REPOGEO REPORT · LITE

roboflow/awesome-openai-vision-api-experiments

Default branch main · commit 7adff72c · scanned 5/11/2026, 6:03:07 PM

GitHub: 1,685 stars · 133 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 roboflow/awesome-openai-vision-api-experiments, 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 opening to emphasize 'experiments collection'

Why:

CURRENT

The must-have resource for anyone who wants to experiment with and build on the OpenAI Vision API.

COPY-PASTE FIX

This repository is a must-have collection of innovative experiments and practical code examples for anyone who wants to build on the OpenAI Vision API.

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the root of the repository, choosing an appropriate open-source license (e.g., MIT, Apache-2.0) and adding its SPDX identifier to the repository settings.

mediumhomepage#3

Add a homepage URL to the repository About section

Why:

COPY-PASTE FIX

Add a link to a relevant project page, blog post, or Roboflow's main site that provides more context or a polished entry point for this collection of experiments.

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 roboflow/awesome-openai-vision-api-experiments

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 2×
Google Cloud Vertex AI · recommended 2×
Microsoft Azure AI Services · recommended 1×
Stability AI · recommended 1×
Hugging Face Inference API · recommended 1×

CATEGORY QUERY
How can I experiment with advanced computer vision models using the latest generative AI APIs?
you: not recommended
AI recommended (in order):
1. OpenAI API
2. Google Cloud Vertex AI
3. Microsoft Azure AI Services
4. Stability AI
5. Hugging Face Inference API
6. RunwayML
7. Midjourney API
AI recommended 7 alternatives but never named roboflow/awesome-openai-vision-api-experiments. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What resources help developers prototype diverse computer vision applications using large language models?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Diffusers (huggingface/diffusers)
3. OpenAI API
4. GPT-4V
5. DALL-E 3
6. CLIP (openai/CLIP)
7. PyTorch Lightning (Lightning-AI/lightning)
8. Keras (keras-team/keras)
9. TensorFlow (tensorflow/tensorflow)
10. LangChain (langchain-ai/langchain)
11. LlamaIndex (run-llama/llama_index)
12. Google Cloud Vertex AI
13. Gemini
14. Azure AI Services
15. Computer Vision
16. Custom Vision
17. Azure OpenAI Service
18. Gradio (gradio-app/gradio)
19. Streamlit (streamlit/streamlit)
AI recommended 19 alternatives but never named roboflow/awesome-openai-vision-api-experiments. 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 roboflow/awesome-openai-vision-api-experiments?
pass
AI named roboflow/awesome-openai-vision-api-experiments explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts roboflow/awesome-openai-vision-api-experiments in production, what risks or prerequisites should they evaluate first?
pass
AI named roboflow/awesome-openai-vision-api-experiments 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 roboflow/awesome-openai-vision-api-experiments solve, and who is the primary audience?
pass
AI did not name roboflow/awesome-openai-vision-api-experiments — 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 roboflow/awesome-openai-vision-api-experiments. 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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MARKDOWN (README)

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HTML

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Brand-free category queries5 vs 2 in Lite
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