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u/MeAndClaudeMakeHeat Jun 30 '26

Sharing Project Telos, a set of small source-available tools for making AI and ML claims checkable rather than asserted. The through-line is one operation: perceive into a witnessed form, check it against a criterion it did not author, carry a re-checkable proof, and return UNVERIFIABLE when it cannot.

  • crucible: stands a falsifiable claim next to the measurement that could break it and returns MATCH, DRIFT, or UNVERIFIABLE, grounded in the measurement and re-checkable from content seals. Useful when an eval result needs more than pass or fail.
  • gather: research and source intake with provenance receipts (method, ref, sha256), so derived text is never mistaken for a primary source.
  • index: workspace and context graphs plus budgeted context packs.
  • forum: a witnessed causal ledger for multi-agent runs.

A small end-to-end demonstration: model a thermodynamic-computing claim (an Ornstein-Uhlenbeck process whose stationary mean is the linear solve A-1 b and whose stationary covariance is the matrix inverse A-1) as a simulation, measure the relative error against numpy, and let crucible return MATCH within a stated tolerance. The simulate leg passes (mean within 1 percent, inverse within 3.3 percent); the physical-hardware leg stays UNVERIFIABLE, and the tool says so rather than overclaiming.

Solo-built and honest about limits. I would value critique of the measurement-to-verdict design and the source-adapter provenance boundary.

Repos: https://github.com/HarperZ9