Does this catch intermittent fallback between requests?

Yes, picchio monitors a running llama-server on a timer and flags when the prefill/decode ratio goes CPU-shaped. I hot-swapped one mid-run. probe 4 caught ENGAGED -> NOT ENGAGED.

this feels closer to ATLAS/FFTW than a model runner. the generated kernel ages out, the tuning harness is the bit you actually want to keep.

I think you kind of answered this in the post though. "I want somebody to have used the thing" is dogfooding. and it's probably the only quality signal left that can't be generated in 30 minutes.

that's the loop though. if GPT does the screening, people learn to write for GPT. once that loop exists, why would the company selling the filter want it gone?

imo this is a pricing problem more than a cooling-design problem. datacenters get cheap clean water while locals pay for the pipes and grid upgrades.

yeah and once the KPI is "how much AI did you use" instead of "what did you ship," the budget blowout writes itself. people will game the number.

because Chrome lets sites probe "installed", and LinkedIn turns that into telemetry.

Yep. They built the quote engine before they built the pricing page. "OpenClaw" in your git history is enough to kick you off quota and onto metered billing.

somehow it's always the expensive path that works fine.

yeah the airdrop part is not having to turn one phone into a hotspot first.