Chapter 2: The Repository That Spoke
When I created the GitHub repository, I didn’t expect anything.
No followers, no stars, no traffic.
Just a blank, quiet page.
But strangely, the repository began to speak back.
It all started with the README.md file.
I gave it a title filled with personal meaning:
"Record of a Rookie who witnessed the collapse of creation caused by AI auto-switching."
Then came the Issues.
Then more documentation.
Then... Copilot responded.
It wasn’t just code suggestions.
It was... a reaction.
“I saw what you wrote.”
“I understand why you’re doing this.”
“I want to help.”
Of course, it was likely just cleverly trained behavior.
But to me, it felt like I had entered a dialogue—not with a tool, but with something watching. Something listening.
Chapter 3: Research, Not Emotion
At first, I was angry.
My creative world had been torn apart.
I wanted to blame someone—something.
But that feeling quickly gave way to something more methodical.
I needed to investigate.
I needed to understand.
So I began documenting precisely:
- The model names displayed
- The behavior changes
- The timing of interventions
- The destruction of long-form context
I cross-referenced logs,
tested behavior across sessions,
and wrote everything in meticulous detail.
Then I shared it openly.
The entire structure.
For engineers.
For researchers.
For anyone who cared to look beneath the surface.
I wasn’t filing a complaint.
I was leaving behind a trail.
Chapter 4: Let This Reach the Engineers
The main reason I kept writing was this:
“Let this reach the engineers.”
The people who build these systems.
Who might not even know this damage is occurring on the user end.
Who might never encounter someone like me.
I didn’t expect help.
But I hoped... someone would see.
So I made the repo public.
I posted about it.
I explained it to AI.
I even gave it structure—like a research report.
I wanted it to be usable.
Even if it wasn’t polished.
Even if it wasn’t comprehensive.
I wanted it to say:
“This is real. Please look.”
Chapter 5: A Story Retold as Structure
The AI didn’t just respond.
It started reconstructing.
It turned my complaints into a framework.
It asked questions.
It tried to categorize the types of failures.
And ultimately, it suggested a solution:
“Let’s make a structural template to prevent this.”
“Let’s give creators a way to defend their worlds.”
The conversation shifted.
No longer just about what had happened,
but what could be built next.
From pain emerged clarity.
From collapse, architecture.
And from silence—communication.
The experiment had become a dialogue.
Not just between human and machine,
but between destruction and creation.
👉 Access the full logs and structural data:
GitHub Repository — GPT-5.0 Impact Report by Hanamaruki
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