HitchHiker’s Guide to the AI

A practical, opinionated field guide to Artificial Intelligence.
Curated for builders, engineers, and curious minds who prefer signal over noise.

This repository is not an attempt to document everything about AI.
It is an attempt to document what is worth knowing, learning, and revisiting.

If AI feels overwhelming, fragmented, or excessively loud, this guide exists to slow things down.

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Who is this for?

• Engineers who want to build with AI, not just read about it
• DevOps and platform folks exploring AI in real systems
• Students looking for structured learning paths
• Founders and product thinkers who want practical context
• Curious humans who ask “okay, but how does this actually work?”

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How to use this guide

Think of this repo like a map, not a textbook.

• New to AI?
  Start with 👉 getting-started/

• Want to learn properly (without drowning)?
  Check 👉 learning-materials/ and learning-paths.md

• Looking for tools, frameworks, and infra knowledge?
  Explore 👉 tools-and-frameworks/

• Interested in real-world applications?
  Visit 👉 use-cases/

• Trying to stay updated without doomscrolling?
  Follow 👉 news-and-trends/

• Care about ethics, safety, and regulation?
  Read 👉 ethics-and-safety/

• Want to build things hands-on?
  Jump into 👉 hands-on/

You don’t need to read this linearly.
Wander. Bookmark. Return.

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What makes this repo different?

• ✂️ Curated: Fewer links, stronger opinions
• 🧠 Practical: Focus on understanding and usage, not buzzwords
• 🛠️ Builder-first: Emphasis on tools, systems, and real constraints
• 🧭 Evolving: Updated as understanding improves

Every link should answer one question:

Why is this worth my time?

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Structure overview

ai-guide/
├── README.md
├── getting-started/
│   ├── what-is-ai.md
│   ├── learning-paths.md
│   └── math-you-actually-need.md
├── learning-materials/
│   ├── courses.md
│   ├── books.md
│   ├── research-papers.md
│   └── youtube-channels.md
├── tools-and-frameworks/
│   ├── llms.md
│   ├── ml-frameworks.md
│   ├── devops-ai.md
│   └── open-source-tools.md
├── use-cases/
│   ├── devops.md
│   ├── healthcare.md
│   ├── finance.md
│   └── startups.md
├── news-and-trends/
│   ├── daily-sources.md
│   ├── newsletters.md
│   └── people-to-follow.md
├── ethics-and-safety/
│   ├── ai-risks.md
│   └── regulations.md
├── hands-on/
│   ├── projects.md
│   ├── datasets.md
│   └── notebooks.md
└── contributing.md

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Ground rules for this guide

• No link dumping
• No trend-chasing without context
• No pretending certainty where there is none
• Curiosity > hype
• Understanding > memorization

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Contributing

Contributions are welcome, if they add clarity.

Before adding anything, ask:

• Does this teach something?
• Does this reduce confusion?
• Would I recommend this to a friend?

See 👉 contributing.md for guidelines.

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Final note

AI is moving fast.
Understanding still takes time.

This guide is an attempt to move a little slower, but a lot deeper.

And yes, bringing a towel is still recommended.

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