Interesting idea. I’ve mostly run into this kind of drift at the LLM output level rather than in the code itself even with similar setups, behavior can change quite a bit between runs. Curious if you’ve explored that side as well.
Interesting idea. I’ve mostly run into this kind of drift at the LLM output level rather than in the code itself even with similar setups, behavior can change quite a bit between runs. Curious if you’ve explored that side as well.
That’s an interesting angle. VibeDrift currently focuses on the artifact i.e. the code that actually lands in your repo rather than the LLM output itself. The reasoning is that regardless of why the drift happened (different sessions, different prompts, different models), the codebase is what you ship and maintain. That said, tracking prompt-to-output consistency is a genuinely different problem and not something I’ve explored yet. Would be curious what patterns you’ve seen there. I’m always open to suggestions and feedbacks. There is always room for improvement