RRepoGEO

REPOGEO REPORT · LITE

kitft/natural_language_autoencoders

Default branch main · commit 1b7f13d9 · scanned 6/11/2026, 3:33:23 PM

GitHub: 788 stars · 105 forks

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 kitft/natural_language_autoencoders, 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
  • highabout#1
    Add a concise, specific repository description

    Why:

    COPY-PASTE FIX
    Open-source library for Natural Language Autoencoders (NLA) to produce unsupervised, human-readable explanations of Large Language Model (LLM) internal activations.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-interpretability, transformer-circuits, activation-explanations, natural-language-autoencoders, nla, llm-activations, machine-learning, deep-learning, ai-explainability
  • mediumreadme#3
    Reposition the README's opening sentence to be more explicit about the core problem

    Why:

    CURRENT
    # Natural Language Autoencoders (NLA)
    
    Open-source library accompanying the Anthropic Transformer Circuits post
    **Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations**.
    COPY-PASTE FIX
    # Natural Language Autoencoders (NLA)
    
    This open-source library provides Natural Language Autoencoders (NLA) for producing unsupervised, human-readable explanations of Large Language Model (LLM) internal activations. It accompanies the Anthropic Transformer Circuits post **Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations**.

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 kitft/natural_language_autoencoders
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
No competitor dominated
  • CATEGORY QUERY
    How can I get natural language explanations for large language model internal activations?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    Tools for mapping LLM activation vectors to human-readable text and reconstructing them?
    you: not recommended
    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 kitft/natural_language_autoencoders?
    pass
    AI did not name kitft/natural_language_autoencoders — 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 kitft/natural_language_autoencoders in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kitft/natural_language_autoencoders 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 kitft/natural_language_autoencoders solve, and who is the primary audience?
    pass
    AI did not name kitft/natural_language_autoencoders — 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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kitft/natural_language_autoencoders — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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