RRepoGEO

REPOGEO REPORT · LITE

therealoliver/Deepdive-llama3-from-scratch

Default branch main · commit c4f85135 · scanned 6/13/2026, 12:49:09 PM

GitHub: 629 stars · 52 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 therealoliver/Deepdive-llama3-from-scratch, 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
    Reposition the README's opening paragraph to explicitly state its educational 'from-scratch' nature

    Why:

    CURRENT
    This project is an enhanced version based on naklecha/llama3-from-scratch. It has been comprehensively improved and optimized on the basis of the original project, aiming to help everyone more easily understand and master the implementation principle and the detailed reasoning process of the Llama3 model.
    COPY-PASTE FIX
    This project is a comprehensive, step-by-step educational guide to implementing the Llama 3 inference process entirely from scratch. It aims to help developers and researchers deeply understand and master the core principles and detailed reasoning behind the Llama 3 model, building upon and enhancing the original naklecha/llama3-from-scratch project.
  • mediumabout#2
    Refine the repository description to emphasize its 'educational guide' and 'from scratch' aspects

    Why:

    CURRENT
    Achieve the llama3 inference step-by-step, grasp the core concepts, master the process derivation, implement the code.
    COPY-PASTE FIX
    A step-by-step educational guide to implementing Llama 3 inference from scratch, designed to help you grasp core concepts, master process derivation, and implement the code yourself.
  • lowtopics#3
    Add 'from-scratch' to the repository topics

    Why:

    CURRENT
    attention, attention-mechanism, gpt, inference, kv-cache, language-model, llama, llm-configuration, llms, mask, multi-head-attention, positional-encoding, residuals, rms, rms-norm, rope, rotary-position-encoding, swiglu, tokenizer, transformer
    COPY-PASTE FIX
    attention, attention-mechanism, from-scratch, gpt, inference, kv-cache, language-model, llama, llm-configuration, llms, mask, multi-head-attention, positional-encoding, residuals, rms, rms-norm, rope, rotary-position-encoding, swiglu, tokenizer, transformer

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 therealoliver/Deepdive-llama3-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anaconda/Miniconda
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Anaconda/Miniconda · recommended 1×
  2. CUDA Toolkit · recommended 1×
  3. pip · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How to understand and implement large language model inference step-by-step?
    you: not recommended
    AI recommended (in order):
    1. Anaconda/Miniconda
    2. CUDA Toolkit
    3. pip
    4. Hugging Face Transformers (huggingface/transformers)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)
    7. Hugging Face Optimum (huggingface/optimum)
    8. bitsandbytes (TimDettmers/bitsandbytes)
    9. AutoGPTQ (PanQiWei/AutoGPTQ)
    10. ExLlamaV2 (turboderp/exllamav2)
    11. ONNX Runtime (microsoft/onnxruntime)
    12. vLLM (vllm-project/vllm)
    13. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    14. Hugging Face TGI (huggingface/text-generation-inference)
    15. FastAPI (tiangolo/fastapi)
    16. Triton Inference Server (triton-inference-server/server)
    17. Hugging Face PEFT (huggingface/peft)
    18. LangChain (langchain-ai/langchain)
    19. LlamaIndex (run-llama/llama_index)

    AI recommended 19 alternatives but never named therealoliver/Deepdive-llama3-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Guide to implementing transformer attention mechanisms and advanced LLM components from scratch.
    you: not recommended
    AI recommended (in order):
    1. NumPy
    2. JAX
    3. PyTorch
    4. TensorFlow
    5. Hugging Face Transformers
    6. OpenAI Triton

    AI recommended 6 alternatives but never named therealoliver/Deepdive-llama3-from-scratch. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 therealoliver/Deepdive-llama3-from-scratch?
    pass
    AI named therealoliver/Deepdive-llama3-from-scratch explicitly

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

  • If a team adopts therealoliver/Deepdive-llama3-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named therealoliver/Deepdive-llama3-from-scratch 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 therealoliver/Deepdive-llama3-from-scratch solve, and who is the primary audience?
    pass
    AI did not name therealoliver/Deepdive-llama3-from-scratch — 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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therealoliver/Deepdive-llama3-from-scratch — 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