RRepoGEO

REPOGEO REPORT · LITE

AmberLJC/LLMSys-PaperList

Default branch main · commit 9f1a2944 · scanned 6/23/2026, 9:58:15 AM

GitHub: 2,145 stars · 111 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
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 AmberLJC/LLMSys-PaperList, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, llm-systems, machine-learning-systems, research-papers, academic-papers, awesome-list, deep-learning, ai-systems, paper-list
  • highlicense#2
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file with the MIT License to clarify usage rights for the repository's content and structure.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/AmberLJC/LLMSys-PaperList

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 AmberLJC/LLMSys-PaperList
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. LLM-Recap · recommended 1×
  5. Hugging Face · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of academic papers on large language model systems?
    you: not recommended
    AI recommended (in order):
    1. Awesome-LLM
    2. Papers with Code
    3. Stanford CRFM
    4. LLM-Recap
    5. Hugging Face
    6. arXiv

    AI recommended 6 alternatives but never named AmberLJC/LLMSys-PaperList. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the system design challenges for efficiently training and serving large language models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA DGX SuperPOD
    2. Google Cloud TPUs
    3. Microsoft Azure ND-series VMs
    4. AWS EC2 P4d/P5 instances
    5. NVIDIA Triton Inference Server (triton-inference-server/server)
    6. vLLM (vllm-project/vllm)
    7. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    8. DeepSpeed-MII (microsoft/DeepSpeed-MII)
    9. Hugging Face Optimum (huggingface/optimum)
    10. ONNX Runtime (microsoft/onnxruntime)
    11. Intel OpenVINO (openvinotoolkit/openvino)
    12. NVIDIA TensorRT (NVIDIA/TensorRT)
    13. bitsandbytes (TimDettmers/bitsandbytes)
    14. PyTorch FSDP
    15. DeepSpeed (microsoft/DeepSpeed)
    16. Megatron-LM (NVIDIA/Megatron-LM)
    17. Ray Serve (ray-project/ray)
    18. Kubernetes (kubernetes/kubernetes)
    19. KubeFlow (kubeflow/kubeflow)
    20. Apache Arrow (apache/arrow)
    21. Parquet
    22. NVIDIA DALI (NVIDIA/DALI)
    23. TensorFlow Data
    24. AWS EC2 Spot
    25. Google Cloud Preemptible VMs
    26. Azure Spot VMs
    27. Slurm (SchedMD/slurm)

    AI recommended 27 alternatives but never named AmberLJC/LLMSys-PaperList. 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 AmberLJC/LLMSys-PaperList?
    pass
    AI did not name AmberLJC/LLMSys-PaperList — 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 AmberLJC/LLMSys-PaperList in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AmberLJC/LLMSys-PaperList 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 AmberLJC/LLMSys-PaperList solve, and who is the primary audience?
    pass
    AI did not name AmberLJC/LLMSys-PaperList — 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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AmberLJC/LLMSys-PaperList — 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