Notes on engineering, AI, hiring, and building systems that hold up in the real world.
As developers increasingly rely on AI-augmented workflows, proprietary SaaS codebases face a silent threat: IP drift. Copying code into consumer LLMs risks leaking core business logic into public training sets. This article outlines a pragmatic CTO framework to secure data using Enterprise AI Gateways, Zero-Data-Retention APIs, and local LLMs without hurting engineering speed.
This article explores why fully autonomous B2B SaaS agents represent a massive compliance and operational risk. It introduces the 'AI Circuit Breaker' architectural pattern—decoupling probabilistic AI proposal generation from deterministic execution—and outlines how to build robust human-in-the-loop validation systems that satisfy regulatory requirements while scaling software capabilities.
