RRepoGEO

REPOGEO REPORT · LITE

zakirullin/gpt-go

Default branch main · commit 9ddbb464 · scanned 6/3/2026, 2:08:16 PM

GitHub: 642 stars · 46 forks

AI VISIBILITY SCORE
35 /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
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 zakirullin/gpt-go, 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
  • highreadme#1
    Reposition the README's opening to clarify purpose

    Why:

    CURRENT
    # gpt-go
    Simple GPT implementation in pure Go. Trained on favourite Jules Verne books.
    COPY-PASTE FIX
    # gpt-go
    A minimal, from-scratch GPT implementation in pure Go, designed for learning and understanding the architecture. Trained on Jules Verne books, this project serves as a companion to 'Neural Networks: Zero to Hero' and provides detailed explanations.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/zakirullin/gpt-go

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 zakirullin/gpt-go
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Gorgonia
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Gorgonia · recommended 1×
  2. GoNum · recommended 1×
  3. TensorFlow Go · recommended 1×
  4. karpathy/llm.c · recommended 1×
  5. gorgonia/gorgonia · recommended 1×
  • CATEGORY QUERY
    How to implement a basic generative pre-trained transformer model in pure Go?
    you: not recommended
    AI recommended (in order):
    1. Gorgonia
    2. GoNum
    3. TensorFlow Go

    AI recommended 3 alternatives but never named zakirullin/gpt-go. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a minimal Go project for learning the fundamentals of GPT architecture.
    you: not recommended
    AI recommended (in order):
    1. karpathy/llm.c (karpathy/llm.c)
    2. gorgonia/gorgonia (gorgonia/gorgonia)
    3. Build a Large Language Model (from scratch) by Jeremy Howard
    4. gonum/matrix (gonum/matrix)

    AI recommended 4 alternatives but never named zakirullin/gpt-go. 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 zakirullin/gpt-go?
    pass
    AI named zakirullin/gpt-go explicitly

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

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

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

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zakirullin/gpt-go — 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