RRepoGEO

REPOGEO REPORT · LITE

janhq/awesome-local-ai

Default branch main · commit 39686924 · scanned 5/30/2026, 7:42:31 AM

GitHub: 1,961 stars · 211 forks

AI VISIBILITY SCORE
23 /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
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 janhq/awesome-local-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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, local-ai, llm, large-language-models, ai-tools, open-source, inference, gpu, cpu
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with an appropriate open-source license (e.g., MIT, Apache-2.0).
  • mediumreadme#3
    Clarify the README's opening sentence to emphasize its 'awesome list' nature

    Why:

    CURRENT
    If you tried Jan Desktop and liked it, please also check out the following **awesome collection of open source and/or local AI tools and solutions.**
    COPY-PASTE FIX
    This is an **awesome collection of open source and/or local AI tools and solutions** for running large language models and other AI models on local hardware.

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 janhq/awesome-local-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
llama.cpp
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 2×
  2. Ollama · recommended 1×
  3. LM Studio · recommended 1×
  4. oobabooga/text-generation-webui · recommended 1×
  5. Transformers · recommended 1×
  • CATEGORY QUERY
    What are the best open-source tools for running large language models on local hardware?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. text-generation-webui (oobabooga/text-generation-webui)
    4. llama.cpp
    5. Transformers
    6. MLC LLM

    AI recommended 6 alternatives but never named janhq/awesome-local-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently run various AI models directly on my own computer's CPU or GPU?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. ONNX Runtime
    3. TensorFlow Lite
    4. OpenVINO Toolkit
    5. NVIDIA TensorRT
    6. MLX
    7. llama.cpp

    AI recommended 7 alternatives but never named janhq/awesome-local-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
    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 janhq/awesome-local-ai?
    pass
    AI did not name janhq/awesome-local-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 janhq/awesome-local-ai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named janhq/awesome-local-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 janhq/awesome-local-ai solve, and who is the primary audience?
    pass
    AI named janhq/awesome-local-ai explicitly

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

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janhq/awesome-local-ai — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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