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hemansnation/AI-Engineer-Headquarters

默认分支 master · commit fd5c64d5 · 扫描时间 2026/6/21 18:08:09

星标 3,666 · Fork 702

本仓库扫描历史

下方为分数趋势（含全部就绪扫描；左旧右新，可横向滚动）。表格明细默认折叠，展开后每页 10 条，最新在上。

分数趋势（左 → 右：旧 → 新）

共 2 条就绪扫描。点击下方按钮展开表格（每页 10 条，可翻页）。

AI 可见性总分
22 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 1 · 警告 1 · 失败 0
客观元数据检查
AI 认识你的名字
1 / 3
直接询问时，AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试：当用户向 AI 提一个不带品牌、本应让 hemansnation/AI-Engineer-Headquarters 浮出水面的问题时，AI 是真的推荐了你，还是推荐了你的竞品？客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highreadme#1
    Reposition README's opening paragraph to clarify its purpose as an advanced AI engineering guide.

    原因：

    当前
    A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This is a drill for people who aim to be in the top 1% of Data and AI experts.
    复制粘贴的修复
    This repository is the **AI Engineer Headquarters**, a meticulously curated and advanced learning path and resource collection for aspiring and current AI professionals aiming to be in the top 1% of Data and AI experts. Unlike generic courses or tools, it provides a structured 'drill' of scientific methods, processes, algorithms, and systems essential for building robust AI models and data-driven solutions.
  • highlicense#2
    Add a LICENSE file to the repository.

    原因：

    复制粘贴的修复
    (Choose and add a standard open-source license file, e.g., MIT, Apache-2.0, or GPL-3.0, to the repository root.)
  • mediumabout#3
    Update the repository's About description for clarity.

    原因：

    当前
    A collection of scientific methods, processes, algorithms, and systems to build stories & models.
    复制粘贴的修复
    A curated, advanced learning path and resource collection for aspiring and current AI engineers, focusing on scientific methods, processes, algorithms, and systems to build robust AI models and data-driven solutions.

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你，还是推荐了别人？

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 hemansnation/AI-Engineer-Headquarters
平均排名
—
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中，你占了多少？
头号对手
Kaggle
在 2 个问题中被推荐 2 次
竞品排行
  1. Kaggle · 被推荐 2 次
  2. MIT OpenCourseware - Mathematics for Computer Science (6.042J) · 被推荐 1 次
  3. Khan Academy · 被推荐 1 次
  4. Python for Everybody (University of Michigan on Coursera) · 被推荐 1 次
  5. Automate the Boring Stuff with Python (Al Sweigart) · 被推荐 1 次
  • 品类问题
    Where can I find a comprehensive learning path for becoming a top AI engineer?
    你：未被推荐
    AI 推荐顺序：
    1. MIT OpenCourseware - Mathematics for Computer Science (6.042J)
    2. Khan Academy
    3. Python for Everybody (University of Michigan on Coursera)
    4. Automate the Boring Stuff with Python (Al Sweigart)
    5. HackerRank
    6. LeetCode
    7. Andrew Ng's Machine Learning (Coursera)
    8. DeepLearning.AI TensorFlow in Practice Specialization (Coursera)
    9. TensorFlow
    10. Scikit-Learn
    11. Keras
    12. Deep Learning Specialization (Andrew Ng on Coursera)
    13. fast.ai Practical Deep Learning for Coders
    14. Stanford CS224n: Natural Language Processing with Deep Learning
    15. Stanford CS231n: Convolutional Neural Networks for Visual Recognition
    16. "Speech and Language Processing" (Daniel Jurafsky and James H. Martin)
    17. Reinforcement Learning Specialization (University of Alberta on Coursera)
    18. "Reinforcement Learning: An Introduction" (Richard S. Sutton and Andrew G. Barto)
    19. Kaggle
    20. GitHub
    21. Hugging Face's Transformers library
    22. Kubeflow
    23. MLflow
    24. AWS SageMaker
    25. Google Cloud AI Platform

    AI 推荐了 25 个替代方案，却始终没点名 hemansnation/AI-Engineer-Headquarters。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    What are the best practical resources for mastering data science and machine learning concepts?
    你：未被推荐
    AI 推荐顺序：
    1. Coursera
    2. DeepLearning.AI
    3. IBM
    4. Google
    5. Kaggle
    6. Kaggle Learn
    7. Pandas (pandas-dev/pandas)
    8. Scikit-learn (scikit-learn/scikit-learn)
    9. fast.ai (fastai/fastai)
    10. Keras (keras-team/keras)
    11. TensorFlow (tensorflow/tensorflow)
    12. Udemy
    13. ChatGPT
    14. Towards Data Science
    15. StatQuest with Josh Starmer
    16. freeCodeCamp.org (freeCodeCamp/freeCodeCamp)

    AI 推荐了 16 个替代方案，却始终没点名 hemansnation/AI-Engineer-Headquarters。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    warn

    建议：

  • README presence
    pass

自指检查

当被直接问到你时，AI 是否还知道你的仓库存在？

  • Compared to common alternatives in this category, what is the core differentiator of hemansnation/AI-Engineer-Headquarters?
    pass
    AI 未点名 hemansnation/AI-Engineer-Headquarters —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • If a team adopts hemansnation/AI-Engineer-Headquarters in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 hemansnation/AI-Engineer-Headquarters

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • In one sentence, what problem does the repo hemansnation/AI-Engineer-Headquarters solve, and who is the primary audience?
    pass
    AI 未点名 hemansnation/AI-Engineer-Headquarters —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

嵌入你的 GEO 徽章

把这个徽章贴进 hemansnation/AI-Engineer-Headquarters 的 README。每次重新扫描都会自动更新，并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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Pro

订阅 Pro，解锁深度诊断

hemansnation/AI-Engineer-Headquarters — 轻量扫描仍免费；本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5，轻量 2
  • 优先行动项8，轻量 3