Most posts here are part of a series, so if one is useful, the one before it probably is too. The SQL for Python Developers series is the place to start if you came for SQL; I Vibe Coded and Lived to Tell if you came for the AI stories.
WITH clauses let you name your subqueries and read top-to-bottom instead of inside-out. Transform nested spaghetti into clean steps.
Karpathy called LLM-based knowledge bases "very useful." Why the architecture works and how to start building one in practice.
Nest queries inside queries, like Python helper functions. Use them in WHERE, SELECT, or FROM to compute intermediate results.
You can't design your way to a good abstraction. You have to earn it through repetition. Vibe coding made that repetition happen faster, not slower.
Connect related tables like looking up values in a Python dictionary. Covers INNER JOIN, LEFT JOIN, and when to use each.
A practical framework for when local wins. Privacy, cost, independence, and learning, grounded in real tool-building.
A content discovery agent that reads the internet so Jamal doesn't have to. Clean architecture, 188 tests, and a URL that took three attempts.
WHERE filters rows before grouping; HAVING filters after. Need "only cities with more than 10 customers"? That's HAVING.
Small local models bring creativity. They don't bring discipline. Here's what I learned testing llama3.2, phi4-mini, and llama3.1:8b so you don't have to.
My first time participating in t-SQL Tuesday, answering the question, what career risks have you taken?