Learn

Four guided paths from beginner to advanced, built around tutorials, guides, and tools.

Path 1

AI-Powered Web Apps

Developers who want to build AI features into web apps

~8 hours

1
Set up your stack
The modern AI app stack: Next.js, Vercel AI SDK, and the tools that tie them together.
2
Learn the AI SDK
Streaming responses, tool calling, and structured output in TypeScript.
3
Build an AI agent
Go beyond chat. Build agents that reason, plan, and take action.
4
Understand RAG
Retrieval-augmented generation: give your AI app access to real data.
5
Take the course
Structured lessons with code examples, quizzes, and projects.
6
Explore tools
Browse the full AI tools directory to find what fits your stack.

Path 2

AI Coding Tools Mastery

Developers who want to code faster with AI

~6 hours

Path 3

AI Agent Development

Developers building autonomous agents

~10 hours

Path 4

Getting Started (Beginner)

Developers new to AI tools

~3 hours

Hands-On Tutorials

Pick a skill, then a tool. Each tutorial is a working walkthrough with code, common pitfalls, and what to build next.

Build a RAG pipeline

RAG

Tutorial

LangChain

LangChain gives you battle-tested abstractions for retrieval-augmented generation: document loaders, text splitters, vector stores, and retrievers. This walkthrough builds a working RAG pipeline in TypeScript that ingests your docs and answers questions over them with citations.

Tutorial

LlamaIndex

LlamaIndex is the data framework for LLM apps. It excels at structured ingestion: parsing PDFs, websites, Notion, and SQL into a queryable index. This guide builds a TypeScript RAG pipeline with the LlamaIndex.TS SDK.

Tutorial

Pinecone

Pinecone is a managed vector database built for scale. You get sub-100ms queries over billions of vectors without operating any infra.

Tutorial

pgvector

pgvector turns Postgres into a vector database. If you already run Postgres, this is the lowest-friction RAG store you can pick.

Build AI agents

Agents

Tutorial

Claude Code

Claude Code is Anthropic's terminal-native coding agent. You can extend it with custom skills, hooks, and MCP servers to build domain-specific agents that own real workflows.

Tutorial

LangGraph

LangGraph models agents as state machines. Instead of free-form ReAct loops, you define explicit nodes and edges, which makes complex flows debuggable and resumable.

Tutorial

CrewAI

CrewAI lets you compose multi-agent teams with role-based prompting. Each agent has a goal, a backstory, and tools - then a Crew orchestrates them.

Tutorial

OpenAI Agents SDK

The OpenAI Agents SDK is a minimal, opinionated framework for building agents on top of GPT models. It bundles tool calling, handoffs between agents, and tracing.

Build an MCP server

MCP

Tutorial

TypeScript

Model Context Protocol (MCP) lets you expose tools, resources, and prompts to any compatible client (Claude Code, Cursor, Claude Desktop). This guide builds a TypeScript MCP server.

Tutorial

Python

FastMCP is the easiest way to build an MCP server in Python. You decorate functions and the framework handles schemas, transport, and routing.

Fine-tune a language model

Fine-tuning

Tutorial

LoRA

LoRA (Low-Rank Adaptation) fine-tunes a model by training a tiny adapter instead of the full weight matrix. You get 90% of the quality at a fraction of the compute.

Tutorial

Unsloth

Unsloth is a drop-in optimization for LoRA fine-tuning that delivers 2-5x speedups and 60% less VRAM. Same code, faster runs.

Tutorial

MLX

MLX is Apple's array framework, optimized for Apple Silicon. mlx-lm fine-tunes LLMs on M-series Macs with unified memory.

Deploy an AI app

Deployment

Tutorial

Coolify

Coolify is a self-hosted Heroku alternative you run on a single VPS. Push to GitHub, Coolify builds and deploys.

Tutorial

Vercel

Vercel is the fastest way to ship a Next.js AI app: connect a repo, push to main, get a URL with edge caching, preview deploys, and built-in observability.

Tutorial

Railway

Railway is the sweet spot between Vercel's polish and Coolify's flexibility. You get hosted Postgres, Redis, and any Docker app from one dashboard.

Add voice to your AI app

Voice

Tutorial

ElevenLabs

ElevenLabs delivers the most natural-sounding TTS available - and a streaming API that lets you ship realtime voice agents.

Tutorial

Gladia

Gladia is a fast, accurate speech-to-text API with realtime streaming and speaker diarization built in.

Get structured output from LLMs

Structured Output

Tutorial

OpenAI

OpenAI's Structured Outputs feature guarantees your responses match a JSON schema. No more parsing failures or missing fields - the model output conforms to your schema or the request fails.

Tutorial

Anthropic

Anthropic's Claude models support structured output through tool use with forced tool calls. Define a schema as a tool, force Claude to use it, and extract the structured data from the tool call arguments.

Tutorial

Instructor

Instructor is the most popular library for structured LLM outputs. It patches the OpenAI and Anthropic SDKs to add automatic validation, retries, and streaming of Pydantic or Zod models.

Not sure where to start?

If you are new to AI development, start with Path 4. If you already know your way around, jump into the path that matches your goal.

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