visionhope
for ml engineers, computer vision researchers
Why this scored 16
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity'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
~2
range 0–14
14-day
~0
range 0–14
30-day
~0
range 0–19
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 124
VisionHOPE — Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
VisionHOPE backbone now available as a PyTorch implementation for image classification tasks. · for ml engineers, computer vision researchers
githubSteady 2 · visionhopeai−7 saturated3h ago - 213
PSRben/VisionHOPE (image-classification)
VisionHOPE offers ready-to-use PyTorch models for image classification tasks. · for ml engineers, computer vision researchers
huggingfaceSteadyai−5 saturated3d ago