In attention markets that price volume more readily than discrimination, @starlisting introduces a quiet inversion: an AI layer that treats linguistic and informational density as a first-order signal rather than a residual byproduct of engagement. Where most discovery mechanisms automate the aggregation of existing social proof, this one automates a delayed reading of the post itself settling scores only after the initial heat has dissipated, and weighting the writing on a scale that can elevate a precise, first-hand observation above a high-follower echo of the same claim. The automation here is not primarily one of distribution or amplification. It is an automation of temporal and qualitative arbitration. In effect, the system attempts to price the scarcity of non-redundant information at the moment it is still cheap, before later volume can overwrite the distinction between a post that merely arrived and one that already contained a usable angle. What remains distinctive is the refusal to reset the ledger. Early substance is not diluted by subsequent noise; the ranking preserves the identity of those who marked a trajectory while the trajectory was still thin. In this respect the protocol is less a content marketplace than a compressed memory of discernment an automated record of who, at a given launch, produced text whose informational content survived the first cooling of engagement. Whether such a mechanism can continue to distinguish genuine early pattern recognition from increasingly sophisticated forms of optimized prose is an open empirical question. For now it simply records, with less delay than the market and more selectivity than raw follower weight, the difference between a voice that waited for the night and one that named a coordinate while the light was still uncertain.