RRepoGEO

REPOGEO REPORT · LITE

no-magic-ai/no-magic

Default branch main · commit 9a876115 · scanned 5/28/2026, 1:52:10 PM

GitHub: 1,334 stars · 104 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 no-magic-ai/no-magic, 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 H1 and opening paragraph to clarify educational purpose

    Why:

    CURRENT
    # no-magic

**Because `model.fit()` isn't an explanation.**

<video src="https://github.com/user-attachments/assets/f107ed4c-6905-4063-b3f6-a4a3c2f16c8e" width="100%" autoplay loop muted playsinline></video>

## What This Is

`no-magic` is a curated collection of single-file, dependency-free Python implementations of the algorithms that power modern AI.
    COPY-PASTE FIX
    # no-magic: Learn AI Algorithms From Scratch

**Because `model.fit()` isn't an explanation.** This repository offers a curated collection of single-file, dependency-free Python implementations of core AI algorithms, designed to demystify modern AI concepts without frameworks or hidden complexity.
  • hightopics#2
    Add specific educational and 'from scratch' topics, correct typo

    Why:

    CURRENT
    ai-algorithms, algorithms, no-dependencies, open-soruce
    COPY-PASTE FIX
    ai-algorithms, algorithms, no-dependencies, open-source, machine-learning-from-scratch, deep-learning-from-scratch, python-algorithms, ai-education, educational-code
  • mediumabout#3
    Update the repository description for clarity

    Why:

    CURRENT
    Because `model.fit()` isn't an explanation
    COPY-PASTE FIX
    Dependency-free Python implementations of core AI algorithms for learning from scratch. Understand modern AI without frameworks or hidden complexity.

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 no-magic-ai/no-magic
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NumPy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NumPy · recommended 1×
  2. SciPy · recommended 1×
  3. scikit-learn · recommended 1×
  4. Python's Standard Library · recommended 1×
  5. math · recommended 1×
  • CATEGORY QUERY
    How to learn core AI algorithms without relying on complex deep learning frameworks?
    you: not recommended
    AI recommended (in order):
    1. NumPy
    2. SciPy
    3. scikit-learn
    4. Python's Standard Library
    5. math
    6. collections
    7. Julia
    8. R
    9. stats
    10. C++
    11. Eigen
    12. Armadillo

    AI recommended 12 alternatives but never named no-magic-ai/no-magic. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking simple, dependency-free Python implementations to understand fundamental AI concepts.
    you: not recommended
    AI recommended (in order):
    1. Make Your Own Neural Network by Tariq Rashid
    2. Neural Networks and Deep Learning by Michael Nielsen
    3. Machine Learning from Scratch by Erik Linder-Norén (eriklindernoren/ML-From-Scratch)
    4. Data Science from Scratch by Joel Grus
    5. Artificial Intelligence: A Modern Approach (AIMA) Python code

    AI recommended 5 alternatives but never named no-magic-ai/no-magic. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 no-magic-ai/no-magic?
    pass
    AI named no-magic-ai/no-magic explicitly

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

  • If a team adopts no-magic-ai/no-magic in production, what risks or prerequisites should they evaluate first?
    pass
    AI named no-magic-ai/no-magic 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 no-magic-ai/no-magic solve, and who is the primary audience?
    pass
    AI named no-magic-ai/no-magic explicitly

    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 no-magic-ai/no-magic. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/no-magic-ai/no-magic.svg)](https://repogeo.com/en/r/no-magic-ai/no-magic)
HTML
<a href="https://repogeo.com/en/r/no-magic-ai/no-magic"><img src="https://repogeo.com/badge/no-magic-ai/no-magic.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

no-magic-ai/no-magic — 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