RRepoGEO

REPOGEO REPORT · LITE

alvinreal/awesome-opensource-ai

Default branch main · commit 8df76368 · scanned 5/18/2026, 6:08:30 PM

GitHub: 3,617 stars · 400 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
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 alvinreal/awesome-opensource-ai, 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
    Clarify repo's nature as an "awesome list" in the README's opening

    Why:

    CURRENT
    Open-source artificial intelligence models, libraries, infrastructure, and developer tools.
    COPY-PASTE FIX
    A curated list of the best truly open-source AI projects, models, tools, and infrastructure. This repository provides a comprehensive guide to open-source artificial intelligence models, libraries, infrastructure, and developer tools.
  • mediumreadme#2
    Add a "Why This List?" section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Why This List?` or `## About This List`, immediately after the initial description, explaining its focus on truly open-source projects and its utility as a discovery tool. Example content: `This list is meticulously curated to highlight truly open-source AI projects, models, tools, and infrastructure. Unlike broader directories, we focus exclusively on projects with permissive licenses and active communities, making it easier for developers and researchers to find reliable, community-driven AI resources.`
  • lowreadme#3
    Add a prominent link to the official homepage in the README

    Why:

    COPY-PASTE FIX
    Add `[Visit our official website for more details: awesomeosai.com](https://awesomeosai.com)` directly below the main description/title block in the README.

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 alvinreal/awesome-opensource-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. pytorch/pytorch · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. scikit-learn/scikit-learn · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    What are the best open-source AI projects for building new applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. scikit-learn (scikit-learn/scikit-learn)
    5. LangChain (langchain-ai/langchain)
    6. Gymnasium (Farama-Foundation/Gymnasium)
    7. Apache Spark MLlib (apache/spark)

    AI recommended 7 alternatives but never named alvinreal/awesome-opensource-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source tools and infrastructure for MLOps and generative AI production.
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Kubeflow (kubeflow/kubeflow)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Accelerate (huggingface/accelerate)
    5. Ray (ray-project/ray)
    6. DVC (iterative/dvc)
    7. Apache Airflow (apache/airflow)
    8. Prometheus (prometheus/prometheus)
    9. Grafana (grafana/grafana)

    AI recommended 9 alternatives but never named alvinreal/awesome-opensource-ai. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 alvinreal/awesome-opensource-ai?
    pass
    AI did not name alvinreal/awesome-opensource-ai — 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 alvinreal/awesome-opensource-ai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alvinreal/awesome-opensource-ai 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 alvinreal/awesome-opensource-ai solve, and who is the primary audience?
    pass
    AI did not name alvinreal/awesome-opensource-ai — 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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alvinreal/awesome-opensource-ai — 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