RRepoGEO

REPOGEO REPORT · LITE

SergioFierens/ai4r

Default branch master · commit 9583d6d5 · scanned 6/10/2026, 8:51:46 PM

GitHub: 720 stars · 123 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
83 /100
Healthy
Category recall
2 / 2
Avg rank #2.5 when recommended
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 SergioFierens/ai4r, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ruby, artificial-intelligence, machine-learning, ai, ml, education, learning, algorithms, transformers, deep-learning
  • mediumreadme#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to your README, for example:

## License

AI4R is distributed under the terms specified in the LICENSE file. Please refer to that file for full details.

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
2 / 2
100% of queries surface SergioFierens/ai4r
Avg rank
#2.5
Lower is better. #1 = top recommendation.
Share of voice
18%
Of all named tools, what % are you?
Top rival
Ruby-FANN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ruby-FANN · recommended 1×
  2. DecisionTree · recommended 1×
  3. NArray · recommended 1×
  4. Rubystats · recommended 1×
  5. GSL · recommended 1×
  • CATEGORY QUERY
    What Ruby library helps understand core AI algorithms through practical implementation and experimentation?
    you: #1
    AI recommended (in order):
    1. AI4R ← you
    2. Ruby-FANN
    3. DecisionTree
    4. NArray
    5. Rubystats
    6. GSL
    Show full AI answer
  • CATEGORY QUERY
    Seeking a lightweight Ruby-based AI playground for exploring machine learning concepts without heavy dependencies.
    you: #4
    AI recommended (in order):
    1. SciRuby (sciruby/sciruby)
    2. Ruby-FFI (ffi/ffi)
    3. Daru (daru-rb/daru)
    4. AI4R (giacomov/AI4R) ← you
    5. Rubystats (sciruby/rubystats)
    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 SergioFierens/ai4r?
    pass
    AI named SergioFierens/ai4r explicitly

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

  • If a team adopts SergioFierens/ai4r in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SergioFierens/ai4r 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 SergioFierens/ai4r solve, and who is the primary audience?
    pass
    AI named SergioFierens/ai4r explicitly

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

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SergioFierens/ai4r — 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