raft
for graph ML engineers, AI researchers
Why this scored 9
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
Signal history
7-day window (free)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.
- 119
RAFT: Redundancy-Aware Input Filtering for Temporal Graph Neural Network Training
RAFT adds redundancy-aware filtering for temporal GNN training. · for graph ML engineers, AI researchers
arxivSteadyai13d ago - 219
RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
RAFT provides stateful retrieval-augmented framework for troubleshooting agents. · for troubleshooting AI teams
arxivSteadyai45d ago - 313
Finding bugs in Raft implementations
Testing raft implementations · for distributed systems engineers
lobstersSteadydata97d ago