Dette er CodeArts blog. Vi deler thought-leadership og tekniske tips og tricks - men som regel på engelsk.
Videos and well executed webinars are key tools in achieving a higher engagement and conversion rate from your visitors and customers. TwentyThree is a powerful video hub and webinar service. I was lucky enough to get an account and access to their API and that escalated quickly into a prototype integration into Episerver.
Over the years I have been involved in quite a lot(!) of Episerver addons and integration projects. A key to a successful add-on is to get the entire project and environment correctly setup and working from the start. This is my recipe.
Automatically tagging your content with topics from a known, well described topic base like Wikipedia can have many cool uses. You can organize your content, suggesting keywords and outbound links, not to mention that you can build up interest profiles of your visitors. These interest profiles can the be used to suggest appropriate content and keep your visitors engaged. Inspired by Episerver Content Intelligence and a couple of earlier projects I've done in the past, I decided to perform an experiment to see how far I could get with a DIY approach as opposed to the traditional cloud-based NLP/AI.
Powerslice might have a few years on it's back, but it's still a great editorial tool, when you are working with large amounts of content and have access to Episerver Find. Here's an example of a recent slice I made that let's editors easily identify unused blocks.
You don't always have to go the full AI route to get AI like results. In this blog post I'll describe an approach I've used several times (and for multiple purposes) with pretty decent results. Instead of classification algorithms, deep learning or neural networks I'll just simply query my favorite search engine.
In 3 previous blog posts I compared various azure storage technologies with regards to performance and scalability in typical web usage scenarios. I was actually done with the series, but with all that interesting data, I decided to throw my current favorite search/storage/no-sql technology into the mix to get an idea about how it all compares. So - ElasticSearch enters the competition!
This is the 3rd post in my Azure Storage Performance comparison. So far we've examined the typical scenario of storing/retrieving data that most dynamic websites of today deal with. In this post, we'll take a closer look at Update and Delete - and finally review the financial aspects.
In this second post of my performance series looking at Azure storage we're going to take a good look at Read speeds for the various storage types.
Almost every project has some data you want to persist, then read, search through, update and eventually delete. With Azure there are loads of great possibilities - for example Blob Storage, Table Storage, CosmosDb, SQL Azure. I've decided to do some simple and fairly naive tests to compare these for some typical usage scenarios and see how they perform.
Azure Storage has a new cool feature in preview - Static Website. But what exactly does it do - and how can I connect my Episerver installation to it? I decided to find out.
