Founder @ Velyr.io -- Building an AI agent that analyzes user behavior, finds conversion drop-offs, and turns them into code fixes for websites and Shopify stores.
Gh issues works surprisingly well as an agent board. Labels for state, one issue per feature. The part i haven't figured out yet is how to know when the output is actually done vs just "looks done" to the agent.
Tried macOS HIG for the same reason and got similar results, less slop, more structure. I think what's happening is that the model has a very specific grammar to pull from instead of averaging over everything web-related it's ever seen.
The SaaS one is interesting as a control, it's essentially asking for the average of all modern web UI, so you get exactly that.