RRepoGEO

REPOGEO REPORT · LITE

haykgrigo3/TimeCapsuleLLM

Default branch main · commit b32ac6d0 · scanned 5/18/2026, 8:02:59 AM

GitHub: 1,915 stars · 72 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
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 haykgrigo3/TimeCapsuleLLM, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, historical-llm, time-capsule, nlp, generative-ai, bias-reduction, historical-data, language-model, ai-research, vintage-llm
  • highreadme#2
    Reposition README H1 and opening sentence for clarity

    Why:

    CURRENT
    # TimeCapsule LLM
*A language model trained **from scratch** exclusively on data from certain places and time periods to reduce modern bias and emulate the voice, vocabulary, and worldview of the era.*
    COPY-PASTE FIX
    # TimeCapsule LLM: Historical Language Models to Eliminate Modern Bias
*TimeCapsule LLM is a unique language model trained **from scratch** exclusively on data from specific historical periods and locations. Its core purpose is to reduce contemporary biases and accurately emulate the voice, vocabulary, and worldview of past eras, unlike general-purpose LLMs focused on memory or context.*
  • mediumhomepage#3
    Add a project homepage link

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://huggingface.co/datasets/postgrammar/london-llm-1800

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 haykgrigo3/TimeCapsuleLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-2 · recommended 1×
  2. GPT-3 · recommended 1×
  3. ChatGPT · recommended 1×
  4. Claude · recommended 1×
  5. Gemini · recommended 1×
  • CATEGORY QUERY
    How to generate text with historical accuracy, avoiding contemporary language biases?
    you: not recommended
    AI recommended (in order):
    1. GPT-2
    2. GPT-3
    3. ChatGPT
    4. Claude
    5. Gemini
    6. Oxford English Dictionary
    7. Google Ngram Viewer
    8. Corpus of Historical American English
    9. Corpus of Historical English

    AI recommended 9 alternatives but never named haykgrigo3/TimeCapsuleLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What large language models are available for generating text reflecting specific historical eras?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus / Sonnet
    3. Gemini 1.5 Pro
    4. Mistral Large
    5. Llama 2
    6. Llama 3

    AI recommended 6 alternatives but never named haykgrigo3/TimeCapsuleLLM. 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 haykgrigo3/TimeCapsuleLLM?
    pass
    AI named haykgrigo3/TimeCapsuleLLM explicitly

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

  • If a team adopts haykgrigo3/TimeCapsuleLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named haykgrigo3/TimeCapsuleLLM 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 haykgrigo3/TimeCapsuleLLM solve, and who is the primary audience?
    pass
    AI did not name haykgrigo3/TimeCapsuleLLM — 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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haykgrigo3/TimeCapsuleLLM — 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