RRepoGEO

REPOGEO REPORT · LITE

archinetai/audio-ai-timeline

Default branch main · commit 48956cc8 · scanned 5/15/2026, 12:57:40 AM

GitHub: 1,912 stars · 72 forks

AI VISIBILITY SCORE
22 /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
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 archinetai/audio-ai-timeline, 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
  • highreadme#1
    Reposition README opening to emphasize curated timeline

    Why:

    CURRENT
    # Audio AI Timeline

Here we will keep track of the latest AI models for waveform based audio generation, starting in 2023!
    COPY-PASTE FIX
    # Audio AI Timeline

This repository provides a curated, historical timeline of the latest AI models for waveform-based audio generation, starting in 2023. It serves as a central resource for tracking key advancements, papers, and code in the rapidly evolving field of Audio AI.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root containing the text of the MIT License.
  • mediumhomepage#3
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to a dedicated project page if one exists, or to the repository URL (https://github.com/archinetai/audio-ai-timeline) as a placeholder.

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 archinetai/audio-ai-timeline
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 2×
  2. arXiv · recommended 2×
  3. GitHub Trending · recommended 2×
  4. Hugging Face Hub · recommended 1×
  5. Python · recommended 1×
  • CATEGORY QUERY
    How can I track the latest artificial intelligence models for audio generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Papers With Code
    3. arXiv
    4. GitHub Trending
    5. Python
    6. PyTorch
    7. TensorFlow
    8. The Batch by DeepLearning.AI
    9. SyncedReview
    10. Towards Data Science
    11. Twitter
    12. Google AI
    13. OpenAI
    14. Meta AI
    15. Stability AI
    16. Reddit
    17. r/MachineLearning
    18. r/deeplearning

    AI recommended 18 alternatives but never named archinetai/audio-ai-timeline. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find an overview of recent machine learning breakthroughs in sound synthesis?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Google AI Blog
    3. DeepMind Blog
    4. The Gradient
    5. arXiv
    6. GitHub Trending

    AI recommended 6 alternatives but never named archinetai/audio-ai-timeline. 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 archinetai/audio-ai-timeline?
    pass
    AI did not name archinetai/audio-ai-timeline — 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 archinetai/audio-ai-timeline in production, what risks or prerequisites should they evaluate first?
    pass
    AI named archinetai/audio-ai-timeline 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 archinetai/audio-ai-timeline solve, and who is the primary audience?
    pass
    AI did not name archinetai/audio-ai-timeline — 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?

Embed your GEO score

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archinetai/audio-ai-timeline — 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