Nothing matters more than speed for the iOS product development team in Schibsted News Media. Soon also VG will launch on their super-fast app.
At Schibsted, we use data science to build models that aggregate user behaviors and preferences. Advertisers can combine these to allocate users in groups, known as targeting segments.
JSLT is a language for querying and transforming JSON that runs 9 billion transforms every day at Schibsted. We are now open sourcing it so anyone can use it.
Kotlin is the new chaos that suddenly penetrated all the tech teams all over the world. Google fully supported Kotlin, integrated it into their Android Studio IDE and considered it a stable language. As a result of this movement, soft or even brute actions need to be considered to be
Imagine this: You’re a marketplace website. You rely on content created by users to deliver a quality product to the general public, with minimum intervention from your side. Because at the end, you’re just a connection between the buyer and the seller. Now, how do you deliver the best user
In an earlier post we discussed a service to estimate the number of unique visitors to a website. In this article we explore the algorithms we used to build the system: HLL (HyperLogLog) and KMV (Kth Minimal Value) and evaluate each.
So you have been working on your e-commerce platform and you are almost ready for joining the Hotsale Season (BuenFin or Black Friday). Below you can find a preparation guide and a checklist to make sure your platform will not let you down.
At leboncoin, we’re migrating to a microservices architecture. Here’s why we’re doing it, and what we’ve learned.
Developing UI at scale can be challenging. At Schibsted, our large, distributed team of iOS developers collaborates on a single codebase. Our modular UI is shared by several apps, each of which applies a unique theme. Our requirements are effectively the worst case scenario for using Storyboards and Interface Builder.
We built a service to estimate the number of website visitors reached by new audience segments in real time, for queries with any combination of user attributes. Here’s how we did it. By Manuel Weiss, with help from the ATE team Audience targeting and segments Schibsted’s Audience Targeting Engine (ATE)
During development you often update the deliverables with the latest version of your code. But don’t forget what you removed. Here is how you can do it with Make.
How we use Java Nashorn to transform data on the fly We were asked to migrate data back and forth between two schema-incompatible systems. At the same time, their schemas were evolving. We decided not to waste our developer energy on writing disposable mapping tables in Excel. Instead, we provided our