RRepoGEO

REPOGEO REPORT · LITE

HenryNdubuaku/maths-cs-ai-compendium

Default branch main · commit 24224ea7 · scanned 6/24/2026, 12:18:07 AM

GitHub: 4,574 stars · 633 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 HenryNdubuaku/maths-cs-ai-compendium, 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 highlight "unconventional" and "MCP Server"

    Why:

    CURRENT
    # Maths, CS & AI Compendium

**Read online**: henryndubuaku.github.io/maths-cs-ai-compendium

## Overview
Most textbooks bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI. This is an open, unconventional textbook covering maths, computing, and artificial intelligence from the ground up. Written for curious practitioners looking to deeply understand the stuff, not just survive an exam/interview.
    COPY-PASTE FIX
    # Maths, CS & AI Compendium: An Unconventional Textbook & AI Assistant Knowledge Base

**Read online**: henryndubuaku.github.io/maths-cs-ai-compendium

## Overview
This is an open, unconventional textbook covering maths, computing, and artificial intelligence from the ground up, designed for curious practitioners looking to deeply understand these fields, not just pass an exam. Unlike traditional resources, it also includes an **MCP Server** that lets any AI assistant (Claude Code, Cursor, VS Code, etc.) use this compendium as a powerful, local knowledge base for research and coding.
  • mediumhomepage#2
    Add homepage URL to repository settings

    Why:

    COPY-PASTE FIX
    https://henryndubuaku.github.io/maths-cs-ai-compendium
  • mediumtopics#3
    Add topics for AI assistant integration and knowledge base

    Why:

    CURRENT
    ai-textbook, algorithms, artificial-intelligence, computer-science, computer-vision, deep-learning, jax, linear-algebra, machine-learning, machine-learning-algorithms, math, mathematics, multimodal-learning, nlp, probability, python, reinforcement-learning, speech-processing, statistics
    COPY-PASTE FIX
    ai-textbook, algorithms, artificial-intelligence, computer-science, computer-vision, deep-learning, jax, linear-algebra, machine-learning, machine-learning-algorithms, math, mathematics, multimodal-learning, nlp, probability, python, reinforcement-learning, speech-processing, statistics, ai-assistant, knowledge-base, rag, local-llm, coding-assistant

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 HenryNdubuaku/maths-cs-ai-compendium
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×
  2. Stanford CS229: Machine Learning · recommended 1×
  3. Stanford CS230: Deep Learning · recommended 1×
  4. Pattern Recognition and Machine Learning by Christopher Bishop · recommended 1×
  5. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive resource to master AI/ML concepts for research engineering interviews?
    you: not recommended
    AI recommended (in order):
    1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    2. Stanford CS229: Machine Learning
    3. Stanford CS230: Deep Learning
    4. Pattern Recognition and Machine Learning by Christopher Bishop
    5. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
    6. MIT 6.S191: Introduction to Deep Learning
    7. Papers With Code
    8. Hugging Face Transformers (huggingface/transformers)

    AI recommended 8 alternatives but never named HenryNdubuaku/maths-cs-ai-compendium. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow integrating an extensive AI/ML knowledge base directly into my coding assistant?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. ChromaDB
    5. LlamaIndex
    6. Haystack
    7. OpenAI API
    8. OpenAI Embeddings
    9. GPT-4
    10. GPT-3.5 Turbo
    11. PostgreSQL
    12. pgvector
    13. Elasticsearch

    AI recommended 13 alternatives but never named HenryNdubuaku/maths-cs-ai-compendium. 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 HenryNdubuaku/maths-cs-ai-compendium?
    pass
    AI did not name HenryNdubuaku/maths-cs-ai-compendium — 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 HenryNdubuaku/maths-cs-ai-compendium in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HenryNdubuaku/maths-cs-ai-compendium 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 HenryNdubuaku/maths-cs-ai-compendium solve, and who is the primary audience?
    pass
    AI did not name HenryNdubuaku/maths-cs-ai-compendium — 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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HenryNdubuaku/maths-cs-ai-compendium — 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