Hi RunAnywhere team,
Saw your YC launch post asking what sucks about current on-device AI
stacks — here's a data point, not a feature request.
I built a small (N=12) reference architecture for a memory substrate that
stays a genuinely fixed size (kilobytes, not a growing log) no matter how
long an agent runs — verified across a 256x range of lifetime length, with
recall quality for early memories barely moving (0.81 to 0.80). The same
architecture individuates per user through lived experience alone: an
agent that lived with one user measurably outperforms one trained on a
different user, and a "stranger" agent can perform worse than a blank one.
This could be a natural memory-layer complement to RunAnywhere's inference
stack — full per-user personalization with no growing storage, fitting the
same memory-pressure constraints your SDK already manages for models.
Preprint + code: https://doi.org/10.5281/zenodo.21122080
Sharing in case it's a useful reference point — not pitching anything,
just curious if this is a direction worth exploring.
— Kimiyasu Igarashi, independent researcher
Hi RunAnywhere team,
Saw your YC launch post asking what sucks about current on-device AI
stacks — here's a data point, not a feature request.
I built a small (N=12) reference architecture for a memory substrate that
stays a genuinely fixed size (kilobytes, not a growing log) no matter how
long an agent runs — verified across a 256x range of lifetime length, with
recall quality for early memories barely moving (0.81 to 0.80). The same
architecture individuates per user through lived experience alone: an
agent that lived with one user measurably outperforms one trained on a
different user, and a "stranger" agent can perform worse than a blank one.
This could be a natural memory-layer complement to RunAnywhere's inference
stack — full per-user personalization with no growing storage, fitting the
same memory-pressure constraints your SDK already manages for models.
Preprint + code: https://doi.org/10.5281/zenodo.21122080
Sharing in case it's a useful reference point — not pitching anything,
just curious if this is a direction worth exploring.
— Kimiyasu Igarashi, independent researcher