AI Without the PhD: A Developer’s Guide

The AI landscape is vast, but you don’t need a PhD in machine learning to make it work for you. By focusing on practical skills, you can build smart apps without getting lost in the math.

Three levels of understanding:

1. Integration Skills: Know how to call an API that provides AI functionality. Example: Converting speech to commands.
2. Conceptual Understanding: Have a general sense of what training an AI model involves. You don’t need to master neural network mathematics, but you should understand the workflow: Collect data → Split into training/testing sets → Train the model → Evaluate → Deploy.
3. Tool Awareness: Recognize that you can train simple models yourself using online tools (e.g. Edge Impulse) or Python libraries (e.g. PyTorch).

A Practical Example: ESP32 Voice Control with AI (No Cloud Needed!)

1. Record voice samples like “light on” or “light off” as training data
2. To expand the dataset, generate synthetic data automatically with OpenAI's Whisper.
3. Split dataset into 80% training and 20% testing.
4. Train the model with a tool.
5. Test accuracy and retrain if needed.
6. Deploy it to the ESP32 microcontroller.

That’s it. You don’t need to know the inner workings of gradient descent or backpropagation to build something that works in the real world.

And most importantly: create 90% of your code with Claude Code.

Music: Sia - Cheap Thrills