meta-models/Muse-Glimmer-30B (image-text-to-text)
Image-text model released
SteadyWhy this scored 0
every term, weightedEngagement gained per hour since the last capture, against the fastest item on its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities are talking about the same entity
Decays to zero over 14 days
Subtracted once something is big and old — we rank what's next, not what's peaked
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On a topic's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Outlook
low confidence · estimate, not a guarantee7-day
~10
range 0–22
14-day
~12
range 0–27
30-day
~13
range 0–32
Signal history
7-day window (free)— — projected trajectory (estimate, not a guarantee)
Entities
Embed a live signal badge
[](https://www.signalcrest.app/topic/hf%3Amodel%3Ameta-models%2FMuse-Glimmer-30B)
Drop this in a README or blog post — it updates automatically as the score moves.