Angular is Costing Companies Billions

I know I’m going to get hate-mail for writing this piece, but, so be it. Someone has got to finally say what many of us as experienced software engineers have been thinking for some time now.

I’ve been a developer for over 20 years working for some of North America’s most prestigious companies. For several years now I’ve been watching the state of UI and how it’s gone from bad to worse. Specifically, I’m talking about “fad-tech”, those cutesy not-so-little pieces of JS and CSS that are supposed to be all the rage with the newbie crowd and now even with some seasoned engineers who should know better.

The snowballing culture of using these frameworks, like Angular, have avalanched us into code hell with no end in sight of when this nonsense is ever going to level off.

Everyday I see job postings come into my email, companies of all sizes scrambling for EXPERIENCED Angular 4, 5, 6, 7, 8, 10, 12 developers with at least 5 years of experience building and maintaining this mess we call “state-of-the-art” UI.

It’s not “state-of-the-art”. It’s a mess.

Several years ago I had an interview with EA (Electronic Arts) during which I was told that the company was junking all of their UI frameworks and returning to simply writing plain vanilla ECMA (JavaScript) enclosures. (That would be JS “plugins” for us jQuery people.) I was surprised but also curious.

Now the rest of us know why.
Keep It Stupid-Simple

I don’t like calling people “stupid”, so I’ve sort of rearranged the classic KISS acronym. But the KISS principle has been utterly lost with the latest versions of Angular. It’s no longer just a UI framework, but a backend service as well. Your UI people are now having to write backend code that goes beyond mere HTML templating. Some people would like to say that is a good thing! But it’s not.

Yes, Angular has some cool “whiz-bang” features—ALL of which are completely unnecessary to write effective and stunning UI or deliver a professional UX.

SPA’s (single-page applications) are out. They are difficult to maintain and wreak havoc with analytics and search engine crawlers which rely on the URL actually changing.

Yes, there are work arounds for these issues, but THAT’S THE POINT! You shouldn’t have to write code to “work around” how the web actually works!

The Last 20 Python Packages You Will Ever Need

Today we are going to look at the 20 Python Packages you should know for all your Data Science, Data Engineering, and Machine Learning projects. Those are the packages that I found most useful during my career as a Machine Learning Engineer and Python Programmer. While such a list can never be complete it surely gives you a few tools for every use case.
1. Open CV
The open-source computer vision library, Open-Cv, is your best friend when it comes to images and videos. It offers great efficient solutions to common image problems such as face detection and object detection. Or as we can see below Edge detection, the process of detecting various lines inside an image. If you are planning to work with Images in data science this library is a must. Open CV gathered a massive 56000 stars on Github and made working with image data several X faster and easier for me.
2. Matplotlib
Data visualization is your main way to communicate with non-Data Wizards. If you think about it, even apps are merely a way to visualize various data interactions behind the scene. Matplolib is the basis of image visualization in python, from visualizing your edge detection algorithm to looking at distributions in your data, Matplolib is your partner in crime. 14000 stars on Github and surely a great library to start contributing to. I made for example this animated lineplot in a recent video using seaborn and matplotlib.