RRepoGEO

REPOGEO REPORT · LITE

mintisan/awesome-kan

Default branch main · commit 8fdcc678 · scanned 6/25/2026, 10:32:55 AM

GitHub: 3,247 stars · 310 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 mintisan/awesome-kan, 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
    awesome-list, kolmogorov-arnold-network, kan, machine-learning, deep-learning, neural-networks, resources, papers, tutorials, research
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the repository root. For example, for MIT: "MIT License\n\nCopyright (c) [YEAR] [COPYRIGHT HOLDER]\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  • mediumreadme#3
    Reinforce 'awesome list' and 'Kolmogorov-Arnold Network' in the README's opening sentence

    Why:

    CURRENT
    A curated list of awesome libraries, projects, tutorials, papers, and other resources related to Kolmogorov-Arnold Network (KAN). This repository aims to be a comprehensive and organized collection that will help researchers and developers in the world of KAN!
    COPY-PASTE FIX
    This is an awesome list: a comprehensive and curated collection of libraries, projects, tutorials, papers, and other resources specifically related to Kolmogorov-Arnold Network (KAN). It aims to be a central hub for researchers and developers in the Kolmogorov-Arnold Network 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 mintisan/awesome-kan
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. ZimingLiu/pytorch-kan · recommended 1×
  3. Hugging Face · recommended 1×
  4. JAX · recommended 1×
  5. YouTube · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources and tutorials for Kolmogorov-Arnold Networks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. pytorch-kan (ZimingLiu/pytorch-kan)
    3. Hugging Face
    4. JAX
    5. YouTube
    6. StatQuest with Josh Starmer
    7. Two Minute Papers
    8. Code Emporium
    9. Medium
    10. Towards Data Science

    AI recommended 10 alternatives but never named mintisan/awesome-kan. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for libraries, projects, and papers related to Kolmogorov-Arnold Network implementations.
    you: not recommended
    AI recommended (in order):
    1. PyTorch-KAN (ZiyaoLi/pytorch-kan)
    2. KAN (KindXiaoming/KAN)
    3. jax-kan (KindXiaoming/jax-kan)
    4. tf-kan (KindXiaoming/tf-kan)
    5. KAN-Numpy (KindXiaoming/KAN-Numpy)

    AI recommended 5 alternatives but never named mintisan/awesome-kan. 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 mintisan/awesome-kan?
    pass
    AI did not name mintisan/awesome-kan — 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 mintisan/awesome-kan in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mintisan/awesome-kan 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 mintisan/awesome-kan solve, and who is the primary audience?
    pass
    AI did not name mintisan/awesome-kan — 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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mintisan/awesome-kan — 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