Become an AI Engineer

First Three Projects Shipped (Beginner to Public App)

Every AI engineer starts with a working Python environment and basic programming intuition. Skip this if you already write Python daily.

5 milestones

1. 1Write and run your first Python program
2. 2Build programs using data structures
3. 3Write functions and handle errors
4. 4Build a real project from scratch
5. 5Ship something others can use

Master Python for Real Projects

Python is the lingua franca of AI engineering. You need to be fluent before you can reason about what a library is doing under the hood.

5 milestones

1. 1Write Pythonic code, not just Python code
2. 2Work with APIs and external libraries
3. 3Write tests for your own code
4. 4Build a data pipeline from files to output
5. 5Publish a Python package on PyPI

Build and Deploy Your First Web App

AI features live inside products. Building a web app first teaches you how to wire an LLM response into a UI that real users interact with.

5 milestones

1. 1Understand how the web works
2. 2Build a working frontend with HTML and CSS
3. 3Add interactivity with JavaScript
4. 4Connect a backend and serve real data
5. 5Deploy your app to a real URL

Ship AI Products in Production

The anchor milestone. Six verified steps from your first API call to a multi-agent system with a real evaluation suite.

6 milestones

1. 1Set up your AI development environment
2. 2Ship your first LLM-powered feature to a public URL
3. 3Build and deploy a RAG system with source citations
4. 4Implement function calling with two live external tools
5. 5Build and evaluate an autonomous multi-step agent
6. 6Ship an AI product with 10 active real-world users

Ship an AI Product to Real Users

Shipping to 10 real users is the proof that you can build something people actually want — not just something that works in a demo.

3 milestones

1. 1Deploy a working prototype to a real URL
2. 2Get 5 real users and document their feedback
3. 3Prove retention: 10 users returning across 4 weeks

Make Your First Open Source Contribution

Optional but strongly recommended. OSS contributions build your professional reputation and force you to read production-quality code at scale.

3 milestones

1. 1Understand a project codebase
2. 2Draft first PR
3. 3PR merged