Learn
A guided path from machine learning basics to agent harnesses—then wire what you learn with curated resources.
- 01
What is Machine Learning?
How computers learn patterns from data instead of following hand-written rules.
5 min · 4 resources
- Explain how ML differs from hand-written business logic.
- Name the main learning paradigms and when each applies.
- 02
What is an LLM?
Large language models: how they work, what they’re good at, and where they break.
6 min · 6 resources
Requires: Machine Learning
- Describe tokens, context windows, and their product implications.
- List core LLM capabilities and hard limitations.
- 03
What is an AI Agent?
Software that pursues goals by combining models, tools, and feedback loops.
6 min · 8 resources
Requires: an LLM
- Define an agent vs a one-shot LLM call or chatbot.
- Map planner, tools, memory, and guardrails in a typical architecture.
- 04
What is an Agent Harness?
The runtime layer that makes agents reliable, observable, and integrable.
6 min · 8 resources
Requires: an AI Agent
- Separate model, harness, and tool responsibilities.
- Evaluate harnesses on tracing, checkpoints, and safety hooks.