Why current LLM coding assistants keep you trapped in engineering deadlocks

Hey builders,

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.

What is the hardest architecture or mathematical deadlock you've faced recently where AI completely failed to understand the bigger picture? Let's discuss.