REPOGEO REPORT · LITE

b7leung/MLE-Flashcards

Default branch main · commit 2204f44b · scanned 5/25/2026, 7:38:14 AM

GitHub: 2,408 stars · 218 forks

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 b7leung/MLE-Flashcards, 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

Prominently state the flashcard format in the README introduction

Why:

CURRENT

250+ flashcards I made as an exercise & reference for myself, after from years of ML research, coursework, & independent study. Hopefully other people can benefit from them as well, for study or interview prep!

COPY-PASTE FIX

This repository contains 250+ detailed flashcards in **PowerPoint format**, designed for quick review and interview preparation after years of ML research, coursework, & independent study. These flashcards cover core concepts in machine learning, computer vision, and computer science.

mediumhomepage#2
Add a homepage URL to the repository settings
Why:
COPY-PASTE FIX
```
https://b7leung.github.io/MLE-Flashcards
```

lowtopics#3

Expand repository topics with specific ML/AI sub-fields

Why:

CURRENT

["ai", "artificial-intelligence", "computer-science", "computer-vision", "flashcards", "interview", "interview-preparation", "machine-learning", "review"]

COPY-PASTE FIX

["ai", "artificial-intelligence", "computer-science", "computer-vision", "deep-learning", "flashcards", "generative-ai", "interview", "interview-preparation", "large-language-models", "machine-learning", "natural-language-processing", "reinforcement-learning", "review", "vision-language-models"]

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 b7leung/MLE-Flashcards

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Cracking the Coding Interview

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Cracking the Coding Interview · recommended 1×
Deep Learning Specialization · recommended 1×
Machine Learning · recommended 1×
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow · recommended 1×
Computer Vision: Algorithms and Applications · recommended 1×

CATEGORY QUERY
How can I quickly review core machine learning and computer vision concepts for interviews?
you: not recommended
AI recommended (in order):
1. Cracking the Coding Interview
2. Deep Learning Specialization
3. Machine Learning
4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
5. Computer Vision: Algorithms and Applications
6. LeetCode
7. InterviewBit
8. Towards Data Science
9. Wikipedia
10. Google Scholar
AI recommended 10 alternatives but never named b7leung/MLE-Flashcards. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good resources for experienced practitioners to refresh advanced AI and ML topics?
you: not recommended
AI recommended (in order):
1. DeepLearning.AI Specializations
2. fast.ai Practical Deep Learning for Coders
3. MIT OpenCourseWare (OCW)
4. Stanford CS224n
5. Papers With Code
6. O'Reilly Media
7. ArXiv.org
8. ArXiv Sanity Preserver
AI recommended 8 alternatives but never named b7leung/MLE-Flashcards. 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 b7leung/MLE-Flashcards?
pass
AI did not name b7leung/MLE-Flashcards — 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 b7leung/MLE-Flashcards in production, what risks or prerequisites should they evaluate first?
pass
AI named b7leung/MLE-Flashcards 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 b7leung/MLE-Flashcards solve, and who is the primary audience?
pass
AI did not name b7leung/MLE-Flashcards — 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 b7leung/MLE-Flashcards. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/b7leung/MLE-Flashcards.svg)](https://repogeo.com/en/r/b7leung/MLE-Flashcards)

HTML

<a href="https://repogeo.com/en/r/b7leung/MLE-Flashcards"><img src="https://repogeo.com/badge/b7leung/MLE-Flashcards.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite