REPOGEO REPORT · LITE

pageman/sutskever-30-implementations

Default branch main · commit 225cba7b · scanned 6/30/2026, 1:52:59 PM

GitHub: 3,281 stars · 446 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

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 pageman/sutskever-30-implementations, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Strengthen README opening to emphasize educational, NumPy-only focus

Why:

CURRENT

# Sutskever 30 - Complete Implementation Suite

**Comprehensive toy implementations of the 30 foundational papers recommended by Ilya Sutskever**

COPY-PASTE FIX

# Sutskever 30 - Foundational Papers in NumPy (Educational Implementations)

**Comprehensive, NumPy-only implementations of the 30 foundational deep learning papers recommended by Ilya Sutskever, designed for interactive learning and understanding core concepts without frameworks.**

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a `LICENSE` file in the repository root with the text of the MIT License.
```

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 pageman/sutskever-30-implementations

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

SethWeidman/deep-learning-from-scratch

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

SethWeidman/deep-learning-from-scratch · recommended 1×
mnielsen/neural-networks-and-deep-learning · recommended 1×
Stanford CS231n: Convolutional Neural Networks for Visual Recognition · recommended 1×
makeyourownneuralnetwork/makeyourownneuralnetwork · recommended 1×
fchollet/deep-learning-with-python-notebooks · recommended 1×

CATEGORY QUERY
How to learn deep learning fundamentals using only NumPy implementations?
you: not recommended
AI recommended (in order):
1. Deep Learning from Scratch by Seth Weidman (SethWeidman/deep-learning-from-scratch)
2. Neural Networks and Deep Learning by Michael Nielsen (mnielsen/neural-networks-and-deep-learning)
3. Stanford CS231n: Convolutional Neural Networks for Visual Recognition
4. Make Your Own Neural Network by Tariq Rashid (makeyourownneuralnetwork/makeyourownneuralnetwork)
5. Deep Learning with Python by François Chollet (fchollet/deep-learning-with-python-notebooks)
AI recommended 5 alternatives but never named pageman/sutskever-30-implementations. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find educational code examples for classic machine learning research papers?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. GitHub
3. Awesome Machine Learning/Deep Learning Lists
4. Kaggle
5. Towards Data Science
6. Fast.ai
7. DeepLearning.AI
AI recommended 7 alternatives but never named pageman/sutskever-30-implementations. 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 pageman/sutskever-30-implementations?
pass
AI named pageman/sutskever-30-implementations explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts pageman/sutskever-30-implementations in production, what risks or prerequisites should they evaluate first?
pass
AI named pageman/sutskever-30-implementations 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 pageman/sutskever-30-implementations solve, and who is the primary audience?
pass
AI did not name pageman/sutskever-30-implementations — 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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MARKDOWN (README)

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pageman/sutskever-30-implementations — 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