RRepoGEO

REPOGEO REPORT · LITE

sunrainyg/RandOpt

Default branch main · commit 1a838c87 · scanned 5/29/2026, 3:03:03 PM

GitHub: 596 stars · 64 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 sunrainyg/RandOpt, 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 to clarify LLM post-training focus

    Why:

    CURRENT
    # RandOpt
**Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights**
    COPY-PASTE FIX
    # RandOpt
**Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights**

This repository provides the official codebase for RandOpt, a method for efficiently post-training large language models by creating diverse task experts around pretrained weights.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a `LICENSE` file to the repository root, specifying the chosen open-source license (e.g., MIT, Apache-2.0).
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to the project page: `https://yulugan.github.io/neural-thickets/`

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 sunrainyg/RandOpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face PEFT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face PEFT · recommended 1×
  2. bitsandbytes · recommended 1×
  3. Fairseq · recommended 1×
  4. Hugging Face transformers · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to efficiently post-train large language models for specific tasks using diverse experts?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT
    2. bitsandbytes
    3. Fairseq
    4. Hugging Face transformers
    5. PyTorch
    6. TensorFlow
    7. Hugging Face TRL
    8. trlx

    AI recommended 8 alternatives but never named sunrainyg/RandOpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods for black-box optimization to enhance reasoning capabilities in transformer models.
    you: not recommended
    AI recommended (in order):
    1. Spearmint
    2. BoTorch
    3. GPflowOpt

    AI recommended 3 alternatives but never named sunrainyg/RandOpt. 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 sunrainyg/RandOpt?
    pass
    AI named sunrainyg/RandOpt explicitly

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

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

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

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sunrainyg/RandOpt — 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