Want to be an Agent Dev? This is Day Zero. No pressure. No deadlines. No certificates. Just getting your footing.
- Mark Kendall
- 3 days ago
- 3 min read
# Day Zero — Start Here (Before You Write Any “Real” Code)
Welcome.
If you made it here, you’re already doing the most important thing: starting.
This repository follows a simple idea:
> You don’t learn AI agents by watching videos.
> You learn them by building, breaking, and teaching the system how to think.
This is Day Zero.
No pressure. No deadlines. No certificates.
Just getting your footing.
---
## What This Is (and What It Isn’t)
This is not:
- A university program
- A bootcamp
- A “get rich quick” AI course
- A $12,000 certificate with weekly Zoom calls
This is:
- A free, open, hands-on path to learning Python and AI agents
- Built for real people with jobs, families, and limited time
- Designed around Learn → Teach → Master
You will need to do the work here.
That’s true in college too — the difference is you own this.
---
## The Stack You’re Learning (No Mystique)
Whether it’s a university program or a corporate bootcamp, the core stack in 2026 is mostly the same:
- Python
- Prompting
- Retrieval-Augmented Generation (RAG)
- Agent workflows
- APIs
The difference here is access and ownership.
You don’t rent knowledge.
You build it.
---
## Step 1: Your Environment (Zero Setup Pain)
We use GitHub Codespaces so you don’t fight your laptop.
### What you need
- A GitHub account (free):
### What you do
1. Fork or clone this repository
2. Open it in GitHub Codespaces
3. Start coding in a browser-based VS Code
GitHub Codespaces:
- Runs in the cloud
- Already configured
- No “works on my machine” problems
If you can open a browser, you can do this.
Learn more about Codespaces here (optional):
---
## Step 2: Python — Just Enough to Be Dangerous
You do not need to be a Python expert to start building agents.
You need:
- Variables
- Functions
- Lists and dictionaries
- Reading and writing files
- Calling APIs
That’s it.
### Free Python Basics (Pick One)
Absolute beginners
Hands-on, interactive
Short and practical
Official documentation (reference, not a tutorial)
You don’t need to finish everything.
You just need enough to recognize what you’re looking at.
---
## Step 3: The Learn → Teach → Master Loop
This repo is built around a loop you’ll repeat constantly:
1. Learn
Read the notes. Skim the code. Don’t panic if it feels fuzzy.
2. Teach
Encode that knowledge into an agent:
- Prompts
- RAG documents
- Instructions
3. Master
Watch the agent use your material to reason and respond.
If you can teach a machine, you understand the concept.
---
## Step 4: trainerNotes.md Is Your Textbook
Instead of lectures, you get trainerNotes.md.
Think of it as:
- A living textbook
- A prompt source
- A knowledge base your agent will learn from
You will:
- Read it
- Modify it
- Feed it into RAG
- Watch the agent reason with it
This is how real-world AI systems are built.
---
## A Word About Jobs, Degrees, and Reality
Let’s be honest.
People pay $10,000–$15,000 for programs like:
- “Post Graduate AI Certificates”
- “Executive AI Bootcamps”
- “University-backed Agent Programs”
They still have to:
- Practice
- Build projects
- Explain what they built in interviews
If you:
- Put in the time
- Build working agents
- Understand why they work
You can absolutely become employable — degree or no degree.
This repo won’t hand you a job.
It will give you something better: real skill and real artifacts.
---
## How to Succeed Here
- Go slow
- Break things on purpose
- Ask “why” more than “how”
- Don’t memorize — understand
There is no schedule.
There is no finish line.
There is only progress.
---
## What’s Next (After Day Zero)
When you’re ready:
- Move to Day One
- Build your first simple agent
- Call a real LLM API
- Watch it reason with your own notes
That’s where things get fun.
You’re not late.
You’re not behind.
You’re exactly where you need to be.
Welcome to the work.
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