We ve all been there: you re designing a complex system architecture or a machine learning pipeline, and you hit a conceptual wall. You turn to Claude or GPT, but all they do is optimize inside your current, broken logic. They suggest refactoring loops or adding try-catch blocks, completely missing the fact that your entire problem representation is fundamentally flawed.
I believe the next generation of AI tools shouldn't just write mechanical code they must manage the topology of the problem itself, forcing "representation shifts" when localized search fails.
I m working on Research Space Navigator (RSN) to automate this mapping. We are launching in 29 days, but I want to build this out in public.
When building complex software or systems, engineers often hit structural deadlocks. Standard AI code assistants just optimize within your current, broken logic. RSN is different. It models your problem space and forces a "representation shift"—mapping your bottleneck into a completely new domain (e.g., continuous to discrete, or vector to graph). It uses a strict 4-Point Anti-Bullshit Filter to eliminate AI hallucinations and outputs an actionable JSON protocol for your next morning's work.
