REPOGEO REPORT · LITE

sentient-agi/OML-1.0-Fingerprinting

Default branch main · commit e3ee78ce · scanned 5/19/2026, 7:47:00 AM

GitHub: 3,512 stars · 233 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface sentient-agi/OML-1.0-Fingerprinting, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening paragraph to highlight unique differentiator

Why:

CURRENT

Welcome to OML 1.0: Fingerprinting. This repository houses the tooling for generating and embedding secret fingerprints into LLMs through fine-tuning to enable identification of LLM ownership and protection against unauthorized use.

COPY-PASTE FIX

Welcome to OML 1.0: Fingerprinting. This repository provides unique tooling for generating and embedding secret, AI-native cryptographic fingerprints into LLMs through fine-tuning. Unlike general watermarking or metadata solutions, OML 1.0 focuses on verifiable identification of LLM ownership and robust protection against unauthorized use, making it a distinct solution for model provenance.

mediumtopics#2

Add more specific topics to clarify the project's niche

Why:

CURRENT

fine-tuning, fingerprint, loyalty, oml, sentient, verifiable-ai

COPY-PASTE FIX

fine-tuning, fingerprint, loyalty, oml, sentient, verifiable-ai, llm-ownership, model-provenance, ai-security

mediumhomepage#3
Add the project's homepage URL to the About section
Why:
COPY-PASTE FIX
```
https://sentient.foundation/
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface sentient-agi/OML-1.0-Fingerprinting

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Eric-Z-Wang/Awatermark

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Eric-Z-Wang/Awatermark · recommended 1×
Invisible Watermark · recommended 1×
Hugging Face Model Cards · recommended 1×
ONNX Metadata · recommended 1×
GnuPG (GPG) · recommended 1×

CATEGORY QUERY
How can I embed a unique identifier into my fine-tuned LLM to prove ownership?
you: not recommended
AI recommended (in order):
1. Awatermark (Eric-Z-Wang/Awatermark)
2. Invisible Watermark
3. Hugging Face Model Cards
4. ONNX Metadata
5. GnuPG (GPG)
AI recommended 5 alternatives but never named sentient-agi/OML-1.0-Fingerprinting. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What techniques exist for identifying unauthorized copies or usage of proprietary LLM models?
you: not recommended
AI recommended (in order):
1. Awatermark
2. Invisible-Watermark-for-LLMs
3. Kong
4. Apigee
5. DetectGPT
6. AWS CloudTrail
7. Google Cloud Logging
8. Azure Monitor
9. GLTR
AI recommended 9 alternatives but never named sentient-agi/OML-1.0-Fingerprinting. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of sentient-agi/OML-1.0-Fingerprinting?
pass
AI did not name sentient-agi/OML-1.0-Fingerprinting — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts sentient-agi/OML-1.0-Fingerprinting in production, what risks or prerequisites should they evaluate first?
pass
AI named sentient-agi/OML-1.0-Fingerprinting explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo sentient-agi/OML-1.0-Fingerprinting solve, and who is the primary audience?
pass
AI did not name sentient-agi/OML-1.0-Fingerprinting — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of sentient-agi/OML-1.0-Fingerprinting. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/sentient-agi/OML-1.0-Fingerprinting.svg)](https://repogeo.com/en/r/sentient-agi/OML-1.0-Fingerprinting)

HTML

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sentient-agi/OML-1.0-Fingerprinting — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite