Building a Full-Stack Habit Tracker with Claude Code - Part 2: Polish, Testing & Deployment Taking the habit tracker from MVP to production-ready with categories, analytics, comprehensive testing, and Vercel deployment In [Part 1], we built a fully functional habit tracker MVP in about 6-8 hours using Claude Code as our AI pair programmer. We had: ✅ Basic CRUD operations for habits ✅ Date-based
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,
The "Deploy" button is not a self-destruct mechanism for your career, despite what your brain screams. We’ve all been there: you’ve poured hours into a project, the code is (mostly) working locally, and then you stare at that final, terrifying button. The one that says "Deploy". It's a mental roadblock, a sudden surge of "what ifs" that can paralyze even experienced developers. But here's the secr
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