Learnetto AI directory

Find useful AI educators, resources, and learning paths.

A compact directory for people who want practical AI skills: prompting, automation, AI product work, coding with AI, RAG, evals, model internals, and production AI engineering.

129

Educators

Paths

184

Resources

Watch first

Fast entry points for prompting, agents, RAG, evals, model internals, and production AI.

All videos and resources

Prompt engineering for developers

DeepLearning.AI · prompting, api, developers

Agents for everything else

AI Engineer · agents, ai engineering, developer tools

LangGraph introduction

LangChain · agents, langgraph, llm orchestration

Hugging Face agents course

Hugging Face · agents, open models, tools

Machine learning crash course

Google for Developers · ml foundations, classification, neural networks

Neural networks from scratch

Andrej Karpathy · model internals, neural networks, coding

Educators to start with

Sorted for range: founders, product teams, developers, evals, model internals, and production systems.

Full educator directory

Educator	Best for	Skills	Start with
Josh Pigford Initial Commit	Indie founders, product builders	Founder workflows, Product ops, Automation	Read the public notes and examples before deciding whether the paid material matches your business.
Brian Casel Builder Methods	Founders, service business owners	Founder workflows, Systems, Automation	Look for workflow breakdowns and implementation examples.
Peter Yang Become an AI Powered Product Leader	Product managers, founders	Product strategy, Writing, AI adoption	Review the Maven syllabus and compare it to your current product workflow.
Lenny Rachitsky How I AI	Product teams, founders	Product, Growth, Team adoption	Browse the How I AI interviews and copy the workflows that match your role.
Ethan Mollick One Useful Thing	Knowledge workers, educators, leaders	AI literacy, Workplace adoption, Strategy	Read recent essays on using AI as a collaborator and on organizational adoption.
Swyx AI Engineer	Developers, AI engineers	AI engineering, Agents, Developer tools	Watch AI Engineer talks for production patterns and tool choices.
Andrew Ng DeepLearning.AI Short Courses	Everyone from beginners to builders	Prompting, Agents, RAG, ML foundations	Start with ChatGPT Prompt Engineering for Developers, then pick a RAG or agents course.
Jeremy Howard fast.ai	Coders learning deep learning	Deep learning, PyTorch, Model training	Practical Deep Learning for Coders, lesson 1.
$Rachel Thomas profile photo$ Rachel Thomas fast.ai	Developers, data scientists	Practical ML, Ethics, Education	Use fast.ai essays and course material alongside hands-on notebooks.
Andrej Karpathy Neural Networks: Zero to Hero	Programmers who want model internals	Neural networks, Backprop, LLM internals	Watch micrograd, then makemore, then the GPT video.
Simon Willison Simon Willison on LLMs	Developers, technical generalists	LLM tools, Prompting, AI safety, Local models	Read recent LLM posts and try the llm command-line tool.
Jason Liu Instructor	Developers building LLM apps	Structured outputs, Extraction, RAG	Try the Instructor examples for extraction and validation.

Choose by skill

Use these as quick routes into the directory.

All learning paths

AI foundations

Useful for: Everyone

Learn: What LLMs can and cannot do; Tokens, context windows, hallucinations; Privacy and data handling

Resources: DeepLearning.AI, Learn Prompting, Ethan Mollick

Prompting and writing

Useful for: Writers, operators, PMs, founders

Learn: Clear task framing; Examples and constraints; Editing, synthesis, and critique loops

Resources: Learn Prompting, DAIR.AI, Peter Yang

Automation and agents

Useful for: Founders, operations teams, developers

Learn: Trigger-based workflows; Tool calling; Agent failure modes; Human review points

Resources: Brian Casel, Josh Pigford, AI Engineer

AI product work

Useful for: PMs, designers, founders

Learn: AI UX patterns; Use-case selection; Prototyping; Measuring quality

Resources: Peter Yang, Lenny Rachitsky, Shreya Shankar

Coding with AI

Useful for: Software developers

Learn: Codebase navigation; Test generation; Refactoring; Reviewing AI output

Resources: Simon Willison, Addy Osmani, AI Engineer

RAG and knowledge systems

Useful for: Developers building AI apps

Learn: Chunking and retrieval; Structured extraction; Reranking; Grounded answers

Resources: Jason Liu, Hamel Husain, Eugene Yan

Evals and reliability

Useful for: AI product teams

Learn: Test sets; Human review; Regression checks; Quality metrics

Resources: Hamel Husain, Shreya Shankar, Chip Huyen

Model internals

Useful for: Engineers who want deeper understanding

Learn: Neural nets from scratch; Transformers; Training loops; Fine-tuning basics

Resources: Andrej Karpathy, Sebastian Raschka, Jay Alammar

Resources worth opening

Courses, guides, repos, books, and video material with a practical reason to use each one.

Full resource library

Resource	Type	Level	Use when
Neural Networks: Zero to Hero Andrej Karpathy	Video course	Intermediate	You want to understand neural networks and language models from code.
Practical Deep Learning for Coders fast.ai	Free course	Intermediate	You can code and want a practical route into training models.
ChatGPT Prompt Engineering for Developers DeepLearning.AI	Short course	Beginner	You want a short, structured intro to prompting for software tasks.
OpenAI Cookbook OpenAI	GitHub repo	Beginner to advanced	You need implementation examples rather than theory.
Microsoft AI Agents for Beginners Microsoft	GitHub repo	Beginner to intermediate	You want a structured agent learning path with code.
Build a Large Language Model From Scratch Sebastian Raschka	GitHub repo / book	Intermediate	You want to build an LLM step by step.
The Illustrated Transformer Jay Alammar	Visual guide	Beginner to intermediate	Transformer architecture still feels fuzzy.
Learn Prompting Sander Schulhoff	Guide	Beginner to intermediate	You need a broad prompt engineering reference.