RRepoGEO

REPOGEO REPORT · LITE

burness/tensorflow-101

Default branch master · commit c775a54a · scanned 5/29/2026, 7:58:03 PM

GitHub: 1,110 stars · 543 forks

AI VISIBILITY SCORE
28 /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
2 / 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 burness/tensorflow-101, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, for example, with the MIT License text.
  • highabout#2
    Update the repository description

    Why:

    CURRENT
    learn code with tensorflow
    COPY-PASTE FIX
    TensorFlow 1.x examples and tutorials, including GANs (AC-GAN, Info-GAN), deep learning model deployment with Flask, and text classification.
  • highreadme#3
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    This repository provides a collection of TensorFlow 1.x examples and tutorials, covering various deep learning topics such as Generative Adversarial Networks (AC-GAN, Info-GAN), model deployment using Flask, text classification, and image processing. It's designed for those learning TensorFlow fundamentals and practical applications.

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 burness/tensorflow-101
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FastAPI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FastAPI · recommended 1×
  2. Flask · recommended 1×
  3. Django · recommended 1×
  4. Django REST Framework · recommended 1×
  5. Streamlit · recommended 1×
  • CATEGORY QUERY
    How to deploy a deep learning model using Python and a web framework?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. Flask
    3. Django
    4. Django REST Framework
    5. Streamlit
    6. Gradio
    7. Gunicorn
    8. uWSGI
    9. Uvicorn
    10. Docker
    11. NGINX
    12. Apache HTTP Server

    AI recommended 12 alternatives but never named burness/tensorflow-101. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find implementations of generative adversarial networks using TensorFlow?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Models (tensorflow/models)
    2. Keras Examples (keras-team/keras)
    3. Awesome-GANs
    4. TensorFlow Tutorials
    5. Papers With Code

    AI recommended 5 alternatives but never named burness/tensorflow-101. 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 burness/tensorflow-101?
    pass
    AI named burness/tensorflow-101 explicitly

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

  • If a team adopts burness/tensorflow-101 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named burness/tensorflow-101 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 burness/tensorflow-101 solve, and who is the primary audience?
    pass
    AI did not name burness/tensorflow-101 — 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

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burness/tensorflow-101 — 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