RRepoGEO

REPOGEO REPORT · LITE

Sumanth077/Hands-On-AI-Engineering

Default branch main · commit 957f3bda · scanned 5/23/2026, 11:03:31 PM

GitHub: 1,209 stars · 395 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
22 /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
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 Sumanth077/Hands-On-AI-Engineering, 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
  • highlicense#1
    Add a 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. Choose a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and include its full text. Then, update the repository's 'About' section on GitHub to specify the chosen license.
  • highreadme#2
    Clarify repository type in README's opening sentence

    Why:

    CURRENT
    A curated collection of practical, production-ready AI projects across multiple modalities, including language models, multimodal models, OCR systems, RAG pipelines, and AI agents.
    COPY-PASTE FIX
    This repository provides a curated collection of practical, production-ready AI projects, offering complete, runnable code for real-world AI applications across language models, multimodal models, OCR systems, RAG pipelines, and AI agents.
  • mediumabout#3
    Expand the repository's 'About' description

    Why:

    CURRENT
    A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.
    COPY-PASTE FIX
    A comprehensive repository of practical, production-ready AI projects, offering complete code for OCR systems, RAG pipelines, and AI agents. Ideal for learning and building real-world AI applications.

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 Sumanth077/Hands-On-AI-Engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Towards Data Science
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Towards Data Science · recommended 1×
  2. Kaggle · recommended 1×
  3. PyTorch Examples · recommended 1×
  4. TensorFlow Examples · recommended 1×
  5. GitHub · recommended 1×
  • CATEGORY QUERY
    Where can I find hands-on examples for building real-world AI applications with Python?
    you: not recommended
    AI recommended (in order):
    1. Towards Data Science
    2. Kaggle
    3. PyTorch Examples
    4. TensorFlow Examples
    5. GitHub
    6. fast.ai
    7. PyTorch
    8. fastai library

    AI recommended 8 alternatives but never named Sumanth077/Hands-On-AI-Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for production-ready Python code examples for RAG, OCR, and AI agent systems.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Tesseract OCR
    5. Google Cloud Vision AI
    6. Amazon Textract
    7. LangChain Agents
    8. CrewAI
    9. AutoGen

    AI recommended 9 alternatives but never named Sumanth077/Hands-On-AI-Engineering. 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 Sumanth077/Hands-On-AI-Engineering?
    pass
    AI did not name Sumanth077/Hands-On-AI-Engineering — 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 Sumanth077/Hands-On-AI-Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Sumanth077/Hands-On-AI-Engineering 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 Sumanth077/Hands-On-AI-Engineering solve, and who is the primary audience?
    pass
    AI did not name Sumanth077/Hands-On-AI-Engineering — 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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Sumanth077/Hands-On-AI-Engineering — 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