I use AI coding agents every day. I believe they are reshaping how we build software, and I think the teams that adopt them deliberately will outperform those that don't. I am not writing this to warn you away from AI-assisted development. I am writing this because the loudest voices in the AI enthusiasm camp are also the most allergic to discussing what can go wrong. And that worries me more than
The DataFrame class (from Pandas) is a work of art. Even if you never "do data", priceless lessons can be gleaned by studying this class. It starts simple enough. Usually you will create a DataFrame by ingesting from a CSV file or database table or something. But you can whip up a small one like this: import pandas as pd df = pd.DataFrame({ 'A': [-137, 22, -3, 4, 5], 'B': [10, 11,
On Second Thought — Episode 06 The ORM hides the SQL. The cache hides the ORM. The service mesh hides the services. The operator hides the YAML, which already hid the kubelet, which already hid the container, which already hid the process. By Tuesday, nobody quite remembers what the original problem was. They are too busy configuring its sixth wrapper. This is the post about that wrapper. When som
Every team experiences incidents. The teams that grow stronger from them are the ones that take postmortems seriously — not as blame sessions, but as structured learning opportunities. Yet most postmortems end up as a wall of text nobody reads twice, filed away and forgotten until the same incident happens again six months later. This guide walks you through writing postmortems that genuinely chan
When we talk about Data Visualization and Dashboards, enterprise tools like Tableau or PowerBI often dominate the conversation. However, for Data Scientists and Developers, these GUI-based tools can feel restrictive. What if you need complex machine learning integration, custom UI logic, or automated CI/CD deployments? Enter the holy trinity of Python visualization tools: Streamlit, Dash, and Boke
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada