RRepoGEO

REPOGEO REPORT · LITE

huggingface/search-and-learn

Default branch main · commit 547502cc · scanned 5/22/2026, 1:18:18 AM

GitHub: 1,132 stars · 131 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
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 huggingface/search-and-learn, 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 the README H1 to specify the technical category

    Why:

    CURRENT
    # Search and Learn
    COPY-PASTE FIX
    # Search and Learn: Recipes for Scaling LLM Inference Compute
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute

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 huggingface/search-and-learn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. OpenVINO · recommended 2×
  3. NVIDIA Triton Inference Server · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. TensorRT-LLM · recommended 1×
  • CATEGORY QUERY
    How can I efficiently scale inference for large language models to reduce latency?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. vLLM
    3. DeepSpeed-MII
    4. TensorRT-LLM
    5. OpenVINO
    6. Ray Serve

    AI recommended 6 alternatives but never named huggingface/search-and-learn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques exist to improve open LLM performance by optimizing runtime computation?
    you: not recommended
    AI recommended (in order):
    1. GPTQ
    2. AWQ
    3. QLoRA
    4. Google's Speculative Decoding
    5. Medusa
    6. FlashAttention
    7. FlashAttention-2
    8. vLLM
    9. NVIDIA TensorRT
    10. ONNX Runtime
    11. OpenVINO
    12. Multi-Query Attention (MQA)
    13. Grouped-Query Attention (GQA)

    AI recommended 13 alternatives but never named huggingface/search-and-learn. 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 huggingface/search-and-learn?
    pass
    AI did not name huggingface/search-and-learn — 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 huggingface/search-and-learn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/search-and-learn 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 huggingface/search-and-learn solve, and who is the primary audience?
    pass
    AI named huggingface/search-and-learn explicitly

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

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huggingface/search-and-learn — 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