RRepoGEO

REPOGEO REPORT · LITE

Infatoshi/fcc-intro-to-llms

Default branch main · commit 86df20cc · scanned 6/16/2026, 4:23:02 AM

GitHub: 833 stars · 338 forks

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 Infatoshi/fcc-intro-to-llms, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A FreeCodeCamp.org course on building Large Language Models (LLMs) from fundamental principles, including practical implementation with PyTorch and Jupyter notebooks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a `LICENSE` file to the repository root, containing the text of your chosen open-source license (e.g., 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 Infatoshi/fcc-intro-to-llms
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neural Networks from Scratch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Neural Networks from Scratch · recommended 1×
  2. Deep Learning · recommended 1×
  3. Attention Is All You Need · recommended 1×
  4. The Illustrated Transformer · recommended 1×
  5. Let's build GPT: from scratch, in code, spelled out. · recommended 1×
  • CATEGORY QUERY
    How can I learn to build a large language model from fundamental principles?
    you: not recommended
    AI recommended (in order):
    1. Neural Networks from Scratch
    2. Deep Learning
    3. Attention Is All You Need
    4. The Illustrated Transformer
    5. Let's build GPT: from scratch, in code, spelled out.
    6. Language Models are Unsupervised Multitask Learners
    7. Hugging Face Transformers Library

    AI recommended 7 alternatives but never named Infatoshi/fcc-intro-to-llms. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best platforms and tools for training LLMs on limited hardware?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. Hugging Face Transformers (huggingface/transformers)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. JAX (google/jax)
    7. Flax (google/flax)
    8. Orbax (google/orbax)
    9. RunPod
    10. Vast.ai
    11. Paperspace Gradient

    AI recommended 11 alternatives but never named Infatoshi/fcc-intro-to-llms. 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 Infatoshi/fcc-intro-to-llms?
    pass
    AI did not name Infatoshi/fcc-intro-to-llms — 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 Infatoshi/fcc-intro-to-llms in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Infatoshi/fcc-intro-to-llms 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 Infatoshi/fcc-intro-to-llms solve, and who is the primary audience?
    pass
    AI did not name Infatoshi/fcc-intro-to-llms — 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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Infatoshi/fcc-intro-to-llms — 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