll start with the Google Cloud Vision API, and I will cover the other APIs in future posts.

Last March, I had the chance to attend and speak at QCon London. I spoke at the event for its first edition, many moons prior, so it was fun coming back and seeing how the conference evolved. 

This year, Eric Horesnyi of Streamdata  was leading the Artificial Intelligence track, and invited me to speak about Machine Learning.

First, I gave an overview of the Machine Learning offering, from the off-the-shelf ready-made APIs like  Vision, Speech,  Natural Language, Video Intelligence. I also mentioned AutoML, to further train existing models like the Vision model in order to recognize your own specific details in pictures. For chatbots, I also covered Dialogflow. And I said a few words about Tensorflow and Cloud Machine Learning Engine  for training & running your Tensorflow models in the cloud. You can watch the video by clicking on the picture below:

Back to GR8Conf Europe in Denmark, for the yearly Groovy community reunion! I had the chance to present two talks.

The first one on Google’s Machine Learning APIs, with samples in Groovy using vision recognition, speech recognition & generation, natural language analysis. I’ll come back on ML in Groovy in forthcoming articles.

And the second talk was an overview of Google Cloud Platform, focusing on the compute and storage options, with demos using Groovy frameworks (Ratpack, Gaelyk, and the newly released Micronaut) and how to deploy apps on Compute Engine, Kubernetes Engine, App Engine. I’ll also come back in further articles on those demos, but in the meantime, I wanted to share my slide deck with you all! Without further ado, here’s what I presented:

Last month, for the first time, I visited Riga (Latvia), for the DevTernity conference. I really enjoyed my time there, and wish to come back with other topics next time. The organizers took very well care of the speakers, and the presentations were very interesting.

I had the pleasure to talk about the pre-trained machine learning APIs provided by Google Cloud Platform, and say a few words as well about TensorFlow and Cloud ML Engine.

InfoQ recently released a video from the APIDays conference that took place in Paris last year. I talked about scaling an Open API based web API using Cloud Endpoints, on the Google Cloud platform.

I spoke about the topic a few times, as web APIs is a topic I enjoy, at Nordic APIs, at APIDays, or Devoxx. But it’s great to see the video online. So let me share the slide deck along with the video:

The JAX conference, in Mainz, Germany, is coming to an end. I was there with my colleagues Mete and Robert, and had the chance to cover two topics: Machine Learning and Scaling Web APIs. It’s a pleasure to come back to this conference where the audience is always very focused, eager to learn, and is engaging in great and interesting conversations.

Machine Intelligence at Google Scale

My first presentation was about Machine Learning, and in particular with the Google Cloud APIs, including Vision, Speech, Natural Language, Translate, and Video Intelligence. Although I’m not an expert in TensorFlow and the Cloud Machine Learning Engine, I got a chance to say a few words about these. I guess I’ll have to play with both at some point to be able to tell even more!

Today was announced the general availability of Google Cloud Endpoints!

Endpoints is the Google Cloud Platform solution for Web API management, which lets you easily protect & secure your API, monitor it, without overhead, and even allows you to implement your API with any language or framework you want.

I’ve spoken about Endpoints a few times already, at Devoxx Belgium, Nordic APIs summit, and APIDays Paris. And you can see the recording of my Nordic APIs appearance, if you want to learn more about Cloud Endpoints: