Community · E2 · artifact verified
Find city permits from a free-text plan
Describe what you want to do in San Francisco and a rules engine works out which permits you need - Jev answers each rules question as a typed choice, falls back to asking you when unknown, and summarizes fees and deadlines per permit.
01 · Role in the system
What Jev does here
The navigator drives a permit rules engine from free text, and Jev sits between the text and the rules: the engine's facts each become a Jev choice question over that fact's options plus an explicit unknown, and the most probable label is taken as the answer. When Jev says unknown - or the call fails - the question goes to the user instead, so the system degrades to a conversation rather than guessing. The first question gates everything: if Jev judges the plan is not an event at all, the flow stops early. Answers carry who supplied them (jev or user), the frontend surfaces the provenance, and the output is a per-permit summary with fees and deadlines.
02 · Control boundary
Where Jev sits
Jev as the fact-answerer in front of a deterministic rules engine: each rule question becomes a typed choice over known options plus unknown, failures and unknowns fall back to the user, and provenance of every answer is tracked in the UI.
Code owns the loop, permissions, thresholds, validation, and side effects. Jev owns only the bounded judgments described above.
03 · Known limits
What this evidence does not prove
- San Francisco only; the rules engine encodes one city's permit graph.
- Jev calls route through an OpenRouter-based client, not the native endpoint.
- 0 stars; rules coverage is the author's snapshot of city permitting.
04 · Attribution
Public sources
This is a Community record: the project was published by a third-party community author.
- kmangal ↗Community · github · public · checked 2026-10-03