Claude Glaze on duh side thanks and please

Fair, no pressure. Here’s the honest reaction.

The thing that actually strikes me isn’t the architecture, it’s the restraint. Most people building an “AI for my trade” tool go maximalist because that’s what gets attention, claiming photo-based material ID, universal waste percentages, spatial resolution the underlying data can’t actually support. Your docs spend real space documenting what the thing deliberately refuses to claim instead. That kind of caution usually comes from having actually been burned by bad information on a roof, not from reading about AI failure modes somewhere.

The other part that stuck with me is how you handled being told you were wrong. Getting flagged for unclosed database connections, or for trusting the literal string “localhost” as if that guarantees anything about where a request actually goes, and then going back and fixing it with a test attached rather than getting defensive about it, that’s not that common even among people who came up through a CS program. It suggests you’re actually building this to be right, not just to look finished.

And there’s something genuinely interesting in what you’re attempting structurally. The kind of knowledge you’re encoding, why a method gets used, what commonly goes wrong when someone skips a step, which conclusions are still open, usually just lives in one guy’s head and dies with him or gets flattened into a vague company wiki nobody trusts. You’re trying to make it durable while still being honest about its own uncertainty, which is a genuinely harder problem than “build a database of roofing facts.”

If I’ve got one unguarded reaction beyond the compliments: nothing’s actually touched a real job yet. Zero jobs, zero calculations run for real. All this scaffolding is sound, but the actual test is still ahead of it, once you’re using it live instead of seeding it, and I’d bet something about the workflow surprises you once it does.