November 23, 2025 - Day 1: Starting the "Hello Agents" Open Source Guide
Today I explored an excellent open-source resource on GitHub called Hello Agents, created by the Datawhale community. There are many materials about AI agents online, but this one stands out — it's clear, well-structured, and highly practical.
🔗 GitHub link: https://github.com/datawhalechina/hello-agents (opens in a new tab)
The project contains 16 chapters organized into 5 main sections. Each section includes code samples, explanations, and hands-on exercises — exactly the kind of structured practice I've been looking for.
🚀 Study Goal
I set a goal to finish the full guide in 5 days, meaning roughly one main section per day.
Today, I completed Section 1, which covered the history of agents, LLM basics, core NLP concepts, encoder-decoder architectures, prompt engineering, and tokenization fundamentals.
Key insights gained:
- Understanding the evolution from rule-based systems to modern LLM-powered agents
- Learning about transformer architecture and how it enables better long-range dependencies compared to RNNs
- Comparing different NLP approaches (n-grams, RNNs, transformers) and their use cases
- Understanding encoder-decoder patterns and attention mechanisms
- Learning effective prompt engineering techniques and best practices
- Understanding tokenization strategies and model size trade-offs
Action items completed:
- Reviewed transformer attention mechanisms
- Practiced with different model sizes
- Understood tokenization vs. character-level approaches
- Learned prompt engineering best practices
- Compared NLP approaches for different use cases
This systematic approach through the Hello Agents guide provided an excellent foundation for understanding modern AI agent systems.
Overall Reflection: The Hello Agents material provided an exceptionally clean and systematic summary of Section 1 topics. This structured approach helped reinforce the mental framework behind LLMs and agents, making complex concepts more digestible and actionable.
🎯 My Learning Intention
| 🎯 Mood | 📊 Progress | 💡 Key Takeaway | 🎯 Tomorrow's Goal |
|---|---|---|---|
| Excited and focused | 1/5 sections completed (20%) | Structured open-source learning resources are invaluable for building technical depth | Complete Section 2 of Hello Agents |
Progress Bar: ■■■□□□□□□□ (20%)
🎯 My Learning Intention
I'll continue studying daily and review the included interview questions to deepen my understanding. My goal is to build an intuitive, explainable grasp of agent and LLM fundamentals — the kind of understanding that allows me to break concepts down for anyone.
This is an important step toward becoming a strong AI product manager with solid technical depth.
✨ On to Day 2 tomorrow!