Most dev blogs get written in hindsight — a clean writeup after the project already shipped, all the messy parts smoothed over. This one's the opposite.

The plan is simple: post the build logs, hackathon postmortems, and learning notes as they happen. Not polished. Not perfect. Just real.

What's coming

• Hackathon retros — what worked, what didn't, what I'd do differently
• Notes from learning SQL and Python on the job
• Occasional detours into AI agents and automation

A taste of the kind of thing I'll be writing about

Here's the kind of SQL I write on a daily basis — nothing fancy, just getting answers out of messy data:

-- Find the top 5 products by revenue this month
SELECT
  product_name,
  SUM(quantity * unit_price) AS revenue
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY product_name
ORDER BY revenue DESC
LIMIT 5;

And the Python that automates the Excel report that used to take 2 hours:

import pandas as pd
 
def generate_report(filepath: str) -> pd.DataFrame:
    df = pd.read_excel(filepath)
    summary = (
        df.groupby("department")["spend"]
        .agg(["sum", "mean", "count"])
        .rename(columns={"sum": "total", "mean": "avg", "count": "entries"})
        .reset_index()
    )
    return summary

If you're tracking a similar path, welcome. Let's see where this goes.