I am fresh grad and I would like to ask a question. I have seen many people in the applied machine learning community working on demo papers showcasing their work i.g [article!](https://arxiv.org/abs/1503.03021), while others focus on sharing their projects with the community through a practical long blog posts. An example of blog posts i.g [article!]( http://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/).

While people in academia say that we need a peer-review to publish and share ideas, it is hard to adapt this mechanism when we write blog posts. Still, it is a very slow process if you want to publish an idea in conferences. There is no doubt that we need to publish theoretical work in academic conferences (NIPS, CVPR, KDD, .. etc), but most of the work around applied machine learning is about providing a solution to a specific problem (How can you build a better music recommendation app ? Recommend a favorite restaurant ? )

When I started looking for a job as a software engineer, I found that people don't really value efforts that have been put in demo research papers (unless it is a very famous conference). How can I make the best out of my work ?. Should I focus on writing blog posts/ Open source projects, in this case how can I get a peer to validate my work ?. Or should I publish demo papers. Doing both is not really a sustainable option as I need to duplicate things and write to different communities. I want to do a career in data engineering, which lays at the intersection of both communities. I don't want to lose time focusing on something that will not help me grow as a software engineer. Based on your experience with HR and managers, what are the most things that can impress them ?. Which one of both will help me as an engineer ?