After building several AI-assisted projects, I noticed something interesting:
Most AI coding tools feel similar during small demos.
But once repositories grow beyond around 40+ files, the differences become much clearer.
The bottleneck no longer feels like pure code generation.
It becomes:
- context management
- repo understanding
- dependency awareness
- multi-file coordination
Some tools work very well for:
- fast prototyping
- brainstorming
- UI iteration
Others become much stronger for:
- repo-wide cleanup
- large refactoring
- architecture consistency
This changed how I evaluate AI coding systems.
I now care less about:
- benchmark demos
- raw code output
and more about:
- workflow continuity
- repo awareness
- long-session stability