First step: I reviewed my own repo from Day 1 —
then I opened muellerzr’s notebook again.
Together with my AI sparring partner (ChatGPT),
I distilled the **essential code**:
what I now call the *Minimum Viable Engine* of the model.
`DataBlock → Learner → Train → Predict`.

Then I closed everything.

(2/9)

Pen on paper.
Line by line.
I rewrote the model setup like a monk in a medieval scriptorium.
No copy-paste.
Just code reconstruction by memory and meaning.
It wasn’t clean. It wasn’t perfect.

But it was mine.

(3/9)

I jumped into Colab and typed everything manually.
No help.
No completions.
Only my notes.
And of course, I hit an error:
`NameError: name 'PAT' is not defined`.

It took me 20 minutes to spot the issue.
I had written ‘PAT’ instead of ‘pat’.

(4/9)

But fixing that one stupid bug felt better
than completing the entire model yesterday.
I learned something deep:
AI makes you fast —
but debugging without it makes you *present*.
Every character matters.
Every layer sticks.

(5/9)

Today’s result?
I trained the model.
Fed it a test image.
Got a clean prediction:
> basset_hound (99.99%)
> 

This time, it was actually right.

(6/9)

(Yesterday, it called a mixed-breed dog a shiba_inu…)

My friend laughed:
“That's not even close.”

But honestly, I was just proud it *ran*.
Today, I’m proud it’s accurate —
and that I understood *why*.

(7/9)

I’m not here to master everything.
I’m here to practice
*thinking like a builder*
- even when the AI isn’t there to help.

Call it autonomy.
Call it sovereignty.
Call it monk mode.

(8/9)

If you’ve been learning in public,
rebuilding from first principles,
or just trying to own your tools —
you’re not alone.

Follow along.
Tomorrow: Day 3.
The Learn Sprint finale.

(9/9)