RRepoGEO

REPOGEO REPORT · LITE

YavorGIvanov/sam.cpp

Default branch master · commit 81002818 · scanned 5/26/2026, 2:27:54 AM

GitHub: 1,275 stars · 62 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 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 YavorGIvanov/sam.cpp, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    Efficient C/C++ inference for Meta's Segment Anything Model (SAM), optimized for CPU and lightweight deployment.
  • highreadme#2
    Reposition the README's opening sentence to highlight its unique value proposition

    Why:

    CURRENT
    Inference of Meta's Segment Anything Model in pure C/C++
    COPY-PASTE FIX
    Lightweight and efficient C/C++ inference for Meta's Segment Anything Model (SAM), optimized for CPU-only deployment.

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 YavorGIvanov/sam.cpp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. OpenVINO Toolkit · recommended 2×
  3. LibTorch · recommended 2×
  4. MNN · recommended 2×
  5. OpenCV · recommended 1×
  • CATEGORY QUERY
    How to perform real-time image segmentation inference using a C++ library?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. ONNX Runtime
    3. OpenVINO Toolkit
    4. TensorRT
    5. LibTorch
    6. MNN
    7. TNN

    AI recommended 7 alternatives but never named YavorGIvanov/sam.cpp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a performant C++ solution to deploy large vision models locally.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. TVM
    5. LibTorch
    6. TensorFlow Lite
    7. MNN

    AI recommended 7 alternatives but never named YavorGIvanov/sam.cpp. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 YavorGIvanov/sam.cpp?
    pass
    AI named YavorGIvanov/sam.cpp explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts YavorGIvanov/sam.cpp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named YavorGIvanov/sam.cpp 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 YavorGIvanov/sam.cpp solve, and who is the primary audience?
    pass
    AI named YavorGIvanov/sam.cpp explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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YavorGIvanov/sam.cpp — 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