falcon
for data engineers, ml engineers
Why this scored 10
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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FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
Offers a framework to create synthetic NL2SQL training data independent of model or dataset. · for data engineers, ml engineers
arxivSteadyai22d ago - 210
Surviving Black Friday: 329 billion requests with Falcon
Falcon shows Ruby can sustain billions of requests, guiding high-scale backend design. · for backend engineers, sre teams
lobstersCoolingdevtools48d ago