Vibe coding is a real and useful phase — the problem is people stop there. The space between ‘I had an idea on a plane’ and ’this runs in an air-gapped Kubernetes cluster’ is where the actual work happens. A generalizable playbook for the middle, starting with: treat the LLM like a very literal child.
Agents got good, code became the cheapest thing in the room, and the gap between product and engineering is closing fast. The people who internalize that — who spend their time deciding what should exist and ripping into what the agents hand back — are going to run circles around everyone else.
A full LLM-driven tuning loop with four real outcomes: a successful apply, an automatic rollback on regression, a safety-layer rejection, and a hint-driven redirect. No recommendations without measurement.
A layered training corpus — domain pairs, public ballast, and a reusable Postgres syntax corpus — is 80% of the work for a NL2SQL LoRA. The training config is YAML and patience.
A step-by-step walkthrough of building a PostgreSQL semantic layer in pg_agents — crawl the schema, enrich it, define your vocabulary, lock down categoricals, and promote the queries that work.
Raw LLMs hit 10-20% accuracy on real enterprise schemas with cryptic column names and tribal-knowledge joins. Here’s why, and the semantic-layer fix that takes you from toy to production.
A top-like live terminal dashboard for monitoring LLM inference servers on NVIDIA DGX Spark.
