I was reading my email this morning, when I stumbled upon an interesting question in the daily newsletter from CodeProject, to which, like the author, I had no idea about the answer: “How many articles there are on CP?”

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Did you know…

Turns out CodeProject is quite a large library after all, with more than 14 million users and 63 thousand articles! We’ve been looking for interesting data-sets to demonstrate how to use the Curiosity Search, and this seemed like an interesting one!

You can reproduce this on your own machine — all the code is available in this repository, and you can download a copy of Curiosity to run locally, or deploy to your favorite containers platform using the Docker image. …

Be the Rockstar Developer you wish, from within your .NET source-code

I recently watched a really interesting video from Dylan Beattie on the NDC called The Art of Code, where he talks about code that has no practical reason to exist except because it is fun, or beautiful, to write it. If you haven’t watched it, I seriously recommend it — it’s interesting and fun!

The Art of Code — Dylan Beattie — YouTube

Towards the end of his talk, he introduces us to his own programming language. He created it in a bar and it’s since become famous worldwide: Rockstar.

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Introducing, ROCKSTAR

While watching the talk, I remembered that I’ve been wanting to get started with source-code generators since the release of NET5.0 for another side project of mine, and what better than using Rockstar as a way to get started? …

We explore the MIND dataset to build a quick news recommendation engine from scratch using Curiosity

Recommendation systems have been a key part of modern website interactions, as it can be very hard to find information otherwise. Websites have sections like “Suggested Read”, “You might also be interested at”, “Relevant for you”, in which these recommendations are embedded in the flow of the website, and are key to help users understand, identify, and provide new opportunities to engage with the content, ultimately improve their experience and increasing retention and interest.

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Recommending similar CodeProject articles using graph-embeddings

In my last post, Exploring CodeProject using Curiosity, I showed how one can build a graph embedding model and use it to recommend similar CodeProject posts in real-time using the built-in HNSW index. In that case, we built our recommendation model based entirely on the extracted entities from the articles’ text content and the relationships that were captured in the knowledge graph. …

How to build an AI-powered search in a day using Curiosity

Anyone that worked on a large enterprise knows this problem: You have lots of documents spread across your company, but it is just very difficult if not impossible to find and navigate any of this knowledge.

Today we’ll take a look on how you can use Curiosity to create your own enterprise search from scratch, in minutes!

To get started, you’ll need:

  1. Curiosity (running on your machine, server or cloud)
  2. Lots and lots of files (we’ll use a nice public dataset from the NASA library)

Installing Curiosity

If you’re running Windows, the easiest way to test-drive Curiosity is to download the installer on our website.

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Alternatively, you can also run it using Docker on any operating…

Search is something we all take for granted: Every day our searches of the internet are easy, comprehensive and instantaneous.

Not so in companies.

It’s hard to believe, but finding information in their company is still a hard problem for employees as part of their everyday work. For many people, hitting “search” is synonymous with getting a cup of coffee while waiting for results and being transported back to the times where Internet Explorer was still a modern browser.

Enterprise search has been stuck since the 90s and it’s not uncommon to see employees in large companies using search systems that look like…


Rafael Oliveira

CTO @curiosity_ai

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