RRepoGEO

REPOGEO REPORT · LITE

MoonshotAI/Kimi-Linear

Default branch master · commit 8c1d85eb · scanned 5/23/2026, 10:58:07 AM

GitHub: 1,392 stars · 69 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
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 MoonshotAI/Kimi-Linear, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods, offering superior performance and hardware efficiency for long-context tasks by reducing KV cache needs.
  • mediumhomepage#2
    Add a homepage link to the paper

    Why:

    COPY-PASTE FIX
    https://huggingface.co/papers/2510.26692

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 MoonshotAI/Kimi-Linear
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FlashAttention / FlashAttention-2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FlashAttention / FlashAttention-2 · recommended 2×
  2. LongFormer · recommended 2×
  3. BigBird · recommended 2×
  4. Reformer · recommended 2×
  5. Performer · recommended 1×
  • CATEGORY QUERY
    Struggling with slow full attention; need efficient architecture for long context LLMs.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2
    2. LongFormer
    3. BigBird
    4. Performer
    5. Reformer
    6. Hyena Hierarchy
    7. Mamba

    AI recommended 7 alternatives but never named MoonshotAI/Kimi-Linear. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a high-performance attention architecture that outperforms traditional methods.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2
    2. LongFormer
    3. Reformer
    4. Performer (Performer-ReLU)
    5. Linformer
    6. BigBird

    AI recommended 6 alternatives but never named MoonshotAI/Kimi-Linear. 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 MoonshotAI/Kimi-Linear?
    pass
    AI named MoonshotAI/Kimi-Linear explicitly

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

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

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

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MoonshotAI/Kimi-Linear — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
MoonshotAI/Kimi-Linear — RepoGEO report