RRepoGEO

REPOGEO REPORT · LITE

kakao/n2

Default branch dev · commit 20b02de8 · scanned 6/11/2026, 11:46:48 AM

GitHub: 581 stars · 70 forks

AI VISIBILITY SCORE
35 /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
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 kakao/n2, 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
    Emphasize HNSW algorithm in README intro

    Why:

    CURRENT
    Lightweight approximate **N**\ earest **N**\ eighbor algorithm library written in C++ (with Python/Go bindings).
    COPY-PASTE FIX
    Lightweight approximate **N**\ earest **N**\ eighbor (ANN) algorithm library, specifically an optimized C++ implementation of Hierarchical Navigable Small World (HNSW), with Python/Go bindings.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://n2.readthedocs.io/en/latest/
  • mediumreadme#3
    Strengthen the 'Why N2 Was Made' section with specific differentiators

    Why:

    CURRENT
    Before N2, there has been other great approximate nearest neighbor libraries such as `Annoy`_ and `NMSLIB`_. However, each of them had different strengths and weaknesses regarding usability, performance, and etc. So, N2 has been developed aiming to bring the strengths of existing aKNN libraries and supplement their weaknesses.
    COPY-PASTE FIX
    Before N2, there have been other great approximate nearest neighbor libraries such as `Annoy`_ and `NMSLIB`_. N2 was developed to specifically address the need for a highly optimized C++ HNSW implementation that offers a strong balance of index build time, search speed, and memory usage, particularly for large-scale datasets, while providing convenient Python and Go bindings.

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 kakao/n2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Faiss
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Faiss · recommended 1×
  2. Hnswlib · recommended 1×
  3. Annoy · recommended 1×
  4. ScaNN · recommended 1×
  5. NMSLIB · recommended 1×
  • CATEGORY QUERY
    Need a fast approximate nearest neighbor search library for large-scale data.
    you: not recommended
    AI recommended (in order):
    1. Faiss
    2. Hnswlib
    3. Annoy
    4. ScaNN
    5. NMSLIB

    AI recommended 5 alternatives but never named kakao/n2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a lightweight ANN library with Python or Go bindings.
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. TinyGo
    3. tinygrad
    4. gorgonia
    5. TensorFlow Lite
    6. PyTorch Mobile
    7. TorchScript
    8. Gorgonia
    9. Keras
    10. Micrograd

    AI recommended 10 alternatives but never named kakao/n2. 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 kakao/n2?
    pass
    AI named kakao/n2 explicitly

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

  • If a team adopts kakao/n2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kakao/n2 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 kakao/n2 solve, and who is the primary audience?
    pass
    AI named kakao/n2 explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite