timestral
for ml researchers
Why this scored 29
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
~10
range 0–22
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.
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Timestral: Mistral's time-series foundation models
Mistral released time-series foundation models for building forecasting applications. · for ml engineers, data scientists
hackernewsSteady 3 · timestralai3d ago - 221
timestral — Repository for Timestral, the time-series foundation model by Mistral AI
Time-series foundation model released by Mistral AI for forecasting tasks. · for ml researchers
githubSteady 2 · timestralai−9 saturated15h ago - 319
mistralai/Timestral-209M (time-series-forecasting)
Timestral-209M model released for time-series forecasting, distributed in safetensors format. · for data scientists
huggingfaceSteady 2 · timestraldata−10 saturated2d ago