RRepoGEO

REPOGEO REPORT · LITE

t8/hypura

Default branch main · commit 7fb2479a · scanned 6/2/2026, 8:11:46 AM

GitHub: 649 stars · 21 forks

AI VISIBILITY SCORE
30 /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
3 / 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 t8/hypura, 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 specific topics to clarify project category

    Why:

    COPY-PASTE FIX
    llm, apple-silicon, macos, inference, out-of-memory, memory-management, mixtral, llama, machine-learning
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0).
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., project website, documentation, or a dedicated GitHub Pages site) to the repository's homepage field.

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 t8/hypura
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. Jan · recommended 2×
  3. Ollama · recommended 1×
  4. llama.cpp · recommended 1×
  5. llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How to run large language models on Apple Silicon Mac exceeding physical memory?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. llama.cpp
    5. llama-cpp-python
    6. MLX
    7. Hugging Face `transformers`
    8. bitsandbytes
    9. AWQ
    10. accelerate

    AI recommended 10 alternatives but never named t8/hypura. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools to efficiently run huge AI models on Mac without out-of-memory errors.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. Ollama (ollama/ollama)
    3. MLC LLM (mlc-ai/mlc-llm)
    4. Hugging Face transformers (huggingface/transformers)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. accelerate (huggingface/accelerate)
    7. PyTorch (pytorch/pytorch)
    8. LM Studio
    9. Jan

    AI recommended 9 alternatives but never named t8/hypura. 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 t8/hypura?
    pass
    AI named t8/hypura explicitly

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

  • If a team adopts t8/hypura in production, what risks or prerequisites should they evaluate first?
    pass
    AI named t8/hypura 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 t8/hypura solve, and who is the primary audience?
    pass
    AI named t8/hypura explicitly

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

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t8/hypura — 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