RRepoGEO

REPOGEO REPORT · LITE

RootbeerComputer/backend-GPT

Default branch main · commit e1cd4c0c · scanned 6/20/2026, 8:58:27 PM

GitHub: 2,931 stars · 220 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
23 /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
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 RootbeerComputer/backend-GPT, 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
  • highreadme#1
    Reposition the README H1 to clearly state the project's core innovation

    Why:

    CURRENT
    # GPT is all you need for the backend
    COPY-PASTE FIX
    # backend-GPT: An LLM-Powered Backend & Database that Infers Business Logic
  • highabout#2
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    An LLM-powered backend and database that infers business logic from API calls, replacing traditional code and databases for rapid application development.

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 RootbeerComputer/backend-GPT
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. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. scikit-learn · recommended 1×
  5. Express.js · recommended 1×
  • CATEGORY QUERY
    How to build backend APIs where an AI infers business logic?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. PyTorch
    3. TensorFlow
    4. scikit-learn
    5. Express.js
    6. TensorFlow.js
    7. Flask
    8. Google Cloud Platform
    9. Cloud Functions
    10. Cloud Run
    11. Vertex AI
    12. AWS
    13. API Gateway
    14. Lambda
    15. SageMaker
    16. Microsoft Azure
    17. API Management
    18. Azure Functions
    19. Azure Machine Learning
    20. Gin
    21. Echo
    22. ONNX Runtime
    23. Spring Boot
    24. Deeplearning4j
    25. ONNX Runtime for Java

    AI recommended 25 alternatives but never named RootbeerComputer/backend-GPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for an LLM-powered backend to replace traditional coding and databases?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI Assistants API
    4. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    5. Google Vertex AI Agent Builder
    6. Dialogflow CX
    7. GPT-4
    8. Claude
    9. Llama 2
    10. Weaviate (weaviate/weaviate)
    11. Pinecone

    AI recommended 11 alternatives but never named RootbeerComputer/backend-GPT. 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 RootbeerComputer/backend-GPT?
    pass
    AI named RootbeerComputer/backend-GPT explicitly

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

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

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RootbeerComputer/backend-GPT — 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