RRepoGEO

REPOGEO REPORT · LITE

lcylmhlcy/Awesome-algorithm-interview

Default branch master · commit fa69e636 · scanned 6/26/2026, 7:58:07 PM

GitHub: 3,020 stars · 453 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 lcylmhlcy/Awesome-algorithm-interview, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ['algorithm-interview', 'computer-vision', 'deep-learning', 'machine-learning', 'ai-engineer', 'interview-preparation', 'awesome-list', 'cv-interview']
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository, for example, using the MIT License template.
  • highreadme#3
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Awesome-algorithm-interview [](https://github.com/sindresorhus/awesome)   

> 算法工程师(人工智能cv方向)面试问题及相关资料的网站收集
    COPY-PASTE FIX
    # Awesome-algorithm-interview [](https://github.com/sindresorhus/awesome)   

A curated collection of interview questions and resources for AI Computer Vision (CV) algorithm engineers.

> 算法工程师(人工智能cv方向)面试问题及相关资料的网站收集

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 lcylmhlcy/Awesome-algorithm-interview
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LeetCode
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LeetCode · recommended 2×
  2. Computer Vision: Algorithms and Applications by Richard Szeliski · recommended 1×
  3. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×
  4. Papers With Code · recommended 1×
  5. OpenCV · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources for computer vision algorithm engineer interview preparation?
    you: not recommended
    AI recommended (in order):
    1. Computer Vision: Algorithms and Applications by Richard Szeliski
    2. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    3. Papers With Code
    4. OpenCV
    5. Coursera's "Deep Learning Specialization" by Andrew Ng
    6. LeetCode
    7. HackerRank
    8. Medium
    9. Google AI Blog
    10. Facebook AI Research Blog
    11. NVIDIA Developer Blog

    AI recommended 11 alternatives but never named lcylmhlcy/Awesome-algorithm-interview. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best deep learning and machine learning interview questions and study guides?
    you: not recommended
    AI recommended (in order):
    1. Cracking the Coding Interview
    2. Machine Learning Interview Questions
    3. Deep Learning Interviews
    4. Machine Learning System Design Interview
    5. Elements of Statistical Learning
    6. Deep Learning
    7. LeetCode

    AI recommended 7 alternatives but never named lcylmhlcy/Awesome-algorithm-interview. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 lcylmhlcy/Awesome-algorithm-interview?
    pass
    AI did not name lcylmhlcy/Awesome-algorithm-interview — 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 lcylmhlcy/Awesome-algorithm-interview in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lcylmhlcy/Awesome-algorithm-interview 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 lcylmhlcy/Awesome-algorithm-interview solve, and who is the primary audience?
    pass
    AI did not name lcylmhlcy/Awesome-algorithm-interview — 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

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MARKDOWN (README)
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lcylmhlcy/Awesome-algorithm-interview — 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