Structured paths, not a firehose.

A ground-up path from "what is AI" to building your first agent.

1. 1. What is AI?The basic idea: systems that perform tasks normally requiring human intelligence.
2. 2. Machine LearningHow systems learn patterns from data instead of following fixed rules.
3. 3. Neural NetworksThe layered, connection-based structure underneath most modern AI.
4. 4. LLMsLarge language models -- what they are, and what they aren't.
5. 5. Prompt EngineeringGetting reliable, useful output from a language model.
6. 6. AI AgentsSystems that plan and take multi-step action, not just respond once.
7. 7. RAGRetrieval-augmented generation -- grounding answers in real, searchable sources.
8. 8. AutomationWiring AI into a repeatable, hands-off workflow.
9. 9. Build an AI AgentPutting the previous eight steps together into one real project.

Practical AI tools for video, writing, and content production.

1. 1. AI Image ToolsGenerating and iterating on visual concepts.
2. 2. AI Video ToolsWhere AI genuinely speeds up production today, and where it doesn't yet.
3. 3. AI Writing AssistantsDrafting, editing, and idea generation without losing your own voice.
4. 4. Thumbnail & Title TestingUsing AI to generate options, not to make the final call.
5. 5. Content RepurposingTurning one piece of research into multiple formats.

Where AI actually changes day-to-day business operations.

1. 1. AI for Customer SupportWhat's realistic today versus overpromised.
2. 2. AI for ResearchFaster information gathering, with the same sourcing discipline as before.
3. 3. AI for ReportingSummarizing and structuring information reliably.
4. 4. Evaluating AI VendorsQuestions worth asking before adopting a tool.

Building repeatable, hands-off AI-assisted workflows.

1. 1. What Should Be Automated?Recognizing genuinely repeatable tasks versus ones that need human judgment.
2. 2. Automation ToolsThe landscape of no-code and code-based automation platforms.
3. 3. Human-in-the-Loop DesignWhere approval gates belong in an automated pipeline -- the same principle Project KAI's own production pipeline uses.
4. 4. Monitoring Automated SystemsKnowing when an automation has quietly started failing.

Using AI to learn faster without skipping the learning.

1. 1. AI as a Study PartnerUsing AI to explain, not to answer for you.
2. 2. Fact-Checking AI OutputWhy every AI claim needs an independent source check.
3. 3. AI for Research PapersWhere AI helps with structure and where it can mislead.
4. 4. Academic IntegrityUsing AI tools without crossing into someone else's work.

A practical tour of the current AI tool landscape.

1. 1. Coding ToolsSee /reviews for Project KAI's own honestly-labeled coverage.
2. 2. Image ToolsSee /reviews for Project KAI's own honestly-labeled coverage.
3. 3. Voice ToolsSee /reviews for Project KAI's own honestly-labeled coverage.
4. 4. Productivity ToolsSee /reviews for Project KAI's own honestly-labeled coverage.

How autonomous AI agents are actually designed and built.

1. 1. Agent ArchitectureSee /agent-builder for Project KAI's own educational walkthrough.
2. 2. Tool UseHow an agent decides which tool to call, and when.
3. 3. MemoryWhat an agent needs to remember, and what it should forget.
4. 4. Human Approval GatesWhy every consequential action should require sign-off -- see Project KAI's own real Agent Network for a working example (/agents).