RRepoGEO

REPOGEO REPORT · LITE

redotvideo/mamba-chat

Default branch main · commit 68e60823 · scanned 6/17/2026, 5:57:58 PM

GitHub: 943 stars · 72 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 redotvideo/mamba-chat, 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 improve categorization

    Why:

    COPY-PASTE FIX
    ["mamba", "state-space-models", "llm", "chatbot", "conversational-ai", "non-transformer", "deep-learning", "pytorch", "huggingface"]
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/havenhq/mamba-chat
  • lowreadme#3
    Refine the README's opening sentence to emphasize the unique differentiator

    Why:

    CURRENT
    **Mamba-Chat is the first chat language model based on a state-space model architecture, not a transformer.**
    COPY-PASTE FIX
    **Mamba-Chat is the pioneering chat language model built on a state-space model architecture, offering an efficient, non-transformer alternative for conversational AI.**

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 redotvideo/mamba-chat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mamba
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mamba · recommended 2×
  2. RWKV · recommended 1×
  3. LSTMs · recommended 1×
  4. GRUs · recommended 1×
  5. TextCNN · recommended 1×
  • CATEGORY QUERY
    Seeking a large language model built on a non-transformer architecture for efficient chat applications.
    you: not recommended
    AI recommended (in order):
    1. RWKV
    2. Mamba
    3. LSTMs
    4. GRUs
    5. TextCNN
    6. ByteNet

    AI recommended 6 alternatives but never named redotvideo/mamba-chat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to fine-tune a state-space model for building a custom conversational AI assistant?
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. Jamba
    3. BlackMamba
    4. Alpaca
    5. ShareGPT
    6. OpenAssistant Conversations Dataset (OASST1)
    7. Hugging Face Transformers
    8. transformers library (huggingface/transformers)
    9. PEFT (huggingface/peft)
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. DeepSpeed (microsoft/DeepSpeed)
    12. Hugging Face `pipeline`
    13. vLLM (vllm-project/vllm)
    14. Triton Inference Server (triton-inference-server/server)
    15. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 15 alternatives but never named redotvideo/mamba-chat. 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 redotvideo/mamba-chat?
    pass
    AI did not name redotvideo/mamba-chat — 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 redotvideo/mamba-chat in production, what risks or prerequisites should they evaluate first?
    pass
    AI named redotvideo/mamba-chat 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 redotvideo/mamba-chat solve, and who is the primary audience?
    pass
    AI named redotvideo/mamba-chat explicitly

    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 redotvideo/mamba-chat. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/redotvideo/mamba-chat"><img src="https://repogeo.com/badge/redotvideo/mamba-chat.svg" alt="RepoGEO" /></a>
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  • Brand-free category queries5 vs 2 in Lite
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