Articles

When you hand a feature to AI without saying what “done” means, it picks the easiest possible definition: the code runs and the happy path works. Everything else (the empty states, the error cases, the “what if two people do this at once”) quietly goes unbuilt, then resurfaces later as “bugs” that were really just scope nobody named. AI is great at spelling out every condition a feature could possibly need to satisfy, but deciding which of those conditions actually count as done for your version, right now, is a judgment call about what your feature has to guarantee for users to trust it. This episode shows how to let AI generate the full menu of finish lines so you can draw the one you’ll actually stand behind.

AI can build you a backup system that runs like clockwork, copying your data on a schedule and rotating out the old copies without you babysitting it. The catch is that “backups exist” and “the thing I can’t afford to lose is safe” are not the same sentence, and plenty of people find that out at the worst possible moment, staring at a backup that doesn’t reach far enough or only holds the already-broken data. This piece covers handing AI the mechanism while you keep the judgment underneath it: which data is precious, how far back it has to reach, and which disaster you’re actually guarding against. It also makes the case for the step almost everyone skips, which is proving you can restore from a backup before you ever have to do it in a panic.

Ask AI to add error handling and it will catch every failure in your app so nothing crashes, which is exactly the boring, thorough work it’s good at. The problem is that catching a failure and handling it are not the same thing, and a user who hits a dead download and sees a generic “an error occurred” is left with no idea whether to wait, retry, or give up. This piece walks through delegating the catching while you keep the real decision: what each failure means to the person hitting it, what they should see, and which failures you’d rather have blow up loudly than get quietly hidden. AI catches everything; you decide what’s worth telling the user and what’s worth telling you.

AI can take any feature and slice it into clean, ordered steps faster than you can read them, which is genuinely useful. The trap is letting it build the whole feature in one swing, because then the first thing that breaks sends you digging through a slab of code you never watched get written. The fix is deciding for yourself where the checkpoints go: after which steps you stop, run the thing, and confirm it works before you let anything build on top of it. AI slices the work; you decide how much to build before you look. This piece shows you how to hand off the decomposition while keeping the one call that keeps every bug small enough to catch the moment it happens.

Ask AI what to build your app with and it will confidently recommend the popular default, the stack most people use, which may or may not be the one you can actually live inside. You won’t find out for weeks, because every stack feels fine at the start, and by the time the friction shows up you’ve built too much to easily switch. This episode shows how to let AI lay out the honest comparison of every option and what each one costs, while you keep the decision that depends on things AI can’t see: which stack fits what you already know, what you’re willing to learn, and how much daily friction you’ll tolerate. Includes a sample prompt and a walkthrough check that reveals whether a stack will fit you or fight you.

Ask AI how to make your app scale and you’ll get a serious-looking plan built for millions of users, handed to you while yours has a few hundred. That list of bottlenecks feels responsible, but treating it as a to-do list means burning weeks on infrastructure for traffic that may never arrive. This episode shows how to let AI map every place your app could strain, in the order the strain would actually hit, while you keep the judgment AI can’t make: deciding which bottleneck is real for your actual growth rate and which ones are someday problems wearing urgent costumes. Includes a sample prompt and a check that separates a near-term risk from a distant fantasy.