RRepoGEO

REPOGEO REPORT · LITE

juliye2025/teaching-boyfriend-llm

Default branch main · commit 5a17cfec · scanned 6/14/2026, 2:08:37 AM

GitHub: 670 stars · 45 forks

AI VISIBILITY SCORE
17 /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
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 juliye2025/teaching-boyfriend-llm, 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
  • highabout#1
    Add a concise English description to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    A systematic learning guide for Large Language Models (LLM), covering fundamentals to advanced topics like fine-tuning, RAG, Agents, and inference optimization.
  • mediumlicense#2
    Add a standard open-source license file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository's root directory containing the text of a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that best suits the project.

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 juliye2025/teaching-boyfriend-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×
  3. Neural Networks and Deep Learning" by Michael Nielsen · recommended 1×
  4. Hugging Face's "Transformers" Course · recommended 1×
  5. The Illustrated Transformer" by Jay Alammar · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide for learning large language models from scratch?
    you: not recommended
    AI recommended (in order):
    1. Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    2. Neural Networks and Deep Learning" by Michael Nielsen
    3. Hugging Face's "Transformers" Course
    4. Hugging Face `transformers` library (huggingface/transformers)
    5. The Illustrated Transformer" by Jay Alammar
    6. Stanford CS224N: Natural Language Processing with Deep Learning
    7. Attention Is All You Need
    8. Language Models are Few-Shot Learners
    9. Training Compute-Optimal Large Language Models

    AI recommended 9 alternatives but never named juliye2025/teaching-boyfriend-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources offer in-depth explanations for LLM fine-tuning, RAG, agents, and inference optimization?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. LangChain (langchain-ai/langchain)
    3. DeepLearning.AI
    4. OpenAI
    5. NVIDIA
    6. TensorRT (NVIDIA/TensorRT)
    7. FasterTransformer (NVIDIA/FasterTransformer)
    8. Triton Inference Server (triton-inference-server/server)
    9. Papers With Code
    10. Microsoft Azure AI

    AI recommended 10 alternatives but never named juliye2025/teaching-boyfriend-llm. 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 juliye2025/teaching-boyfriend-llm?
    pass
    AI did not name juliye2025/teaching-boyfriend-llm — 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 juliye2025/teaching-boyfriend-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named juliye2025/teaching-boyfriend-llm 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 juliye2025/teaching-boyfriend-llm solve, and who is the primary audience?
    pass
    AI did not name juliye2025/teaching-boyfriend-llm — 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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juliye2025/teaching-boyfriend-llm — 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