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一分钟完成验证Evidence-First Financial AI — the Science Trust Layer for Financial AI. Open-source, runs on your own hardware. Every claim traces to an SEC filing; every method is locked before the data arrives; every result is kept in a public, hash-linked record built to be recomputed by anyone. Research & education only — not investment advice. Apache-2.0.
翻译准备中
正在加载完整 README。 以下为已保存的摘录。
Research & education only. Not investment advice.
Evidence-First Financial AI
The Science Trust Layer for Financial AI.
Evidence before answers.
Financial AI normally gives you an answer.
YUCLAW gives you the evidence — what was known, when it was known, what it can support, what it cannot, and whether the conclusion survived.
Make financial AI accountable to evidence.
• Trace every claim to the filing it came from.
• Lock every method before the data arrives.
• State every conclusion’s limits.
• Keep every result — including the ones we wished had come out differently.查看完整 README ↗Financial AI normally gives you an answer.
YUCLAW gives you the evidence — what was known, when it was known, what it can support, what it cannot, and whether the conclusion survived.
Make financial AI accountable to evidence.
A public, hash-linked record, built to be recomputed by anyone.
Become the Science Trust Layer for Financial AI.
The evidence infrastructure that AI systems, researchers, and institutions use to decide what deserves to be believed.
| Principle | Practice |
|---|---|
| We don’t ask you to trust us. | We give you the hash. |
| We don’t predict. | We register, compute once, and disclose. |
| We don’t hide the days we were wrong. | We chain them. |
| For | What you get |
|---|---|
| Analysts | The evidence behind every label, and the label’s limits. |
| Builders | Machine-readable receipts — passports, endpoints, and a registry you can walk line by line. |
| Institutions | A record that can be audited without asking us. |
Statistics is one instrument. Evidence is the foundation. Science is the discipline.
AI is the market. Trust is the product. Accountability is the mission.
Designed for reproduction from published artifacts. One affiliated external-machine reproduction recorded; unaffiliated replications: 0.
First touch — install, ask for help, check a claim, reproduce the Lab (expected exit code in the comment):
pip install yuclaw # installs the CLI
yuclaw --help # command list with one-line descriptions · exit 0
yuclaw check-claim --text "NVDA reported an insider sale in May 2026" # Evidence Passport JSON · exit 0
yuclaw check-claim --ticker NVDA --accession 0001045810-26-000019 # passport for one cited filing · exit 0
yuclaw check-claim --accession 0001045810-26-000019 # accession alone: unique → same passport · exit 0
# (ambiguous accession → exit 2 with the candidate tickers)
yuclaw replay-lab # rebuilds every published Lab statistic · exit 0 = reproduced
Exit-code contract for every command: 0 = success · 1 = ran, negative result (e.g. a replay mismatch) · 2 = usage or validation error · 3 = environment unsupported. The transcript below is generated from the release-candidate wheel and regenerated every release:
Transcript produced from the release-candidate wheel yuclaw-9.1.0-py3-none-any.whl (yuclaw 9.1.0, Python 3.12) by tools/cli_transcript.py; the replay-lab run uses the documented local-bundle path. Every command ran with YUCLAW_CORPUS=snapshot: check-claim answers from the corpus snapshot bundled in the wheel and never looks for a research node (without that setting, a host that reaches a research node answers from it and its passport has no corpus block). No date: release verification compares this block byte-for-byte with a fresh transcript of the final artifact, made the same way.
以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗