RRepoGEO

REPOGEO REPORT · LITE

qibin0506/Cortex

Default branch master · commit 9ebe1d85 · scanned 5/28/2026, 4:12:43 PM

GitHub: 2,657 stars · 207 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 qibin0506/Cortex, 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
    Add a direct, one-sentence project purpose statement at the very top of the README

    Why:

    CURRENT
    The project's core purpose is introduced under `## 📖 项目简介`.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title (and before any badges/links):
`Cortex 是一个致力于让个人开发者也能承担训练成本的 LLM 项目，实现了从零开始构建大模型的全过程，代码完全开源且解耦。`
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["large-language-model", "llm-training", "pretraining", "rlhf", "moe", "deep-learning", "machine-learning", "ai", "llm-as-judge", "open-source-llm", "chinese-chip-adaptation"]
  • mediumreadme#3
    Add a concise 'Why Cortex?' section highlighting key differentiators

    Why:

    CURRENT
    Key features are listed under `### 🌟 Cortex 3.1 核心特性`.
    COPY-PASTE FIX
    Add a new section, e.g., `## ✨ Why Cortex?` or `## 🚀 核心优势`, near the top of the README (after the initial purpose statement), summarizing the key differentiators:
`Cortex 致力于让个人开发者也能承担训练成本，通过极致轻量 MoE 架构实现低算力设备上的超高推理吞吐，并引入 LLM as Judge 驱动的 PPO 训练，同时支持国产芯片适配，提供从零构建大模型的完整开源实践。`

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 qibin0506/Cortex
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Accelerate · recommended 2×
  3. PEFT · recommended 2×
  4. DeepSpeed · recommended 2×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    What open-source frameworks enable building a complete large language model with limited compute resources?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. bitsandbytes
    3. Accelerate
    4. PEFT
    5. PyTorch Lightning
    6. DeepSpeed
    7. Megatron-LM

    AI recommended 7 alternatives but never named qibin0506/Cortex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement a lightweight MoE large language model and use LLM as Judge for alignment?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. PEFT
    4. LoRA
    5. QLoRA
    6. Llama 2
    7. Mistral
    8. DeepSpeed
    9. Fairseq
    10. PyTorch FSDP
    11. OpenAI API
    12. GPT-4
    13. GPT-3.5 Turbo
    14. Anthropic Claude
    15. Opus
    16. Sonnet
    17. Haiku
    18. Google Gemini API
    19. Gemini 1.5 Pro
    20. Mistral Large
    21. Mixtral 8x7B
    22. vLLM

    AI recommended 22 alternatives but never named qibin0506/Cortex. 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 qibin0506/Cortex?
    pass
    AI named qibin0506/Cortex explicitly

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

  • If a team adopts qibin0506/Cortex in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qibin0506/Cortex 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 qibin0506/Cortex solve, and who is the primary audience?
    pass
    AI named qibin0506/Cortex explicitly

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

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qibin0506/Cortex — 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