Saturday, June 10, 2017


GraphQL vs REST: Things to Consider @ InfoQ

REST API downfalls, and dawn of GraphQL – Otto von Wachter – Medium
"The basic premise of both GraphQL and Falcor is that the server exposes a comprehensive data schema to the client, and the client decides exactly what it needs. Unlike with discrete REST endpoints, all the data for any given UI (page) can be sent in one trip to the client.
Ultimately, GraphQL is the more flexible and complete solution of the two, while Falcor provides 
out-of-the-box simplicity and is GraphQL-like."

GraphQL vs REST: Overview | Phil Sturgeon

Is GraphQL The End of REST Style APIs? | Nordic APIs |

Netflix Falcor: One Model Everywhere (JavaScript data library)
GraphQL Logo.svg

GitHub GraphQL API is Out of Early Access @ InfoQ

"Announced at GitHub Universe last year, GitHub GraphQL API aims to add more flexibility to GitHub API. The main advantage of GraphQL is its ability to define exactly what data are required, which makes it possible to replace multiple REST request with a single call. Additionally, GraphQL schemas are strongly typed and introspective."

Introducing GitHub Marketplace and more tools to customize your workflow @ GitHub

GitHub API | GitHub Developer Guide @ GitHub

GraphQL  spec, by Facebook, @GitHub

GraphQL - Wikipedia

GraphQL | A query language for your API

Code | GraphQL

From REST to GraphQL (Marc-Andre Giroux) - Full Stack Fest 2016 - YouTube

Zero to GraphQL in 30 Minutes – Steven Luscher - YouTube

Apollo GraphQL - YouTube

AI: Apple Core ML

Apple Announces Core ML: Machine Learning Capabilities on Apple Devices @ InfoQ

"At WWDC 2017 Apple announced ways it uses machine learning, and ways for developers to add machine learning to their own applications.
Their machine learning API, called Core ML, allows developers to integrate machine learning models into apps running on Apple devices with iOS, macOS, watchOS, and tvOS. Models reside on the device itself, so data never leaves the device."

"Supported machine learning tools are Keras (with Tensorflow backend), Caffe, Scikit-learn, libsvm and XGBoost. It is not possible to import an existing Tensorflow model into Core ML, which would be possible with Tensorflow Lite on Android."

Core ML integrates a trained machine learning model into your app.

The machine learning stack

Is Core ML related to Apple's acquisition of Turi last year?
Apple execs explain why the tech giant acquired machine learning startup Turi – GeekWire

While typically used from Python, Turi's GraphLab ML toolkit is written in C++,
so Apple could have embedded in the Core ML to be used from any supported language.

Is TensorFlow better than other leading libraries such as Torch/Theano? - Quora

Amazon goes open source with machine-learning tech, competing with Google’s TensorFlow – GeekWire

Microsoft/CNTK: Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit