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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 "o
by softwarewright 10d ago
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
- kgcgfva 9d agoOne thing that falls out of Model Minimalism is adding native WunderOS model hosting, which I've been working on this week, natively in Zig, NIF'd into BEAM/OTP. IMO vertical integration in AI infra is underrated; by adding model serving I can exploit a range of optimizations that 'best of breed'/glue code architecture makes harder. This week's example: native semantic entropy implementation -- following Spanda -- in Zig, such that hallucination detection at K=5 (batch size) is 650us per turn, i.e., in the noise. I've spec'd how to do QLoRA, too, but it's unclear when or if I'll implement it, not least because it's not clear that I should bother given the training data integration issues. I'm glad someone else is thinking about this stuff!