Project Telos is looking for field testers for telos: a shared perceive-and-make surface where a person and model can inspect the same artifact and produce a re-checkable certificate.
The question this repo should help answer:
Can human-AI work produce a visible receipt trail that is useful in real domains, not only in demos?
Useful test lanes:
- Doctor/admin: summarize and check public policy or guidance material. Do not use private patient data.
- Artist/studio: track transforms, exports, gallery artifacts, and human-vs-automatic decisions.
- Media/newsroom: separate supported claims, source links, assumptions, and unverifiable claims.
- Token economy/routing: record which tool/service was chosen, why, what it cost, and what receipt came back.
- Reasoning workflows: compare a model's explanation against shared witnessed state.
What I am looking for:
- Verification and testing against real public workflows.
- Clear examples where the receipt trail helps a human make a safer decision.
- Cases where the UI/report hides too much, exposes too much, or labels uncertainty poorly.
- Early traction from doctors/admins, artists, journalists, engineers, researchers, and builders who need checkable AI-assisted work.
- Pointers to modest grassroots research funding or labs that would stress-test this properly.
Project links:
Stage label: public, solo, early, pre-revenue, not independently audited. Please test with public, synthetic, or non-sensitive material first.
Project Telos is looking for field testers for
telos: a shared perceive-and-make surface where a person and model can inspect the same artifact and produce a re-checkable certificate.The question this repo should help answer:
Useful test lanes:
What I am looking for:
Project links:
Stage label: public, solo, early, pre-revenue, not independently audited. Please test with public, synthetic, or non-sensitive material first.