Dette er CodeArts blog. Vi deler thought-leadership og tekniske tips og tricks - men som regel på engelsk.
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.
This is a little code snippet that I often find quite handy. It's a piece of c# code that opens a gzipped json file and iterates through the items in it. Since it takes it piece by piece (as opposed to loading everything in memory) it's can pretty much handle files of any size.
The best way to learn coding - and in particular c# and .NET Core - is to code. CodeArt is proud to make all the teaching materials to a newly created course online and freely available for self-study.
Here is a classic error that happens to me a lot. It's extremely simple, yet sometimes I find myself spending too much time trying to remember what it is that goes wrong.
As a little experiment I just launched a basic annotation service, that let's you put comments on screenshots of a web page in order to collaborate during creation.
A long time ago I did a small weekend project to fix a problem my wife was having with her e-reader. I shared it on my blog and then forgot all about it. Until now, that is.
If you have a site with a lot of different content types, it can be a good idea to help Episervers Automatic Content Type suggestion feature along. Here is a basic Admin mode tool - in good old webforms (yes, I washed my hands after I made it) that will let administrators / and super-editors configure exactly which content types to suggest when.
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.
