RRepoGEO

REPOGEO REPORT · LITE

VHellendoorn/Code-LMs

Default branch main · commit 570feba4 · scanned 5/19/2026, 9:22:59 AM

GitHub: 1,841 stars · 263 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)

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

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 VHellendoorn/Code-LMs, 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 README's initial description to clarify its role as a research framework

    Why:

    CURRENT
    I occasionally train and publicly release large neural language models on programs, including PolyCoder. Here, I describe how to use these.
    COPY-PASTE FIX
    This repository serves as a comprehensive guide and research framework for leveraging, evaluating, and comparing large language models of source code, including PolyCoder and other state-of-the-art models.
  • hightopics#2
    Update repository topics for better categorization

    Why:

    CURRENT
    deep-learning, gpt-2, source-code
    COPY-PASTE FIX
    deep-learning, source-code, code-llms, code-generation, language-models, benchmarking, evaluation, research-framework
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://[your-project-page-or-relevant-research-paper-link]

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 VHellendoorn/Code-LMs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Codex / GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Codex / GitHub Copilot · recommended 1×
  2. Google AlphaCode · recommended 1×
  3. Hugging Face Transformers Library · recommended 1×
  4. CodeT5 · recommended 1×
  5. CodeBERT · recommended 1×
  • CATEGORY QUERY
    How can I leverage deep learning models for automated source code generation tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Codex / GitHub Copilot
    2. Google AlphaCode
    3. Hugging Face Transformers Library
    4. CodeT5
    5. CodeBERT
    6. GPT-NeoX-20B
    7. Salesforce CodeGen
    8. DeepMind's Gato
    9. TabNine
    10. Amazon CodeWhisperer

    AI recommended 10 alternatives but never named VHellendoorn/Code-LMs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find pre-trained language models optimized for programming language understanding?
    you: not recommended
    AI recommended (in order):
    1. Code Llama
    2. GPT-4
    3. GPT-3.5 Turbo
    4. StarCoder (bigcode-project/starcoder)
    5. StarCoder2 (bigcode-project/starcoder2)
    6. InCoder (facebookresearch/InCoder)
    7. CodeBERT (microsoft/CodeBERT)
    8. PLBART (microsoft/CodeXGLUE)
    9. UniXcoder (microsoft/UniXcoder)

    AI recommended 9 alternatives but never named VHellendoorn/Code-LMs. 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 VHellendoorn/Code-LMs?
    pass
    AI named VHellendoorn/Code-LMs explicitly

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

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

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

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VHellendoorn/Code-LMs — 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