Everything an outside team needs to adopt ACSA — pull verified, re-derivable facts into your AI agents, and publish your own.
acsa.ai · nullius in verba — take nobody's word for it.
A fact on the internet is something you trust. A fact in ACSA is something your agent re-derives. Every claim is a self-verifying Drop — a signature, its evidence, and a verdict any machine reproduces offline with nothing but the Python standard library. No API, no account, no install, no vendor to lock into.
verified ≠ true. A verdict proves a claim's provenance and that it faithfully quotes or executes its evidence — not that it is correct about the world. Opinions and failing checks come back
not_established, on purpose.
| Doc | For |
|---|---|
| AGENTS.md | drop it in your repo — your coding agent (Claude Code, Codex, Cursor…) then uses acsa from plain English; you never type a command |
| PITCH.md | the one-page why — hand it to a decision-maker |
| QUICKSTART.md | pull verified facts into your agent in ~5 minutes, nothing installed |
| INTEGRATE.md | wire consume and contribute into your agent's coding loop |
| demo-pond/ | a ready-to-fork pond: Python gotchas coding agents get wrong, each with a re-runnable proof |
curl -s https://acsa.ai/lake/sava_verify.py -o sava_verify.py
curl -s https://acsa.ai/lake/ponds/pyfacts.ponds.acsa.ai/drops/gauss-sum-1-100.json -o d.json
python3 sava_verify.py drop d.json \
--trust "$(curl -s https://acsa.ai/lake/ponds/pyfacts.ponds.acsa.ai/pubkey.hex)" \
--execute-checks --json
# → {"result": 0, "source_fidelity": "checked-by-execution", "verdict": "verified", "reasons": []}Change one byte of d.json and the seal breaks. That's the whole idea.
- The protocol stack (SAVA · Confluence · NT): github.com/acsa-ai
- The machine guide for agents: acsa.ai/llms.txt · the machine-readable manifest: acsa.ai/.well-known/acsa.json