π€ Agentic Coding β Master Map of Content
A comprehensive knowledge base of 1,200+ interconnected concepts for F1βF5 Hong Kong secondary school students. Each note includes a motivation, concrete example, and analogy to make complex AI concepts accessible.
π How to Use This Vault
- Start anywhere β every note links to related concepts. Follow the
[[links]]to explore. - Look for the MOC at the top of each domain folder for a structured overview.
- Use Obsidianβs Graph View to see how concepts connect visually.
- Each note has: Motivation (why it matters), Example (how it works), Analogy (what itβs like).
πΊοΈ Domain Map
| # | Domain | Description | Notes |
|---|---|---|---|
| 01 | [Programming Fundamentals](01-programming/01-programming - MOC.md) | Variables, loops, functions β the building blocks of code | 89 |
| 02 | [Data Structures](02-data-structures/02-data-structures - MOC.md) | How data is organized: lists, trees, graphs | 81 |
| 03 | [Algorithms](03-algorithms/03-algorithms - MOC.md) | Step-by-step methods to solve problems | 81 |
| 04 | [Computer Systems](04-computer-systems/04-computer-systems - MOC.md) | How computers and networks work | 63 |
| 05 | [Internet & Web](05-internet-web/05-internet-web - MOC.md) | How the internet and web work | 56 |
| 06 | [Software Engineering](06-software-engineering/06-software-engineering - MOC.md) | Methods for building software reliably | 59 |
| 07 | [AI Fundamentals](07-ai-fundamentals/07-ai-fundamentals - MOC.md) | The basics of artificial intelligence | 61 |
| 08 | [Math & Logic for AI](08-math-logic/08-math-logic - MOC.md) | Mathematical foundations of AI | 65 |
| 09 | [NLP Fundamentals](09-nlp/09-nlp - MOC.md) | How computers understand language | 69 |
| 10 | [LLM Concepts](10-llm-concepts/10-llm-concepts - MOC.md) | How large language models work | 66 |
| 11 | [Prompt Engineering](11-prompt-engineering/11-prompt-engineering - MOC.md) | How to communicate with LLMs effectively | 62 |
| 12 | [Agent Fundamentals](12-agent-fundamentals/12-agent-fundamentals - MOC.md) | What AI agents are and how they work | 58 |
| 13 | [Agent Architectures](13-agent-architectures/13-agent-architectures - MOC.md) | How agents are structured and organized | 56 |
| 14 | [Tools & Functions](14-tools-functions/14-tools-functions - MOC.md) | How agents use tools to interact with the world | 52 |
| 15 | [Memory & State](15-memory-state/15-memory-state - MOC.md) | How agents remember things across conversations | 46 |
| 16 | [Code Generation](16-code-generation/16-code-generation - MOC.md) | How AI writes, fixes, and improves code | 47 |
| 17 | [Agentic Coding](17-agentic-coding/17-agentic-coding - MOC.md) | AI agents that program software autonomously | 45 |
| 18 | [Safety & Ethics](18-safety-ethics/18-safety-ethics - MOC.md) | Keeping AI safe, fair, and responsible | 47 |
| 19 | [Evaluation](19-evaluation/19-evaluation - MOC.md) | How to measure if AI models are good | 45 |
| 20 | [Data & Datasets](20-data-datasets/20-data-datasets - MOC.md) | The data that feeds AI models | 41 |
| 21 | [MLOps](21-mlops/21-mlops - MOC.md) | Deploying and maintaining AI in production | 41 |
π Total: 1230 concept notes across 21 domains
π― Learning Path Suggestions
For F1βF2 Students (Beginners)
- Variable β Function β Loop β If Statement
- Algorithm β Sorting β Search
- Artificial Intelligence β Machine Learning β Neural Network
For F3βF4 Students (Intermediate)
- API β REST API β JSON β Web API
- Large Language Model β Token β Context Window β Prompt
- AI Agent β Agent Loop β Tool Use β Function Calling
For F5 Students (Advanced)
- Agentic Coding β Code Agent β Autonomous Software Development
- Multi-Agent System β Hierarchical Agent β Agent Coordination
- Alignment β RLHF β Safety β Red Teaming
π Key Cross-Domain Connections
- Programming β AI: Function β Function Calling β Tool Use
- Data β Models: Dataset β Training Data β Pre-training β Fine-tuning
- LLMs β Agents: Large Language Model β Agent Loop β ReAct Pattern β Agentic Coding
- Web β APIs β Tools: HTTP β API β Function Calling β Tool Use
- Safety β Evaluation: Alignment β Red Teaming β Benchmark β Model Evaluation
ποΈ Structure
This vault supports a progressive workshop curriculum:
| Session | Topic | Domains |
|---|---|---|
| 1 | Programming Basics | 01β03 |
| 2 | Computer & Internet | 04β05 |
| 3 | Software Engineering | 06 |
| 4 | AI Fundamentals | 07β09 |
| 5 | LLMs & Prompting | 10β11 |
| 6 | AI Agents | 12β14 |
| 7 | Memory & Code Gen | 15β16 |
| 8 | Agentic Coding | 17 |
| 9 | Safety & Ethics | 18 |
| 10 | Evaluation & MLOps | 19β21 |