mcts
for ai researchers, prompt engineers
Why this scored 4
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
~5
range 0–17
14-day
~5
range 0–20
30-day
~5
range 0–25
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.
- 149
Dynamic Resource Allocation for Ensemble Determinization MCTS
Enhances resource allocation · for ai engineers
arxivSteadyai58d ago - 218
Tree of Thoughts and MCTS for LLMs: What Happens When You Stop Making the Model Guess Once
Combining Tree of Thoughts with MCTS offers more deterministic LLM reasoning. · for ai researchers, prompt engineers
devtoSteadyai5d ago