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C++11 introduced move semantic, which allows, as its name suggests, to move objects instead of copying them. The move process typically involves copying pointers to some resources, and then setting the original pointers to nullptr, so they cannot be used to access the resources anymore. Of course, all of this is done transparently to the user of the class.

With this power comes a complexity, which is enhanced when it is coupled with copy elision. Copy elision is the general process where, when returned from a function, an object is not copied nor moved, resulting in zero-copy pass-by-value semantics. It includes both return value optimization (RVO) and named return value optimization (NRVO).

While the links on cppreference provided above probably contain all the information you might want to know about the interaction of move semantic with copy elision, they can be a bit of an arid reading. The goal of this post is to give a quick and incomplete introduction on the subject. This introduction is then used to present an interesting difference between the g++ and clang++ 3.8 compilers.

At Coveo, there’s one thing we like above all: making sure we have all the data we might need at the same place. Coveo already offers a plethora of connectors to do so, but there are still many more places where you could find relevant data.

Enter Zapier, a web service that allows you to connect different applications or services together, to perform actions in an application when something happens in another. With over 1000 available apps, it soon became clear that a Zapier integration would be a great solution to surface all that data.

If you’re one of the many passengers on the deep learning hype-train (welcome aboard), you’ve at some point almost certainly experienced the great confusion of having to choose a deep learning framework.

If you’re trying to choose a framework, or maybe re-evaluating your choices, I might be able to help!

I’ve done a lot of the leg work for you, having made a pretty comprehensive report on the state of deep learning frameworks that compares their strengths and weaknesses.

Maps are awesome. A good map implementation reacts to user’s current context and provides relevant information. Since maps are great at visualizing relevance, the Coveo for Sitecore product team decided to experiment with that concept. While exploring the concept, several use cases related to customer requests have emerged. This article will serve as a guide for these specific cases while remaining primarily focused on geospatial research.

Before we start, you can refer to the code here, and have a look at the live map here

On The Menu

• Geospatial Search
• Persistent Queries
• Custom TypeScript Coveo Components
• Push API
• Advanced Relevance Tuning

Storing parameters securely accross multiple environments with employees having different level of access in each of those can be challenging. AWS Systems Manager Parameter Store offers a nice solution to that problem with lots of advantages such as:

• being hosted, highly available, and scalable
• history tracking
• encryption with KMS
• audits in CloudTrail
• notifications with CloudWatch or SNS
• granular security using IAM policies

Every security team’s dream.

When we decided to store our deepest secrets in the Parameter Store, I searched right away for an integration with Spring Boot/Cloud for our Java microservices. I was left empty-handed.

This post is the second part on how we built our Elastic Search demo. You can read the previous post The Elastic Search Demo, Part 1: Build the Index on how we got the content inside our index.

Use case

• Build a demo on top of Coveo for Elasticsearch to show what you can do with the platform.
• Use public content that everybody is familiar with.
• Build it in 2-4 weeks time.

This is the second blog post of a new series entitled “Build it with the Coveo Cloud Platform”. The series will present innovative use cases for the Coveo Platform, always including full code samples. Read the previous post Building a resource locator.

Use case

• Build a demo on top of Coveo for Elasticsearch to show what you can do with the platform.
• Use public content that everybody is familiar with.
• Build it in 2-4 weeks time.

Search is great. It gives you the power to find information without having to go through every page of a website. But sometimes, search alone is not enough, and you need filters and sorting options to help you find what you’re looking for. One fantastic way to narrow large sets of content is faceted navigation. In fact, faceted navigation may be the most significant search innovation of the past decades.

Now that we have created and tested our custom component, we want to integrate it like any other component in the Coveo for Sitecore Hive Framework.

This post offers a step by step approach to adding a custom component in the Coveo for Sitecore integration.

Update: Links to the Blitz registration page were added.

Blitzday

Saturday. 7 AM. Normally I’d be sleeping in. But alas, this Saturday is not like your regular Saturday. I’m wide awake and super hyped. No, this isn’t the first chapter of my new fantasy book, it’s real. It’s Blitzday. I’m ready to crush through code like never before.