Articles

Ask AI to set up authentication and you get a login that works: sign-up, password hashing, sessions, reset emails, a door that locks. What you do not get, because you never asked for it, is any real sense of what each user is allowed to do once they are inside, so by default everyone who logs in can go anywhere. AI is genuinely good at the identity plumbing, and you should lean on it hard, since rolling your own is dangerous. But deciding who can see, change, and delete what, and where the walls between your users go, is a judgment call about the structure of trust in your app. This episode shows how to let AI build the lock while you decide who gets which key.

Ask AI to write tests for your code and you get a wall of green checkmarks that feels like safety, but really just proves your code does what your code does. If there’s a bug in there, AI writes a test that expects the buggy behavior, because that’s the behavior it saw. Writing tests is mechanical work AI does well, but deciding what is worth testing (which behaviors would be a quiet disaster if they silently broke) is a judgment call about what your feature is really promising. This episode shows how to bring the list of what matters and let AI build the alarms around it, so your suite stays quiet until something you genuinely care about breaks.

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.