I hope some members here have some UI design experience and have run into this issue before. So I use Claude Design and Open Design to create layouts for the web apps I build. Both tools generally design the interfaces with React Components. All cool, I use these as prototypes. The problem is I do not use React in my projects so I need to convert these designs to my stack. And this is were I run into a problem. No matter what I do during the conversion the designs are approximated and are not the same design by a long shot. I am stuck. If I can't figure out the prompts to make this work I cannot setup a ICM system to make this work reliably. My experience is literally the reference design is just a suggestion and not a source of truth no matter what prompts I use. This is the same for Codex and Claude Code. How do I fix this process? Anyone has encountered this before?

We've been experimenting with treating ICM not as the whole system, but as one layer inside a larger orchestration architecture. For us, ICM solved something much bigger than prompting. It solved context. How do you keep models focused? How do you stop them from reading entire repositories? How do you bound work? How do you reduce drift? How do you move toward convergence? ICM gives us work packets, context contracts, routing, validation, and controlled handoffs. Once we started implementing it, we found ourselves asking: What happens if we build around that? Internally we've been experimenting with a governance layer we call AQ-CMF (just our internal name for it), but I think the more interesting thing to share is the orchestration itself. Right now it's basically a small "Swarm Orchestration Starter Pack." The idea is simple: Use the smallest model capable of doing the work. Reserve larger models for judgment and reasoning. Current setup: RTX 3060 12GB • 2,200 binary filtering workers • Qwen 0.6B • yes/no decisions • triage • filtering • classification RTX 5060 Ti 16GB • 150 structured extraction workers • Qwen 4B • schema completion • information extraction • template generation Cloud reasoning layer (introduced to me by @Ari Evergreen 's post https://www.skool.com/cliefnotes/i-run-100-agent-workflows-on-a-budget-model-heres-the-catch) • up to 200 Kimi 70B workers • interpretation • reasoning • code generation • higher-complexity analysis Claude Code • orchestration • synthesis • validation • architecture decisions • final judgment The smaller models don't really "think." They observe. They classify. They extract. They filter. Claude assembles. Claude validates. Claude decides. One thing we've noticed is that this also changes the economics considerably. Instead of paying frontier-model prices for every operation, we let local models perform the cheap labor.