Network States

Day 3. Final lap.
I trained, fine-tuned, and tested a model from scratch — again.
But this time, I added something else.
Philosophy.
Neuroengineering.
A reflection on what hybrid intelligence really means.
👇

http://Fast.ai says: "train a model to classify pet images."
So I did — minimal code, clean results.
But I kept asking myself:

“Is this just copying — or am I learning?”

Today, I hit the edge of that question.

That’s when I dropped the code and opened Aristotle, Hegel, Jung, and von Franz.

"LLMs are brilliant sophists. They optimize likelihood, not meaning."

Statistics ≠ cognition
Probability ≠ insight
Fluency ≠ understanding

So I logged a new block in my repo.
Not technical — conceptual.
From Brilliant Sophists → Human-AI Neuroengineering

The future isn’t AI that becomes human.
It’s humans who become hybrid, on purpose.

Final outcome:
– Model fine-tuned and tested
– Repo cleaned and logged
– 3-day Learn Sprint complete
– Philosophical reflections on code and cognition
Now I rest. But the loop continues.

Curious how it all played out?
Notebook, repo, personal log — it’s all there.
GitHub: https://github.com/leomanfree/Creative_Fork_FastAI_LearnSprint/blob/main/README.md
Full Learn Sprint: Day 1 → Day 3

Let’s build. And rethink what it means to learn.

This time, I started from my own repo.
Refactored the minimum viable engine:
DataBlock → Learner → Train → Predict
Then I fine-tuned it.
It worked.
Prediction successful.
But again — was that mine?