I work with databases for a living. I write Java. I know what a heap file is, what a B-tree does, why your query is slow. I am comfortable in the world of things I understand.
AI made me uncomfortable.
Not in a dramatic way — I wasn't afraid of it replacing me. More like the feeling of being in a conversation and missing every third reference. Everyone was speaking a language I had the alphabet for but not the grammar.
So I decided to learn it. Not by watching YouTube videos at 1.5x speed. By building things, breaking them, and writing down what I find.
What I Actually Knew on Day Zero
Not much. Here's the honest list:
- Neural networks are loosely inspired by the brain
- GPT means Generative Pre-trained Transformer
- Transformer ≠ the robot from the movie
- Something something embeddings
- Something something vectors
That's it. That's the inventory.
The First Question I Asked Myself
What does an LLM actually do?
Not "how does it work internally" — I'll get there — but functionally: what is the thing doing when I type a prompt and get a response?
The answer I landed on: it predicts the next token, over and over, until it decides to stop.
That's it. There's no understanding. No reasoning in the human sense. It's a very sophisticated pattern-completion engine trained on a significant fraction of human writing. The magic — and there is something that feels like magic — is that pattern completion at scale starts to look a lot like thinking.
This reframing helped me. I stopped anthropomorphizing it and started thinking of it as a system I could reason about.
The First Thing I Built
A script. Embarrassingly simple. It calls the Anthropic API, sends a prompt, prints the response.
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-opus-4-5",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain what a transformer is. I'm a backend engineer."}
]
)
print(message.content[0].text)
The response was good. Better than I expected. And then I spent two hours just talking to it — asking progressively more technical questions, pushing on the edges, seeing where it confused itself.
That was day one.
What I Want to Build Through This Series
Not a tutorial. There are enough of those. This is a log — a working document of someone learning in public.
Each entry will be one thing I learned, one thing I built, and one thing that confused me. Honest accounting.
The eventual goal: build something real with AI integrated into it. Not a demo. Not a wrapper around a chat UI. Something with actual engineering decisions, tradeoffs, and failure modes.
I'll figure out what that is as I go.
Next: what actually is an embedding, and why does everyone keep talking about vectors?