applesilicon
for mobile AI engineers
Why this scored 31
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
~12
range 0–23
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.
- 133
Laya on Mac M4 CoreML Offline
Laya runs offline on Mac M4 using CoreML, enabling local inference without cloud. · for mobile AI engineers
hackernewsSteady 3 · layaai2d ago - 233
laya-mlx — Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.
laya-mlx provides native MLX inference on Apple silicon for low-latency decision models. · for mobile ML engineers
githubSteady 3 · layaai−6 saturated3d ago