Category: Computer Science

Categories
Computer Science

Setting up your DEV machine in ONE script

Photo by Claude Piché on Unsplash

A new computer is great 💻🎉 – It’s a clean slate. It’s snappy. It’s great… but it is missing all the tools I rely on every day! My work requires me to jump around in a variety of tools and technology stacks, hence I need to install a lot of different tools and be able to support different development environments.

Existing package manager for CLI tools

Over time developers load command line tools like people are buying toilet paper these days. From maven, via tl;dr, stopping at kubectl to tmux. We use all of those little nifty tools. Hence setting up a new machine can seem scary task at times, but fear not! For CLI tools we solved that problem long long ago with Homebrew, or as you probably know it: brew
(The windows folks probably know Chocolatey)

You can install maven for example by running: brew install maven

Fear not! The same tool that installs CLI tools can install (almost) all our applications!

me

Brew can do more!

Continue reading “Setting up your DEV machine in ONE script”
Categories
Computer Science

Set up GitLab CI with an iOS project that uses Cocoapods

We all write tests for our applications and keep them in version controlled systems like GitLab or GitHub. However, how can we configure GitLab to run Unit and UI tests for us?

I couldn’t find a lot of documentation explaining how to set up continuous integration for iOS on GitLab, therefore I am writing this article.

Victor Peschenkov explains in his article how to use GitLab CI with Fastlane. My article focuses on how to run everything yourself.

Continue reading “Set up GitLab CI with an iOS project that uses Cocoapods”
Categories
Computer Science UI/UX

ID3 Decision trees ?

As part of the Machine Learning class in (CIS 678) at GVSU, Camila and I created a decision tree tool that analyzes and processes a data-set, where each entry has a different combination of attributes and an expected outcome. The system then finds the «best» attributes, that allow deciding which outcome will occur with the least amount of questions, for previously seen or  new data.

The website & tool takes data-sets in the form of a simple text-file, then parses & processes it and finally creates a decision tree ? based on that data. Please check it out and let us know what you think and how you like it.

The tool can be accessed on this sub-site.

 

Tree example