Start Here

Truth Machine is a way to maintain shared knowledge that stays explainable as new evidence arrives, people disagree, and decisions change.

Use it when a group needs more than “the latest note”: people must be able to see what the current account is, what supports it, and how it was changed.

The essential idea

• A source claim is evidence; it is not automatically the team’s current answer.
• Authority is specific. Being confident, senior, or important does not make a source authoritative for everything.
• A proposed change says exactly what would change, why, and what evidence supports it.
• Review applies to that exact change. Stale, invalid, or partly applicable work does not quietly become current.
• The resulting account keeps its reason and ancestry with it.
• Unknowns and disagreements stay visible.
• An AI collaborator can help substantially, but does not gain authority by sounding convincing.

Is this a fit?

The architecture is worth considering when all of these are true:

1. people act on durable shared claims;
2. sources can be incomplete, conflicting, or differently authoritative;
3. changing the current account has real consequences; and
4. the earlier account and reason for change need to remain recoverable.

If that is not your situation, use the simpler tool that fits. Truth Machine is a way to introduce clarity where the cost of ambiguity is already real.

When you need precision

The normative specification is under /specification/. Requirements use stable identifiers such as TM-EVD-001 and the BCP 14 terms MUST, MUST NOT, SHOULD, and MAY. Conformance applies only to those identified requirements—not to examples, profiles, patterns, or this orientation.

Optional practical material

• Git-native profile — one concrete implementation using commits, text packets, and an offline reader.
• AI Peer profile — how to make a truth system safely inhabitable by an AI collaborator.
• Facets — disposable, non-authoritative projections at an outside boundary.

None is required for core conformance.

Machine-readable entry points

The published site provides:

• /llms.txt — curated reading map;
• /llms-full.txt — complete documentation context;
• /context/core.md — compact normative context;
• /manifest.json — versions, classifications, paths, and content hashes;
• page-level Markdown at each HTML route plus index.html.md; and
• /schemas/ — reference packet schemas.

All are generated from the same documents as the human site. This lets a person and an AI collaborator start from the same maintained material.