graphrag
for LLM developers, data engineers
Why this scored 12
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.
- 118
The Same GraphRAG Comparison Wins and Loses. It Depends Which Instrument Judged It.
Benchmark outcomes for GraphRAG differ by evaluation metric, so choose metrics wisely. · for ml engineers, ai researchers
devtoSteadyai47d ago - 215
When should I use GraphRAG instead of traditional vector-based RAG?
Use GraphRAG when you need graph-based reasoning instead of plain vector similarity. · for LLM developers, data engineers
stackoverflowSteadyai44d ago