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Articles/AI Models
AI Models/2026-06-17Advanced

Where to Stop Letting Rork Fix Your Bugs: A Triage Routine for the 30% That Need You

Most bugs you hand Rork get fixed in a couple of regenerations. A stubborn minority loop forever, each fix spawning a new symptom. Here is the triage routine I use to split what to delegate from what to take over by hand, with retreat lines, regression guards, and a decision log.

Rork415AI29Bug FixingOperations3Debugging9

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I meant to tweak one setting, and somehow I had run nine regenerations by the end of the night. The culprit was a wallpaper app I keep in production: on certain devices, the thumbnail grid would occasionally render completely blank.

When I described the symptom to Rork's chat, the first fix looked plausible. But when I checked it against a production build, scrolling broke on a different list. I asked Rork to fix that, and the original blank screen came back. Fix and regression, back and forth, well past midnight.

The next morning I realized what had gone wrong: I had unconsciously assumed I could just keep asking until it was fixed. Rork's bug fixing really is strong. In my own experience, roughly 70% of the bugs I hand it get resolved with almost no manual work. The trouble is the remaining 30%. Approach that minority with the same posture and you end up in last night's loop.

As an indie developer running several apps in parallel, how fast I can spot that 30% and switch to hands-on work decides the productivity of my whole day. Below is the triage routine I built out of that late night: how to decide whether to keep delegating a bug to the AI or take it over yourself, including retreat lines, regression guards, and a decision log.

Where the self-healing 70% splits from the 30% that needs you

The first thing to internalize is that Rork has a clear bias toward certain kinds of bugs.

It is strong on bugs that reproduce reliably and have a single, contained cause. "Tapping this button always crashes," "the spacing breaks only on this screen" — these usually resolve in one regeneration round. Rork narrows down the location from the symptom description and returns a fix that tidies the surrounding code too.

It struggles with bugs that appear only probabilistically, bugs with several interacting causes, and bugs whose behavior differs between production and preview. Last night's blank screen was a race between image-cache eviction and rendering — exactly the probabilistic, compound kind. Because the reproduction condition cannot be pinned down in words, Rork attacks a different "likely cause" each time, and I was stepping through those ad-hoc fixes one by one.

Leave this judgment to instinct and you will keep telling yourself "just one more prompt." So before I dive in, I always run three filters.

Narrow triage down to three axes

When I meet a new bug, before I even open Rork's chat, I check three things out loud.

1. Does the symptom reproduce deterministically?

If the same action reproduces it 100% of the time, hand it to Rork. Attach the reproduction steps as a bullet list and the fix quality rises further. The moment words like "sometimes" or "only on certain devices" appear, the odds of self-healing drop sharply. For probabilistic bugs, pinning down the reproduction condition on the human side is faster.

2. Is the fix local or wide-reaching?

A fix confined to one screen or one component keeps the regeneration blast radius small. But areas that ripple across the whole app — the ad (AdMob) initialization order, the purchase-state check — risk the regenerator "tidying" unrelated files. For wide-reaching bugs, do not hand the whole thing to Rork; set the boundary yourself, then ask for a scoped change.

3. Is failure recoverable?

A layout glitch is undoable. But a build right before store review, or the receipt-verification logic for purchases, is territory where a mistake reaches users who already shipped. In unrecoverable places, insert one round of human review before adopting any AI suggestion.

Running just these three axes shows that last night's blank screen was "probabilistic, wide-reaching, low-risk," and that I should have spent the time investigating the reproduction condition from the start.

Thank you for reading this far.

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WHAT YOU'LL LEARN
A triage table that classifies a bug by reproducibility, blast radius, and failure cost, so you decide in seconds whether to keep delegating or take over
A retreat line (the 3-regeneration rule) and the concrete trigger for switching from prompting to hands-on debugging
A Jest regression guard that protects manual fixes from being overwritten, plus a decision-log format that makes handoffs to the AI reproducible
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