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.
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?
GROUP BY creates buckets and counts them. It's like Python's `collections.Counter` or pandas `groupby()`. Learn COUNT, SUM, AVG, MIN, and MAX.
Filter rows with conditions, the SQL equivalent of list comprehension `if` clauses. Covers AND/OR, IN, BETWEEN, LIKE patterns, and NULL handling.
Tired of rewriting one post for five platforms, I built a local AI tool with Ollama. What broke, why two passes fixed it, and whether small models can carry it.
SQL returns rows in no guaranteed order; the same query can come back differently twice. ORDER BY gives you control, like Python's sorted() with keys.