RRepoGEO

REPOGEO REPORT · LITE

Exorust/TorchLeet

Default branch main · commit 23b78d12 · scanned 5/14/2026, 7:07:42 PM

GitHub: 2,044 stars · 263 forks

AI VISIBILITY SCORE
30 /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
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 Exorust/TorchLeet, 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
    Clarify project's core purpose and language in README intro

    Why:

    CURRENT
    > I struggled to grind for ML/AI interviews so I went back to the basics and created a list after careful research. 

TorchLeet is broken into two sets of questions:
    COPY-PASTE FIX
    TorchLeet is a collection of PyTorch practice problems and LLM implementation challenges, designed to help you prepare for ML/AI interviews. It's broken into two sets of questions:
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    pytorch, deep-learning, machine-learning, llm, interview-preparation, leetcode, practice-problems, education
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Choose an appropriate open-source license like MIT or Apache-2.0 and add it as a LICENSE file in the repository root.)

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 Exorust/TorchLeet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. DeepLearning.AI PyTorch Specialization · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. Fast.ai's "Practical Deep Learning for Coders" · recommended 1×
  • CATEGORY QUERY
    Where can I find practice problems to improve my PyTorch and deep learning skills for interviews?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. PyTorch Examples (pytorch/pytorch)
    3. DeepLearning.AI PyTorch Specialization
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. Fast.ai's "Practical Deep Learning for Coders"
    6. fastai library (fastai/fastai)
    7. LeetCode

    AI recommended 7 alternatives but never named Exorust/TorchLeet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources exist for practicing LLM implementation and understanding large language models from scratch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. minGPT (karpathy/minGPT)
    3. nanoGPT (karpathy/nanoGPT)
    4. PyTorch
    5. PyTorch Lightning
    6. TensorFlow
    7. Keras
    8. transformers-from-scratch (SethWeidman/transformers-from-scratch)
    9. The Annotated Transformer (harvardnlp/annotated-transformer)
    10. OpenAI API
    11. Anthropic's Claude
    12. Google's Gemini

    AI recommended 12 alternatives but never named Exorust/TorchLeet. 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 Exorust/TorchLeet?
    pass
    AI named Exorust/TorchLeet explicitly

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

  • If a team adopts Exorust/TorchLeet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Exorust/TorchLeet 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 Exorust/TorchLeet solve, and who is the primary audience?
    pass
    AI named Exorust/TorchLeet explicitly

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

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Exorust/TorchLeet — 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