RRepoGEO

REPOGEO REPORT · LITE

360er0/awesome-lego-machine-learning

Default branch main · commit 2bec504c · scanned 6/4/2026, 10:28:03 PM

GitHub: 516 stars · 24 forks

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 360er0/awesome-lego-machine-learning, 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 README opening to clarify its role as a guide

    Why:

    CURRENT
    A curated list of resources dedicated to Machine Learning applications to LEGO bricks.
    COPY-PASTE FIX
    This curated list helps you discover and explore resources dedicated to Machine Learning applications to LEGO bricks, guiding you to the right tools and projects for your needs.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and adding the necessary license text.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant homepage URL to the repository settings, such as a project website, blog post, or a link to the GitHub Pages for this repo if applicable.

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 360er0/awesome-lego-machine-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. OpenCV · recommended 2×
  3. Google Cloud Vision API · recommended 2×
  4. YOLO · recommended 1×
  5. Mask R-CNN · recommended 1×
  • CATEGORY QUERY
    How to automatically identify and classify LEGO bricks from images or video?
    you: not recommended
    AI recommended (in order):
    1. YOLO
    2. Mask R-CNN
    3. TensorFlow Object Detection API
    4. PyTorch
    5. OpenCV
    6. Google Cloud Vision API
    7. Amazon Rekognition Custom Labels

    AI recommended 7 alternatives but never named 360er0/awesome-lego-machine-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help sort and categorize a large LEGO collection using computer vision?
    you: not recommended
    AI recommended (in order):
    1. Brickit
    2. OpenCV
    3. TensorFlow
    4. PyTorch
    5. LabelImg
    6. VGG Image Annotator (VIA)
    7. Roboflow
    8. Python
    9. scikit-image
    10. Pillow
    11. NumPy
    12. Google Cloud Vision API
    13. AWS Rekognition
    14. Azure Computer Vision
    15. Google Cloud AutoML Vision

    AI recommended 15 alternatives but never named 360er0/awesome-lego-machine-learning. 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 360er0/awesome-lego-machine-learning?
    pass
    AI did not name 360er0/awesome-lego-machine-learning — 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 360er0/awesome-lego-machine-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 360er0/awesome-lego-machine-learning 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 360er0/awesome-lego-machine-learning solve, and who is the primary audience?
    pass
    AI did not name 360er0/awesome-lego-machine-learning — 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 360er0/awesome-lego-machine-learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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360er0/awesome-lego-machine-learning — 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