RRepoGEO

REPOGEO REPORT · LITE

android/ai-samples

Default branch main · commit 8f47ab7d · scanned 6/5/2026, 11:43:00 AM

GitHub: 595 stars · 165 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 android/ai-samples, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    A catalog of official Android sample applications demonstrating how to integrate Google's Generative AI models (like Gemini) and related tools into mobile apps.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    android, generative-ai, gemini, ai-samples, mobile-development, google-ai, machine-learning, firebase
  • highlicense#3
    Add an Apache 2.0 LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the Apache License 2.0.

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 android/ai-samples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/llama
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/llama · recommended 3×
  2. Google Cloud Vertex AI · recommended 1×
  3. Firebase ML Kit · recommended 1×
  4. OpenAI API · recommended 1×
  5. GPT-4 · recommended 1×
  • CATEGORY QUERY
    How can I integrate generative AI features into my mobile applications?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vertex AI
    2. Firebase ML Kit
    3. OpenAI API
    4. GPT-4
    5. DALL-E 3
    6. Hugging Face Transformers (huggingface/transformers)
    7. ONNX (onnx/onnx)
    8. TensorFlow Lite (tensorflow/tensorflow)
    9. ONNX Runtime Mobile (microsoft/onnxruntime)
    10. Meta Llama (facebookresearch/llama)
    11. Llama 2 (facebookresearch/llama)
    12. Code Llama (facebookresearch/llama)
    13. MLX (ml-explore/mlx)
    14. PyTorch Mobile (pytorch/pytorch)
    15. Apple Core ML
    16. Microsoft Azure AI Services
    17. Azure OpenAI Service
    18. AWS Bedrock
    19. Amazon Titan
    20. AI21 Labs Jurassic
    21. Anthropic Claude
    22. Stability AI Stable Diffusion (Stability-AI/StableDiffusion)

    AI recommended 22 alternatives but never named android/ai-samples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples for building AI-powered functionalities on mobile devices.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. Core ML
    3. ML Kit (Firebase)
    4. Kaldi
    5. ARKit
    6. ARCore

    AI recommended 6 alternatives but never named android/ai-samples. 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 android/ai-samples?
    pass
    AI named android/ai-samples explicitly

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

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

Drop this badge into the README of android/ai-samples. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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android/ai-samples — 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