We open sourced drone-cache, a plugin for the popular Continuous Delivery platform Drone. It allows you to cache dependencies and interim files between builds to reduce your build times. This post explains why we are using Drone, why we needed a cache plugin, and what I learned while trying to release drone-cache as open source software.
Read on for the story behind drone-cache or if you want to jump into action directly, go to the github.com/meltwater/drone-cache, and try it for yourself.
We Germans are known for our love of renewable energy, energy saving lamps, and recycling. I am allowed to say that, as I am one of “them” :) Our office in Berlin had another idea to reduce waste: Lunch boxes (“Brotdose” being one possible translation of that).
Most of us like to leave the office for lunch break. Once in a while we also pick up food from nearby stores, to eat it in our kitchen in the office. That means you will get a doggy bag to transport your food, mostly a box box made of styrofoam, or plastic. “ewwwww”
At Meltwater, we have been running a quarterly employee survey with 350+ people in the Product & Engineering group for three years. This post explains our journey, our approach and what we have learnt.
Read on if you are wondering how an employee survey can help you understand your organization better and create strategies to improve engagement on various levels.
Our team was challenged with a project that involved performing actions based on plain-text requests. Having little experience in Data Science, Machine Learning (ML) and Natural Language Processing (NLP), our initial approach amounted to nothing more than “AI based on if-else statements”.
To improve our approach, we invited our Data Science team from London to visit us in Berlin. We learned about some services available to us, best practices and the like, but the biggest takeaway was nailing down a methodological workflow, which has become our go-to approach to tackle any data problem.
Read on to learn our Data Science workflow, with a practical example, and see how it can help you in your projects.
My team and I have built a solution that mines a stream of online articles for real-time insights for our customers. This component’s logic could be dramatically simplified if we could assume that it never receives near-duplicates of articles. While deduplication of identical documents is simple, detection of near-duplicates (i.e. “same thing, just slightly different”) is a complex but well-researched problem space.
To solve our problem, we ended up building a locality-sensitive hashing library for Elixir. Read on to find out why and how we built and open-sourced ExLSH.