Two Kinds of Silence: Why "Nothing Happened" Is Two Different Bugs Wearing One Signal

In a recent discussion on Indie Hackers, the author observed a recurring pattern across three unrelated posts: a reconciliation job, a fridge-scanning app, and an API growth writeup. Each of these systems produced the same observable output for two situations that required opposite responses. The author identified two distinct failure modes that should be distinguishable.

In the reconciliation job, a run that did nothing wrote a row indicating so, while a run that didn't happen at all wrote no row, which the morning report flagged as a gap. Two failure modes, two distinguishable signals. However, inside the "warn" alert, both situations appeared identical, making it difficult to differentiate between a minor issue and a significant problem.

Similarly, in the fridge-scanning app, the product hid recipe results until a photo scan found 5+ recognizable items. However, both the failure to recognize items and the genuine presence of items in the fridge resulted in zero recipes being shown. The issue was that one was a bug in the vision model, while the other was simply Tuesday. The founder's beta-test plan included testing a nearly-empty shelf to generate the input that was indistinguishable from the software's failure mode.

Lastly, in the API growth report, 744 signups were recorded, but only 284 had a billed API call. The gap of 460 people could not be distinguished based on whether they created a key and never issued a request or issued a request, found it unappealing, and left. The aggregate number couldn't provide any insight into the issue, requiring different fixes for each scenario.

The author argues that the issue isn't about logging more data; instead, it's about extracting the existing data and using it to distinguish between the two situations. The reconciliation job could simply count the number of "warn" occurrences within a rolling window and flag anything that crosses a certain threshold as an error.

The fridge app could utilize the raw scan result (item count and confidence per item) and not discard it when deciding how to handle a situation where no recipes are shown. Similarly, BeatAPI could join request timestamps per key to identify and address the issue.

The author acknowledges that in some cases, such as the one they personally care about (StareBrain's confirm-before-execute layer), the distinguishing data may not exist yet. In this scenario, the remote system's true state cannot be known without taking a risk (like retrying), which could be dangerous in certain situations. The author suggests that they need to first determine whether the distinguishing signal already exists or needs to be captured, before implementing a solution.