RRepoGEO

REPOGEO REPORT · LITE

HazyResearch/aisys-building-blocks

Default branch main · commit fc7356ad · scanned 6/16/2026, 8:03:18 PM

GitHub: 627 stars · 27 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 HazyResearch/aisys-building-blocks, 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 H1 to specify it's a curated resource collection

    Why:

    CURRENT
    # Building Blocks for AI Systems
    COPY-PASTE FIX
    # Curated Resources: Building Blocks for Efficient Foundation Models
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-systems, foundation-models, ml-systems, efficient-ai, research-collection, llms, deep-learning-research
  • mediumlicense#3
    Add a LICENSE file and mention it in the README

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text for the Creative Commons Attribution 4.0 International (CC-BY-4.0) License. Add the line "The content of this repository is licensed under CC-BY-4.0." to the README.

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 HazyResearch/aisys-building-blocks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. FlashAttention · recommended 2×
  3. Apache Arrow · recommended 2×
  4. Parquet · recommended 2×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    How can I find resources to understand the building blocks for efficient foundation models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch FSDP
    3. DeepSpeed
    4. FlashAttention
    5. bitsandbytes
    6. NVIDIA Apex
    7. NVIDIA CUDA
    8. AWS EC2
    9. Google Cloud TPUs
    10. Azure ML
    11. Hugging Face Datasets Library
    12. Apache Arrow
    13. Parquet
    14. WebDataCommons
    15. The Pile
    16. OpenVINO
    17. TensorRT
    18. ONNX Runtime

    AI recommended 18 alternatives but never named HazyResearch/aisys-building-blocks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key system optimizations for developing performant large AI models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed (torch.distributed)
    2. DeepSpeed
    3. Megatron-LM
    4. Automatic Mixed Precision (AMP)
    5. torch.cuda.amp
    6. torch.utils.checkpoint
    7. DeepSpeed's ZeRO-Offload
    8. PyTorch DataLoader
    9. WebDataset
    10. Apache Arrow
    11. Parquet
    12. pyarrow
    13. NVIDIA NCCL (NVIDIA Collective Communications Library)
    14. InfiniBand
    15. NVIDIA A100 / H100 GPUs
    16. NVLink
    17. High-Bandwidth RAM (HBM)
    18. TorchDynamo (PyTorch 2.0)
    19. Inductor
    20. XLA (Accelerated Linear Algebra)
    21. TensorFlow
    22. FlashAttention
    23. Triton

    AI recommended 23 alternatives but never named HazyResearch/aisys-building-blocks. 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 HazyResearch/aisys-building-blocks?
    pass
    AI did not name HazyResearch/aisys-building-blocks — 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 HazyResearch/aisys-building-blocks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HazyResearch/aisys-building-blocks 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 HazyResearch/aisys-building-blocks solve, and who is the primary audience?
    pass
    AI did not name HazyResearch/aisys-building-blocks — 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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  • Brand-free category queries5 vs 2 in Lite
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