panavm

One thing I've noticed building domain-specific SaaS with AI assistance: the first few weeks feel like magic, but then the codebase becomes hard to maintain.

The issue isn't the AI output quality — it's that most builders (myself included, initially) use AI reactively. Ask a question, accept the answer, move on. No structure for maintaining context between sessions or verifying that new additions stay coherent with the existing system.

The builders who get the best results seem to treat Claude/Cursor more like a junior dev: useful, but you review everything, and you explicitly maintain shared context about the state of the project.

Domain-specific SaaS is actually a great use case for this because the problem space is bounded — you can give the AI a really tight context. "We are building scheduling and invoicing for pest control companies. Current architecture is X. Today we are adding Y." That specificity makes the output dramatically better than generic prompting.

Good luck with the build — the insight to go learn the domain in person before building is genuinely rare and gives you a huge moat.

This resonates a lot. The CLAUDE.md hierarchy + indexed artifacts is exactly the kind of structural thinking that most "how to prompt Claude" content skips over entirely.

The piece I keep running into with solo builders is that even when they have a good structure, the failure mode is trusting Claude's output too uniformly — treating fast generation as a proxy for correctness. The code looks clean, tests pass, ships fine... and then a month later nobody (including you) can reason about it because the decisions that shaped it never got persisted anywhere. Your decision capture artifact solves exactly that.

One thing I've been exploring from a complementary angle: rather than scaffolding the SDLC, building a clearer internal model of how Claude reasons — where it's reliable vs. where it needs human review gates. Working on a free starter pack around this (panavy.gumroad.com/l/skmaha) — would be curious if any of this maps to patterns you've seen with the scaffold.