RRepoGEO

REPOGEO REPORT · LITE

NarimanN2/ollama-playground

Default branch main · commit db6d48f4 · scanned 6/5/2026, 7:33:17 AM

GitHub: 532 stars · 182 forks

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 NarimanN2/ollama-playground, 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 H1 and opening paragraph to highlight practical examples

    Why:

    CURRENT
    # Ollama Projects
This repository contains the code for the projects I built using Ollama's open-source models for my YouTube channel. Make sure to check out the videos to see how I built them, and also subscribe to the channel for more content like this.
    COPY-PASTE FIX
    # Practical Ollama Projects: RAG, Agents, Multi-Agent Systems & More
This repository is a curated collection of practical, runnable examples demonstrating advanced LLM applications built using Ollama's open-source models. Explore real-world implementations of Retrieval-Augmented Generation (RAG), AI Agents, Multi-Agent Systems, and more, all designed to be easily experimented with locally.
  • hightopics#2
    Add comprehensive topics for better categorization

    Why:

    COPY-PASTE FIX
    ollama, llm, large-language-models, rag, retrieval-augmented-generation, ai-agents, multi-agent-systems, python, machine-learning, ai, examples, projects
  • mediumhomepage#3
    Add homepage link to the YouTube channel

    Why:

    COPY-PASTE FIX
    https://www.youtube.com/@NarimanN2

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 NarimanN2/ollama-playground
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
llamaindex/llamaindex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. llamaindex/llamaindex · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. deepset-ai/haystack · recommended 1×
  4. facebookresearch/faiss · recommended 1×
  5. UKPLab/sentence-transformers · recommended 1×
  • CATEGORY QUERY
    How to build a RAG system for querying PDFs using open-source models?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (llamaindex/llamaindex)
    2. LangChain (langchain-ai/langchain)
    3. Haystack (deepset-ai/haystack)
    4. Faiss (facebookresearch/faiss)
    5. Sentence-Transformers (UKPLab/sentence-transformers)
    6. PyMuPDF (pymupdf/PyMuPDF)
    7. Ollama (ollama/ollama)

    AI recommended 7 alternatives but never named NarimanN2/ollama-playground. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical examples of building AI agents for web scraping or research tasks.
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. Puppeteer
    3. OpenAI GPT-4
    4. GPT-3.5 Turbo
    5. LlamaIndex
    6. Llama 3
    7. Ollama
    8. LangChain
    9. SerpAPI
    10. Google Search API
    11. requests
    12. BeautifulSoup
    13. Anthropic Claude 3
    14. Scrapy
    15. PostgreSQL
    16. smtplib
    17. Twilio
    18. Autogen

    AI recommended 18 alternatives but never named NarimanN2/ollama-playground. 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 NarimanN2/ollama-playground?
    pass
    AI did not name NarimanN2/ollama-playground — 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 NarimanN2/ollama-playground in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NarimanN2/ollama-playground 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 NarimanN2/ollama-playground solve, and who is the primary audience?
    pass
    AI named NarimanN2/ollama-playground explicitly

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

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NarimanN2/ollama-playground — 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