Hacker News Reader

Comment by kgcgfva | Hacker News Reader

Good stuff here, much of which resonates with me. FWIW, my notion of an OS for agents isn't as close to the metal yet, ie, my view of agent lifecycle is that they look a lot like WhatsApp or Discord traffic. So, based on that DEEP analysis, I decided to build on BEAM/OTP for the control plane: basically, Elixir for the UX and Gleam for everything else. And the data plane is pure Zig: data sidecar into BEAM/OTP via NIF.

So that gives me a certain freedom for deploy: bare metal, containers, VMs, even K8S. And since there are some tools for pickling all that into a single binary, and running BEAM/OTP on u-kernel sorts of things, I can get all the way to the metal in the way that you are. Whether or when that happens remains to be seen, etc.

Thanks for sharing! See https://pentad.ai/PLRN for more about what I'm up to.

View on HN

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 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!

Reply on news.ycombinator.com