I’m going on a short vacation this week, so this post is coming out a bit earlier than usual. I actually had a different, more “useful” topic in mind — something educational, something responsible. But then I came across this fascinating article: I don’t like Tailwind. Sorry not sorry written by @freshcaffeine , and I couldn’t get it out of my head. So I decided to write a response instead. I actu
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,
🤔 Why v0 Output Alone Isn't Production-Ready If you've used v0.dev to spin up a landing page, you've probably hit the same wall on the next step. The component looks clean inside v0, but the moment you drop it into your Next.js project the design tokens drift, dark mode breaks, metadata is empty, and Lighthouse scores land in the 60s. This isn't a v0 limitation — it's that v0's output is "desig
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