RRepoGEO

REPOGEO REPORT · LITE

ckd0817/LLM-Interview-Code

Default branch master · commit 4ed436c8 · scanned 6/10/2026, 6:38:54 AM

GitHub: 648 stars · 37 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 ckd0817/LLM-Interview-Code, 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 repository description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Essential code implementations for Large Language Model (LLM) interview preparation, covering attention, normalization, PEFT, and more from scratch.
  • mediumlicense#2
    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 repository root with your chosen open-source license (e.g., MIT, Apache-2.0) to clarify usage terms.

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 ckd0817/LLM-Interview-Code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NumPy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NumPy · recommended 1×
  2. JAX · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    How to implement common large language model components from scratch for interview preparation?
    you: not recommended
    AI recommended (in order):
    1. NumPy
    2. JAX
    3. PyTorch
    4. TensorFlow
    5. Keras
    6. SciPy
    7. Matplotlib
    8. Seaborn

    AI recommended 8 alternatives but never named ckd0817/LLM-Interview-Code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking code examples for modern LLM attention, normalization, and PEFT techniques from first principles.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. peft library (huggingface/peft)
    3. minGPT (karpathy/minGPT)
    4. llm.c (karpathy/llm.c)
    5. The Annotated Transformer
    6. PyTorch nn.Transformer (pytorch/pytorch)
    7. flaxformer (google/flaxformer)
    8. nanoGPT (karpathy/nanoGPT)

    AI recommended 8 alternatives but never named ckd0817/LLM-Interview-Code. 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 ckd0817/LLM-Interview-Code?
    pass
    AI named ckd0817/LLM-Interview-Code explicitly

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

  • If a team adopts ckd0817/LLM-Interview-Code in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ckd0817/LLM-Interview-Code 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 ckd0817/LLM-Interview-Code solve, and who is the primary audience?
    pass
    AI did not name ckd0817/LLM-Interview-Code — 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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ckd0817/LLM-Interview-Code — 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