Open sourcing TonY - native support of TensorFlow on Hadoop (2018) - an example of building an application on YARN (Hadoop’s scheduler), which is similar to what Hadoop, Hive, Spark etc. do, but with a focus on TensorFlow. Size is quite small in the examples, but it’s still a neat example.
Algorithms behind modern storage systems (2018) - an ACM Queue article about B-trees and LSM-trees, which are the two major storage implementations for OLTP-type databases. Doesn’t go into a lot of details, but enough to get an intro on them.
Book review - how to write a lot - a practical guide to productive academic writing (2018) - a review of a book review. So meta! It’s a nice distillation and a lot of the suggestions apply to blog writing or any sort of technical writing a developer might find themselves doing - documentation, design docs, launch announcements etc. Very much similar to the advice from the deep work review - write a lot in a dedicated time for writing basically.
It doesn’t have to be Turing complete to be useful (2018) - a case for small Turing-incomplete languages as DSLs for narrow tasks. OTOH, it is nice to have a Turing complete language in such cases - Gradle is an example which comes to mind from recent work.
Google Maps’ moat (2017) - a “moat” is any feature/process/audience/etc a product has which protects it from its competitors. This is an analysis of Google Maps’ moat. It comes down to massive amounts of data and a lot of engineers thrown at the problem. What’s interesting is that the baseline, from personal experience, to getting a usable experience with open source tools and data is not that hard to achieve. But getting to the level of Maps is nigh impossible.