The fingerprint is where the bill comes from. Trackers group events by a hash of the exception type plus the top frame [10], which is a syntactic key rather than a causal one, so one broken date helper called from nine places arrived as nine separate issues [11]. That is nine stack traces to open for what turns out to be one decision.
The first agent pass in this pipeline therefore never reads code. It gets metadata only, and the merge test it is given is whether fixing one line would plausibly resolve both issues: different call sites into the same helper count as one cause, the same exception type from unrelated modules as two [12]. That pass took 340 issues down to 112 causes [13].
Now the arithmetic, at the author's own rate. He assumes a careful engineer needs 4 minutes per issue to open the trace, find the corresponding code and decide whether it is real, which puts 340 issues at roughly 22 hours [4]. 112 causes at the same 4 minutes is 448 minutes, under 7.5 hours [1]. That reduction happens before anything has looked at the repository. Note what the 22 hours actually is: an assumption the author states, not a timed pass [4]. If your engineers are slower per issue, or your fingerprints already happen to track causes, the figure moves in both directions.
The second cost control is the payload shape. The builder filters stack frames on `if f.get("in_app")`, keeps only the deepest six, and truncates the message at 2,000 characters [8][15]. That matters because an unfiltered React trace in this codebase ran 40 frames of framework internals around 3 frames of application code [9]. It also means the filter inherits your SDK's configuration: `.get` returns None when the key is absent, None is falsy, so a tracker that never marks app paths yields an empty frame list and a verdict rendered on the issue title alone.
The other failure mode the write-up names is over-merging, and it is handled by instruction rather than by code. Unsure clusters stay separate, on the stated grounds that over-splitting is cheap and over-merging hides bugs [12]. That asymmetry is the part of the design I would copy unchanged, because the expensive stage scales with cluster count and the silent stage does not.
The remaining risk sits in the branch nobody watches. Clusters that come back "not real" are auto-muted with a reason attached [6], which is at least an audit trail, though the source text available here breaks off as the per-cluster verdict stage begins [16]. Eleven causes survived out of 112, about one in ten [14][3]. The one that justified the exercise was a null dereference limited to accounts created before a 2024 schema change, sitting at rank 180 with six events [3], which is seven hundredths of a percent of the week's volume [4]. Sorting by volume is what buried it, and sorting by volume is what every dashboard does by default.
