Throughout last year I had the opportunity to participate and collaborate on multiple research initiatives in the field of Natural Language Generation (NLG) in addition to my responsibilities as a Data Scientist at trivago. NLG is the process of automatically generating text from either text and/or non-linguistic data inputs. Some NLG applications include chatbots, image captioning, and report generation. These are application areas of high interest internally within trivago as we seek to leverage our rich data environment to enrich the user experience with potential NLG applications.
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Insights, experiences and learnings from trivago's tech teams.
When migrating your data to new technologies, validation of the data becomes challenging as your data structures might change. Rebase tries to make this easier while also giving your more flexibility on your data.
Around a year ago, in our large scale refactoring project also known as Project Ironman, we stepped away from image sprites that we used for our icons. In this post we will explain our reasoning behind this decision and how it improved maintainability and website performance.
At trivago we are building and using a Pattern Library which is based on Brad Frost's Pattern Lab adapted to our needs; our patterns are written in Twig. This Pattern Lab is based around Brad's Atomic Design, which is also something that we are embracing.
When using webpack to build your assets, it's only a matter of time until you wish for targeted builds. Whether it's the output of the library you're working on (CJS, UMD, AMD, Var, etc.), or the specific feature set (IE8 support, no IE8 support).
parallel-webpack can run those builds in parallel.
One of our core values at trivago is fanatic learning. Twice a year, the trivago software developers gather to have a 2 day internal hackathon. This December saw another round of ambitious creativity, relaxed atmosphere, and good food.