01 — The Vision: What We're Building & Why
🎯 Your Question
"Manus is amazing. How do they do it? Can we build something like that?"
Short answer: Yes! Not identical — Manus has hundreds of engineers and millions in funding. But we can build a "child version" that captures the core idea and teaches you every concept along the way.
🤖 What Is Manus AI?
Manus (acquired by Meta) is an AI agent — not just a chatbot. Here's what makes it special:
1. It Actually DOES Things (Not Just Talks)
| ChatGPT/Claude | Manus |
|---|---|
| "Here's how to find Python files..." | Actually runs the command and shows you |
| "Here's a script idea..." | Writes, tests, and deploys the code |
| "I can help you plan..." | Plans, executes, and verifies |
2. Three Specialized Agents Working Together
Manus uses three sub-agents that coordinate:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ PLANNER │────▶│ EXECUTOR │────▶│ VERIFIER │
│ │ │ │ │ │
│ "Break this │ │ "Run shell │ │ "Check if │
│ into steps"│ │ commands" │ │ it worked" │
│ │ │ │ │ │
│ Strategize │ │ Navigate │ │ Quality │
│ multi-step │ │ web, write │ │ control │
│ path │ │ code, use │ │ & fix │
│ │ │ tools │ │ errors │
└─────────────┘ └─────────────┘ └─────────────┘
3. Persistent Cloud Environment
Manus runs in a cloud VM (virtual machine):
- Files persist between sessions
- Can install software (
pip install,npm install) - Works while you sleep (asynchronous)
4. Can Browse 50+ Websites Simultaneously
For research tasks, Manus spawns many parallel agents to gather info.
🔬 What We're Building: "Mini-Manus"
Our Simpler Architecture
Instead of three separate agents + cloud VM, we use ONE model with a loop:
User: "Find all Python files and count them"
│
▼
┌─────────────────────────────────────────┐
│ MCP-Agent-1.7B (Our Model) │
│ │
│ ┌─── THINK ───┐ │
│ │ "I need to │ │
│ │ list .py │ │
│ │ files" │ │
│ └──────┬───────┘ │
│ │ │
│ ┌─── ACT ─────┐ │
│ │ shell_exec({│ ◀── ONE MODEL plays │
│ │ "command": │ ALL three roles │
│ │ "find . │ (planner + │
│ │ -name │ executor + │
│ │ '*.py'" │ verifier) │
│ │ }) │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ (Result: "main.py, test.py, utils.py")
│ │
│ ┌─── VERIFY ──┐ │
│ │ "Got 3 │ │
│ │ files. Now │ │
│ │ count." │ │
│ └──────┬───────┘ │
│ │ │
│ ┌─── ACT ─────┐ │
│ │ python_exec({│ │
│ │ "code": │ │
│ │ "print(3)"│ │
│ │ }) │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ (Result: "3") │
│ │
│ ┌── RESPOND ──┐ │
│ │ "Found 3 │ │
│ │ Python │ │
│ │ files! ✅" │ │
│ └─────────────┘ │
└─────────────────────────────────────────┘
Key Differences from Manus
| Feature | Manus | Mini-Manus (Ours) |
|---|---|---|
| Agents | 3 specialized (Planner/Executor/Verifier) | 1 model, all roles |
| Environment | Cloud VM | Local/Gradio Space |
| Parallelism | 50+ simultaneous | Sequential (one at a time) |
| Cost | $$$/month | $3 one-time |
| Model Size | GPT-4 class (100B+) | 1.7B (100× smaller!) |
| Persistence | Files persist forever | Session-based |
| Web Browsing | Real browser | DuckDuckGo search API |
Why This Still Impresses People
- It runs LOCALLY — No API keys, no cloud costs, no rate limits
- It actually DOES things — Not just text, but real shell commands, file operations, Python execution
- It's 100× smaller than Manus's models but still functional
- It's OPEN SOURCE — Anyone can use, modify, improve it
- YOU trained it — From base model to agent in one project
🧠 The Core Insight: Why Small Models CAN Work for Agents
You might think: "How can a 1.7B model compete with GPT-4?"
The secret is FOCUS.
GPT-4 is a generalist — it knows about history, science, poetry, coding, everything. Our model is a specialist — it ONLY knows about tool-calling.
Think of it like this:
- GPT-4 = A professor who can teach any subject
- Our model = A skilled technician who only knows how to use tools
The TinyAgent paper proved this: a 1.1B model fine-tuned on tool-calling data matched GPT-4-Turbo at function-calling tasks. Not because it's smarter, but because it's focused.
📋 What Makes This a "WOW" Project
When you show this to people, they'll be impressed because:
1. "You trained your own AI agent?"
Most people think you need a PhD and a supercomputer. You don't.
2. "It runs on a laptop?"
1.7B parameters = 4GB in memory. Runs on any gaming laptop.
3. "It can actually modify files?"
Not just text generation — real file system operations, shell commands, Python execution.
4. "It costs $3?"
Compared to Manus's pricing (or OpenAI API costs), this is almost free.
5. "You built this yourself?"
From research → data → training → app. Full pipeline.
🎓 What You'll Learn From This Project
By the end, you'll understand:
- ✅ How AI agents work (ReAct pattern)
- ✅ What MCP is and why it matters
- ✅ How to pick base models for different budgets
- ✅ LoRA: the magic of cheap fine-tuning
- ✅ SFT: supervised fine-tuning step-by-step
- ✅ How to tune hyperparameters (learning rate, batch size, epochs)
- ✅ How to build an agent harness
- ✅ How to deploy ML models
- ✅ How to read research papers and apply them
If you can train a 1.7B model, you can train a 70B model. The concepts are identical — only the scale changes.
🔜 Next Step
Read 02-research.md to see what papers and datasets we found, and why we made the choices we did.