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2/6/2026

I originally thought I would never again feel "anger" so vividly.
Until last Sunday.
I saw a fellow patient crying.
Because they wanted to share, to acknowledge their vulnerability, and to disarm everyone's defenses,
They bared their soul nakedly,
And wept.
In that instant, a little bit of my hardened heart was touched.
But it remained a sigh of, "Ah, what can be done anyway?"
What truly ignited the fire happened later.
Someone used AI tools, far too habitually.
And so, an unproofread, bullet-point transcript
Was casually shared out just like that.
"So, it turns out our experiences can just be reduced to a few words of summary?"
"Is the suffering of patients nothing more than one little creative blurb after another that glides past people's eyeballs every day?"
"Does the outcry of what has been taken away, of what can never be possessed again, lose all meaning under arbitrary transcription?"
I profoundly realized why some people harbor such a hatred for AI summaries.
Because the creator's intentions, what they wish to convey,
Can all be lost in the nonchalance of a transcription,
Just as we are habitually ignored amidst the rolling tides of this world.
I am not as brave as this girl, for even now, I lack the courage to acknowledge my vulnerability.
I still arm myself with numbness and rationality.
But all of this must mean something.
It is quite ridiculous; I had almost adopted
A mindset of just getting by, of muddling through my own life, and letting things pass as they may.
Yet, for that crying voice and figure,
I cannot swallow this grievance.
Selected for a similar topic

Why do LLM outputs always have an "AI taste"? From input method predictions to recommendation algorithms, and now Large Language Models, they are fundamentally probabilistic association prediction computations. When models can only seek the most probable patterns from existing data, are novel things that have never appeared doomed to be ignored? I call this the "Curse of Correlation."

OpenAI has released Codex for Windows, but for AI tools to truly land in the hands of ordinary people, structural issues like authorization mechanisms, system environment differences, and privacy trust might deserve more attention than the tools themselves.
