RRepoGEO

REPOGEO REPORT · LITE

evanmiller/LLM-Reading-List

Default branch main · commit 100c74e0 · scanned 6/3/2026, 3:08:06 PM

GitHub: 749 stars · 39 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 evanmiller/LLM-Reading-List, 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 the README's opening sentence to clearly state its value

    Why:

    CURRENT
    Just helping myself keep track of LLM papers that I‘m reading, with an emphasis on inference and model compression.
    COPY-PASTE FIX
    A curated reading list of essential LLM research papers, with a specific focus on inference optimization and model compression techniques.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, research-papers, inference, model-compression, deep-learning, machine-learning, reading-list, transformer-architectures, foundation-models, kv-cache, position-encoding
  • mediumlicense#3
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file with the CC0-1.0 Public Domain Dedication to clarify usage rights for the reading list content.

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 evanmiller/LLM-Reading-List
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-LLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-LLM · recommended 1×
  2. Papers with Code · recommended 1×
  3. Stanford CRFM · recommended 1×
  4. Google AI Blog · recommended 1×
  5. Meta AI Blog · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of essential papers on large language model research?
    you: not recommended
    AI recommended (in order):
    1. Awesome-LLM
    2. Papers with Code
    3. Stanford CRFM
    4. Google AI Blog
    5. Meta AI Blog
    6. Hugging Face

    AI recommended 6 alternatives but never named evanmiller/LLM-Reading-List. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key research papers for optimizing LLM inference and reducing model size?
    you: not recommended
    AI recommended (in order):
    1. LLM.int8()
    2. GPTQ
    3. AWQ
    4. Lottery Ticket Hypothesis
    5. SparseGPT
    6. DistilBERT
    7. TinyLlama
    8. FlashAttention
    9. Mamba

    AI recommended 9 alternatives but never named evanmiller/LLM-Reading-List. 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 evanmiller/LLM-Reading-List?
    pass
    AI did not name evanmiller/LLM-Reading-List — 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 evanmiller/LLM-Reading-List in production, what risks or prerequisites should they evaluate first?
    pass
    AI named evanmiller/LLM-Reading-List 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 evanmiller/LLM-Reading-List solve, and who is the primary audience?
    pass
    AI did not name evanmiller/LLM-Reading-List — 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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evanmiller/LLM-Reading-List — 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