RRepoGEO

REPOGEO REPORT · LITE

ninehills/blog

Default branch gh-pages · commit 18b6d1ba · scanned 6/30/2026, 5:48:13 AM

GitHub: 2,673 stars · 229 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
35 /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
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 ninehills/blog, 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 'about' description for the repository

    Why:

    COPY-PASTE FIX
    Source code and content for Ninehills' technical blog, featuring in-depth articles on LLM inference, AI agents, SRE, and related AI/ML topics.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license, such as `MIT License`.
  • mediumtopics#3
    Expand repository topics to reflect content areas

    Why:

    CURRENT
    hugo
    COPY-PASTE FIX
    hugo, llm, ai-agent, sre, inference-optimization, technical-blog, machine-learning, artificial-intelligence

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 ninehills/blog
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
jekyll/jekyll
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. jekyll/jekyll · recommended 1×
  2. gohugoio/hugo · recommended 1×
  3. gatsbyjs/gatsby · recommended 1×
  4. vercel/next.js · recommended 1×
  5. 11ty/eleventy · recommended 1×
  • CATEGORY QUERY
    What are popular static site generators for publishing technical articles and notes?
    you: not recommended
    AI recommended (in order):
    1. Jekyll (jekyll/jekyll)
    2. Hugo (gohugoio/hugo)
    3. Gatsby (gatsbyjs/gatsby)
    4. Next.js (vercel/next.js)
    5. Eleventy (11ty/eleventy)
    6. Astro (withastro/astro)

    AI recommended 6 alternatives but never named ninehills/blog. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find in-depth articles on LLM inference, AI agents, and SRE topics?
    you: not recommended
    AI recommended (in order):
    1. Towards Data Science
    2. Google AI Blog
    3. Google Cloud Blog
    4. OpenAI Blog
    5. Hugging Face Blog
    6. Transformers (huggingface/transformers)
    7. TGI (huggingface/text-generation-inference)
    8. Netflix TechBlog
    9. The New Stack
    10. ACM Queue
    11. IEEE Spectrum

    AI recommended 11 alternatives but never named ninehills/blog. 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 ninehills/blog?
    pass
    AI named ninehills/blog explicitly

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

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

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

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Pro

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ninehills/blog — 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
ninehills/blog — RepoGEO report