GrokNerf: Model Quality Regression Monitor

Cross-version quality regression alerts for developers who rely on one model

Problem

Developers who use a model as their daily default notice it getting dumber, fumbling answers, and going off-track within a week, but have no proof beyond asking on forums whether it got nerfed. Vendors silently retune or downgrade models, so users rely on gut feel — and only notice after work is already delayed.

Solution

Users subscribe a fixed eval set (their own prompts plus expected-output criteria); the service calls the target model on a schedule and scores it automatically, alerting on significant drops. It also compares same-priced models side by side to advise whether to switch. The core is an automated pipeline for private benchmarks.

Users

Heavy AI coding/writing users and small teams standardizing on a single model

MVP

Upload 10–50 representative prompts with scoring rules; run a daily scoring job and plot the curve; compare multiple models side by side; email/Feishu alert when score drops below threshold; one-click export of a switching comparison report

Revenue

Tiered subscription by prompt count and number of models; free tier limited to 1 model and one run per week

Why now

Model release/retune cycles are faster than ever and silent downgrades are common; users are highly sensitive to tools quietly getting worse. An indie can build on existing APIs without training models.