Opportunity ledger / Dossier O-0399
AI teams need monitoring to detect silent LLM behavior drift in production, where model outputs quietly change and create a reliability gap.
First seen 2026-07-16 · Last updated 2026-10-16 · Recalculated daily
These observations describe collected samples, not platform-wide prevalence; they cannot establish whether a problem is widespread or accelerating. Collection scope changed on 09-16 and 09-27; monthly counts must not be read as trends.
Post title: since the September updates, one of my agent flows quietly got less reliable
prosumer who pays for the top tiers and notices wh…
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Post title: [TLDR] Opus 5 doesn't finish tasks, it manufactures them [via r/ClaudeCode]
Same prompt on 4.6 vs 5, I measured it: 4.6 ended …
The public layer keeps only a minimal excerpt. Open the source for full context.
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Scoring summary
Evidence confidence = strength × evidence volume × source diversity × payment × competition × trend × 10. Counts use independent content items; only first-hand pain, current spend, explicit willingness to pay and concrete feature requests affect the score. Repeated posts by one author are discounted. Scoring and status changes follow deterministic rules.
“Payment-backed” means first-hand payment evidence exists in the record. It does not mean the business is worth building. “Fading” is a recency tag shown alongside any evidence level, not a lower level. Read the full methodology.