miniagi
for ml engineers
Why this scored 16
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
~20
range 0–32
14-day
~15
range 0–30
30-day
~4
range 0–23
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.
- 128
Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM
Mini-AGI shows dynamic continual learning on just 8 GB VRAM, lowering hardware barriers. · for research engineers, ml developers
hackernewsSteady 2 · miniagiai3d ago - 211
mini-AGI — Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data.
Shows a low-VRAM continual-learning model feasible on laptops. · for ml engineers
githubSteadyai−7 saturated2d ago