lfm3-1.7b
for ml engineers
Why this scored 25
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
~8
range 0–20
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.
- 120
AtomicChat/LFM3-1.7B-Uncensored-GGUF (text-generation)
Quantized LFM3 1.7B model released for local text-generation workloads. · for ml engineers
huggingfaceSteadyai−7 saturated2d ago - 24
LiquidAI/LFM3-1.7B-GGUF (text-generation)
LiquidAI released a 1.7B LFM3 model in GGUF format for llama.cpp inference. · for AI researchers, ML engineers
huggingfaceSteadyai−11 saturated3d ago