Not a hype post about "10x with AI." A concrete walkthrough of where AI actually sits in my daily work as a full-stack developer – code, content pipelines, translations, tests, deploys – and where I still do the work myself.
The most expensive Python I review is for order in orders after a SELECT *. Last row per group, month-over-month, filtered counts, and upserts are queries. How I write them in Postgres instead of downloading the table.
The SQL can be right and the API still dies: unbounded .all(), f-string queries, ORM N+1, a new connection per request, pandas as a warehouse on the web box. How I keep Python thin around Postgres in 2026.
Your product already has – or is hiring – an ML Engineer for model pipelines, embeddings, vector indexes, and Python algorithms. I am the Front-End Heavy partner who takes 100% ownership of the client-side UI, pixel-perfect execution, streaming, and user experience. Not a replacement for ML, but the dedicated front-end leader beside them.