Insights

Non-fiction books can be insightful, but I have noticed that I'd expect those insights to come back to me right when they would be helpful –– like when there's an obstacle in a project, I will have an “a-ha” moment and remember that great idea I have read about years ago.

Recently, a customer reported that the performance of the web application we are building, an AngularJS front end using REST services from a  Java back end, is unsatisfactory. I had a fun time finding out what the problem was, understanding what was causing it and thinking up solutions.

When developing or testing, having a database that comes pre-filled with relevant data can be a big help with implementation or a scenario walk-through. However, often there is only a structure dump of the production database available, or less.

Previously, we've built a simple PV-DBOW-'like' model. We've made a couple of choices, e.g., about how to generate training batches, how to compute the loss function, etc. In this blog post, we'll take a look at the choices made in the popular gensim library.

In my previous blog post I wrote about creating your own search DSL using Antlr. In that post I discussed the Antlr language for constructs like AND/OR, multiple words and combining words. In this blog post I am showing how to use the visitor mechanism to write actual elasticsearch queries.

As an (elastic)search expert, I regularly visit customers. For these customers I often do a short analysis of their search solution and I give advice about improvements they can make. It is always interesting to look at solutions customers come up with.