Vibe coding is a good starting point, but it is not where serious AI-assisted development ends. The next step is agentic engineering: using AI coding agents inside a controlled engineering workflow, with context, tests, review and clear boundaries. Vibe coding often focuses on the generated output: Ask for feature -> get code -> run it -> ask for fixes Agentic engineering focuses on the system ar
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,
This post was created with AI assistance and reviewed for accuracy before publishing. Cursor can use project rules and documentation to steer behavior. Exact file names and mechanisms evolve; check Cursor documentation for the current layout (for example rules in .cursor or legacy .cursorrules patterns). Short, enforceable bullets beat long essays: stack versions, test commands, “no new dependenci
Vibe coding is one of those terms that sounds unserious until you notice how many people are actually doing it. The basic idea is simple: describe what you want, let an AI coding tool generate the implementation, run it, adjust the prompt, and keep going. It can feel magical. It can also go wrong very quickly. Vibe coding works best when the problem is visible and forgiving: small prototypes inter
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