Applications

A sample of the (quick-and-dirty) data applications I developed. For fun & learning.

League scheduler

League scheduler

An implementation of constrained time-relaxed double round-robin (2RR) sports league scheduling using a tabu search based heuristic algorithm. A mouthful just to say a tool to do automatic scheduling of sports calendars.

Pick me quick

Pick me quick

A silly and simple random names or teams generator. Primarily an evening JavaScript practice, but also useful because wysiwyg and totally ad-free.

Lyrical Arctic Monkeys

Lyrical Arctic Monkeys

A scrollytelling web page that takes you on a journey through all released Arctic Monkeys albums. From song section importance to lyrical variation and lexical dispersion. Developed with the JavaScript library D3.js.

Futsal Friend

Futsal Friend

A handy application that acts as your digital futsal companion to help find a friendly opponent, look for a team to join, analyze vanity metrics, and receive tactical advice from a coachbot.

Cycling similarity

Cycling similarity

A Streamlit application that exposes an algorithm to detect the most similar cyclists given a selected rider, based on the race results over the past seasons. Partly built on a tiny AWS cloud infrastructure.

Fluks jobscan

Fluks jobscan

An R Shiny application to scan your job happiness. This is the password-protected development version. To see the wholesome beauty and utility of it, reach out to me so I can bring you in touch with Madame Fluks.

Pet image screener

Pet image screener

What breed is your pet? A simple tool that allows to detect the breed of your pet (actually, just cats or dogs), based on a single picture. Uses a computer vision AI model under the hood.

Cycling similarity v0

Cycling similarity v0

A more clunky predecessor of the cyclingsimilarity.com Streamlit application, with comparable functionality but using different data and Dash as framework. Deployed with Azure App Service.

sentometrics.app

sentometrics.app

With this R Shiny application created during my PhD days you can compute both textual sentiment and aggregated time series. Enjoy fooling around with the various parameters!