A small, reproducible research baseline trained on real, authorized aggregate X post observations. It did not beat a simple baseline in the measured comparison. PyTorch 2.14.0, CPU, one thread, 400 Adam steps per fit. Each post has a separate curve: count(age) = b + A (1 - exp(-age / tauseconds)) Three positive float64 trainable parameters per curve, enforced through softplus. The saved release contains two fitted curves, six learned scalar parameters in total. The JSON state is 2,929 bytes; the raw parameter payload is 48 bytes across the two fits. Training uses mean squared log1p-count error. b is a fitted intercept, not an invented age-zero observation. The two final fits use 19…
Independent publisher
LIghtJUNction
LIghtJUNction
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Models on Hugging Face2
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