Every vibe-coding demo looks amazing. You get a functional CRUD app in minutes, and it's easy to imagine moving your entire team onto this workflow. The problem starts once the project stops being a demo.
In most existing tools, each new session starts without much knowledge of what was built before. The AI reads a sample of files, guesses at intent, and sometimes rewrites things that already worked. For a one-day project, this is invisible. For a project that grows over weeks with entities, endpoints, and cross-file dependencies, it becomes a daily obstacle: you keep re-explaining to the tool what it's building.
Why this happens
This is an architectural problem, not a model-quality one. A tool that rescans files fresh every session and feeds them to the language model as raw context hits the context-length ceiling once a project passes a certain size. The result: the model gets a partial picture and starts "guessing" instead of knowing.
What it costs teams
- Wasted time re-explaining design decisions already made.
- Changes that clobber existing code that worked fine.
- Inconsistency between endpoints generated in different sessions.
How we think about it at Vibe Coder
Vibe Coder maintains persistent context on the project's structure, entities, and API contract across sessions, so the tool's understanding doesn't erode as the project grows. More on the conceptual model (without implementation internals) is on the Product page and in the Documentation.