In this article, we dive into how TomTom’s Traffic API works and how we can leverage the Traffic suite of web services (which are based on the real-time traffic data from TomTom Traffic) to retrieve detailed traffic information about an area, which you can use for your web and mobile applications.

This article explores how developers can make deep-learning applications faster and more efficient by taking advantage of tools that optimize deep-learning code.

This article takes a look at a variety of tools available from Intel: Intel® Movidius™ Neural Compute Stick, Intel® Python Distribution for Python™, Intel® Math Kernel DNN Library, Intel® Data Analytics Acceleration Library, Intel Distribution of OpenVINO™ Toolkit

In this article, I’m going to introduce you to the Routing API with a few examples. We’ll also discuss consumption models for gasoline and electric vehicles and use these models to plot the best route for your vehicle.

This post discusses strategies for improving the performance of Python applications by making them run faster and use fewer resources.

In this article, I will introduce you to Intel’s Cryptographic API by walking you through how to optimize the popular RSA cryptographic algorithm.

In this article, I’m going to introduce you to API functionality which can be leveraged specifically for drivers of electric vehicles.

In this article, I’m going to introduce you to some of the APIs available from TomTom and demonstrate how you can leverage them to turn location data points into actionable information about your fleet.

In this article, we'll look at how we can use Azure Functions and SignalR Service to broadcast real-time document changes in Azure Cosmos DB to clients over WebSockets.

Azure Cosmos DB is Microsoft’s globally distributed multi-model database, when we team it up with Azure Functions, Azure’s serverless compute service, the result is the ideal database for the serverless era.