Interesting link/content and it seems complimentary.
One thing that is missing from both of our approaches is the ability re-train (fine-tune) coding models "overnight" so that they can "learn" from the prior day and changes since their training cutoff date.
I have found some things I can do to improve my work based on this, thanks.
_Pentad idea_ -/- _MLOS relevance_ -/- _Action_
Closed autonomic loops -/- Very high -/- Adopt architecture vocabulary
Deterministic replay -/- Very high -/- Strengthen event/replay contract
Model minimalism -/- Very high. -/- Extend later to compute-placement ladder
Durable vs active population -/- High -/- Define registered vs resident capacity metrics
Standing queries -/- High -/- Future policy/watch abstraction
Provenance by construction -/- High -/- Record policy decision causality
No model/NLP in hot path -/- High -/- State explicitly as invariant
One thing that is missing from both of our approaches is the ability re-train (fine-tune) coding models "overnight" so that they can "learn" from the prior day and changes since their training cutoff date.
I have found some things I can do to improve my work based on this, thanks.
_Pentad idea_ -/- _MLOS relevance_ -/- _Action_
Closed autonomic loops -/- Very high -/- Adopt architecture vocabulary
Deterministic replay -/- Very high -/- Strengthen event/replay contract
Model minimalism -/- Very high. -/- Extend later to compute-placement ladder
Durable vs active population -/- High -/- Define registered vs resident capacity metrics
Standing queries -/- High -/- Future policy/watch abstraction
Provenance by construction -/- High -/- Record policy decision causality
No model/NLP in hot path -/- High -/- State explicitly as invariant