A Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data
Demonstrates continual-learning models can be trained on a laptop with 8 GB VRAM.
Why this scored 10
every term, weightedEngagement gained per hour since the last capture, against the fastest item on its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities are talking about the same entity
Decays to zero over 14 days
Subtracted once something is big and old — we rank what's next, not what's peaked
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On a topic'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)Embed a live signal badge
[](https://www.signalcrest.app/topic/lob%3Agxjhqo)
Drop this in a README or blog post — it updates automatically as the score moves.