Engineers demo end-to-end feature extraction on Apple M1 in 3.2 ms

- Engineers demonstrated an end‑to‑end feature extraction pipeline on Apple M1 silicon that processed 200 candles and 24 indicators in 3.2ms, finishing at about 4ms when paired with a DQN. - The benchmark numbers cited were 3.2ms for the indicator pass and ~4ms total with downstream DQN inference on the same host. - This shows sub‑5ms feature pipelines are feasible on low‑power hosts, informing edge inference and low‑latency model placement decisions. (x.com 1) (x.com 2)