xpost·Content and growth
App Store keywords from the real competition
I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.
Everton Carneiro
@everton_dev
I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.
Evaluate input state and return typed decision for App Store keywords from the real competition.
Jev sits between the incoming context and the next system action. Rather than generating lengthy, slow natural language that requires brittle regex parsing, Jev returns non-autoregressive, calibrated probabilities that downstream code can immediately execute.
01actionPrimary action
02confidenceConfidence score
03fallbackFallback route
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