It is often said that developers are lazy and that is (usually) a good thing. It just means that instead of repeating the same task or code over and over they tend to find ways to prevent that repetition and optimising their time.
Enums are (and have always been!) a recommended way to model a fixed set of constants. Most commonly an enum only provides a set of possible constants and nothing more. But being full classes, enums can also carry helper methods and fields (both instance and static) or even implement interfaces.
Since we announced adding Kotlin as an officially-supported language on Android, we’ve recommended a staged approach when adopting Kotlin. At the beginning, this means writing code such as tests and new features in Kotlin, which typically means calling from Java code into Kotlin.
How do you know if some code is over-engineered? What does that even look like? How do you know if you’re over-engineering the code that you’re writing? What if you recently started at a new company, how do you know if the code you’re working with is over-engineered? In this episode, Kaushik and Donn go back and forth on this topic.
I’ve written about lots of computer vision and machine learning projects like object recognition systems and face recognition projects. I also have an open source Python face recognition library that is somehow one of the top 10 most popular machine learning libraries on Github. Together, that means that I get asked a lot of questions from people new to Python and computer vision.