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MotherDuck's Jev is a decision model that classifies text by picking from predefined answers, running 30x faster than GPT at 1% of frontier model costs. The article walks through five real use cases—grading AI agent logs, analyzing Hacker News sentiment, screening sales calls, and others—with actual SQL queries and datasets you can attach and rerun.
- Jev classifies 100,000 items in 82 seconds at ~1% the cost of frontier models by constraining outputs to a closed list you define, returning typed columns with confidence scores.
- On Hacker News mentions, 89% of pre-2023 "Claude" and "Anthropic" matches were false positives (Claude Shannon, the anthropic principle), but Jev's name-checking reduced false positives to 0.8% post-2023 when the products actually launched.
- The pattern separates facts from judgment: SQL extracts exact counts from logs or data, Jev only answers subjective questions like "was this turn efficient?" or "is this negative?", keeping costs down while maintaining accuracy.