quarto
for data scientists
Why this scored 7
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.
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Set Quarto chunk options such as fig-height from an R object or expression
Shows how to set Quarto chunk options like fig-height from R objects or expressions. · for R data scientists
stackoverflowSteadydata42d ago - 215
Alternative to output_dir in quarto::quarto_render for rendering multiple reports?
Quarto lacks an output_dir alternative for batch report generation in R. · for r users, data scientists
stackoverflowSteadydevtools48d ago - 314
sklearn.learning_curve(n_jobs=-1) crashes with TerminatedWorkerError in Quarto but not Jupyter
scikit-learn learning_curve fails in Quarto due to multiprocessing differences, not in Jupyter. · for data scientists
stackoverflowSteadydevtools35d ago