Recent Posts

An exploration of the machine learning approach behind the Stock Advisor, examining how financial data is transformed into predictions while focusing on uncertainty estimates and feature interpretations that help separate signal from noise in the stock market.

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.

My complete blogging process, from idea generation to deployment, including tools like Logseq, Typora, LanguageTool, Hugo, Netlify, and more.