Open-Weight Models on Jaiman.org

What I learned running an AI verification pipeline over 20,000 publisher profiles

Tue, 07 Jul 2026 17:31:00 +0530

This is Part 2 of a three-part series. Part 1 tells the story of why this system exists. Part 3 collects the rules I now follow. This part is the engineering.

PublishersGlobal runs a verification pipeline over an active directory of ~20,000 publishers and service providers. Every profile needs to be continuously verified and re-verified, because data on the internet degrades very quickly. In the first few months we ran close to 30,000 verification passes. This post recounts some of what I learned building and running the verification system on open-weight models.

A survey of what a full-stack developer learned doing AI engineering on a live production system

Tue, 07 Jul 2026 17:30:00 +0530

This is Part 3 of a three-part series. Part 1 is the story of why the system exists; Part 2 is the engineering detail. This part is the distillation: the rules I now follow, each one paid for by a specific mistake¹. I’d bet that this list is far from complete and I will have the opportunity to add to it.

I came to AI engineering with decades of full-stack habits. Some transferred directly, some had to be unlearned, and a few new ones had to be acquired at the cost of production incidents. Here are twelve, in roughly the order the pipeline taught them to me.