RRepoGEO

REPOGEO REPORT · LITE

EvanZhouDev/llm.pdf

Default branch main · commit c2aabbbd · scanned 5/29/2026, 10:23:19 AM

GitHub: 758 stars · 49 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 EvanZhouDev/llm.pdf, 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 README opening to emphasize executable PDF

    Why:

    CURRENT
    > Run LLMs inside a PDF file.

Watch how llm.pdf was built on YouTube.

## What is llm.pdf?

This is a proof-of-concept project, showing that it's possible to run an entire Large Language Model in nothing but a PDF file.
    COPY-PASTE FIX
    > Run LLMs *directly inside* a PDF file. This project demonstrates a unique proof-of-concept: an entire Large Language Model (LLM) compiled and embedded within a standard PDF, allowing for client-side, offline inference without external dependencies. Watch how llm.pdf was built on YouTube.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clarify usage rights.

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 EvanZhouDev/llm.pdf
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 1×
  2. PyInstaller · recommended 1×
  3. Nuitka · recommended 1×
  4. TensorFlow.js · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to embed a small language model directly into a document for offline use?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. PyInstaller
    3. Nuitka
    4. TensorFlow.js
    5. PyTorch
    6. TorchScript
    7. torch-wasm
    8. ONNX.js
    9. Hugging Face Transformers.js
    10. SQLite
    11. sqlite3
    12. pickle
    13. FlatBuffers
    14. Protocol Buffers

    AI recommended 14 alternatives but never named EvanZhouDev/llm.pdf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow running local LLM inference entirely client-side within a file?
    you: not recommended
    AI recommended (in order):
    1. MLC LLM (Web) (mlc-ai/web-llm)
    2. Transformers.js (huggingface/transformers.js)
    3. llama.cpp (WebAssembly Port) (ggerganov/llama.cpp)
    4. ONNX Runtime Web (microsoft/onnxruntime)
    5. Web LLM (Mozilla Research) (mozilla-ai/web-llm)

    AI recommended 5 alternatives but never named EvanZhouDev/llm.pdf. 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 EvanZhouDev/llm.pdf?
    pass
    AI named EvanZhouDev/llm.pdf explicitly

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

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

Drop this badge into the README of EvanZhouDev/llm.pdf. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/EvanZhouDev/llm.pdf.svg)](https://repogeo.com/en/r/EvanZhouDev/llm.pdf)
HTML
<a href="https://repogeo.com/en/r/EvanZhouDev/llm.pdf"><img src="https://repogeo.com/badge/EvanZhouDev/llm.pdf.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

EvanZhouDev/llm.pdf — 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