Desert Eagle

HiveLens: the beekeeping agent that checks itself before it speaks. Varroa mites destroy honey-bee colonies worldwide, and beekeepers still count them by eye, one by one. Meta’s models alone can’t solve this. On a real 49-megapixel sticky board, SAM 3.1 finds 0 of 21 mites, and Muse Spark guesses “24” with nothing to back it up. HiveLens finds 20 and shows every single one. HiveLens is a true agent, not a model wrapper. SAM 3.1 reads the board tile by tile. Muse Spark votes on 194 uncertain spots, forms its own hypotheses about the colony and tests each one with check_claim against hard measurements. In the saved inspection it caught and corrected two of its own wrong guesses across 24 tool calls. Physical checks rejected 9 debris spots, each with a stated reason. Only verified findings reach the report, and a rule-based colony status that the model cannot override turns them into clear actions: now, this week, next inspection. It runs a whole apiary, not just a photo: farms and hives on a world map, full inspection history, live weather with “can bees fly today”, and a to-scale yard map that pinpoints which neighbours a heavily infested hive is spreading mites to. The numbers: on 12 sticky boards with 128 expert-marked mites, HiveLens reaches F1 0.72, 6.5× the classic method’s 0.11. Its average count error is 3 mites per board, 47× lower than the classic darkness threshold’s 142. It also ships with real field data from 57 hives in a published EU study. Live now: open “Explore a saved analysis” and see every claim checked against its measurement, or press “Run a live sample” to watch SAM 3.1 and Muse Spark work in real time, no sign-in needed.