In our project, we’re using GraphQL, and we’re using graphql-codegen to generate types for our queries. While this is very convenient, it’s almost impossible to extract sub-types from these generated types. Let me explain.Weiterlesen
In many projects, one needs to track changes in data in your database. With pgMemento, you can automatically generate audit information like that. It extends your PostgreSQL tables by an audit_id column, which in turn refers to the specific audit event in the pgMemento tables, which are by default stored in a separate database schema.
So far, so good.Weiterlesen
Following philosopher Yuval-Noha Harari, cooperation is the key to our human being and what humans achieved in evolution, making us humans more successful than other creatures on earth. „It is the unprecedented ability to cooperate flexibly in large numbers“ that makes us unique. No other creature on earth can cooperate in a way like this, ants and bees, f.e. also cooperate, but in a very rigid way or only close relatives. So you can’t compare an ant workforce and their cooperation with humans.
Interactions with strangers and building new social cooperation networks in such an effective way is a human privilege.Weiterlesen
PHP is sooo yesterday. It’s slow. It’s not hip. Don’t use it.
That’s what people say.
Well, people, listen up! Because RoadRunner has come to pimp PHP performance in astounding dimensions. So let’s take a look at what RoadRunner is.Weiterlesen
In Image similarity search with LIRE we explained how to compare and find similar images using the Java library LIRE. The idea was to transform the complicated problem of comparing a large bunch of pixels to the simpler problem of comparing vectors representing histograms and other higher-level properties. In other words, if we can compress the information inherent in a bunch of pixels to a point in n-dimensional space (an array with n entries – the so called feature vector of the image), we can regard the distance between two such points as a similarity measure for the corresponding images. We can then find the images similar to a search image by selecting those images whose feature vectors have a small distance to the feature vector of the search image.
However, a naive approach to the problem – comparing the feature vector of the search image to all feature vectors in the database – is rather slow if our database is large. In this article, we show how to implement a fast similarity search for even very large databases.
In this article we will take a closer look at PHPWord and three different ways to create Word documents with it: basic easy templating, the creation of Word documents from scratch, and (going a little crazy there) the combination of both by merging existing templates with dynamically created documents. Hopefully, after reading through the text, you will have an idea of how to implement the perfect Word creator for your needs.
Last thursday, Dan Abramov gave a talk on JSConf Iceland called „Beyond React 16“. A few hours later, react-etc.net featured an article titled RIP Redux: Dan Abramov announces future fetcher API. While I agree with almost nothing that article has to say about Redux, the article got one thing right: Somewhere between 6 or 12 months from now, the way we are using Redux (at least when starting a new project) will be drastically different from the way we are using it today.
But before we take a gaze into the crystal ball, let’s take a step back and see what happened.