conorbronsdon/ai-learning-resources
Curated learning path from 'what is AI?' to building with Claude Code and MCP. Quality over quantity.
Platform-specific configuration:
{
"mcpServers": {
"ai-learning-resources": {
"command": "npx",
"args": [
"-y",
"ai-learning-resources"
]
}
}
}Add the config above to .claude/settings.json under the mcpServers key.
A curated learning path from "what even is AI?" to building your own AI-powered workflows. Quality over quantity — every resource here is the best of its kind.
This isn't a dump of links. It's a path. Start at Stage 1 and work forward, or jump to wherever you are.
Git: Claude Code and many AI tools use Git for version control. If you're new to it, GitHub's Git Handbook will get you up to speed in 10 minutes. Prefer video? Git Explained in 100 Seconds (Fireship, 3 min) covers the essentials.
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You don't need a CS degree. You need mental models for what's happening under the hood so you can reason about what AI can and can't do.
The single best general-audience walkthrough of how LLMs are built: pretraining, finetuning, RLHF. No technical background needed. If you watch one thing, make it this. *Shorter version: Intro to Large Language Models (1 hr)*
The gold-standard visual explainer. Starts with "what is a neural network?" and builds through backpropagation to transformers and attention. Chapters 5-7 cover LLMs specifically.
Deep, readable explainer covering embeddings, tokens, transformers, and the philosophical "why" behind next-token prediction. Also a book.
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