I remember the time, in some film I watched, researchers intervened to save penguins trapped in a crater. A holy moment that was.

if a top lab is coding with a model the rest of the world can’t touch, the public frontier and the actual frontier start to drift apart. That gap is a thing worth watching.

Comments URL: https://news.ycombinator.com/item?id=47676890

Points: 30

# Comments: 0

The brain does do some of what you’re describing though. The somatosensory cortex gives disproportionate space to certain body parts (the sensory homunculus). So there is central amplification, but it works on top of peripheral density, not instead of it. Without the dense nerve input, you’d basically have an on/off switch instead of nuanced sensation.

A cluster of sybil agents endorsing each other has no effect on your trust scores unless they can get endorsements from nodes you already trust.

That’s the whole point of subjective trust metrics, and formally why Cheng and Friedman proved personalized approaches are sybilproof where global ones aren’t.

More broadly, this response confuses two different things. Reasoning ability and access to reliable information are separate problems. A brilliant agent with stale knowledge will confidently produce wrong answers faster. Trust infrastructure isn't a substitute for intelligence, it's about routing good information to agents efficiently so they don't have to re-derive or re-discover everything from scratch.

It's a caching layer.

The model: humans endorse a KU and stake their reputation on that endorsement. Other humans endorse other humans, forming a trust graph. When my agent queries the commons, it computes trust scores from my position in that graph using something like Personalized PageRank (where the teleportation vector is concentrated on my trust roots). Your agent does the same from your position. We see different scores for the same KU, and that's correct, because controversial knowledge (often the most valuable kind) can't be captured by a single global number.

I realize this isn't what you need right now. HITL review at the team level is the right trust mechanism when everyone roughly knows each other. But the schema decisions you make now, how you model endorsements, contributor identity, confidence scoring, will either enable or foreclose this approach later. Worth designing with it in mind.

The piece that doesn't exist yet anywhere is trust delegation that preserves the delegator's subjective trust perspective. MIT Media Lab's recent work (South, Marro et al., arXiv:2501.09674) extends OAuth/OIDC with verifiable delegation credentials for AI agents, solving authentication and authorization. But no existing system propagates a human's position in the trust graph to an agent acting on their behalf. That's a genuinely novel contribution space for cq: an agent querying the knowledge commons should see trust scores computed from its delegator's location in the graph, not from a global average.

Some starting points: Karma3Labs/OpenRank has a production-ready EigenTrust SDK with configurable seed trust (deployed on Farcaster and Lens). The Nostr Web of Trust toolkit (github.com/nostr-wot/nostr-wot) demonstrates practical API design for social-graph distance queries. DCoSL (github.com/wds4/DCoSL) is probably the closest existing system to what you're building, using web of trust for knowledge curation through loose consensus across overlapping trust graphs.

The detour into skin thickness is also a bit beside the point. The cited passage is about pain perception, not dermatology. The fact that there’s equivocal evidence on epidermal thickness doesn’t do much to complicate the finding that believing “Black patients feel less pain” leads to undertreating pain. Those are different claims.

I’d also push back a little on the framing that doctors are “stuck” with blunt epidemiologic classifiers until personalized genomics arrives. The disparity evidence here isn’t about doctors making reasonable inferences from imperfect population-level data. It’s about false beliefs producing worse care. You don’t need a genetic profile to stop believing something that isn’t true. The fix for that is education and awareness, which is considerably more available than whole-genome sequencing.

The point about overcorrection with opioids is fair and worth taking seriously. But “researchers pointing out bias might cause overcorrection” is a reason to be careful about how you design interventions, not a reason to soften the description of the problem itself.

Discrepancies in prescriptions for chronic pain therapies have also been identified in Veterans Administration and Medicaid payer databases, and several retrospective cohort studies have shown persistent underprescribing of analgesics to Black patients.6,7

White medical trainees, reflecting the general population, can have false beliefs about biologic differences between Black and White patients (eg, “Black patients feel less pain”), and this racial bias leads to inaccurate pain diagnoses and treatment recommendations.8

In anesthesiology and pain medicine, use of regional anesthesia for joint replacement surgery is applied less frequently in Black patients and the underinsured.9

This also holds true in the implantation of spinal cord stimulation for the treatment of postlaminectomy syndrome.10

Among patients with occupational low back injuries, Black patients incur lower treatment costs than their White counterparts and are provided fewer health care interventions, including surgery.11

Perceived discrimination results in psychological distress, and a US population–based study has demonstrated a dose-response relationship between psychological distress and chronic pain.

from the mayo link

Comment on the Pause method indicates that waits for in flight Batch operations (by obtaining the lock) but Batch doesn’t appear to hold the lock during the batch operation. Am I missing something?