Building an AI visibility tracker requires evolving from a simple script that checks if a brand appears in AI model responses into a scalable data platform. The article explains the core algorithm, demonstrates a Python example using OpenAI's API, and illustrates how scale multiplies complexity: monitoring 1,000 brands across 50 prompts and 3 models daily generates 4.5 million monthly responses, making architecture and cost management critical challenges.
This article explores ten years of Postgres logical replication evolution since its introduction in Postgres 10. The author describes building a hub-and-workers architecture for distributed write loads using logical replication instead of older tools like Londiste, and outlines a series covering three key architectures and how successive Postgres releases have simplified the required SQL.