A detailed exploration of building a modern data engineering stack: how careful tool selection and cloud-native design enable reliable data pipelines, machine learning workflows, and interactive reports with zero operational costs.
From cautious ETF investor to data engineer on a mission: I built a personal stock advisor that combines financial data with machine learning to predict long-term US stock performance relative to S&P500, running on a completely free tech stack while providing interpretable insights for investment decisions.
This post is a reply to my three-year-old post on my previous attempt to create a personal site, in which I explained why I chose R Distill (now called Quarto blog) + Firebase Hosting. I’ll outline what has changed and what hasn’t since then, which made me land on Hugo + Netlify. In the next post I’ll describe my “blogging stack”, which comprise all the tools I use from ideation to published blog post. In summary, I am happy I made the switch to Hugo and my current blogging stack works well.
An interactive dashboard designed to facilitate the exploration and understanding of a potential model for calculating a Click-Through Rate (CTR) based ranking score. It uses the Binomial-Beta conjugate prior relationship for modelling CTR uncertainty and the Beta quantile function to determine ranking order.
An interactive dashboard for visualizing article rankings on a (hypothetical) newspaper front page. Made with Svelte, running purely in the front-end. The post outlines the motivation for creating it, the ranking method used and some tooling considerations.