Community · E2 · artifact verified
Sift emoji out of the pile by description
Type a description and matching emoji fly out of the pile: one request carries the query as shared state with one score question per emoji, answered in parallel, and code ranks and floors the results.
01 · Role in the system
What Jev does here
Keyword search cannot answer things a magnet could attract (no emoji name mentions metal) or i need to lose weight (a goal, not a description). The request sends the query once as state and asks one independent score question per emoji - a position on a four-level scale from unrelated to an obvious answer - so all answers come in parallel, the payload stays the same size whatever the user typed, and the user’s text never enters the instructions, a stronger boundary than escaping it. Ranking rather than yes/no per emoji is the load-bearing choice: things that make noise is true of almost any object, so a fixed bar returned 27 matches for one query and 2 for another; ranking asks which emoji answer best. Code owns what happens next - rank by score, drop below a floor, take the top few - and the gateway key stays server-side.
02 · Control boundary
Where Jev sits
Query lives in state once; one score question per emoji in a single parallel request; deterministic code ranks, floors, and truncates.
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
- A repo to run locally (Node 22+, gateway key server-side); no hosted demo at capture time.
- Calls go through Vercel AI Gateway rather than the native TypeSafe endpoint.
- The four-level scale and the floor are code’s choice; the README’s hard-query examples were not independently rerun here.
04 · Attribution
Public sources
This is a Community record: the project was published by a third-party community author.
- nischal94 ↗Community · github · public · checked 2026-09-22