Every model has a boundary. The useful question is not whether a system sees everything, but whether it names what it can see, what it cannot see, and what it is only guessing.
GTCode applies that habit to AI engineering and machine-learning research: explicit assumptions, clear interfaces, observable failure modes, and evaluation loops that expose drift before it becomes architecture.
Useful starting points: