https://lalitm.com/post/building-syntaqlite-ai/ I found that AI made me procrastinate on key design decisions[34](https://lalitm.com/post/building-syntaqlite-ai/?utm_source=Robin_Sloan_sent_me#sn-procrastination). Because refactoring was cheap, I could always say “I’ll deal with this later.” And because AI could refactor at the same industrial scale it generated code, the cost of deferring felt low. But it wasn’t: deferring decisions corroded my ability to think clearly because the codebase stayed confusing in the meantime. The vibe-coding month was the most extreme version of this. Yes, I understood the problem, but if I had been more disciplined about making hard design calls earlier, I could have converged on the right architecture much faster. Tests created a similar false comfort[35](https://lalitm.com/post/building-syntaqlite-ai/?utm_source=Robin_Sloan_sent_me#sn-tests-insufficient). Having 500+ tests felt reassuring, and AI made it easy to generate more. But neither humans nor AI are creative enough to foresee every edge case you’ll hit in the future; there are several times in the vibe-coding phase where I’d come up with a test case and realise the design of some component was completely wrong and needed to be totally reworked. This was a significant contributor to my lack of trust and the decision to scrap everything and start from scratch. Basically, I learned that the “normal rules” of software still apply in the AI age: if you don’t have a fundamental foundation (clear architecture, well-defined boundaries) you’ll be left eternally chasing bugs as they appear.