The complete AI curriculum
24 lessons in 6 tracks — from what an LLM is to building agents, automating workflows, running private AI and selling AI services. Each lesson has clear objectives, step-by-step instructions, runnable code, a knowledge check and official links.
AI Foundations
How AI and LLMs actually work, choosing assistants, and prompting like a pro.
What AI Really Is
Cut through the hype. Understand AI, machine learning, deep learning and generative AI — what each one is, how they relate, and what they can and can't do for you.
How Large Language Models Work
Tokens, context windows, training, temperature and hallucination — the handful of concepts that explain almost every LLM behavior you'll see.
Choosing & Using AI Assistants
Claude, ChatGPT, Gemini, Copilot, open-source apps — how to pick the right assistant, use its power features, and set up a daily AI workflow that actually saves time.
Prompt Engineering Fundamentals
A repeatable framework for writing prompts that get great results the first time — with before/after examples you can copy.
Advanced Prompting Techniques
Few-shot examples, step-by-step reasoning, prompt chaining, structured JSON output, system prompts and simple evaluations — the techniques behind reliable AI systems.
AI Agents
Tools, memory, RAG, your first agent, design patterns, MCP and agent harnesses like Hermes.
What AI Agents Are
Agents are LLMs that take actions in a loop until a goal is met. Learn the agent loop, the six building blocks, and when you should — and shouldn't — use an agent.
Tools & Function Calling
How a model “uses” a tool: schemas, tool calls, results and the round trip. Write tool definitions models understand, and design tools that are safe.
Memory, Knowledge & RAG
Give AI your business knowledge. Learn embeddings, vector search and Retrieval-Augmented Generation — then build a working mini RAG system with a local model.
Build Your First Agent
A complete, runnable Python agent on a local model: real tools, a system prompt, step limits and a human-approval gate. Understand every line.
Agent Design Patterns & Multi-Agent Systems
Prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer and multi-agent teams — when to use each, with business examples.
MCP: Connecting Agents to Everything
The Model Context Protocol is the open standard for plugging tools and data into AI apps. Connect existing MCP servers, then build your own in about 20 lines of Python.
Agent Harnesses: Hermes, Claude Code & More
Stop writing agent loops from scratch. Install Hermes Agent, connect it to a local Ollama model, give it real work and reach it from Telegram — plus how to choose among other harnesses.
Workflows & Automation
Automation fundamentals and a complete hands-on AI lead workflow in n8n.
Workflow Automation Fundamentals
Triggers, actions, data mapping and where AI fits. How to find the tasks worth automating, choose a platform, and design workflows that don't break.
Build an AI Lead Workflow in n8n
Hands-on: install n8n free, then build a complete lead pipeline — form intake, AI qualification to JSON, CRM logging, a drafted personal reply and Telegram approval before anything is sent.
Local AI
Run private AI on your own hardware with Ollama, Open WebUI, LM Studio and more.
Why Run AI Locally (and What Hardware You Need)
Privacy, zero per-token cost, offline resilience and control — plus an honest guide to RAM, GPUs, model sizes and quantization so you know exactly what your machine can run.
Install & Master Ollama
Install Ollama on Mac, Windows, Linux or Docker, run your first model, and learn every command you'll use day to day — plus configuration, storage and troubleshooting.
Ollama API, Modelfiles & Integrations
Call local models from code, use the OpenAI-compatible endpoint so existing tools just work, get structured JSON, and package custom assistants with Modelfiles.
Local AI Apps: Open WebUI, LM Studio & Private RAG
Give yourself or a whole office a private ChatGPT-style workspace with document chat — Open WebUI, LM Studio, AnythingLLM and Jan — and deploy it for a client.
AI for Business
Where AI creates value, choosing use cases, rollout, ROI, safety and governance.
How AI Creates Business Value
The four ways AI pays off — time, cost, revenue and quality — mapped across every department, with concrete examples for small businesses and freelancers.
Finding & Prioritizing AI Use Cases
A practical method to discover AI opportunities, score them on impact, feasibility and risk, and pick a first pilot that's almost guaranteed to succeed.
Implementation Roadmap & Measuring ROI
A 90-day plan to roll out AI, the change-management moves that make people actually use it, and how to calculate and report ROI credibly.
AI Safety, Privacy & Governance
Protect client data, defend against prompt injection, keep humans accountable and write a one-page AI policy — including the Philippine Data Privacy Act basics.
Freelancing with AI
Package, price and sell AI services — then prove it with a capstone project.
Selling AI Services as a Freelancer
Turn what you've learned into income: service offers, niching, pricing, packaging a paid pilot, finding clients, and recurring care plans.
Capstone: Build a Complete AI Solution
Put it all together. Choose one of three capstone briefs, build it end to end with a local or cloud model, measure it, and turn it into a portfolio case study.